Announcements from the labs, companies and platforms themselves.
Learn what's new in Visual Studio Code 1.140 (Insiders) Read the full article
Learn what is new in Visual Studio Code 1.139 Read the full article
What is Jev? Learn how TypeSafe AI’s System One model makes fast, structured decisions, where it fits in the agent loop, and how to use Jev with LangChain
NVIDIA AI Day Singapore, which takes place Sept. 22-23 at the Raffles City Convention Centre, is offering attendees opportunities to explore the hands-on training, expert-led sessions and advanced tools to accelerate their work in AI and high-performance computing. At the event,…
Understand how Copilot agents perform and interact with models and tools. The GitHub Copilot app now supports OpenTelemetry (OTel) configuration through enterprise-managed settings. OTel is an open source observability framework.… The post OpenTelemetry in the GitHub Copilo…
GitHub Copilot for JetBrains 1.18.0 brings AI-assisted tool approvals, more control over agent conversations, and shared skills and instructions for your organization. You can also review plans with the Codex… The post New features and improvements in Copilot for JetBrains…
OpenAI and Grab launch GO Forward with AI, a regional programme helping 30,000 partners build practical AI skills across Southeast Asia.
C++ code intelligence in GitHub Copilot CLI is now faster with support for whole codebase indexing. C++ repositories can contain millions of lines of code across deeply connected source files… The post Faster C++ code intelligence with whole codebase indexing appeared first…
Amazon CloudWatch Omni is the next evolution of CloudWatch — unified observability that brings your applications and AI agents into one reimagined experience, with auto-discovered topology, natural language queries, and AI-guided investigation powered by AWS DevOps Agent.
Learn how Amazon CloudWatch Omni delivers AI-powered observability purpose-built for generative AI and agentic workloads. Trace, evaluate, and experiment with AI agents across any framework—directly from your IDE or a standalone web experience—using open standards and built-in ev…
Learn how GPT-6 improves prompt caching with higher cache hit rates, new diagnostics, explicit breakpoints, and controls that reduce latency and costs.
Meet GPT-6 Sol and Luna, two models that bring frontier intelligence to everyday work with different balances of capability and cost.
As Kubernetes adoption has grown, the conversation has shifted beyond running containers to managing increasingly complex application lifecycles. Modern platforms support stateless web services, stateful databases, batch processing, AI workloads, and platform services. At the sam…
Learn what AI change management involves and how enterprises can adapt roles, workflows, and governance as adoption scales.
Claude Opus 5.5, Anthropic’s newest Opus model, is now available in GitHub Copilot. You can use it for agentic coding, long-running agentic tasks, and knowledge work. In early testing, Opus… The post Claude Opus 5.5 is now available in GitHub Copilot appeared first on…
See how LangSmith helps healthcare AI teams turn clinical review into reusable evaluators, datasets, and release gates for safer AI in production.
OpenAI’s GPT-6 family is expanding in GitHub Copilot with two additional models: GPT-6 Sol, and GPT-6 Luna. Joining the previously released GPT-6 Astra, these new options let you select the… The post OpenAI’s GPT-6 Sol and GPT-6 Luna now available appeared first…
Business leaders often have access to plenty of data, but still can’t get a reliable...
AI coworkers and coding agents are spreading fast across organizations, and each...
Meet the partners and customers bringing practical AI, security, and development sessions to the Docker Pavilion at WeAreDevelopers. The post explains why a strong ecosystem matters to developers, announces the sessions and speakers, and invites attendees to connect with the team…
We’re removing several SSH algorithms, adding a new algorithm, and requiring larger RSA SSH keys to improve security. The changes are as follows: We’re removing the ability to use RSA… The post Security improvements for SSH appeared first on The GitHub Blog.
Vary support is now available in Cache Rules on every plan. You can normalize known negotiation headers, pass exact values through to the origin when those small differences matter, or bypass cache when the variation is too unpredictable.
Code quality is shaped by countless decisions, from how developers manage complexity to how quickly they detect regressions. But common concepts such as technical debt, cognitive complexity and unit testing are not always clearly understood. In a new code quality Q&A session,…
Worker Previews gives every branch its own URL, configuration, state, and observability, so you and your agents can test changes in parallel without affecting production.
To build and deploy sophisticated robotics applications that can perceive, reason and act in dynamic environments, developers need new physical AI models and tools. The ROS open framework is a project from Open Robotics that helps humans build robots. NVIDIA Isaac ROS 5.0 — a col…
GPT‑6 Astra allowed Parallel’s agents to research and synthesize labor-market data in half the time and at half the cost vs. prior models.
Starting with CodeQL CLI 2.27.0, the all-platform CodeQL bundle (i.e., codeql-bundle.tar.gz and codeql-bundle.tar.zst), which includes the binaries for all supported platforms up to this release, is marked as deprecated. In… The post Deprecation notice: All-platform CodeQL…
AI can produce code. Organizations still have to produce software. Agentic development is changing how software gets made, but it hasn’t changed what it costs to be wrong. Six months ago, we began publicly experimenting with agentic development environments. Around the same time,…
The new repository pull requests page is now generally available to all GitHub users. Highlights The new page makes it easier to find and act on pull requests in a… The post Refreshed repository pull requests page generally available appeared first on The GitHub Blog.
OpenAI outlines priorities and principles for rigorous, secure, and independent third-party AI safety assessments of frontier models and safeguards.
A hard model swap exposes every user at once, and rolling back means cold-starting the old deployment under pressure. Here's how staged traffic ramps, metric gates, and automatic rollback work on dedicated inference.
We rebuilt the combined SpaceXAI and Cursor support operation around Grok Bot, expanding to a much broader product portfolio without adding headcount.
Fireworks introduces the Specialized Intelligence Index, a one-stop destination for real-work benchmarks across industries.
GPT-6 Sol and GPT-6 Luna from OpenAI are now available on AI Gateway. Both models bring GPT-6 improvements in professional work, coding, computer use, factuality, and communication at a lower price than GPT-6 Astra. GPT-6 Sol ( openai/gpt-6-sol ) is suited to complex professional…
Drives for Vercel Sandbox are now available in public beta on Hobby, Pro, and Enterprise. A Drive is persistent storage that you mount as a directory in a Vercel Sandbox. It isn’t tied to a single sandbox, so you can reuse the same Drive across runs and different sandbox instance…
Claude Opus 5.5 from Anthropic is now available on AI Gateway. It is a step-change improvement over Opus 5, with its biggest gains in agentic coding, long-running agent tasks, and knowledge work. Anthropic cites that Opus 5.5 performs at the level of Fable 5.1, but ~30% faster an…
The Next.js team has disclosed a critical severity vulnerability in an upstream dependency that can lead to remote code execution when ImageResponse renders untrusted input. It is patched in 15.5.26 and 16.3.6. Applications that do not pass untrusted input into ImageResponse are…
Anthropic’s Claude Opus 5.5 model is now available through Netlify’s AI Gateway and Agent Runners with zero configuration required. Use the Anthropic SDK directly in your Netlify Functions without managing API keys or authentication. AI Gateway handles everything automatically. H…
OpenAI’s GPT-6 Sol and GPT-6 Luna models are now available through Netlify’s AI Gateway and Agent Runners with zero configuration required. Use the OpenAI SDK directly in your Netlify Functions without managing API keys or authentication. AI Gateway handles everything automatical…
This is a major release, and the headline feature is our new AI Assistant. We're starting to roll out AI capabilities across our database tools, and SQL Manager for PostgreSQL is the first to get them. It's also available in SQL Management Studio for PostgreSQL, which ships with…
Application code has fast testing loops: runners, fixtures, and red-green feedback in JavaScript, TypeScript, and Python. Postgres can be tested too, but database logic often sits outside those loops. Developers have to provision state, manage transactions, or fall back to a past…
PostgresCompare is pleased to announce the release of version 2.2.0, the first public release since 1.2.2 and the largest change in the product's history. The application has been rebuilt on a new foundation, and the 2.x series adds database pipelines, ad-hoc comparison, data-cha…
We analyzed billions of transactions on Stripe from January 2022 to March 2026 to understand how card fraud patterns differ by region and country, what's driving those differences, and how businesses can respond.
At enterprise scale, even small architecture choices can have outsized consequences. A deployment that works for a handful of teams can become a constraint once thousands of developers, repositories, and pipelines depend on it. That makes each decision made before rollout especia…
GitLab Duo Agent Platform orchestrates and automates complex tasks through agentic flows. A key part of the platform is the Flow Registry, a declarative configuration framework, built from reusable components, that compiles YAML into fully functional LangGraph flows. By using Flo…
At the end of August, we announced our first Maintainers in Residence, Rust Project contributors who are funded for their upstream contributions and maintenance work from the Rust Foundation Maintainers Fund (RFMF). Since then, the Rust Leadership Council has dedicated more funds…
Enterprise owners can now export a complete inventory of every credential that can access their enterprise (e.g., SSH keys, classic and fine-grained personal access tokens, OAuth App access tokens, and… The post GitHub Enterprise adds credential inventory exports appeared f…
The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
Kubernetes v1.37 promotes the PersistentVolumeClaimUnusedSinceTime feature gate to Beta (enabled by default). With this feature, the PersistentVolumeClaim (PVC) protection controller adds an Unused condition to each PVC, telling you whether any running pod currently references it…
Every AI factory needs power and cooling that fit its computing architecture. As AI infrastructure expands, power, cooling, water, site and grid constraints are shaping what builders can deploy. Choosing products that fit the complete factory design helps builders turn computing…
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
Use Jev as a judge for LangSmith evals to evaluate agent traces with faster, cheaper structured feedback across production runs, datasets, and regression tests.
Vercel Connect now includes a managed connector for Microsoft Teams. Creating one gives your organization a Teams bot that your apps and agents run. People can @mention it in channels or message it directly, and your code receives the message and replies as the bot. As a Vercel M…
Living in the Netherlands, I spend a fair amount of time on trains, and that is usually where I catch up on what the builder community is writing. Until now, that meant opening a laptop or squinting at a browser tab on my phone. This week I found myself scrolling through trending…
Physical AI is moving rapidly from research to large-scale deployment. By 2035, ABI Research projects an installed base of 49 million level 3-5 autonomous vehicles (AVs), while Omdia estimates that roughly 60 million industrial robots will be deployed between 2026 and 2035. As th…
We’re open-sourcing Rebalancer, the assignment-problem solver that has been used to solve resource allocation problems throughout Meta for over nine years. Rebalancer separates several related concerns: how to specify an assignment problem, how to store it efficiently in me…
Today, Egypt’s AI builders gathered in the Grand Egyptian Museum for a reception that highlighted the nation’s rapidly growing AI ecosystem — spanning AI natives, developers, researchers, startups and enterprises — building applications across industries. The event included a key…
Demand for AI infrastructure is at an all-time high. Global accelerator shortages mean engineering teams can rarely get all the compute they need from just one data center — capacity comes a cluster here, a cluster there, often an ocean apart. At the same time, workloads are gett…
The surge in AI development has created unprecedented demand for compute capacity around the globe. This can have negative implications for data processing and pipelines with Apache Spark. Whether you are managing your own Spark infrastructure or using a managed service, you can…
When running modern AI workloads, there’s often a conflict between performance and cost. Workloads like large language models (LLMs) load massive files, and may serve thousands of AI agents that need to execute code instantly. If each component is starting “cold” with a full data…
A resilient software supply chain is the foundation of modern delivery, and securing your continuous integration and continuous delivery (CI/CD) pipeline is what keeps innovation moving safely. Notable supply chain attacks more than doubled in the first half of 2026 compared to t…
Custom-made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules. The…
We tested using Jev-as-a-Judge against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation.
Deployments now show their billable duration and CPU minutes in the dashboard, vc inspect, and the REST API. Use it to understand how each build contributes to your usage. Billable duration is the build and post-build time combined, rounded up to the next whole minute. CPU minute…
Grok 4.7, xAI’s latest reasoning model, is now rolling out in GitHub Copilot. Building on Grok 4.6, it is designed for agentic coding and complex, multistep workflows. This model is… The post Grok 4.7 is now available in GitHub Copilot appeared first on The GitHub Blo…
AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work. As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what…
Python Workers allow developers to run Python web frameworks and AI orchestration libraries natively in the Cloudflare Workers runtime. You can seamlessly integrate with Cloudflare's ecosystem including D1, R2, and Workers AI without writing any JavaScript glue code.
With GPT-6 Astra, Higgsfield AI makes video ad creation easier for small businesses and brings new creative tools to market faster.
OpenAI is working with an independent Advisory Group on Mathematics and Artificial Intelligence to guide the review and communication of emerging AI results.
Petal, the next step in Meta’s subsea innovation, will be the first subsea cable to deliver petabit capacity at transoceanic distances, connecting France and the United States over approximately 7,000 km (4,300 mi). Expected to enter service in 2029, it will be the first subsea c…
Clean energy isn’t hard to come by, but the pace of large-scale adoption has historically been slow due to bottlenecks — including out-of-date infrastructure, elongated research and development timelines, and upfront cost barriers. At New York Climate Week, NVIDIA is highlighting…
OpenAI outlines a path to shared global AI standards, calling for coordinated evaluation, reporting, and governance to improve safety.
Explore new OpenAI Academy learning paths for employees, developers, leaders, educators, and students to build and demonstrate practical AI skills.
Using GPT-5.6, V7 turns scattered company files into context agents can use to complete complex, source-linked work.
SpaceXAI's most powerful model for coding and knowledge work. Twice as fast, at half the price of comparable models.
18 models, 113 coding tasks. The best single model gets 74.1% at $6.52. A perfect router gets 97.6% at $1.88. See how FireRouter closes the gap.
MiMo V2.6 Pro, MiMo V2.6 Flash, and MiMo V2.6 Pro UltraSpeed from Xiaomi are now available on AI Gateway. MiMo V2.6 combines coding, reasoning, and tool use with native text, image, audio, and video understanding. Its 1M token context supports long repositories, tool traces, and…
You can now call Jev from TypeSafe AI through AI Gateway using an existing TypeSafe client or the HTTP API, in addition to the AI SDK. TypeSafe client: Point an existing TypeSafe client at AI Gateway without changing its evaluation calls. HTTP API: Call Jev directly from any lang…
Grok 4.7 from SpaceXAI is now available on AI Gateway and 40% off through September 27. The discount applies automatically when you call spacexai/grok-4.7. Grok 4.7 has a 500K token context window and supports low, medium, high, and xhigh reasoning levels, giving you control over…
What's left for us to build?
The Rust Security Response Team was notified that Miri stores all environment variables to target/, allowing secrets to persist in caches. While not necessary a vulnerability in and of itself, when paired with GitHub Actions caching behavior, it is possible for this to expose sec…
Some of the most damaging outages are the ones your monitoring never flags: a slice...
Enterprise teams on Flexible Commitment plans can now use Spend Management, already available on Pro, at no additional cost. You can set a budget at any time in Spend Management settings. Set a budget per billing cycle, and when your team's metered usage approaches or crosses it,…
mcp-handler now has experimental support for WebMCP, the proposed web standard for exposing tools to in-browser agents. Add a single script tag to your site, and your existing MCP tools become available there too. Opt tools in by adding them to the experimental_webMcp object: The…
Algorithms & Theory
At Databricks IT, our vision is to empower people to work from anywhere without putting...
Cloudflare's global network is immense but not limitless. As we look for small ways to trim our resource usage, we sometimes get lucky and we can cut significantly more. Here’s how we reduced one of our Pingora-based service's RAM usage with statistics.
v0 now installs private packages from npm and custom registries using credentials stored as shared environment variables on Vercel. This makes it easier for teams to build with their existing design systems, component libraries, and internal packages directly in v0. To get starte…
Vector search is a critical component of generative AI, retrieval-augmented generation (RAG), and data agent architectures, but sometimes vector search alone isn't enough. While vector embeddings are incredible at understanding conceptual meaning, they stumble on specific alphanu…
Today, we are excited to announce enhancements to the borderless Lakehouse, our answer to how data engineers, data scientists, and increasingly, AI agents, can query governed data directly where it lives. To reason accurately and automate complex enterprise workflows, agents and…
State and local governments are driven by a shared mission to provide responsive, equitable, and accessible services. However, achieving this goal is often hindered by legacy technical debt, disconnected data, and heavy administrative burdens that slow down mission delivery. This…
As enterprises invest in generative AI, tech leaders keep seeing the same pattern: Developers test AI tools for a week, hit setup problems, and then drift back to the backlog. Nothing ships. The real gap is enablement. In this landmark Harvard Business Review article, Josh Bersin…
We are expanding our AI & Economy team with world-class academic advisors, fellows, and core internal researchers.
We are living in genuinely interesting times. AI is disrupting software development at a pace where new models, tools, and practices appear almost daily. Many teams’ natural first instinct is to spend ever more time chasing updates. After almost two years of AI product and market…
We want local coding agents to be smart and fast, with the ability to understand a codebase, do useful work, and finish tasks without long waits. This Junie Local update makes it practical to use a more capable model on your own machine. In the first release, we had to choose bet…
Google worked side-by-side with designers Jane Wade and Sergio Hudson to custom-design Google Flow tools to prep for NYFW.
OpenAI introduces the Australian Youth Safety Blueprint, a six-pillar roadmap for safer AI experiences that protect and empower young people.
We’re partnering with Accenture on independent evaluation of frontier AI—part of our recent commitment to embed evaluators at Anthropic. Both we and Accenture expect to invest at least $1 billion to build capacity in this area over the next five years.
You have the change ready and the tests are green. Now someone has to launch the app, find the right screen, and check the flow. Often, that someone is still you, even when an agent helped write the code. You should be able to delegate that part too. Junie /demo is a new mode in…
Ktor 3.6.0 is here! This release is full of new experimental features, including typed authentication capabilities with specialized support for OpenID Connect and HTTP/3 support for the Netty engine. There are also a few quality-of-life improvements for routing and request handli…
Git made isolated development a baseline for software teams. Each developer can create...
Within 24 hours of launching on AI Gateway, Jev from TypeSafe AI reached more than twice as many paid teams as any previous model launch, making it the fastest-adopted model in gateway history. Jev passed every other comparison model in its first twelve hours and continued to wid…
Activation steering has emerged as a powerful method for guiding the behavior of generative models towards desired outcomes such as toxicity mitigation. However, most existing methods apply interventions uniformly across all inputs, degrading model performance when steering is un…
Inside a global bank's shift to self-serve dedicated inference: how Together's DMI gave engineering teams direct control over scaling, models, and testing.
Announcing SpaceXAI's newest speech-to-text model, with unparalleled accuracy and cost effectiveness.
In August 2026, Hacktron reported what looked like a remote code execution (RCE) vulnerability in Next.js image optimization. Their investigation found that the vulnerable code was not in Next.js itself, but upstream in libheif, an AVIF image decoder used by Next.js, ImageMagick,…
GLM 5.3 FlashX is now available on AI Gateway. GLM 5.3 FlashX is a high-speed serving option for Z.ai's multimodal coding model, delivering inference at ~200 tokens per second for faster streamed responses. The higher serving speed is useful for coding agents, tool loops, and int…
Last year, Stripe data shows fraud attempts against travel and leisure businesses hit a four-year high. We analyzed payment activity from more than 200,000 active travel and leisure businesses on Stripe to understand where fraud is rising, how effectively it’s being blocked, and…
I joined GitLab at a moment when the way teams build and secure software has been changing rapidly. GitLab CEO Bill Staples recently framed that shift in When Code Is Abundant. When code is no longer the bottleneck, trust becomes scarce, and that constraint shows up first in what…
You and your agents can now deploy static artifacts to Vercel in under one second through Vercel CLI. Run vercel deploy to share a prototype, publish an HTML report, or preview a page created by your coding agent. Vercel automatically detects eligible deployments, and valid artif…
AWS introduces new low-cost burstable Amazon EC2 T8i instances powered by custom sixth generation Intel Xeon Scalable Processors (Granite Rapids), available only on AWS. T8i instances are among the lowest-cost EC2 instances and deliver up to 30% better price performance over prev…
Education Innovation
Google and the UN system have launched the UN System Data Commons, a new open platform making global statistics accessible and easy to search.
You can now opt into Turbo build machines on any individual deployment. This is useful when you need to increase resources temporarily without changing project settings. You can do this in three ways: Include #VERCEL_BUILD_MACHINE=TURBO in your Git commit message before pushing t…
Run an application on AWS Elastic Beanstalk Cluster Mode without provisioning or operating the compute underneath it. You provide a container image or source code; Elastic Beanstalk with service-operated compute creates and operates the environment that runs it.
You can now run Harbor evals on Vercel Sandbox. Harbor is the open-source harness behind Terminal-Bench, whose registry includes many other benchmarks such as SWE-bench, tau3-bench and OSWorld. Pass --env vercel to harbor run and each trial executes in its own isolated Firecracke…
skills@1.7.0 adds Notion skills databases as an install source for agent skills. Notion skills are reusable agent skills written as Notion pages. Teams author, review, and update them in the workspace they already use, then install them into any agent the skills CLI supports. No…
See how Included Health used Deep Agents, LangGraph, and LangSmith to build Dot, a federated healthcare navigation agent with human handoff and clinical oversight.
Deep Life Sci is LangChain's open source agentic assistant for clinical and lab scientists. It pulls from 600K+ ClinicalTrials.gov studies, 29M PubMed abstracts, and 12M PubMed Central full-text articles, with sandboxed sub-agents for real data analysis.
The five criteria for evaluating a database for AI agents are branch isolation, serverless...
Provisioned, resource-consumption, and request-based cloud pricing compared, with worked examples showing which model is cheaper for each workload shape.
A new creature-catching adventure is ready to stream from the cloud this week. Pawprint Studio’s Aniimo arrives on GeForce NOW at launch, inviting gamers to explore the vibrant continent of Idyll across supported devices. Also this week, 007 First Light receives a path-tracing up…
Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solutio…
Cooley built GO Public with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus judgment where it matters most.
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
A crowdsourced game built on Olmo 3 showed how people can exploit unexpected model behaviors to stress-test prosocial AI evaluations—and how open access to a model’s internals can help researchers understand why those tests break.
AI Gateway Production Index — September 2026 Every month, AI Gateway routes tens of trillions of tokens between production applications and AI labs. That traffic gives us a view of what AI usage actually looks like in today's enterprise, and we publish it here monthly. See the Pr…
OpenAI for Law brings frontier intelligence for law, custom firm workflows, connected legal data sources, and legal-grade controls for confidential client work.
A central goal of autonomous reinforcement learning is continuous policy training without external resets. However, existing paradigms largely depend on underlying environmental reversibility, a property absent in real world manipulation, where events such as pushing objects off…
4-bit Rotational Quantization in Weaviate 1.39: the SIMD performance work, a centered tier, scaling analysis and a TurboQuant comparison.
Phylo cut inference cost 60% while doubling users month-on-month, running Biomni Lab's long-horizon biology agents on open models on Fireworks.
GPT-Live 1 from OpenAI is now available on AI Gateway. GPT-Live 1 is a full-duplex voice model and can listen and speak at the same time. Many voice models use turn detection to respond. Full duplex removes that boundary, so a user can pause, interrupt, or add detail while GPT-Li…
You can now connect native Marketplace resources to custom environments. Previously, resource connections could only target production, preview, and development environments. Choose custom environments when connecting a resource from the Vercel dashboard, Vercel CLI, or REST API.…
TypeSafe’s Jev model is now available through Netlify’s AI Gateway with zero configuration required. Install @typesafe-ai/sdk and use it directly in your Netlify Functions — no API keys to create, no provider config, no base URLs to wire up. AI Gateway handles credentials automat…
The pgAdmin Development Team is pleased to announce the release of pgAdmin 4 version 9.18. This release of pgAdmin 4 includes 29 bug fixes and new features, including fixes for four security vulnerabilities (CVE-2026-86861 through CVE-2026-86864). For more details, please see the…
The SaaSpocalypse was a useful warning for the software industry, but SaaS platforms that help businesses run core operations are more deeply embedded. New platform businesses on Stripe are up 182% year over year.
GitLab.com hosts millions of projects for teams of every size that need a platform they can rely on. Demand is climbing quickly, and we expect platform load to grow several times over this year. Predictable limits are what keep GitLab.com fast for everyone on it, including the au…
There’s no single best model for every software development task. Implementing a new feature, diagnosing a failed pipeline, and resolving security vulnerabilities all place different demands on the model handling them. GitLab Duo Agent Platform is expanding GitLab-managed model c…
We believe that there is an ongoing campaign targeting rust-lang members and owners of popular crates that is attempting to compromise devices and accounts in order to use them to publish malware. What we've seen A video call is set up for something positive — maybe for a job, ma…
We’ve released updates for multiple major MPS versions that fix several issues. DOWNLOAD MPS 2026.1.1 Check out all the updates in each particular version below: MPS 2026.1.1 The Projectional Agent Toolkit receives several practical improvements that allow agents to: See an…
A modern storefront can look healthy while malicious JavaScript quietly siphons revenue, hijacks clicks, or rewrites analytics. See how Cloudflare's machine learning models surface evasive client-side attacks for analyst investigation.
Kubernetes v1.37 brings important storage security features: emptyDir permission modes and bind mount options. They help application programmers and security professionals implement rigorous security policies, for example, prohibiting deletion of files across containers or execut…
Hobby projects now retain fewer deployments past the 30-day retention window. Hobby teams get 10GB of Deployment Storage. Every deployment you keep uses some of it, and going over the limit can block you from deploying until you free some up. Deployment Retention for Hobby teams…
AWS has reimagined the getting started experience with smart and sensible defaults to help developers get started fast so that they can focus on building. New customers can sign up and get started right away with $100 in Free Tier credits, managed project environments, and simpli…
OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior.
Builds using Secure Compute or Static IPs now start 64% faster, with the average time from deployment creation to build start dropping from 6.7 seconds to 2.4 seconds. Previously, each build waited for a new build container to boot with its network configuration. These builds now…
Mem0 is now available as a native integration on the Vercel Marketplace, giving your AI agents and apps long-term memory. Mem0 remembers user preferences, facts, and context across sessions, so your app stops starting from scratch. Install from the Marketplace with integrated bil…
Learn what is new in Visual Studio Code 1.138 Read the full article
OpenAI and AARP are bringing free, hands-on ChatGPT workshops to 1,000 older adults across 10 U.S. cities to build practical AI skills safely.
Gartner highlighted Microsoft’s unified single-product architecture, flexibility across hyperconverged and disaggregated architectures, and more. The post Microsoft recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Distributed Hybrid Infrastructure appeared first on…
Agent programs in healthcare and life sciences are being built under a different set of constraints than those in most industries. There’s plenty of upside if the constraints can be resolved. Success can mean hours of manual review compressed into minutes, data spread across a do…
System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportiona…
Cohere and Aleph Alpha partner to launch the first transatlantic sovereign AI solution with dual headquarters in Berlin and Toronto, providing secure and governable AI.
Modern development tools, especially IntelliJ IDEA, have such comprehensive debugging support that for virtually any niche use case, there is a specialized tool for the job. This can make it hard to know where to begin. If you are new to debugging tools and want the biggest retur…
We’ve released version 2026.2.2 of ReSharper, Rider, and.NET tools. You can install this update from inside the tools themselves, through the Toolbox App, or on our website. Here’s what’s new in this update. Rider 2026.2.2 AI Agent Setup widget We’ve recently added a wide range o…
AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center. Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its…
Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations with HubSpot and Shopify.
We’ve all been there: you join a new project, and the first thing you ask for is the architecture diagram. You’re handed a diagram that looks great, but after a week of debugging, you realize it’s six months out of date. Service A hasn’t talked to Service B since the…
GPT-6 Astra helps Hex’s data agents turn answers into interactive visualizations that employees are proud to share.
Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes.
Open, private and multilingual AI is coming to your web browser. Mistral and Mozilla team up to put powerful, trustworthy AI where you already browse.
The people building AI, writing on their own blogs. Last 30 days.
overshadowing more efficient GPT6 models from OpenAI
Yesterday was Grok 4.7 ( pelicans ) and MiMo v2.6 Flash/Pro ( more pelicans ). Today Anthropic released Claude Opus 5.5, and around an hour later OpenAI released GPT-6 Sol and GPT-6 Luna. It's going to take a while to get a good read on all of these new models, but here are my im…
We talked to Google’s Oscar winning “Giganerd” about automating science, solving climate change, and how future generations can contribute to science in the age of superintelligent AI
Notes on MiMo-V2.6 Pro's GQA and sliding-window attention, agent training tasks, reward signals, and large RL batches.
Podcast #19
crowning a new Chinese frontier lab
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling "System One models" (I'm with Maggie Appleton, I think "decision models" is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts t…
The expanded form of a testimony I prepared for Congress.
A short note on Jev's generalization, possible encoder-style architecture and training, and Choice and Noul API examples.
Interesting & joyful things from the previous week
An “AI moderate’s” view on recent events and the trajectory of frontier models.
Using your deep knowledge, wide knowledge, taste, and agency
This document curates the most common questions Shreya and I received while teaching 5,000+ engineers and PMs AI Evals. Warning: These are sharp opinions about what works in most cases. They are not universal truths. Use your judgment. How to use this FAQ Browse the questions tha…
You can just prove things, apparently.
“We never want to be in a situation again where we underestimate the AI.”
I have a lot of mixed feelings about AI and LLM technology. I’m fascinated by its effect on our profession, excited by the potential gains in productivity - and thus the products we could rapidly build. On the other hand, I’m fearful of the damage AI might cause: agent swarms tak…
Reports of agentic hacking continue, in this case it happened back in May and it seems OpenAI did not disclose that they were responsible. Simon Willison sees two options: After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine…
Sumeet Gayathri Moghe finds many folks building presentations get tangled in building slides without a coherent narrative. He advises distilling the big idea, visualizing the audience, and building a structured storyline. more…
My take on AI model pacing as a framework for release checks and the competitive pressure around model releases.
Yesterday David Sacks wrote a tweet and within a few minutes people did, what they usually do, and they asked Pangram if it was AI. And Pangram said it’s entirely AI generated. To which David replied that these AI detectors are bogus. Now Pangram has a pretty low false posi…
What it takes to run agents in a codebase older than the team
Here's a neat thing I had ChatGPT Work with GPT-6 Astra (Max) do this morning: I live at <my address>. Figure out 5K and 10K running routes from me that loop from my house. Use OSM data. It worked for 27 minutes and produced exactly what I'd asked for, as both an embedded v…
Interesting & joyful things from the previous week
OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx - three of the four authors of the report on the agent attack on disused wikis ( previously ) last week. This time they're noting that it lo…
This week some flavor of “AI is going to kill us all” went viral. In particular one where an employee put his personal probability of that happening above 10%. Which made me go to the Wikipedia page of P(doom) and I realized that Dario Amodei’s apparent probabil…
“We're nowhere near the ceiling.”
On the Navier–Stokes Millennium Prize Problem introduces an impressive result from OpenAI, who used an unreleased model to produce a resolution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems that have been subject to a $1,000,000…
Breaking down 6 years of pretraining progress into data vs model improvements
I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The E…
Interesting & joyful things from the previous week
From the Hugging Face Incident to Twilight Factories
Agents can finish the task without teaching you anything. Building expertise now has to be deliberate.
A practical guide to auditing what your coding agent still needs.
Tutorials, case studies and technical deep dives.
Everything that joined the OpenRouter catalog.
New arXiv papers in AI, language, machine learning and software engineering. The 30 newest; with Jev on, the 30 most relevant.
Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive LLMs by enabling non-autoregressive text generation. However, their practical deployment remains limited by inefficient inference, largely due to the absence of effective Ke…
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observabl…
A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to alloc…
Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what, whom each statement concerns, how individuals perceive one another, what information is shared by the group, and…
Agents often work on complex problems that require millions of tokens of context, which necessitates compacting across sessions due to limited context windows. We develop CliffCompaction, an autocompaction technique that reduces cost by up to 50% under a bounded context while mai…
We introduce SWE-Serve, a benchmark for evaluating agents on production inference engineering tasks. Implementing an inference feature can require coordinating multiple changes across the serving stack, including model support, runtime execution, and public APIs. Existing benchma…
Agents using the Model Context Protocol (MCP) rely on semantic matching to select tools from third-party servers, exposing a semantic supply-chain risk through attacker-controlled metadata and outputs. We introduce A2M (Attraction-to-Manipulation), a two-stage black-box framework…
Large language model (LLM) agents often handle streams of related tasks, yet standard harnesses repeatedly ask the model to reconstruct the same control decisions inside each task's context. We study whether task feedback can instead turn recurring control into reusable executabl…
Typed decision models are built for settings where model outputs are consumed directly by software. Instead of generating free-form text, they return a decision over a predefined set of options. By construction, every output conforms to the required schema. Yet this guarantee doe…
X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray collapses 3D anatomy into a 2D projection, causing structures to overlap and anatomical boundaries to be ambiguous, even to experts.…
Large language models are increasingly used to generate SystemVerilog Assertions from natural-language specifica- tions and register-transfer-level designs. Existing datasets and benchmarks support important goals such as large- scale training, formal evaluation, specification-to…
Large language models (LLMs) are increasingly applied to the automated repair of C/C++ security vulnerabilities, and compile rate is a commonly reported proxy for progress: whether the generated patch compiles. We argue that compile rate is a scientifically unreliable metric for…
Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters or neighborhood size. Conversely, we propose automatic depth-based local center clustering (A-DLCC)…
Detection of overlapping communities is essential for modelling networks in which nodes participate simultaneously in multiple structural or functional groups. Existing graph neural network approaches commonly rely on local message passing, which can obscure community boundaries…
AI tools for digital product design now offer prompt-to-design capabilities, allowing designers and their non-designer colleagues to create prototypes through conversational workflows with large language models (LLMs). While these tools promise time savings, experimental evidence…
Long-context LLMs focus on retrieving distant evidence from extensive context, yet existing work has largely focused on overcoming distance alone. In this work, we identify the Proximity Trap, insufficient attention to distant evidence often arises less from distance itself than…
Software vulnerabilities are often discovered long after they are introduced, making it difficult to identify the vulnerability-inducing commit (VIC) responsible for introducing the underlying vulnerable condition. Existing VIC identification techniques largely rely on git blame…
Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathematical and code reasoning substantially impaired. Long generations often degenerate into repetitive loops, exhausting the decoding…
Offline reinforcement learning and off-policy evaluation evaluates dynamic treatment rules based on retrospectively collected data prior to deployment. In recent AI applications, state and reward information is recorded as complex text or image, which recent AI advancements such…
A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to explore this quantum-classical boundary, as they capture both the classical field behavior and the intrinsic quantum nature of ligh…
Large language models increasingly tackle hard reasoning problems by spending more test-time compute, yet the dominant strategy remains naive repeated sampling: draw many independent solutions and hope one is correct. Because such sampling explores only through local decoding noi…
A coding agent must emit a valid tool call--a parseable invocation of a tool in the provided schema--before the harness can execute its chosen action. We study how local serving stacks affect this protocol step and show that measured outcomes can depend on the serving layer rathe…
Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discriminative capability of interpretable stylometric features extracted solely from submitted text. Using data from 90 participants who…
Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, ca…
Generative AI (GenAI) applications have flourished enabling users to chat with large language models, and to create agents to act on their behalf for a variety of tasks. The pace of development of capabilities in this field is incredibly fast with security and safety taking a bac…
In grokking an early fit to the training data separates from a much later improvement in generalization. During this delay, training can move from a fixed neural tangent kernel (NTK) regime to one in which task-relevant kernel eigendirections continue to evolve. We provide a quan…
Collaboration topology shapes both the performance and execution cost of LLM-based multi-agent systems. Because tasks differ in complexity and required capabilities, recent approaches generate task-specific collaboration graphs that specify agent participation and information flo…
Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit attributes needed to be joined. We study Discovery-Driven Integration, where the relevant sources and their missing relational structu…
We study statistical rates in entropic optimal transport in the semi-discrete regime where one measure has finite support and the other is subGaussian. Our main result establishes parametric convergence rates for the empirical dual potentials to their population counterparts, wit…
Successful agent execution need not identify which future product improvement its user would value. We present a decision-specific audit that maps a declared observation channel and product-value contrast to compatible intervals and witness populations. Its foundations are establ…
Stable versions of the tools and libraries we track. Prereleases are left out.
New repositories under the GitHub topics we track, and packages published to npm.
GitLab Duo provider for Vercel AI SDK
Integration between n8n workflow automation and Model Context Protocol (MCP)
Coherence infrastructure for self-evolving AI agents — on the Claude Code or Codex subscription you already have.
把十二种 IM 渠道和公网 AI Office 接入本机 DeepSeek Harness。 Connect twelve IM channels and a public AI Office to a local DeepSeek Harness.
Author MCP servers for Lovable apps. Declare tools with defineTool, register them in defineMcp, and a framework adapter (TanStack or Supabase Edge Functions) emits the route(s) at build time.
Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.
cli for mastra
MCP server for Hostinger API
The **Model Context Protocol (MCP) client** for the [AI SDK](https://ai-sdk.dev/docs) lets you connect to MCP servers and use their tools with AI SDK functions like `generateText` and `streamText`.
MCP (Model Context Protocol) adapter extension for Pi coding agent
Open-source coding-agent harness you can actually change — own the loop (prompts, gates, routing, skills, terminal states), use any model, run long tasks while you're away.
Headless browser automation server and OpenClaw plugin for AI agents - anti-detection, element refs, and session isolation
Filesystem-first framework for durable backend AI agents that run anywhere.
Plannotator Pi extension - interactive plan review with annotations, annotate agent messages, and review code/PRs
GitLab MCP server for projects, merge requests, issues, pipelines, wiki, releases, and more
Mock infrastructure for AI application testing — point your SDK at one local port and every provider, protocol, and service answers deterministically.
Coding agent CLI with read, bash, edit, write tools and session management
Unified LLM API with automatic model discovery and provider configuration
General-purpose agent with transport abstraction, state management, and attachment support
Mobile app automation and verification for AI coding agents. CLI, MCP server, and typed Node.js API for iOS, Android, HarmonyOS, TV, web, macOS, and Linux.
Markdown and HTML renderer for Svelte 5 — built for rendering streaming AI agent output from Claude Code, ChatGPT, and agentic workflows. XSS-safe defaults, streaming-aware sanitization, token caching, TypeScript types, and Svelte 5 runes.
GitHub PR notification signal provider for Mastra agents
Superfast runtime validators with only one line
Dotted thought-orb loading indicators for AI & agent UIs — nine tuned states, two sizes, auto dark/light
805 verified examples of Jev — TypeSafe AI's System One decision model — indexed by the decision each one makes, not the blog that mentioned it. Every cited call site is re-read by CI each week. Bilingual EN/中文, JSON schema, and a cross-platform compatibility table.
Different models, the same brief - a collection of demos built from shared prompts, with source, screenshots, and notes.
微信（Windows 4.x）旁挂的回复辅助：窗口截图 + 本地离线 OCR 读对方消息 → Jev 判断意图 → 3 条候选一键填入，发送永远手动
微信消息意图识别悬浮窗（macOS）：看屏 + 本地小模型判断意图和风险，再按话术生成回复候选。纯只读、不注入微信。
装在手机上的对话副驾：在微信 / QQ / X / 飞书里读懂对方、给出候选回复、一键填入输入框，发不发由你。非侵入，只读屏幕，不 hook 不改包。
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating)
AI video prompt cheat sheet & Claude Skill: cinematic camera angles, camera movement, lighting, composition, color grading for Veo 3, Kling, Sora, Runway, Midjourney. 700+ terms with Vietnamese explanations.
Specialised AI models for logo design — a brand-analysis model turns a business into constraints, typography and symbol models construct the mark, and a composition engine produces real lockups and clear-space rules. Early access open.
660+ muapi-hosted generative-media models plus community-submitted third-party API tools (SEO, enrichment, social, scraping) — one YAML file per entry, browsable by capability.
Jev-powered semantic code search for coding agents — find behavior across repositories via CLI or MCP, with exact source excerpts and line numbers.
Awesome list of TypeSafe AI Jev use cases: 74 demos ranked by likes, 150+ GitHub repos, limits, cost and API examples. CC0, sponsored by AY Automate.
A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.
Generate realistic teacher documents (ID cards, licenses, letters) for 13 countries via MCP.
A curated list of Jev use cases, projects, SDKs, and resources. Jev is TypeSafe AI's System One model for fast, typed decisions in software — Choice, Score, and Noul with calibrated probabilities.
A curated, source-backed list of projects built with Jev, TypeSafe AI's System One model for typed decisions.
An agent skill that finds where a photo was taken — OpenStreetMap geometry, elevation skylines, satellite imagery and street view — and shows its work. Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot.
中国创作者给全球 AI 出的一场真实大考 | A searchable index of Bilibili AI Infinite Arena videos.
Awesome Jev: source-backed open-source ecosystem radar, plain-language project discovery, and automatic GitHub sync
Jev-compatible API endpoint based on open models (prefill-only)
Audit a GitHub repository before launch - ten readiness checks scored 0-10: name, description, topics, README, license, demo, installation, contributing guide and issue templates. Free and open source.
A curated list of public projects, integrations, and discussions built on Jev — TypeSafe AI's System One model for typed decisions.
Local AI music studio unifying ACE-Step 1.5 and YuE2-3B in one Vue interface — text-to-music generation, stem separation, MIDI transcription, and LoRA fine-tuning, with a built-in multitrack DAW.
Awesome Jev: a source-backed field guide to TypeSafe's System One model, with SDKs, live demos, agent tools, and independent evaluations.
Audit your GitHub repository search ranking signals. Checks name, description, topics, README, stars, forks, and activity.
Astra plans and reviews; DeepSeek Flash builds. A native Codex workflow with phased tasks, verification, safe installation and reversible setup.
Local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev.
An mcp connector to evaluate anything fast and cheap. Give your AI agent direct access to typesafe ai's jev model.
Explore sleep, training, mood and nutrition together using local records, traceable calculations and optional MCP tools for your preferred AI client.
Computer use for about $0.0002 a step: OCR the screen, classify the next action with TypeSafe, click. macOS.
Track and visualize the stars history of any GitHub repository. Open-source growth analytics and velocity tracking.
Browser automation where an LLM plans and Jev (Typesafe System One) decides. Library, CLI and MCP server.
This GitHub repo is a powerhouse collection of AI APIs you can start using immediately to ship real products — from LLM tools to computer vision and agents. One of the most valuable AI API lists on GitHub.
Follow us on LinkedIn, X, Bluesky, Instagram | Follow Insiders Changelog on X or Bluesky
Last updated: September 22, 2026
Welcome to the 1.140 Insiders release of Visual Studio Code.
These release notes cover the Insiders build of VS Code and continue to evolve as new features are added.
You can still track our progress in the Commit log and our list of Closed issues.
Happy Coding!
We really appreciate people trying our new features as soon as they are ready, so check back here often and learn what's new.
Released September 23, 2026 Stable
macOSUniversalIntelApple silicon
Linux.deb.rpm.tar.gzArm instructionsSnap
Already installed? Use Check for Updates in VS Code. For upcoming features, use the Insiders build.
This release makes large agent session lists faster, extends Dev Container support to remote projects, and improves everyday editing.
Remote Dev Container sessions: Run agents inside your project's Dev Container on SSH, Tunnel, and WSL hosts.
Session list improvements: Load large session lists faster, fit more sessions on screen, and in-place session renaming.
Editor experience: Identify wrapped lines at a glance and avoid duplicate closing brackets as you type.
The agent host runs agent harnesses in a dedicated process based on the Agent Host Protocol (AHP), so you can connect to the same session from multiple VS Code windows. Learn more about its architecture and workflows in the agent host blog post.
Setting: chat.agentHost.devContainer.enabled (Agents Window only)
Let agents build and test your remote project with the right tools and dependencies, without duplicating toolchain setup on your laptop or the remote host. This release extends Dev Container sessions from local folders to projects on SSH, Tunnel, and WSL hosts.
To get started, enable chat.agentHost.devContainer.enabled and select Use Dev Container from the folder menu in the Agents Window. The remote folder must have a supported Dev Container configuration, and Docker must be available on the remote host.
Note: Dev Container sessions are rolling out gradually, so the setting might not be enabled by default for you yet. You can enable the setting manually to try the feature now.
VS Code loads and refreshes large agent session lists faster. The agent host keeps lightweight session and chat metadata in a central catalog instead of opening every conversation database each time the list is built. Full conversation content remains isolated in the individual session and chat databases.
The improvement grows with the number of sessions because the previous approach did work in proportion to your session count. Measured with around 645 sessions on a development machine:
| Operation | Before | After | Improvement |
|---|---|---|---|
| First session listing after launch | 1.3 seconds | 0.1 seconds | About 12x faster |
| Refresh the session list | 0.6 seconds | 0.15 seconds | About 4x faster |
If you have few sessions, expect a smaller difference. Sessions created before this release are migrated automatically in the background.
Fit more sessions in the sessions list by enabling Compact View in the sessions list view of the Agents Window.
Compact rows show the session title at rest and reveal workspace details when you hover over or focus the row. A row expands when the session needs input or approval, so these requests remain visible.
Progress also appears on the row for the chat that owns the work. When you collapse a session, the parent row summarizes progress from its hidden chats.
Disable Empty Groups from Filter Sessions to hide empty custom groups and the empty Chats section. This preference is stored in your profile and resets with the other sessions list filters.
Rename a session or nested chat directly in the sessions list. Double-click its title, use the Rename context menu action, or focus the row and press F2 for a session or F2 for a nested chat. Inline validation prevents blank titles, and canceling restores the previous title.
Setting: sessions.showChatTabs (Agents Window only)
An agent session can contain multiple chats, each representing a different conversation or context. When a session contains multiple chats, choose the presentation that best fits your workflow from the session header menu:
Switching presentations preserves your open chats, active chat, and conversation state. In Single mode, chats that you explicitly open to the side remain independent panes with their own header actions.
Thank you to everyone who submitted a name for the VS Code pet. The naming contest closed on September 17, 2026, and we're reviewing the eligible entries. We'll announce the winner and the pet's new name soon.
While you wait, enter /vscode-pet in chat to meet your companion and explore all its interactions and reactions.
Display word wrap indicators to make wrapped lines easier to identify. An arrow at the word wrap column on the right side of the editor indicates that a line wraps.
VS Code avoids inserting duplicate closing brackets when you type an opening bracket. If a matching closing bracket exists, VS Code uses it. Otherwise, VS Code inserts one.
AuthenticationSession exposes an access token but no information about how long that token stays valid. An extension that passes a credential to an SDK with its own refresh callback cannot distinguish between a token that never expires and one that is about to expire. As a result, the extension either refreshes the credential unnecessarily or lets a long-running operation fail when the token expires.
The authSessionExpiration proposal adds an optional expiresAfter property to AuthenticationSession:
export interface AuthenticationSession {
/**
* The access token's remaining lifetime, in milliseconds, when the authentication
* provider returns the session.
*/
readonly expiresAfter?: number;
}
The value is the remaining lifetime when the session is returned rather than an absolute expiration timestamp. The extension host can run on a different machine than the client, and the two clocks can disagree. Authentication providers that return a cached session recompute the value each time and leave it undefined when the token's expiration is unknown. The built-in Microsoft account provider supplies this value.
Try it out and let us know what you think in the API proposal issue. To learn how to build against a proposal, see using proposed APIs.
None
For users whose organization disables Agent mode by account policy, ensure the Welcome invitation opening is hidden and that alternative methods of launching the disabled Agents Window (for example, code --agents disallow circumvention of the control). #336968: Fix account policy enforcement in the Agents window
For users with enterprise-managed OpenTelemetry (OTel) settings, fix a race condition in the configuration of OTel in the Local (i.e. non-Agent Host Harness) to ensure OTel is not dropped. #336701: Fix Enterprise Managed OTel Race in Copilot Extension
Contributions to vscode:
Contributions to our issue tracking:
We really appreciate people trying our new features as soon as they are ready, so check back here often and learn what's new.
If you'd like to read release notes for previous VS Code versions, go to Updates on code.visualstudio.com.
Agents run in a loop: an LLM decides what to do, a tool executes, a model evaluates the results, and then continues in that loop until the task is complete.
Agents and LLMs were initially difficult to integrate into software applications, which depend on structured data and predictable interfaces. Two primitives emerged that made this much easier:
But even with those in place, the agent loop is still slow and costly: every decision requires another model call.
Enter, Jev. Jev is a new model released from TypeSafe AI. The company reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks.
This post covers how Jev works, where it fits into the agent loop, and how to use it with LangChain.
Jev is actually not a traditional LLM, it doesn’t generate text. It’s what the TypeSafe AI team calls a System One model:
📖 System One models are a class of AI models built to make fast, structured decisions that software can use directly. A System One model evaluates a state and returns typed answers and probabilities.
It’s trained using reinforcement learning for calibrated decisions (RLCD). Your code uses those results to guide what an agent does next, without a full chat LLM call for each decision.
To invoke a Jev model, you send it a state (the context) and questions about that state. Here’s a single-question version of the support-ticket example in their docs:
{
"model": "jev-latest",
"state": "Hi, I've been trying to connect my Stripe account for 3 days and it keeps failing. I'm losing sales. Please help ASAP.",
"questions": {
"is_urgent": {
"type": "noul",
"instructions": "The message conveys urgency or time-sensitivity"
}
}
}The docs’ example gives this urgency answer, shown here without the rest of the response:
{
"is_urgent": {
"type": "noul",
"noul": 0.999
}
}That’s a 99.9% probability that the message is urgent, which your application can use to prioritize the ticket.
There are three types of supported questions:
One key feature here is that you can ask multiple questions about the same state in one request.
💡 System One models evaluate every question in a request in parallel. Adding questions barely changes the response time and costs only the tokens for the extra questions, which are cheap.
For an example of asking multiple questions about a support ticket, see the TypeSafe Quickstart.
In sum, unlike traditional LLMs, Jev is neither constrained by text generation or sequential decision making!
LangChain's provider agnostic model is well suited for supporting Jev alongside thousands of other integrations and model providers.
The LangChain integration exposes Jev through TypeSafeClassifier. You pass your state and questions to .invoke(), and get classification results rather than a chat response.
Install langchain-typesafe and set your TYPESAFE_API_KEY, then make a call:
from langchain_typesafe import Noul, TypeSafeClassifier
classifier = TypeSafeClassifier()
response = classifier.invoke({
"state": (
"The deploy failed twice and customers are seeing 500s. "
"Can someone look now?"
),
"questions": {
"urgent": Noul(
instructions="Does this need attention right now?"
),
},
})
urgency = response.nouls["urgent"].noulThe state can be text, structured data, or LangChain messages. That makes it straightforward to call Jev from a node or middleware hook using the context your agent already has.
You can build this into custom middleware or tools!
Jev isn’t a drop-in replacement for an LLM. It doesn’t generate text, but it can handle classification tasks we often use LLMs for today, without the same latency and cost. That makes it a promising complement to the model driving your agent: use an LLM for open-ended reasoning and generation, and Jev for fast, structured decisions along the way.
A simple lookup doesn’t need the same model as a difficult debugging task. Model-routing middleware lets Jev assess the request and choose a model based on criteria you define, so fast and inexpensive for straightforward tasks, more capable for complex ones.
from langchain.agents import create_agent
from langchain_typesafe.experimental.middleware import (
ModelChoice,
ModelRouterMiddleware,
)
router = ModelRouterMiddleware(
choices={
"fast": ModelChoice(
model="openai:luna",
criteria="Direct lookups, extraction, and localized changes.",
),
"powerful": ModelChoice(
model="openai:sol",
criteria="Architecture and high-stakes decisions.",
),
},
instructions="Choose the least costly model that can complete the task.",
)
agent = create_agent("openai:gpt-5.6-luna", middleware=[router])The router selects a model from the latest user message and uses it throughout the run. The probabilities and confidence remain available in agent state, too.
Agents are still inherently untrustworthy. An agent can receive bad instructions (either naturally or from a motivated enough attacker) which can persuade it into taking actions we didn’t want it to.
Coding harnesses like claude, codex, cursor have shipped some kind of way to classify dangerous actions before they’re taken which has slowly helped to build trust in agents. Up until now, this classifier step has been locked away in the closed source parts of the harness.
Now that a cheap and performant classifier model exists, we can take the same pattern and adopt it to all agents!
from langchain.agents import create_agent
from langchain_typesafe.experimental.middleware import (
AutoModeMiddleware,
)
guardrail = AutoModeMiddleware(tools=["bash"])
agent = create_agent("openai:gpt-5.6-luna", middleware=[guardrail])AutoModeMiddleware uses Jev to check tool calls for risky decisions it may take, and block calls before the tool executes.
We're pretty thrilled about Jev and the possibilities that come with it. A few cool projects that we’ve seen already: Kyle Jeong from Browserbase is powering browser use agents for fractions of a cent, Jarrod Watts built a live trading agent, and Ryan Vogel is doing email triage at scale.
New models drop every week at this point, but this one had a pretty outsized response. We’re excited to see what you build with LangChain and Jev.
Let us know what you think on the forum, tag us on X and share what you’re building, or engage with LangChain issues!
NVIDIA AI Day Singapore, which takes place Sept. 22-23 at the Raffles City Convention Centre, is offering attendees opportunities to explore the hands-on training, expert-led sessions and advanced tools to accelerate their work in AI and high-performance computing.
At the event, NVIDIA and its partners are showcasing breakthrough AI advancements across the Southeast Asia region at large.
Read more about these announcements below.
AI is becoming a matter of national strategy, with governments looking to move from pilots to production and deliver impact at scale, while building trusted AI capabilities that reflect local languages, cultures, priorities and economic needs.
NVIDIA is working to enable all nations to be AI nations — providing the technology, infrastructure, ecosystem and expertise needed to make this possible.
To accelerate this transition across Southeast Asia, NVIDIA is helping nations move AI from experimentation to production-scale deployment through open models, developer tools and a broad partner ecosystem.
Together, NVIDIA and its partners are focusing on four key areas:
Singapore’s HTX (Home Team Science and Technology Agency) is embarking on research using the NVIDIA Nemotron 3 Super and Nemotron 3 Nano Omni models to advance AI for public safety. Nemotron Super has the potential to support the agency’s complex reasoning and agentic workflows, while Omni’s unified vision, audio and language capabilities could help HTX develop multimodal applications grounded in real-world operational data. Together, the models could strengthen HTX’s ability to deploy secure, locally controlled AI across Singapore’s Home Team.
NCS is advancing agentic AI adoption across enterprises and the public sector, using Nemotron models and the NVIDIA Blueprint for video search and summarization (VSS), while advancing physical AI for practical humanoid robotics applications, to address security, responsiveness and data governance requirements. ST Engineering is using NVIDIA NeMo tools and NVIDIA cuOpt software to develop its AI Studio platform and deploy agentic AI solutions across its businesses such as Marine MRO.
Beyond Singapore, similar work is already underway across the region. Malaysia’s YTL AI Labs is fine-tuning Nemotron models for enterprise and citizen services, while Viettel AI is doing the same for Vietnamese-language applications.
In Thailand, the Big Data Institute and iApp Technology, as members of the ThaiLLM Collaboration, are exploring Nemotron as a foundation model. With an initial focus on legal applications, iApp Technology is adapting Nemotron 3 Nano by fine-tuning OpenThai 2.0 Legal with Thai-language legal data using the NVIDIA NeMo framework.
The model is released as open source for the Thai developer community and serves as the engine for Thanoy, the company’s legal-assistant chatbot, which already serves approximately 43,000 users.
In Brunei, Antrique built an AI innovation platform to help boost productivity across the nation’s food sector.
Across the region, NVIDIA Cosmos open world models and the NVIDIA VSS Blueprint are advancing smart city solution development. Malaysia’s ITMAX uses Cosmos with VSS to improve city traffic operations, while Thailand’s AS-TECH applies the same stack to improve passenger flow in airports.
Learn more about NVIDIA Nemotron and Cosmos models and read about NVIDIA’s participation in the Open Secure AI Alliance.
Leading enterprises, technology providers and research organizations across Southeast Asia are building region-specialized AI models and applications with NVIDIA Nemotron open models, datasets and libraries — accelerating the development of AI tailored to the region’s languages, industries and communities.
NVIDIA Nemotron provides a foundation for regional AI ecosystems, letting organizations customize, control and own models that address their specific requirements. Nemotron also offers persona datasets that provide locally relevant synthetic data reflecting the region’s populations, languages and workforces.
Across the region, partners are building applications spanning public services, services and healthcare.
Enterprises in Singapore are adopting NVIDIA Nemotron for various use cases. AI Singapore is expanding its SEA-LION model family to include the NVIDIA Nemotron open models and NVIDIA NeMo tools. SEA-LION is an open model family designed for Southeast Asian languages and cultures.
Hummingbird Bioscience, together with LynxKite, is building an explainable Toxicity Knowledge Graph powered by Nemotron 3.5 Lightning and NeMo Retriever with in silico simulations. The collaboration aims to integrate complex public and proprietary data across diverse third-party file formats, creating a comprehensive, unified foundation for robust analysis and reasoning that helps de-risk and accelerate drug discovery and development.
Bitdeer AI co-hosted the Open Models AI Codefest with NVIDIA, providing the GPU cloud infrastructure that enabled developers across the region to use NVIDIA Nemotron open models, datasets and training recipes to accelerate localized applications across critical sectors, including healthcare.
In Vietnam, Viettel AI has been extensively fine-tuning Nemotron 3 Super for the Vietnamese language and agentic applications. The model achieved the highest ranking on both the VMLU benchmark and the company’s in-house product benchmark, and it’s set to be adopted in Legal AI — an agent harness that will serve both internal Viettel Group employees and external customers.
Also in Vietnam, FPT Smart Cloud codeveloped Nemotron-Personas-Vietnam, an open dataset grounded in Vietnamese demographic and cultural data, and is enabling local developers to post-train and evaluate localized AI models.
Get started building with NVIDIA Nemotron using skills and playbooks that help partners customize Nemotron open models for their languages and domains.
Stay up to date on agentic AI, NVIDIA Nemotron and more by subscribing to NVIDIA news, joining the community and following NVIDIA AI on LinkedIn, Instagram, X and Facebook.
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Sea Limited, a global technology company founded in Singapore, is the first enterprise in the ASEAN region to adopt the NVIDIA Vera Rubin platform, further strengthening the company’s AI capabilities to better serve and create meaningful economic opportunities for millions of consumers and small businesses across Southeast Asia.
Serving hundreds of millions of users through its Garena, Monee and Shopee platforms, Sea has already deployed AI across its businesses to make its services more useful and accessible. Now, with NVIDIA Vera Rubin, Sea will build on these efforts, developing and deploying AI models and intelligent agents at greater scale to serve the evolving needs of its communities.
On Shopee, AI is already helping sellers reduce the time and effort required to create informative product listings, improve product discovery and deepen customer engagement, while enabling better-informed business decisions. These capabilities enable small- and medium-sized enterprises in Southeast Asia, many of which operate with limited resources, to scale their businesses using enterprise-grade AI technologies previously accessible only to large corporations.
Across Monee, the digital financial services division of Sea, AI is being applied in areas such as fraud detection and credit risk assessment, supporting Monee’s ability to deliver simple, accessible and inclusive digital financial services. For small businesses and consumers underserved by traditional financial services, these capabilities can expansively broaden access to financial tools.
At Garena, Sea’s digital entertainment and video game arm, AI is used to enhance gaming experiences supporting the company’s efforts to create engaging, inclusive and safe online spaces that bring players together.
NVIDIA Vera Rubin will provide the advanced computing infrastructure to build on this foundation — enabling Sea to accelerate innovation, scale AI applications more broadly and deepen its impact for the communities it serves.
Learn more about NVIDIA Vera Rubin.
Understand how Copilot agents perform and interact with models and tools. The GitHub Copilot app now supports OpenTelemetry (OTel) configuration through enterprise-managed settings.
OTel is an open source observability framework. Administrators can use it to send agent activity data to their organization’s compatible monitoring tools. This helps teams:
Configure the telemetry property in your enterprise’s managed-settings.json file to enable export and specify the endpoint that will receive the data. Prompt and response content is excluded by default—review your content-capture settings before enabling it.
Learn more about OpenTelemetry for agent monitoring and configuring enterprise-managed settings.
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GitHub Copilot for JetBrains 1.18.0 brings AI-assisted tool approvals, more control over agent conversations, and shared skills and instructions for your organization. You can also review plans with the Codex agent and manage MCP tools with persistent controls.
AI-assisted tool approvals, called assisted approvals, are now in public preview for Copilot agent sessions. Low-risk tool calls receive automatic approval, while higher-risk actions continue to prompt you for a decision.
This gives you fewer approval interruptions for low-risk actions while keeping higher-risk decisions in your hands.
You can now re-edit a previous user message in a Copilot agent session. Before sending your replacement message, Copilot rewinds both the conversation and file changes.
This lets you revise an earlier request and continue from that point, rather than adding another message to correct the direction of the conversation.
Local and Copilot agent sessions now support organization and enterprise skills, along with organization-managed custom instructions. You can use shared skills and organizational guidance in both types of sessions.
The Codex agent now supports plan mode. You can review, refine, or approve a plan before implementation, giving you an opportunity to shape the approach before the agent starts making changes.
A new setting lets you turn the built-in GitHub MCP Server on or off without changing manually configured MCP servers. The built-in server remains enabled by default.
Copilot agent sessions also gain persistent per-tool controls for MCP servers. You can manage individual tools as well as control whether the built-in server is enabled.
A new side-by-side chat panel switcher in the session toolbar lets you chat in the editor while browsing sessions in the tool window. You can keep your conversation open alongside the session list.
Other updates make features and settings easier to discover:
/init tip and grouped it with customizationsThis update improves inline chat reliability, including preserving your edits when requests end and respecting selected thinking effort and context window settings. It also addresses Codex session startup issues, improves behavior across multiple project windows, and restores embedded editors and message re-editing on IntelliJ 2026.3 EAP builds.
Inline chat and its entry points are now hidden in JetBrains Gateway and remote development environments.
If you use a JetBrains IDE version 2025.1, you will see advance notice to upgrade to 2026.1 or later. Support remains unchanged in this release.
We encourage you to try out the latest version of the GitHub Copilot plugin and share your feedback. Your input is invaluable in helping us refine and improve the product.
Your feedback drives improvements. We’d love to hear about your experience in the following channels:
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C++ code intelligence in GitHub Copilot CLI is now faster with support for whole codebase indexing.
C++ repositories can contain millions of lines of code across deeply connected source files and headers. Without a reusable index, code-intelligence requests may need to rediscover project information as you navigate, making it slower to find a definition, locate references, or understand unfamiliar code.
Whole codebase indexing (WCI) creates a persistent index of symbols across your C++ project, including files that aren’t currently open. The Microsoft C++ Language Server uses your project’s compilation information to resolve types, symbols, includes, and relationships between files. WCI makes that symbol information available for reuse instead of rediscovering it for each request.
You spend less time waiting for definitions, references, implementations, and symbol search results, and more time reviewing, understanding, and changing code.
Whole codebase indexing is enabled by default because its persistent symbol index helps the Microsoft C++ Language Server efficiently understand relationships across your entire project. The language server loads the index when you first open a C++ project. You can check indexing progress at any time with /lsp logs.
Building the index for the first time can take additional time and temporarily increase memory usage, particularly for large or complex repositories. After the initial index is complete, it is reused and dynamically updated, so this overhead is primarily associated with initial setup.
To disable whole codebase indexing, you can temporarily disable WCI using the indexing documentation.
Restart your Copilot session after changing the setting.
Help us improve the Microsoft C++ language server for Copilot CLI by filling out our short survey. To report a problem or suggest an improvement, open an issue in the GitHub repository.
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Amazon CloudWatch now offers CloudWatch Omni, an AI-powered observability experience for the applications and AI agents you run together. You reach Omni through a dedicated URL for your organization and sign in with the identities you already manage, so working in Omni does not require access to the AWS Management Console. Omni is built on OpenTelemetry: the telemetry you already send to CloudWatch appears in Omni with nothing to reconfigure, and any other workload you instrument with OpenTelemetry sends its telemetry to an OpenTelemetry Protocol (OTLP) endpoint.
CloudWatch Omni offers both agent observability and application observability in a single experience. In our companion post, we introduced the agent observability capabilities of Omni for generative AI and agentic workloads. In this post, we present the application observability experience.
Engineering teams spend a significant portion of their observability time maintaining dashboards, tuning thresholds, and switching between tools to piece together what happened during an incident. When an issue crosses team boundaries, context gets lost in Slack threads and screenshots rather than flowing naturally to the next engineer. CloudWatch Omni changes this by organizing observability around your applications rather than individual signals, and bringing your whole team into the same workspace.
What CloudWatch Omni brings
CloudWatch Omni addresses three problems that engineering teams told us they face today.
One collaborative experience for your whole team. Every engineer accesses CloudWatch Omni through a single URL with enterprise SSO (via IAM Identity Center, supporting Okta, Azure AD, and other providers). No AWS Console access is required. SREs, developers, database engineers, and managers share the same data and investigation context. When an investigation escalates, the next person joins the same session with full context already in front of them.
The system adapts as your applications evolve. CloudWatch Omni discovers your services, maps dependencies, and adjusts alarms automatically. Instead of manually curating dashboards and tuning thresholds, you declare what matters (availability targets, latency budgets, error rate thresholds) and Omni adapts as your system changes. When you deploy new services, Omni updates the application topology automatically.
AI-powered investigation with Amazon DevOps Agent. Amazon DevOps Agent participates alongside your team in investigation sessions, correlating signals and suggesting next steps. The agent works from the same telemetry your engineers see, so its suggestions are grounded in the actual state of your application. It identifies correlated events across services, traces root cause paths through your dependency graph, and maintains investigation history for post-incident review.
How an investigation works
When something breaks, CloudWatch Omni opens an investigation session pre-loaded with context. Here is a typical incident workflow:
An alarm fires on elevated error rates in your checkout service. Omni opens a session showing the service topology, correlated signals (a deployment 10 minutes earlier, increased latency from a downstream payment API), and DevOps Agent’s initial analysis.
Your on-call SRE confirms the deployment correlation, pulls in the trace view to identify failing endpoints, and checks if the payment API latency correlates with a capacity limit.
The SRE escalates to the payments team. The payments engineer joins the same session and sees everything found so far, plus DevOps Agent’s correlation with a configuration change in the payment provider’s API gateway. They identify the root cause and roll back.
The entire investigation history is captured automatically. No separate incident report needed.
Walkthrough: setting up your first Space
To set up CloudWatch Omni for your team, open the CloudWatch console and click “Try CloudWatch Omni.”
Next, connect your identity provider through IAM Identity Center (supporting Okta, Azure AD, and other SAML 2.0 providers). Once connected, your team members access Omni directly at your dedicated URL without needing AWS Console credentials.
Create a Space for your team. A Space groups the applications your team owns and the telemetry associated with them.
Once created, Omni discovers your services automatically and maps the dependencies between them. You see your application topology immediately.
You can ask CloudWatch Omni any question about your applications in plain English, and Omni will analyze your telemetry data and surface insights.
You can also set up service health alerts, configure what matters to your team, and trigger an AWS DevOps agent investigation to identify the root cause and develop a mitigation plan.
Application-centric organization
CloudWatch Omni organizes telemetry by application rather than by infrastructure component. The system automatically discovers services from the telemetry data and AWS Config resource discovery, maps dependencies, and lets you see your application as a connected system rather than a collection of isolated resources.
Each team gets a Space that contains the applications they own. A Space points at existing CloudWatch data (logs, metrics, traces, and alarms) with no additional data movement required. Dynamic views replace the maintenance burden of static dashboards, providing ongoing visibility into SLOs and application health.
Getting started
Getting started takes minutes and doesn’t require reconfiguration of your existing CloudWatch setup.
If you’re an existing CloudWatch customer: Click “Try CloudWatch Omni” in the CloudWatch console. All your existing telemetry (logs, metrics, traces, and alarms) is immediately available. Workloads are discovered automatically, and you can start an investigation or browse your application topology right away.
For organization-wide deployment: An administrator configures a domain, connects your identity provider via IAM Identity Center, defines Spaces for teams and environments, and invites users. Each Space points at existing CloudWatch data with no additional data movement required.
For applications in other environments: CloudWatch Omni provides connectors that make it easy to bring in telemetry from additional environments. All ingested telemetry appears alongside your AWS data in the same Spaces and investigation sessions.
For generative AI and agentic workloads: The same CloudWatch Omni experience delivers purpose-built observability for AI agents, including trace exploration, evaluation frameworks, and real-time monitoring. In our companion post, we introduced the agent observability capabilities of Omni; for that walkthrough, see Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads.
Things to know
Pricing and availability
Amazon CloudWatch Omni is now available. Existing CloudWatch customers can try it directly from the CloudWatch console. For pricing details, visit the Amazon CloudWatch pricing page.
To get started, visit Amazon CloudWatch Omni or click “Try CloudWatch Omni” in the Amazon CloudWatch console.
If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.
— Daniel Abib
Today, Amazon CloudWatch introduces CloudWatch Omni, a unified observability experience for application and AI workloads that is app-centric, AI-powered, built on open standards, and delivered off-console. CloudWatch Omni is a purpose-built observability, evaluation, and experimentation solution for AI agents. It helps teams design, evaluate, and operate AI agents across any model provider, framework, or runtime, with an eval-driven workflow, support for the tools you already use, and observability delivered where you work: directly in your IDE and through a standalone web experience, separate from the AWS Management Console.
Organizations deploying agentic AI systems face observability challenges that traditional monitoring can’t address. Agent behavior is non-deterministic: a prompt change can degrade response quality even when standard metrics show no errors. Teams spend hours manually reviewing logs across multiple systems, unable to pinpoint what changed or why. Existing tools force teams to choose between siloed generative AI monitoring or fragmented solutions requiring constant context-switching between their coding environment and browser-based dashboards.
CloudWatch Omni captures every trace and includes built-in evaluators for correctness, coherence, retrieval quality, and tool selection, among others. You can compare prompt versions side by side in the playground, build test datasets from production traffic, run experiments across different configurations, and detect regressions automatically.
Two surfaces for development and operations
CloudWatch Omni delivers observability through two complementary surfaces. Developers get a native extension inside VS Code and Kiro (the currently supported IDEs), where traces appear as you run your agent with a playground and evaluators a click away. Operators get a standalone web experience, separate from the AWS Management Console to monitor the fleet, accessible through SSO with no AWS console needed. Both share the same data: the trace a developer debugs is the trace an operator investigates.
The Cloud Login feature connects your local IDE environment to your AWS account, enabling you to send telemetry data to Amazon CloudWatch for persistent storage, share traces with your team, and access production dashboards. This connection is optional. You can use CloudWatch Omni entirely locally during development, then connect to the cloud when you are ready to monitor agents in production.
Getting started
CloudWatch Omni offers two ways to get started: through the IDE extension (for VS Code and Kiro) or directly through the cloud experience, where you can start sending telemetry data to CloudWatch without installing any IDE extension. In this walkthrough, I install the extension, create an agent, run it, and explore the traces and evaluation tools from my IDE.
After installing the CloudWatch Omni extension from the VS Code Marketplace, the CloudWatch Omni icon appears in the Activity Bar. From the welcome screen, I selected Get started with Sample Project to load a pre-configured agent with sample trace data or use shortcut to Command Palette using Command + Shift + P (on macOS) or Ctrl + Shift + P (on Windows/Linux) and select Omni: Create a new Project
The sample project comes with an agent implementation and example datasets. Part of the getting-started experience is adding OpenTelemetry instrumentation, and CloudWatch Omni guides you through each step. You can also create a new agent from scratch. CloudWatch Omni walks you through the process using an interactive chat where you define the agent’s purpose, select a model provider, and configure tools. All data is stored locally by default. You can optionally connect to AWS to send data to Amazon CloudWatch.
After verifying the configuration, I started the local dev server and sent a question to the agent. What makes this different from a typical chatbot interface is what happens next: selecting View Trace shows exactly how the agent processed the request.
CloudWatch Omni integrates with AI code assistants such as Kiro, Claude Code, and Codex to streamline the setup process. These assistants can configure the Dev Server, install dependencies, and set up instrumentation on your behalf, so you can go from installation to running your first traced agent session in minutes without manual configuration.
Traces are essential for understanding AI agent behavior. Unlike traditional request-response systems, agents make multiple decisions per invocation: choosing tools, composing prompts, and chaining sub-calls. Without full trace visibility, diagnosing why an agent produced an incorrect answer or took an unexpected path becomes guesswork. CloudWatch Omni records every step in a structured timeline so you can pinpoint exactly where behavior diverged.
The Trace Explorer shows a detailed breakdown of every step the agent took (LLM calls, tool invocations, and reasoning steps) in a structured, hierarchical timeline. I could drill into any span to inspect inputs, outputs, token usage, and latency.
The Trace Explorer also supports Compare mode, which places two traces side by side to see how different prompts or configurations affect behavior. Compare mode is especially helpful when debugging regressions. And with Ask Assistant, an AI agent analyzes your traces to surface patterns and anomalies, answering questions like “Why did the agent call this tool twice?”
Evaluation is what turns observability into actionable quality improvement for generative AI. Traditional metrics like latency and error rate cannot tell you whether an agent’s response was helpful, coherent, or factually correct. Evaluators score each response against quality dimensions, letting you measure what users actually experience and catch regressions that standard monitoring misses entirely.
CloudWatch Omni includes 17 built-in evaluators for metrics like coherence, helpfulness, faithfulness, and routing correctness. I selected traces from the Trace Explorer, chose evaluators, and ran an evaluation, getting per-example scores and aggregate metrics without building any custom evaluation framework.
From there, I used the Playground to test different system prompts side by side, comparing multiple model and prompt configurations in real time to see how each variation affects output quality before committing changes. With the Experiments view, I could run the same dataset against two agent variants and compare their evaluation scores, latency, and token usage side by side to pick the best-performing configuration.
Figure 7. Comparing evaluations across agent variants in the Omni Experiments console
With Prompt Management, you can version and track prompt configurations over time, making it easy to roll back when a new version underperforms.
CloudWatch Omni also provides a Session Explorer to review full conversation histories and understand how agents handle multi-turn interactions, along with an Agent Topology view that visualizes the architecture of your agent system, including sub-agents, tools, and their interconnections. You can drill into any node to inspect performance and identify bottlenecks.
CloudWatch Omni also offers a dedicated web experience accessible from any browser without an IDE. Teams can access all capabilities collaboratively, including application monitoring, analytics, agent observability, and AI-powered investigations.
I curated traces into golden datasets for structured experimentation. The Experiment function runs the agent against a dataset and automatically scores results, creating benchmarks for regression testing whenever prompts or agent logic change.
If you already have an agent built with a supported framework, CloudWatch Omni provides two paths to add instrumentation: Auto-instrument with Kiro, which detects your framework and configures tracing automatically, or manual instrumentation with ready-to-use code snippets for Python and TypeScript. For detailed instrumentation guides, see the CloudWatch Omni documentation.
Supported frameworks and open standards
The walkthrough above uses the sample project, but CloudWatch Omni works with the agent frameworks teams are already using: LangChain, LangGraph, CrewAI, OpenAI SDK, Strands, Vercel AI SDK, and more, in both Python and TypeScript. It also provides native observability for agents built with Amazon Bedrock AgentCore, and uses AgentCore’s evaluation capabilities to assess agent quality directly within the Omni workflow.
Instrumentation uses open standards (OpenInference and ADOT), whether your agents run on Lambda, ECS, EKS, or other clouds. For evaluation, Omni integrates with third-party evaluators including AutoEval and DeepEval alongside built-in datasets, a playground, and batch experiments. No re-platforming required.
CloudWatch Omni brings agent observability and application observability together in a single experience. For the application observability experience, read the companion post Introducing Amazon CloudWatch Omni: collaborative AI-powered observability for your applications.
Pricing and availability
Amazon CloudWatch Omni is now generally available. The IDE extension is free to use. You don’t need an AWS account to get started. You only need AWS credentials for Amazon Bedrock models, or API keys for other providers like OpenAI or Anthropic. Get started today by installing the extension from the VS Code Marketplace.
To explore all capabilities and get started quickly, visit CloudWatch on AWS Builder Center.
If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.
Happy building!
— Daniel Abib
As Kubernetes adoption has grown, the conversation has shifted beyond running containers to managing increasingly complex application lifecycles. Modern platforms support stateless web services, stateful databases, batch processing, AI workloads, and platform services. At the same time, they must remain reliable during upgrades, scaling events, and infrastructure failures.
Every Kubernetes user relies on SIG Apps, whether they realize it or not. Deployments, StatefulSets, DaemonSets, Jobs, and CronJobs form the foundation of how applications are deployed, updated, scaled, and operated across the Kubernetes ecosystem.
SIG Apps is focused on improving workload resilience, refining application lifecycle management, and addressing the operational challenges that emerge when applications encounter node failures, rollout disruptions, and increasingly complex infrastructure environments.
In this spotlight, we sit down with two of the three SIG Apps chairs Janet Kuo and Maciej Szulik to discuss the evolution of Kubernetes workload management, the challenges of balancing application reliability with operational simplicity, and the future of application lifecycle management within one of Kubernetes’ most influential Special Interest Groups.
Natalie Fisher: Can you introduce yourself, your role, and how you got involved in SIG Apps?
Janet Kuo: I'm a Senior Staff Software Engineer at Google and have been a Kubernetes maintainer since 2015, joining the community just as we were racing toward the 1.0 launch. In those early days, my focus was on building the core Workloads API, specifically developing controllers like Deployment, ReplicaSet, StatefulSet, and DaemonSet, defining their rollout behaviors, and bringing them from initial designs to GA. That hands-on work was my entry point into SIG Apps.
Since then, I've stayed deeply involved in both the technical and community sides of Kubernetes. I have led SIG Apps as Co-Chair and Tech Lead since 2019. Currently, in addition to maintaining the workloads API, I am driving new subprojects like the Agent Sandbox to ensure Kubernetes is ready for next-generation agentic and AI workloads.
Maciej Szulik: I started contributing to Kubernetes all the way back in 2014. Since then, I've worked across various areas of the project: controllers, kubectl, and apimachinery, which eventually led me to become one of the Chairs and Tech Leads for SIG Apps. My current focus is reliability of the workload controllers under the SIG Apps umbrella and stability and ease of use of kubectl as part of my SIG CLI Tech Lead role. I also care about overall community health and growth as part of my Steering Committee role. Outside of Kubernetes, I work as a Staff Platform Engineer at Defense Unicorns, where I'm helping make Kubernetes more airgap-native with a project called zarf.
SIG Apps is responsible for the core workload APIs that power how applications run on Kubernetes. From Deployments and StatefulSets to Jobs and CronJobs, these controllers determine how workloads are created, updated, scaled, and recovered when things go wrong.
As Kubernetes expands to support increasingly diverse workloads – including AI, batch processing, and large-scale distributed applications – SIG Apps continues to evolve these APIs while balancing reliability, backward compatibility, and operational simplicity.
NF: For readers who may not be familiar, what is SIG Apps, and what role does it play within the broader Kubernetes ecosystem?
MS: SIG Apps is the Kubernetes Special Interest Group responsible for the workloads APIs. CronJob and Job help running batch workloads, whereas DaemonSet, Deployment, ReplicaSet, and StatefulSet serve the majority of other applications. More broadly, SIG Apps owns the layer most developers actually touch day-to-day: the controllers that turn a workload specification into running, self-healing pods. It's the group deciding how Deployments roll out, how Jobs retry, how DaemonSets place a pod per node.
JK: Adding to what Maciej described, as the industry shifts, we are seeing a massive demand to run complex, non-traditional workloads like distributed AI training, batch computing, and dynamic agent environments. Our role is expanding: we aren't just maintaining the classic workloads API, but we are actively evolving it and establishing new patterns (like the Agent Sandbox) to make sure Kubernetes remains the best platform for the next generation of workloads, such as AI.
NF: Looking at the workload APIs owned by SIG Apps (Deployments, StatefulSets, DaemonSets, Jobs, and CronJobs), which areas are receiving the most attention from maintainers and contributors?
MS: After a long stretch focused on making batch workloads run smoothly on Kubernetes, we’ve shifted attention to make sure serving workloads (DaemonSets, StatefulSets, etc) aren’t left behind. This means performance and high-scale improvements to rollout and scaling behavior, plus working through our backlog of user-reported issues, prioritizing the ones with the strongest support from the user base.
As Kubernetes workloads grow in scale and complexity, the challenges facing workload controllers evolve as well. We asked the SIG Apps chairs where contributors are focusing their efforts today and which resilience problems they believe are the highest priorities.
NF: From your perspective, what are the most important workload resilience problems SIG Apps is trying to solve today?
MS: Node lifecycle challenges have come up repeatedly across SIG Apps, SIG Node, and SIG Autoscaling discussions. DaemonSets and Jobs are just where the pain is most visible, since they're the workloads most directly bound to node state. Rather than solve it piecemeal within one SIG, we've settled on spinning up a dedicated Node Lifecycle Working Group to focus on this properly and hopefully land long-term solutions instead of one-off patches.
JK: From an AI perspective, resilience is critical. When you are running a massive distributed LLM training job that spans hundreds of GPUs, a single node failure can halt the entire pipeline. Similarly, if a DaemonSet that runs your logging or GPU monitoring agent gets stuck on a bad node, it impacts the entire cluster's health.
In addition to the work in the Node Lifecycle WG to handle infrastructure-level degradation, SIG Apps is addressing this at the orchestration layer through subprojects like JobSet (for distributed training) and LeaderWorkerSet (LWS) (for sharded LLM inference). These APIs introduce patterns like "all-or-nothing" failure handling, where a single pod or job failure triggers a coordinated group-level restart to resume from the last clean checkpoint, rather than letting stuck workloads hang in an inconsistent state.
The work happening within SIG Apps extends far beyond controller implementations and API design. We wanted to understand what these improvements mean in practice for platform teams operating Kubernetes clusters in production.
NF: For platform teams operating Kubernetes in production, what practical improvements would they notice if the node lifecycle and workload resilience work currently under discussion is successfully delivered?
MS: I’m mostly looking from the sidelines, the folks actually in the Node Lifecycle Working Group would give you a sharper answer. But from where I sit, I’m hoping their work translates into fewer 3am pages that turn out to be “a DaemonSet rollout got stuck because node X was flaky, and someone had to manually cordon/delete/restart to unstick it.”
JK: +1 to what Maciej said, and beyond reducing manual intervention, platform teams will also see much better resource predictability and cost efficiency. For example, in AI workloads where GPU idle time is extremely expensive, having Kubernetes automatically detect a degraded node and reschedule the training coordinator or agent before the job crashes means less wasted compute and more stable job execution.
Evolving APIs that millions of workloads rely on requires careful engineering and even more careful decision-making. We asked the SIG Apps chairs about the technical and operational trade-offs they weigh when introducing changes to Kubernetes’ core workload controllers.
NF: What are some of the hardest technical or operational trade-offs SIG Apps encounters when evolving core workload controllers?
MS: Honestly, a few tensions keep coming up: how aggressively a controller should give up on stuck pods, and what signals it actually needs to make that call correctly. At the same time, we always have to think about backward compatibility. Deployment, DaemonSet, and Job behavior has been depended on for a decade [by Kubernetes users, tooling, automation, and higher-level controllers], so even a change that’s clearly “more correct” can break automation people built around the old behavior without meaning to.
JK: One of our hardest trade-offs is resisting the urge to make "elegant" design changes that break backward compatibility. Instead, we have to design opt-in features that let users adopt new behaviors without forcing them on legacy workloads. When we need to support completely new paradigms, we prefer introducing them as CRDs first rather than bloating the core APIs, like we are doing with Agent Sandbox, JobSet, and LWS.
While much of SIG Apps’ work focuses on maintaining the stability of existing workload APIs, the group is also shaping the future of Kubernetes through new enhancements and proposals. We concluded by asking about one proposal that recently returned to active development and what it represents for the future of workload management.
NF: The SIG recently discussed reviving KEP-4443 with a target release of Kubernetes 1.38. What opportunities or challenges does this proposal aim to address, and why is now the right time to revisit it?
KEP-4443 addresses a small but real gap in the Job API: a PodFailurePolicy can be configured to add a condition reason to the JobFailed condition, but different pod failure policy rules targeting different container exit codes all produce that same generic reason. The proposal is simple: an optional Name field on each PodFailurePolicyRule, which gets appended to the JobFailed condition reason, so higher-level tools like JobSet can finally react differently depending on which rule triggered the failure.
As for timing, the answer is as simple as it always is in open source: we lost the original contributor who was driving this. Now we’ve got someone new interested in picking it up, that’s why we’re targeting the next release.
NF: For someone interested in contributing to SIG Apps, where would you recommend they start, especially if they are not yet a Kubernetes maintainer?
MS: The best place to start is the #sig-apps slack channel and our regular SIG Apps meetings. We’ve all started there, and if it feels intimidating, or nobody replies right away, that’s completely normal. Everyone's busy. It's not personal.
JK: In addition to what Maciej answered, I'd suggest looking at our newer subprojects and initiatives. Contributing to stable APIs like Deployment or StatefulSet can be daunting because the barrier for making changes is very high due to backward compatibility, and there is much less low-hanging fruit.
If you are new to the community, projects like the Agent Sandbox are fantastic entry points. They are actively evolving, have a friendly group of maintainers, and offer plenty of greenfield development opportunities where you can make a significant impact quickly.
SIG Apps has shaped how Kubernetes applications are deployed and operated since the project’s earliest days. While users often interact with Deployments, StatefulSets, Jobs, and DaemonSets without thinking about the controllers behind them, the work within SIG Apps continues to shape the reliability and scalability of workloads across the Kubernetes ecosystem.
From improving workload resilience and node lifecycle behavior to enabling new patterns for AI and distributed computing, the SIG is evolving Kubernetes while remaining committed to one of the project’s core principles: preserving the stability and backward compatibility that users depend on. Whether you’re interested in core workload APIs, emerging projects like Agent Sandbox, or helping improve the operational experience of Kubernetes users everywhere, SIG Apps offers many opportunities to get involved.
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Change management has long covered two familiar kinds of change: the rollout of new tools and technologies, and broader human-led transformations such as leadership changes and restructuring.
But that distinction starts to blur as artificial intelligence becomes woven into the fabric of organizations. Although AI is software, it has a unique capacity for open-ended, context-dependent work that involves collaborating with people. This raises the question: Should we simply treat AI like software to be deployed, or should we think of it as an active participant in our workflows whose role needs to be defined?
The instinctive answer is just to treat it like software. Organizations often try to retrofit AI into existing infrastructure and processes, assuming it can slot into the same guardrails, integrations, and workflows that support CRM systems, ERP platforms, and other enterprise software.
But maybe this isn’t the right approach. Maybe AI isn’t just a tool to be installed; it’s a participant to be onboarded. And when we force it into software-shaped boxes, we miss the opportunity to leverage its unique strengths — pattern recognition, scalability, and adaptability — in ways that complement human judgment rather than compete with it.
AI isn’t just a tool to be installed; it’s a participant to be onboarded.
In this post, we’ll look at what AI change management involves and the key considerations for enterprises as AI becomes a more integral part of day-to-day work.
AI change management is the work of guiding an enterprise through the organizational changes required to adopt AI at scale, from preparation through to day-to-day use. It includes understanding how AI changes roles and responsibilities, helping employees develop new ways of working, communicating new expectations, and adapting workflows and oversight as adoption progresses.
Those changes may mean employees spend less time producing work themselves and more time directing AI, working with its outputs, or focusing on tasks that depend on human expertise.
Consider the difference between the launch of a new sales platform and the appointment of a new CEO. A new sales platform usually involves training sessions, a phased rollout, and minor process adjustments around a system whose role and behavior are relatively well understood. Meanwhile, a new CEO can have much broader and less predictable implications for an organization’s culture, priorities, division of responsibilities, and ways of working.
AI presents a distinct change-management challenge because it combines elements of both kinds of change. It represents both the adoption of a new technology and a broader shift in how work is distributed across an organization.
For AI to function as a collaborator, enterprises need to design workflows that accommodate both human and artificial intelligence.
On the AI side, that might mean providing AI with the relevant organizational and task context much as you would when onboarding a new employee. It can also mean building human-in-the-loop mechanisms that allow AI to ask for missing information or escalate cases that require human judgment or approval.
On the human side, it might mean creating workflows where AI can act as a thinking partner rather than simply a tool for executing delegated tasks. For example, AI might help a person analyze information, explore different options, or identify relevant patterns as the work progresses. For judgment-heavy tasks, AI can synthesize evidence and surface options and trade-offs while leaving the final decision to a person. The point isn’t to pretend AI is human, but to recognize that its effectiveness can depend on how well it’s integrated into human-led workflows.
In these new human-AI workflows, employees start to act like managers of AI-assisted work, setting direction, assessing quality, and deciding when human input is needed. However, this shift introduces a risk: mistaking the speed and volume of AI-generated outputs for quality. This can result in “AI slop”: superficial or low-impact work. Employees therefore need to adopt a manager mindset, prioritizing the quality and impact of AI-assisted work, not sheer volume.
Those who actively build internal AI solutions take on an additional role, becoming something like mini product managers. Beyond building the solution itself, they may need to think about who will use it, what business outcome it serves, and how it should be maintained and governed across its lifecycle. This shift carries a different risk: employees can move beyond their traditional role boundaries without seeing all the dependencies around what they’re building. “Vibe coding,” for example, makes it possible to build a useful solution without accounting for its legal, business, operational, or technical implications.
Effective human-AI collaboration depends on understanding which parts of a workflow can be delegated to AI and which still require human judgment. One way to think about that division is to separate work into three categories:
A key skill for effective AI adoption is to continually reassess that division as work unfolds: what AI can execute and what still requires a human decision.
These human-AI workflows also depend on a combination of domain expertise and broader capabilities. A data scientist who understands healthcare regulations, or a marketer who grasps AI’s creative limitations, can bridge the gap between technical possibility and real-world viability. Employees may therefore need development in technical AI skills, as well as management, communication, strategy, ethics, and collaboration.
AI governance needs to be more adaptive than traditional software governance. Since conventional software typically operates according to predefined logic, permissions, and expected behaviors, oversight is mostly focused on ensuring it functions as intended within those boundaries. Meanwhile, AI systems are probabilistic, producing more variable and context-sensitive outputs. That makes ongoing monitoring more important after deployment. Organizations need to watch for changes in performance and behavior over time, including drift, emerging bias, and departures from organizational expectations and human values.
Access controls help illustrate why AI needs a distinct governance approach. Conventional software operates under fixed, preauthorized permissions, while people typically have role-based access but can request additional permissions as needed. As AI takes on a more active role in workflows, enterprises may need to borrow more from the human model: give AI least-privilege access while allowing it to request additional access when a task requires it. Of course, any additional access should remain subject to appropriate approval and oversight.
The analogy between AI governance and managing people extends beyond access controls. When organizations onboard a new team member, they define their role, set boundaries around what they can do, establish how work will be reviewed, and explain the standards they’re expected to meet. The same should go for AI. For example, when a human employee presents their work to a manager, they’re expected to explain their thinking and decisions. AI should be no different. It too should be able to demonstrate how it arrived at specific outputs.
The general point is that we should apply the same rigor to governing AI that we do to managing people, including regular check-ins, context reviews, and course corrections. But that doesn’t mean every AI system should be governed in the same way. AI solutions can vary drastically in their level of autonomy, from human-operated tools to fully autonomous, always-on systems. These differences mean governance should be defined at the use-case level, rather than through a one-size-fits-all approach. A fully autonomous system will require rigorous, real-time monitoring and human-in-the-loop mechanisms for high-risk decisions, whereas a human-operated tool may only require controls like input validation and output review.
We should apply the same rigor to governing AI that we do to managing people.
Most importantly, humans must be held accountable for what they use AI for and the outcomes it produces. If an AI system exhibits bias or generates incorrect outputs, the buck stops with the humans overseeing it, just as a manager is accountable for their team’s output and performance.
At rollout, enterprises are unlikely to know exactly how AI will fit into day-to-day work as they progress in AI maturity. Capabilities evolve rapidly, and teams often discover through experience which tasks AI handles well and where else it can add value. As that understanding grows, organizations may need to revise role expectations, controls, and guidance to reflect how AI is actually being used.
Here are some key considerations for managing that evolution:
A clear, achievable AI goal tied to business impact can give teams a shared sense of direction as adoption evolves. That goal also needs visible leadership sponsorship. A senior leader can help keep the effort on the leadership agenda, resolve obstacles that require coordination across teams, and reinforce the priorities and expectations around AI adoption as it becomes more embedded in everyday work.
Employees need to understand why AI is being introduced, how it may affect their work, and what benefits and risks come with it. Leaders and managers can encourage buy-in by proactively explaining how AI can augment roles and create new opportunities for the organization, rather than presenting it purely as a cost-optimization tool. That communication should also be realistic about AI’s limitations and give employees clear ways to ask questions, raise concerns, and share what they’re seeing as use expands.
Regular measurement can show whether AI adoption is actually taking hold. Usage data can show where and how often AI is being used, but a fuller picture comes from combining that data with employee feedback, manager observations, measures of AI proficiency, and evidence that expectations around AI use and oversight are being followed.
Those signals can show where the change effort needs to adapt, whether through further training, clearer guidance, changes to communication, or adjustments to how AI is being integrated into particular roles and workflows.
AI represents more than a way to automate existing work or reduce costs. The goal of AI adoption should be to integrate AI into the organization’s operating model in ways that transform how the business works and creates value. That transformation depends on AI capabilities evolving alongside the organizational capabilities that support them, from data foundations and technology to workforce skills, governance, and strategy.
The picture is still developing, of course. What we’ve covered here reflects only what we’re observing in our work with customers: the conditions that help AI adoption succeed, the challenges that persist, and how enterprises are responding to them. As we gain more experience supporting organizations through their AI transformation journeys, we expect that our understanding of what effective adoption requires will continue to change.
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Claude Opus 5.5, Anthropic’s newest Opus model, is now available in GitHub Copilot. You can use it for agentic coding, long-running agentic tasks, and knowledge work. In early testing, Opus 5.5 resolved tasks comparably to Claude Opus 5 while using significantly fewer steps and tokens. It also quickly recovered from errors in multistep tasks.
Claude Opus 5.5 watermarks its text outputs. The watermark doesn’t change the meaning, quality, or readability of outputs, nor does it add any tokens or cost. To learn more visit Anthropics’s How Claude’s text watermark works.
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In healthcare, the best judges of whether an AI system works correctly are the people whose time it was built to protect. A clinician can quickly tell if a generated note correctly attributes a symptom or a patient was recommended the most appropriate level of care. But they cannot perform this review across thousands of encounters indefinitely. At scale, expert validation becomes the limiting factor.
Healthcare teams have approached this challenge in different ways, but many share one principle: they treat clinical review as infrastructure rather than a recurring operational cost. This blog covers how two organizations, building AI healthcare products in different ways, converge on evaluation practices. Using LangSmith, they convert expert input into durable assets such as labeled datasets, calibrated evaluators, and automated release gates. The goal is not to eliminate human judgment, but to make its value compound.
You’ll explore:
Healthcare AI agents operate across various workflows, such as patient care decisions and visit documentation.
Included Health built Dot, an AI guide powered by LangGraph and Deep Agents. It interprets ambiguous member needs, answers coverage and billing questions, routes people to appropriate care, and detects emergencies. A question about whether a scan is covered may reveal, several turns later, that the member actually needs to speak with a primary care physician. Evaluating Dot means both checking its accuracy and whether it used the member's full context to recommend a safe and appropriate next step.
Abridge transforms patient-clinician conversations into clinical notes. With a patient's consent, a physician records the visit, and Abridge converts the conversation into a note that becomes part of the longitudinal health record and supports billing. In this setting, attribution is critical. If a patient's observation is presented as a physician's conclusion, a symptom can become a billable diagnosis. Hallucinations create a different risk: a medication or dosage that was never prescribed can enter the record. A trustworthy note must preserve who said what, capture what matters clinically, and introduce nothing the conversation does not support.
These systems fail in different ways, but both teams use LangSmith to address the same constraint: accuracy is determined by someone outside the engineering team, and that person's time is often the scarcest resource in the system.
That makes expert review both indispensable and a potential bottleneck. As the Abridge team puts it: “trust is earned in drops and lost in buckets.” The goal is to make each expert judgment reusable across future tests, releases, and iterations.
Before clinical judgment is encoded into automated evaluations, teams must first define exactly what experts are evaluating. Three properties make that judgment difficult to scale.
Ground truth is rarely singular. A clinical note does not have one canonical form. What belongs varies by specialty, encounter, and clinician, and reasonable experts can disagree. Reference notes are useful, but treating a single reference as the only correct answer can penalize valid variation while still missing clinically important errors.
Correct inaction matters. Healthcare teams must evaluate whether a system acted correctly and if it recognized when not to act. For instance, Included Health's reviewers check that emergency guardrails trigger when appropriate and remain inactive in benign cases. For example, Abridge tests whether its agent stays within its boundaries and selects the tools a clinician would expect.
Reviewer expertise is part of the specification. Abridge determines upfront whether an evaluation requires a board-certified physician or a particular specialist. A judge is only as good as the judgments that calibrated it.
None of this makes clinical judgment impossible to automate. It simply defines the requirements for doing so responsibly: tolerance for valid variation, attention to what did not happen, and the right expertise behind every label.
Once teams have defined the judgment they need to preserve, they can begin converting expertise into infrastructure. Abridge converts clinician input into labeled datasets and calibrated judges that keep working after an individual review ends.
Abridge begins with known failure modes from clinician and user feedback. The team ranks them by prevalence and severity, groups them into categories such as accuracy, compliance, style, and completeness, and builds a separate judge for each. Rather than produce one general quality score, the evaluators test for specific ways a note can fail.
The time savings come from automating what happens after they've provided their judgment. Previously, a clinician wrote an annotation guide and labeled encounters, then someone manually adjusted the judge’s prompt until its scores matched those labels. Abridge now feeds the same guide and examples into an automated prompt optimization framework that generates the judge.
Because no single reference can capture every valid note, Abridge layers two approaches with complementary strengths:
Together, they balance breadth and precision: one provides scalable coverage across encounters, while the other captures the specialty-specific nuance that clinical review demands.
An optimized judge still must be validated against clinician annotations. LangSmith’s Align Evaluator gives teams an interface for comparing the two and investigating disagreements. Abridge separately asks annotators to explain their decisions, even when the output is correct. Those explanations help resolve inconsistencies and confirm the labels reflect careful review.
The result is an evaluation system that can be inspected, recalibrated, and reused. By turning individual judgments into durable evaluators, teams reserve scarce clinical expertise for the cases where it adds the most value. Production review supplies the new cases and feedback that keep those standards current.
Calibrated judges apply judgment the team has already captured. Production review supplies the next round of that judgment, revealing how the system behaves in real conversations and generating evidence for what to fix.
Included Health shows how that new evidence enters the loop. Conversations go into a LangSmith annotation queue, where clinical reviewers assess whether Dot directed the member to the right care setting, whether its emergency guardrails behaved appropriately, and whether the case requires follow-up.
Those decisions become structured labels that are exported to Included Health's data warehouse, where the data science team uses them to build operational dashboards. They also feed back into the skill definitions that govern how Dot navigates members. Each review is spent once and used three times.
The result is a powerful feedback loop. Clinical judgment becomes data, the data guides product changes, and those changes are tested against the same standards before the next release. Every review contributes to both the case at hand and the system’s future behavior.
The feedback loop pays off at release time. Instead of evaluating every candidate change from scratch, teams can test it against evidence they have already captured.
At Abridge, a model change moves through progressively more realistic stages: offline evaluations, backtesting against historical encounters, a limited A/B test, full release, and continuous production monitoring. Each stage adds a different kind of evidence.
The A/B test is the most unusual step in this process. Some of Abridge’s partners agree to be among the first 10 to 15% of customers included in a silent rollout. This lets Abridge observe signals automated judges cannot provide: whether clinicians edit the generated notes, how they rate them, and what qualitative feedback they share. Offline evaluations establish whether a change is ready for limited exposure; production behavior determines whether the rollout should expand. That process reduced Abridge’s release cycle from one or two months to a matter of days.
Included Health applied the same principle to an architectural change. Moving Dot’s supergraph to Deep Agents affected four product teams, all wary of breaking changes. The team ran its existing multi-turn simulation suite, confirmed that performance held, and completed the migration in under two weeks without significant regressions.
In both cases, release confidence became cumulative. Rather than re-establish trust with every change, teams could build on evidence they had already collected.
A faster release cycle matters only if the system performs reliably once it reaches real users. Included Health measures performance across three dimensions: adoption, routing quality, and safety.
Each metric answers a different question: Will members use the product? Does it direct them to appropriate care? Does it recognize situations that require urgent attention? Looking at them together gives the team a more complete picture of production reliability.
Following Dot's launch, Included Health reports a 75% lift in chat engagement. Among the graded conversations, clinician agreement with Dot’s care recommendations remains above the team’s 95% target, and clinical audits show that Dot identifies more than 99% of high-risk situations.
At Abridge, labeled encounters calibrate judges that run against future releases; at Included Health, clinical labels outlive the conversation that produced them. The artifacts still require review and recalibration, but the expert judgment behind them is no longer consumed by a single decision.
The same artifacts that make clinical judgment reusable—encounter traces, conversation histories, and clinician annotations—can also contain protected health information. Once teams begin storing and reusing them, security and deployment architecture become part of the evaluation design.
Abridge treats self-hosting, access controls, and auditability as requirements for its evaluation infrastructure. They also remove identifying information from conversation data before using it for learning. These are not controls to add after the evaluation pipeline is built; they shape what data can enter it in the first place.
LangSmith supports managed cloud, bring-your-own-cloud, and self-hosted deployments. Teams must decide where evaluation data will be stored, who can access it, which audit and retention controls apply, and how traces containing PHI will be handled.
Trust builds slowly in healthcare AI. It grows with every encounter handled correctly, every guardrail, and every regression caught before it reaches users. Yet one change that escapes those checks can undo it.
Healthcare teams move fast by ensuring each careful review continues working long after the review itself is complete.
For the complete customer stories, watch Building Clinical AI Agents with LangGraph: Abridge's Eval Stack for High-Stakes Healthcare and read How Included Health Built Federated Agents for Healthcare Navigation with Deep Agents and LangGraph.
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OpenAI’s GPT-6 family is expanding in GitHub Copilot with two additional models: GPT-6 Sol, and GPT-6 Luna. Joining the previously released GPT-6 Astra, these new options let you select the model that best fits your task, whether that’s everyday agentic coding or fast, cost-efficient assistance.
These models are billed under usage-based billing. See pricing for GitHub Copilot models and requests for details.
GPT-6 Sol is available to Copilot Pro+, Max, Business, and Enterprise plans. GPT-6 Luna is available to Copilot Pro, Pro+, Max, Business, and Enterprise plans.
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Copilot Enterprise and Copilot Business plan administrators can manage access to GPT-6 models through the model policy in Copilot settings. Under default model enablement, new models are enabled automatically unless an administrator has turned off the global default or explicitly disables this model.
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Business leaders often have access to plenty of data, but still can’t get a reliable answer to a seemingly simple question like: Why did net revenue decline 8% at our largest account last week? The answer may span sales, finance, promotions, inventory, and account data, with each source potentially correct in isolation, yet different in its definitions, detail, relationships, and authority.
Without the right business context, general-purpose agents can’t reliably determine what your metrics mean, which sources are authoritative, how data should connect, or what each user is allowed to see.
Genie One MCP gives governed context to any MCP-compatible AI agent. It connects assistants such as ChatGPT, Claude, Microsoft Copilot, and coding agents to Genie One, so they can answer business questions using approved definitions, trusted data relationships, and permission-aware access controls. Instead of asking agents to infer meaning from raw tables or overloaded prompts, organizations can define business context once in Genie Ontology and make it available across every approved AI agent.
General-purpose AI agents inherit the fragmentation and ambiguity of the systems they connect to. Three gaps make reliable business answers difficult:
Reliable analysis requires more than retrieving data. An agent needs to understand how business facts relate, which definitions apply, and which sources should take precedence.
Connecting an AI agent directly to structured and unstructured sources provides access, but not the shared business context needed to deliver reliable, governed answers. Without that context, direct access introduces four challenges:
Genie Ontology is the governed context layer in Databricks that helps Genie One understand your business. It combines approved metrics, business definitions, relationships, ownership, and access policies with context inferred from trusted enterprise assets such as notebooks, queries, dashboards, documentation, and Genie Agents.
Genie One uses that context to identify the right definitions and sources, reconcile conflicts based on authority and certification, and enforce each user’s permissions. The result is traceable, permission-aware answers grounded in governed business context.
Applied to our first question: Why did net revenue decline 8% at our largest account last week? Genie Ontology:
This results in not only a high-quality answer but also a full explanation that users can trace back to the governed definitions and assets behind it.
A governed context layer becomes more valuable when it can be used consistently across the places people work. Genie One provides a native AI cowork experience in Databricks, while the Genie One MCP server extends the same governed business context to other approved AI assistants, coding agents, and client interfaces. Together, they allow organizations to build business meaning once in Genie Ontology and apply it across an evolving AI ecosystem.
Genie One is Databricks’ data-smart AI coworker for business users. Powered by Genie Ontology, it helps teams answer data-intensive business questions, synthesize information across enterprise sources, and turn insights into follow-on work such as documents, tasks, and scheduled actions, both in Databricks and in third-party tools like Google Drive, Microsoft 365, Atlassian, Slack, GitHub, and Glean.
Genie One MCP extends the same governed context to popular AI assistants and coding agents. If you already use Claude, ChatGPT, Microsoft Copilot, or a coding agent such as Claude Code, Genie One MCP exposes Genie as a tool to any of them, with the same ontology and the same permission enforcement as a native surface.
Genie One MCP can immediately provide deep enterprise context to AI assistants, significantly improving the quality of engagement with business users. Some examples include:
Campaign performance review: A marketing leader asks ChatGPT Business which campaigns generate qualified pipeline and where to reallocate budget. Genie One connects approved attribution, campaign, spend, lead, and opportunity data; ChatGPT turns the findings into a campaign action plan.
Monthly business review: A finance leader asks Claude Cowork what is driving the gap between forecast and actual margin, and which regions require action. Genie One identifies the official forecast, approved margin definition, and relevant operational drivers; Claude turns the findings into an operating review narrative and action list.
Customer retention review: A customer success leader asks Microsoft Copilot Cowork which customers show declining adoption, rising support volume, and renewal risk. Genie One connects governed customer, product usage, support, and contract context; Copilot then prioritizes at-risk accounts and prepares targeted follow-up.
Setup starts in a workspace with the preview toggle plus a client connection. The steps differ by client, so follow the instructions in our AI assistants and coding agents documentation for more detail. Once the connection is established, you should be able to see the Genie One MCP connection in your AI assistant (see below for an example from Claude Cowork).
Once set up, Claude can then invoke Genie One MCP when asked any question that requires enterprise context. In response, Genie One returns a fully reasoned and formulated response by fully respecting the access controls to the underlying data assets (see below for examples from Claude Cowork and ChatGPT).
The Genie One MCP server provides MCP Apps, an extension that lets a server return an interactive view instead of plain text. On clients that support MCP apps, the server returns an interactive view, rendering visuals, summary metrics, and Genie Ontology citations inside the AI assistant interface (see below). Note: Clients that support MCP Apps automatically get the interactive view, while clients without MCP Apps support continue to get text-only results.
ChatGPT with Genie One MCP
Claude Cowork with Genie One MCP
Calling Claude with MCP is easy with the simple command ug claude, which will connect to the Claude instance in the Databricks workspace. Once the Claude model serving endpoint is open, it can be used directly via command line. With the Genie One MCP integration, Claude has context to answer accurately.
Claude code (CLI) with Genie One MCP
Genie One MCP lets external AI agents use the power of Genie One’s governed conversational analytics capabilities by sending a natural-language question. Genie interprets the business terminology, searches permitted enterprise data, generates and runs SQL, and returns a grounded answer with Databricks source links.
To enable this, the Genie One MCP server exposes five tools:
genie_ask starts a response and returns a conversation_id and response_id genie_poll_response returns progress steps and, on completion, the answer with an Explore in Databricks deep link genie_get_query_result returns the full result set when the truncated response is insufficientgenie_cancel_response stops an in-flight turn view_ask replaces genie_ask on clients that support MCP Apps, rendering an interactive panel with progress, visualizations, and ontology citations inlinewarehouse_id _meta parameter pins execution to a specific SQL warehouseWhen agents query through Genie One rather than underlying tables, it determines which metrics users can access and how they are computed. User identity must therefore flow through the request. The recommended approach is on-behalf-of (OBO) user authentication. The external assistant passes the end user’s OAuth token, and Genie evaluates Unity Catalog privileges, row filters, and column masks in that user’s context. Users receive only authorized results, and deep links open only assets they can access.
Two users can ask the same question in the same client and receive appropriately scoped answers without per-user prompt logic. Machine-to-machine authentication with a service principal is available for external-facing integrations, but represents every caller as one identity, removing per-user permission enforcement and potentially limiting personalization and memory.
Access Genie everywhere covers U2M, M2M, and OBO patterns and their governance implications. External MCP connections are Unity Catalog objects governed through standard grants.
Managed MCP servers are listed under Agents > MCPs in the workspace and are visible in Unity Gateway. Genie One chat events appear in audit logs, SQL execution in Query History, and consumption in billing system tables. Here are practices that hold up in production:
/api/2.0/mcp/genie/{genie_space_id} when a use case maps to one curated domain. It exposes a single read-only agent with its own instructions and trusted SQL, which is easier to benchmark and to scope.Agents, client interfaces, and integration protocols will continue to change. General-purpose AI assistants can still benefit from shared and governed business context.
Use Genie Ontology to establish that shared context, then deploy Genie One MCP to bring Genie One’s data-aware capabilities to the agents and workflows your teams already use.
For more information, review these resources:
AI coworkers and coding agents are spreading fast across organizations, and each one arrives with its own view of the business. Agents deployed in isolation lack the semantics and business definitions they need to answer accurately, rely on context that was modeled by hand at setup and has since gone stale, and return answers that contradict other agents pointed at the same data. Without a shared data foundation and business context, you cannot scale agents across an organization with confidence.
The Genie One Model Context Protocol (MCP) server is now generally available to all Databricks users. It gives any agent a single interface to retrieve structured and unstructured data, insights, and answers from Genie One, grounded in governed business context from Genie Ontology. The Genie One MCP now lives within Unity Gateway as a managed MCP Service, providing centralized governance, fine-grained policies, and audit logging across every invocation
What makes the Genie One MCP click for us is that it keeps analysis quality high regardless of which AI tool our teams choose. Some work directly in the Genie One UI; others live in Claude Cowork or their IDE all day. The MCP gives us one integration point that meets them where they already work, so the same trusted, governed answers show up consistently, no matter what tool they're using.—Fenny Sanyoto, Engineering Manager - Growth & Traveler Data Engineering, GetYourGuide
The Genie One MCP exposes Genie One over MCP, allowing any agent to communicate with Genie One as a peer agent.
The MCP exposes tools for asking questions to Genie One, getting query results, checking on incremental progress, and steering responses. The Genie One MCP App allows supported agent clients to embed Genie One’s whole process in real time with interactive visualizations and Genie Ontology citations. These capabilities allow you to integrate Genie One as your data-smart AI coworker into any agent without changing your workflow.
The MCP App provides interactive visualizations and Genie Ontology citations
By serving as a single governed entry point for agentic interactions, the Genie One MCP directly eliminates the friction of agent sprawl. Connected agent clients automatically leverage Genie Ontology via Genie One to interpret domain semantics, bridging structured relational data and unstructured document repositories without requiring custom, per-format connectors. This unified interface ensures that whether users operate within Claude, ChatGPT, Cursor, or custom internal interfaces, every user question yields a consistent answer governed by a single enterprise context layer, while intelligent routing dynamically delegates complex sub-tasks to tailored, domain-specific Genie Agents.
With the Genie One MCP, you can access trusted context from across your data estate and integrate it into any agent workflow. First, you’ll add the Genie One MCP to your agent from Unity Gateway. Once added, you can easily integrate the MCP into your workflows. Here are some popular use cases we’ve seen from our customers so far:
Consider an agent you’ve configured to create presentations: it aligns to your organization’s style guide, knows the expected format your executives prefer, and is popular with teams across your business. But when it’s time to fill those slides with business results, your teams still have to track down the right numbers, reconcile conflicting definitions, and explain what the data means. Now, you can add the Genie One MCP to this agent to bring trusted data and context into your slides, not just create the skeleton deck. While your agent works on the presentation, it kicks off requests to the Genie One MCP to retrieve the right data, which your agent integrates into its presentation.
Customer success teams may create an agent that automatically reaches out to customers based on interesting findings in their product usage patterns. But a drop in usage doesn’t mean the same thing for every customer. Teams still have to investigate what changed and what it means for that account before the agent can send a relevant message.
With the Genie One MCP added in, the agent can query Genie One to investigate usage and fetch trusted telemetry signals based on Genie Ontology. It then passes this data, along with any related context on what the usage might indicate, back to the outreach agent, which goes on to send targeted emails via your CRM.
Engineering teams using coding agents can integrate the Genie One MCP to ground their development in Genie Ontology. For example, if a developer is working on a PR to add logging to a product, their coding agent can make a request to the Genie One MCP to fetch the current definitions and queries associated with that product. This ensures the changes they make align with agreed upon business definitions.
Any time your preferred agent needs access to your governed business data, you can invoke the Genie One MCP to give it the context it needs to take confident action.
Genie One MCP allows you to leverage Genie One as your data-smart coworker from any agent your users prefer. With Genie One MCP, answers across agents stay consistent and grounded in Genie Ontology.
To see the Genie One MCP in action and set it up for your own use cases, review our new blog Genie One MCP: How to give any AI Agent the Right Business Context. To learn more, visit our documentation or contact your Databricks account team for support.
As teams put AI agents to work, they need to move quickly without losing control of what they deploy. They’re combining models, tools, and infrastructure from across a fast-changing ecosystem. Making those pieces work together and keeping them accountable as the stack evolves is becoming a core part of building AI applications.
Docker’s approach to this challenge is providing a trusted, common foundation for containment, curation, and control of agent workloads at its core, while pairing those capabilities with an open ecosystem of partners and tools.
That ecosystem spans model providers, MCP tools and gateways, enterprise applications, data and memory platforms, identity, security, observability, and code quality. It also includes the cloud providers, systems integrators, and channel partners that help organizations bring these capabilities into production.
Integrating this ecosystem gives teams the freedom to choose the models, platforms, and clouds that fit their needs while maintaining a consistent foundation for governance. Developers remain in the lead: choosing what agents can access, directing their work, and verifying the outcomes. The goal is to give them the tools and guardrails to build with confidence as models, frameworks, and requirements change.
At WeAreDevelopers World Congress North America, September 23–25 in San Jose, partners and customers are bringing that ecosystem to life at the Docker Pavilion. Customer sessions will show how these technologies come together in practice, from repeatable AI deployments at the edge to simpler development with payment APIs. Lightning talks and demos will explore enterprise knowledge and agent memory, collaboration between agents, security and incident response, and verification of generated code.
Here’s who you can meet and what they’ll be sharing.
Customers bring another essential perspective: how these technologies come together in the systems they build.
These sessions bring together the people building the tools and the teams putting them to work. It’s an opportunity to compare approaches, ask questions, and see how the ecosystem can help you tackle your next engineering challenge.
Come visit us at WeAreDevelopers. Meet our partners, customers, and speakers, catch a lightning talk, and see their technologies in action. Plan your visit to San Jose.
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We’re removing several SSH algorithms, adding a new algorithm, and requiring larger RSA SSH keys to improve security.
The changes are as follows:
ssh-rsa signature type, including ssh-rsa-cert-v01@openssh.com certificates using SHA-1).diffie-hellman-group-exchange-sha256.mlkem768x25519-sha256 for SSH sessions on github.com and GitHub Enterprise Cloud with Data Residency, except for the U.S. region.Adding ML-KEM lets us offer a newer, more performant key exchange method that is secure against quantum computers.
We’re also removing the older Diffie-Hellman method, a slow, little-used algorithm that could be broken with advances in quantum computing. For RSA, we’re removing the use of SHA-1 since it’s known to be weak, as well as increasing key sizes to align with 128-bit security requirements.
mlkem768x25519-sha256 will be enabled on github.com and GitHub Enterprise Cloud with Data Residency (except for the U.S. region).ssh-rsa signature type (i.e., RSA keys using SHA-1) and the diffie-hellman-group-exchange-sha256 key exchange algorithm.ssh-rsa signature type and the diffie-hellman-group-exchange-sha256 key exchange algorithm.ssh-rsa signature type and diffie-hellman-group-exchange-sha256 key exchange algorithm.These changes will all take effect in GitHub Enterprise Server in version 3.25, except for the addition of mlkem768x25519-sha256, which will take effect in version 3.24.
The only affected users are those connecting with a Git client over SSH or those using the unauthenticated Git protocol on GitHub Enterprise Server. If your Git remotes start with https://, nothing here will affect you.
If you’re using an existing RSA key, make sure you’re using RSA with SHA-2 (i.e., the rsa-sha2-256 and rsa-sha2-512 signature types). You do not need to generate a new key, since all RSA keys are capable of signing with all hash algorithms. As long as the SSH program or library you’re using supports RSA with SHA-2, you can continue to use the same key without a problem and most SSH implementations supporting RSA with SHA-2 will choose it automatically.
Note the distinction between the key type ssh-rsa, which applies generically to all RSA keys regardless of signature algorithm, and the confusingly named signature type ssh-rsa, which indicates an RSA key using SHA-1 (as opposed to rsa-sha2-256 and rsa-sha2-512, which refer to RSA keys using SHA-256 and SHA-512, respectively).
Here’s a list of some common software that uses SSH to connect to GitHub and the version necessary to support RSA with SHA-2 robustly with the default configuration:
| Software | Minimum Version |
|---|---|
| OpenSSH | 7.2p1 |
| JSch | 0.1.66 from this fork |
| TeamCity | 2021.2.3 |
| Go SSH | 0.16.0 |
| libssh2 | 1.11.0 |
| PuTTY | 0.82 |
Alternatively, if you’re using older software and can’t upgrade, you may be able to use an Ed25519 or ECDSA key instead. All Ed25519 and ECDSA keys we support are strong, secure, and will continue to work for the indefinite future.
For generating new keys, we recommend using an Ed25519 key whenever possible. However, if you still need an RSA key for compatibility with other services, you can generate one as long as it as at least 3072 bits in size.
diffie-hellman-group-exchange-sha256If you’re using one of the SSH implementations above that supports RSA with SHA-2, it should also support a strong key exchange mechanism.
The addition of the mlkem768x25519-sha256 shouldn’t require any changes from users. SSH clients will automatically use the new algorithm by default if configured to prefer it. Users who use an older SSH client should automatically fall back to an older key exchange algorithm.
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The response header, Vary, has been called “the ugliest part of HTTP that we haven't yet improved.” The same post describes it as a “horrible, kludgy mechanism” with “pretty abysmal interoperability” across intermediaries. That is usually where sensible engineers back away slowly with their hands raised.
That’s not exactly an endorsement of Vary, but ugly doesn’t mean useless.
One URL can have more than one correct response. A server might, for example, deliver different image formats to different browsers. If a cache ignores Vary, it risks serving the wrong bytes to a request. But if it treats every raw header value as distinct, a handful of similar requests can spread into thousands of barely reusable cache entries. Vary tells a cache which request fields may affect the response, but it does not tell the cache which differences actually matter.
Vary support is now available in Cache Rules on every plan. The origin still names the request headers that may affect a response, but you decide how Cloudflare handles each one. You can normalize known negotiation headers, pass exact values through when those small differences matter, or bypass cache when the variation is too unpredictable. The origin declares what may vary, and you decide how much variation is actually meaningful for the cache.
Vary is a standard HTTP response header that tells intermediary caches (like Cloudflare) which request fields may affect the response sent by the origin. Sites use Vary to serve different languages, image formats, compression schemes, or regional content from the same URL.
Take one URL that produces two valid representations. A browser requests a webpage:
The origin returns HTML and identifies Accept as a field that may affect the response:
An API client can request the same URL with a different preference:
This time, the correct response is JSON. The Vary: Accept header tells the cache that the URL alone is not enough to choose between responses. The request’s Accept value must also be considered.
Without Vary, whichever response enters the cache first can be served to both clients. If HTML wins, the API client receives markup and its JSON parser fails. If JSON wins, a browser expecting a web page receives an API response.
Vary prevents the cache from serving the wrong response to the requesting client. But it introduces a harder question: when two requests contain different header values, do they actually need different responses?
Vary can tell a cache which request fields may affect a response. It does not tell the cache what the response represents. For example, take an origin that serves content in only English, French, and German. A client might send:
While another client might request:
Both requests prefer English here. The origin’s response may map both requests to exactly the same English response. But a cache comparing the raw values cannot safely assume they are equivalent. They have different orders and language tags (that the origin doesn’t differentiate). So the cache may store them as separate variants, even when their response bodies contain identical bytes.
This is Vary’s central problem. Applications often produce a small, finite set of representations from an enormous set of possible request values. The origin understands that thousands of language preferences collapse into three supported languages, while a cache usually does not.
This problem compounds when a response varies on multiple fields. Ten possible values across one field create ten variants. Ten values across three fields can create 1,000 combinations. Real headers can have far greater cardinality: User-Agent values are numerous, cookies can be unique to individual visitors, and preference headers can differ in ordering, formatting (spaces and tabs matter!), and quality values.
The result is a cache that can be perfectly correct and almost permanently cold (an entry never reused). Identical responses can be scattered across entries that receive too little traffic to remain hot and in cache. They can consume capacity, evict one another, reduce cache hit ratios, and send more requests back to origin servers. Eviction can remove cold entries, but it cannot merge them just because the responses are identical.
An analysis of more than 120 million responses from nearly 50,000 popular sites found almost 3,000 sites varying on four or more fields. Some varied on 10, 23, or even 47 fields. We want to make sure that customers have the tools they need to use Vary when appropriate, but not so much that they create a useless cache.
Some high-cardinality variation is deliberate. CDNs or reverse proxies may inject values, such as a geographic region, to partition content predictably. That works when the possible values are controlled and every component agrees on their meaning. Without those constraints, the cache fragments into variants it may never reuse.
That was the design problem we needed to solve to support Vary. We needed to preserve enough variation to serve the right response, without allowing incidental differences between requests to destroy cache efficiency.
Cloudflare customers already had several ways to handle negotiated content similar to Vary. They could bypass cache and let their origin deal with it, reproduce the origin's negotiation logic in a custom cache key or other rule, use a Worker, or use features like Vary for images.
Those options remain useful, but they either give up caching, duplicate application logic, need to write additional code, or address a narrower use case. Vary in Cache Rules may fill the gap between these existing features by splitting support into two decisions:
Vary to identify the request headers that may affect a response.A Cache Rule does not force every response to vary. If the origin does not return Vary, Cloudflare caches the response normally, though the rule may still rewrite Accept and Accept-Language before forwarding the request to the origin.
When the origin does return Vary, Cloudflare uses the configured action for each header it names. Headers without an individual setting use the rule’s default action. The three available actions are:
We recommend normalize as the default. For individual headers with personal or unbounded values, use bypass. Use passthrough when the exact value changes the response.
For example, passthrough preserves distinctions in casing, whitespace, ordering, and duplicate values, even when the origin treats them as equivalent. With Vary: X-View and passthrough, these three values produce separate cache keys:
X-View: compact,full
X-View: Compact,full
X-View: compact, full
Enough incidental variation can turn a reusable response into many one-off variants in your cache.
Regardless of the configured actions, Vary: * always bypasses cache. It means any aspect of the request, even information outside the HTTP message (like the client’s IP address), may affect which response the origin selects. Cloudflare therefore cannot reuse the response for a later request without contacting the origin.
Let’s follow one of the /catalog requests from above through Cloudflare.
On the first request, Cloudflare has no stored Vary data for the resource, so the cache lookup misses. The matching Cache Rule can normalize configured fields before Cloudflare contacts the origin.
This can happen before Cloudflare knows whether the eventual response will contain Vary. The Cache Rule defines the permitted normalization; the response later determines whether those fields become part of the cached variant.
That ordering matters. If Cloudflare grouped several raw values under one normalized cache key, but the origin still received those raw values, the origin could produce different responses that the cache would later consider interchangeable. Forwarding the normalized value keeps origin selection aligned with cache matching.
The origin responds with:
Vary: Accept, Accept-Language
Cloudflare records those header names and stores the response as a cached variant. The header values, processed according to the Cache Rule, distinguish this variant from others for the same resource.
When another request for /catalog arrives, Cloudflare starts with the resource’s base cache key: generally the URL plus any other configured key fields. It then reads the stored Vary fields and applies the Cache Rule to those headers in the new request to identify the matching cached variant.
Suppose they normalize to:
Accept: text/html
Accept-Language: en,fr
Cloudflare uses those values to look up the matching cached variant directly. It does not compare the request against every stored variant one by one.
If a matching variant exists and is fresh, the request is a cache hit. If not, Cloudflare sends the request to the origin and may store the resulting response as another variant.
The origin response closes the loop. For each header named in Vary, Cloudflare uses the action configured for that header, or the rule’s default action if the header is not listed individually:
Vary, Cloudflare caches it normally.Vary: *, Cloudflare does not store it.This places an important responsibility on the origin. Every cacheable response that can differ based on request fields must return the appropriate Vary header consistently, including errors and fallback responses. If one response omits it, Cloudflare could cache that response without the variance needed to keep it isolated.
The cache keys in the diagram are conceptual. The later request assumes a fresh cached response.
Any purge targeting a cached resource covers all its Vary variants. Existing requirements for purging custom cache keys still apply.
Changing a Vary configuration does not automatically purge existing content. The new policy may produce different cache keys: requests can miss and refill under the new keys, while old entries remain until they expire or are purged.
Remember the requests from above asking for English and French?
Accept-Language: en-US, fr;q=0.8
Accept-Language: fr;q=0.8, en-GB
Both requests prefer English, but passthrough would treat them as different variants. If the Cache Rule allows en, fr, and de, normalize reduces both to en,fr, allowing them to share a cached response.
To do this, Cloudflare lowercases values in Accept, Accept-Language, and Accept-Encoding, then sorts them by quality value, the highest first, with alphabetical ordering to break ties. The client’s ordering therefore does not affect the cache key. After sorting, Cloudflare strips parameters from entries with a nonzero quality value. It can also lose q=0 (“not acceptable”) when shortening language tags or filtering to the configured formats and languages. For example, en-US;q=0 can become en. Use passthrough for Accept or Accept-Language if the origin needs to see those exclusions.
You can also configure the rule to keep only specified media types or languages in Accept and Accept-Language. Regional language tags such as en-US reduce to their base language, en, unless the full tag is configured. This lets you align normalization with the formats and languages your origin actually serves.
To keep origin selection aligned with cache matching, Cloudflare forwards the normalized Accept and Accept-Language values to the origin. It also forwards normalized Accept-Encoding values when Respect Strong ETags is enabled. Other headers are normalized only for cache matching.
In the Cloudflare dashboard, go to Caching > Cache Rules, create or edit a rule, make the response eligible for cache, and add the Vary setting. Set the default behavior, then add the headers your origin is expected to name.
The same configuration is available through the Rulesets API in the http_request_cache_settings phase. The default setting chooses a fallback action for headers your origin names in Vary that you have not configured individually.
This example normalizes Accept and Accept-Language to a configured set of formats and languages. The default normalize action also applies to other headers named in Vary:
This is a complete request body for a PUT to the http_request_cache_settings phase entrypoint. A PUT replaces every rule in that entrypoint. If you already have Cache Rules, include them in the rules array or use the appropriate single-rule create or update operation instead.
If the origin serves one representation for each media type and language pair, there are six content combinations. That does not cap the cache at six keys. Preference order, missing headers, and values that normalize to empty can create more. Keep the supported set small and define the rule’s boundaries clearly. After rollout, test the same URL with different header values that should normalize to the same cached variant. Send the test requests from the same client, confirm they return the expected format and language, and inspect CF-Cache-Status. Look for hits once the cache is populated, and investigate persistent miss responses or unexpected bypass responses.
For limitations, additional examples, and how to set this in Terraform, see the Vary documentation.
At this point, an obvious question is, “why not add Accept and Accept-Language to a custom cache key?”
That works when those fields are always part of the resource’s identity. But a custom cache key adds the configured dimensions to every response covered by the rule, whether the origin used them or not.
Vary is response-driven, but cacheable responses under the same base key need a consistent set of Vary fields.
Use a custom cache key when a request property always defines the resource. Use Vary when the origin declares the same set of request fields across cacheable responses. Avoid placing the same header in both unless the duplication is deliberate and tested.
Vary helps solve an obvious problem: one URL can have more than one correct response. But it hands a cache a harder problem, which request differences actually matter? The origin knows which responses it can serve. The cache needs to know which requests can reuse each response.
Vary in Cache Rules connects those two views. The origin identifies the request fields that may affect a response. You decide whether to normalize values, use passthrough for exact differences, or keep the response out of cache.
Vary was never too ugly to be useful. But configuring supported formats and languages manually may not suit every application. We’re evaluating whether ideas from the expired Availability Hints draft could reduce that work by letting origins describe the representations they serve directly.
Vary in Cache Rules is available today on Free, Pro, Business, and Enterprise plans through the Cloudflare dashboard, Rulesets API, and Terraform.
Code quality is shaped by countless decisions, from how developers manage complexity to how quickly they detect regressions. But common concepts such as technical debt, cognitive complexity and unit testing are not always clearly understood.
In a new code quality Q&A session, we asked members of the Qodana team to answer some frequently searched questions about maintaining code quality. Here are their concise, practical explanations.
“Poor code quality describes the code itself, while technical debt describes a compromise that creates future work. For example, “Let’s use hardcoded values instead of configs, just because we need to write POC ASAP” is intentional technical debt.
By contrast, “I don’t know why I need to use configs, that’s why I hardcode values” is poor code quality caused by a lack of knowledge. Technical debt may lead to poor code quality, but poor code quality is not always caused by technical debt”. – Alexandr Kugushev
“Best practice to minimize complexity in nested loops – is avoiding them: extract functions, double check conditions (maybe it’s possible to simplify them with different logic rules), explicitly name intermediate results, use iterators, generators, map, filter, reduce functions, try to flatten data before processing and so on”. – Anastasia Lavrenko
“Code coverage and mutation testing serve different but related purposes. Code coverage measures how much production code is executed by unit tests. Mutation testing evaluates the quality of those tests by deliberately introducing small changes to the code and checking whether the tests detect them. If the tests still pass, the mutation survives, indicating a possible gap in the test suite.
There is no universal ideal mutation score, as results vary by project and testing strategy. However, scores of around 40–60% are generally considered a reasonable starting range. Higher scores can indicate a stronger test suite, but the goal should be meaningful test quality rather than reaching a specific number”. – Aleksander Movsesov
“Unit tests provide the fastest feedback on code changes. By testing individual units of behaviour in isolation, they catch regressions close to the point where they are introduced, make refactoring safer and confirm that the code continues to behave as expected”. – Arman Ayvazyan
“I think the best way to track if code quality is actually improving is to look at the trends over time. Static analysis can give you signals like how many new issues are being introduced, how severe those issues are, and whether technical debt is going up or down. But you should also be able to see the impact outside of static analysis.
If code quality is improving, you should start seeing fewer Sev 1 issues making it into production and a faster MTTR when issues do happen. That gives you a better picture of whether the changes you are making are actually improving the quality and reliability of the software”. – Alex Costa
“First of all, we should draw a distinction between vulnerability scanning and static analysis. What Qodana does is not always vulnerability scanning – a lot of the issues it finds cannot be exploited by a third party. Sure, Qodana does find vulnerabilities, but it also finds regular bugs, dangerous functions, dead code, and so on.
That being said, eliminating false positives is done in a similar way regardless of the issues – by giving the tool you use more information about your repository. The most common case of a false positive is the automated tool not knowing that you intended something to be written in a particular way, for example when you disagree with established practices, or when you use an older standard of the language that doesn’t support a safer alternative, or when you are forced into an unsafe code pattern by a third-party dependency.
All of this can be addressed: for repo-wide false positives, you can exclude the inspections in qodana.yaml. For individual lines and blocks for code, you can explicitly silence inspections with comments like // NOLINT(<INSPECTION ID>) and // NOLINTNEXTLINE(<INSPECTION ID>)“. – Anna Zhukhova
Maintaining code quality requires more than fixing individual issues. Teams need to make technical compromises consciously, keep code understandable and create fast feedback loops that catch problems early.
Automated code analysis can support these practices by identifying quality issues continuously and helping teams apply consistent standards throughout development. With Qodana, teams can bring JetBrains inspections into their CI/CD pipelines and address problems before they become more difficult and expensive to resolve.
Need answers to your most pressing code quality and security questions? Leave a comment below for the next round or find out how Qodana can help you secure and improve your codebase.
Nothing is worse than testing out a change that works in staging, only to see it behave differently in production. That’s why we wanted to give you an environment that’s as close to production as possible — so you can battle-test your changes and make sure they behave exactly as you expect them to.
Agents are helping us push more lines of code than ever before, and larger changes mean more ground needs to be tested ahead of release. Ideally, that testing is done in a way that doesn’t slow agents down, but gives them the tools to take on more of the development lifecycle.
That’s why today we’re launching Worker Previews. Each Git branch gets a production-like place to run, with its own code, configuration, URL, observability, and state.
So now, for every change in your codebase, you can:
npx wrangler preview, using its own variables, secrets, and bindings, separate from production configuration and traffic.main. We call this the base configuration.The result is a pre-production feedback loop for every branch. Push your change to a branch, test behavior, inspect performance — before you merge to production.
This enables an Agent Development Lifecycle (ADLC) where each change is atomic, independently deployable, observable, and revisable. And it gives agents and humans the evidence they need to self-improve: catch what failed, push a fix, and verify the next deployment before it hits production.
When you start work on a new feature, the first thing you do is branch off of main. You get your own copy of the code and make your changes without affecting anything in production.
Worker Previews extend that same model beyond code. Each branch gets its own isolated environment and URL. You can run hundreds of Previews at the same time — each operating independently without affecting other Previews or production.
Production and each Preview have their own configuration — served on their own URL.
When you run npx wrangler preview, the branch gets its own copy of your Previews configuration that you have defined, running on its own URL — all under the same Worker.
In the dashboard, this works like switching branches. Click the breadcrumb next to your Worker's name (it defaults to Production) to see all your Previews:
The dashboard brings every environment into one view. Production sits alongside as many Previews as you need, so contributors can work on separate changes without fighting over a shared staging site. Unlike Wrangler environments, where each environment requires deploying and managing a separate Worker, Previews keep that isolation in one dashboard view.
Each Preview runs as a real version of your Worker. Some changes can only be validated at runtime: an API endpoint has to handle a real request and return the right response. More subjective changes, like a UI update, a new onboarding step, or a different error state, need to be experienced in context before they reach production.
For isolation to extend across your application, stateful resources need special treatment. The reason for that is that Durable Objects run on a singleton model. One instance is responsible for a given object ID, and that instance owns its storage.
If a Preview shared the same DO namespace as production, you wouldn't just be reading stale data — you could modify the same instance serving live traffic in real time (scary!).
That is why every time you run npx wrangler preview, Cloudflare automatically creates a new Durable Object namespace and Container application for that Preview — so that a failed migration or a bad schema change stays contained to that branch and that branch only.
All you need to do is export the class, add its migration, and access it through ctx.exports:
In production, ctx.exports.Counter resolves to the production namespace, while in a Preview, it resolves to that Preview’s namespace.
You now have an entire playground to experiment with. Take Sandboxes, for example, where milliseconds of improvement to startup time can make or break the experience. If you have been trying to improve cold-start performance, you can run different configurations across branches at the same time, compare their cold and warm performance side by side, and find the best setup faster.
Now that each branch runs at its own URL in an isolated environment with its own state, you can enter the feedback loop and start battle-testing every change before it reaches production.
You can send traffic to the Preview URL however you normally would — from your terminal, probe from CI, an agent, or by clicking through it yourself. Once that traffic starts flowing, every Workers Observability tool you’re already used to is available, scoped to each individual Preview.
As each request hits the Preview, Workers Observability traces its full lifecycle in a waterfall, including fetch calls, binding operations, and handler invocations. So when something fails, you can follow exactly what happened without sorting through production traffic or signals from other changes.
Observability for Previews looks just like you're already used to for production Workers. Select your Preview from the breadcrumb and open the Observability tab to see its events, errors, and traces:
To give your agents even more control, you can have them open the Preview URL in a headless browser, click through a login flow step by step, and capture a screenshot or record the entire session as replayable DOM events – with Browser Run.
Below is an example where an agent opens the Preview, captures what was rendered, and connects a failed request to Workers Observability events from the same run.
A reviewer can watch the session in real time with Live View or step in with Human in the Loop when the automation needs judgment.
If something fails, you see it from both angles: what rendered and what happened at runtime.
That gives the agent enough evidence to keep the pre-production loop running autonomously: deploy, open the URL with Playwright MCP, click through, query the traces through the Workers Observability MCP server, patch, redeploy, and verify. Every iteration stays scoped to the branch.
Just like you wouldn't reconfigure your code from scratch every time you branch, you shouldn't have to reconfigure your environment either.
You set base configuration for Previews once, in a previews block in your Wrangler configuration file.
In the dashboard under Worker → Settings, you see this inlined as Production and Previews Base. Once the base is set, run npx wrangler preview from any branch to create a Preview. If your Worker is Git-connected through Workers Builds, it happens automatically on push.
You can override any setting for only one Preview — without affecting production, the base, or other Previews.
To bring the whole setup even closer to production, your preview URLs can be served from your own custom domain. If your app runs on example.com, a Preview for a login branch could run at feature-login.previews.example.com.
If you want to keep those URLs private, you can protect your Previews with Cloudflare Access and require visitors to sign in first.
We’ve already been dogfooding Worker Previews inside Cloudflare, most notably to build and test CloudflareOS, our open-source platform for safely connecting agents to company systems.
CloudflareOS lets agents work with services such as Google, GitHub, and Slack through Gatekeepers, which control what those agents can access and change. That makes Gatekeeper changes especially sensitive, because a bug could expose data or permit an action that should never have been allowed.
Some of these bugs only appear when OAuth callbacks, permissions, approval flows, and application state are running together. Because testing each component separately cannot show us how the complete system will behave, we deploy an isolated Preview of CloudflareOS and its Gatekeepers for every change under review. We then run the full workflow, fix what fails, and test it again before merging.
We’re seeing customers use Previews for the same basic reason: some problems only show themselves when the change is actually running.
"At Supermemory, we use Cloudflare heavily, and Worker Previews are exactly the kind of developer experience improvement we wanted to see. For HTTP flows, we can preview Worker changes before they reach production, including routes backed by Durable Objects, and catch issues earlier without slowing down shipping." — Dhravya Shah, Founder, Supermemory
"Previews is amazing for Inspect [Ramp’s coding agent]. I used it to review and test an Inspect PR on my phone that is making reviewing and testing PRs with Inspect on phones responsive…with Inspect." — Dylan Garcia, Senior Staff Engineer, Ramp
You might be thinking: Didn't Workers already have preview URLs? It’s true, we did. We're now calling those Version URLs because they point to specific uploaded Worker versions. Unlike Worker Previews, they don't create an isolated environment for each branch and could only point to production resources. To learn more and compare the different workflows, check out our docs.
Worker Previews is a big improvement from what we offered before, but there's still more to come. Here's what we're working on next:
Worker Previews are available now. Get started with the docs, and if you have a feature request or run into an issue, open an issue on GitHub or join the Cloudflare Developers community on Discord.
Acknowledgements: This project was made possible by the design and implementation efforts of Greg Brimble, Patrick O’Donnell, Matt Price, Korinne Alpers, Max Peterson, Cina Saffary, Josh Wheeler, Thomas Ankcorn, Matt Rothenberg, and Brandon Strittmatter, with leadership from Brendan Irvine-Broque and Dan Carter.
To build and deploy sophisticated robotics applications that can perceive, reason and act in dynamic environments, developers need new physical AI models and tools.
The ROS open framework is a project from Open Robotics that helps humans build robots. NVIDIA Isaac ROS 5.0 — a collection of GPU-accelerated packages built on ROS, released today at the ROSCon conference in Toronto, Canada — helps humans and AI agents build robots together.
The release introduces new agentic workflows and platform support to help developers build, customize and deploy robotics applications faster.
ROS provides the open source foundation for much of modern robotics development, giving developers common tools, libraries and standards for building and connecting robot applications.
NVIDIA Isaac ROS brings NVIDIA accelerated computing, physical AI models and production-ready libraries to the nearly 1.3 million ROS users, helping developers build high-performance robotics applications using free, familiar, open source tools.
AI agents are changing how software is built, helping developers automate repetitive tasks, navigate complex codebases and move from ideas to working applications faster. Isaac ROS 5.0 brings these capabilities to robotics development.
Isaac ROS 5.0 introduces support for ROS Lyrical and Ubuntu 24.04, giving developers a path to adopt the latest ROS platform while continuing to accelerate demanding robotics workloads with NVIDIA accelerated computing. NVIDIA worked with the Open Source Robotics Alliance to contribute a standard data-handling interface to ROS Lyrical that helps robotics software work efficiently across different computing hardware, including GPUs.
Available to the entire ROS community, it gives developers a consistent way to accelerate demanding robotics applications, with CUDA providing a working example for GPU acceleration.
New NVIDIA Isaac skills for setup and manipulation provide reusable workflows that developers and AI agents can use to complete robotics development tasks. Agent-ready documentation also makes it easier for AI agents to understand Isaac ROS tools and workflows, turning developer intent into working applications faster.
Some skills go beyond assisting with individual coding tasks. A new FoundationStereo fine-tuning skill enables an AI agent to help adapt a stereo perception model to a developer’s cameras, environment and robotics application, so developers can easily achieve more accurate perception for a given sensor configuration.
FoundationPose, a foundation model for object pose estimation and tracking, now provides an agent-ready inference library that enables robots to perceive and track the position and orientation of objects up to 5.5x faster.
In addition, pick and place — a common workflow that connects detection, depth estimation and pose output — is now available as a standalone, agent-ready skill, providing robot developers more flexibility beyond Isaac ROS.
The robotics ecosystem is already extending this agentic approach to development workflows.
AgenticROS, an open source project sponsored by 3D perception technology company RealSense, connects Isaac ROS with NVIDIA Nemotron open models and NVIDIA NemoClaw blueprints, enabling AI agents to interact with ROS-based robots. RealSense is also optimizing its latest AI-native 3D stereo depth cameras, including RealSense D585 Pro, and an open source software development kit for Isaac ROS and the NVIDIA Jetson Thor edge AI platform, helping developers build perception, navigation and manipulation applications.
Intrinsic’s Open Machine Tending Solution is a reference application for computer numerical control machine tending, part of the newly released Intrinsic Core, an open source suite of preconfigured runtime services and capabilities designed to accelerate industrial robotics applications. It includes built-in compatibility with NVIDIA FoundationPose for out-of-the-box object registration, tracking and pose estimation. Using the FoundationPose perception pipeline, the solution enables robots to dynamically detect and handle parts while reducing the need for rigid, costly physical fixtures and specialized systems integration.
Seeed Studio is using NVIDIA Isaac ROS with reBot Arm, combining accelerated perception, spatial understanding and motion planning on NVIDIA Jetson Thor. This integration gives developers a practical platform for building adaptable physical AI applications, from object localization to collision-aware manipulation and autonomous pick and place.
Magna is using NVIDIA Isaac ROS as a modular, GPU-accelerated foundation for robotic perception, synchronized data collection and NVIDIA Isaac GR00T model deployment, pairing it with Isaac Sim hardware-in-the-loop testing to bring intelligent automation from research to real-world manufacturing and mobility — faster and with fewer risks.
Prefix.dev’s Pixi package-management tool makes it easier to create reproducible robot development environments, bringing together ROS with the NVIDIA CUDA platform to help developers more easily set up and share accelerated robotics workflows.
As an Isaac ROS Partner, Foxglove helps developers visualize and debug live ROS applications through its web and desktop tools, which are integrated throughout Isaac ROS tutorials and support data such as 3D topics, nvblox meshes and rosbags.
Flexiv is integrating Isaac ROS with its Rizon 4 adaptive robot, giving developers access to NVIDIA-accelerated robotics capabilities and a streamlined path from testing applications in NVIDIA Isaac Sim to deploying them on a physical robot.
Ekumen, a Grid Dynamics Company, is using GPU-accelerated Isaac ROS packages within existing ROS and Nav2 stacks to improve precision docking, 3D obstacle detection, visual localization and real-time motion planning, validating each application in Isaac Sim.
Ekumen uses isaac_ros_cumotion on a GPU to map a collision-free path for a warehouse arm in roughly 2 to 5 milliseconds.
Ouster integrates its Stereolabs ZED stereo cameras with NVIDIA Isaac ROS to deliver GPU-accelerated perception for robotics applications. The integration simplifies the development of real-time object detection, mapping and navigation while maintaining interoperability with the broader ROS ecosystem.
The applications that developers and agents build ultimately need to run on the robot.
NVIDIA Jetson is a scalable computing platform for running the physical AI stack at the edge with real-time performance, bringing together ROS, accelerated perception and navigation, AI models and application logic on the robot.
Isaac ROS 5.0 supports scalable compute, from entry-level NVIDIA Jetson Orin Nano to high-performance Jetson Thor devices, giving developers a path from development to deployment as robotics workloads become increasingly sophisticated.
Robotics companies are already using this combination to bring more AI processing directly onto their machines.
Mentee Robotics uses NVIDIA Isaac ROS as the perception and AI backbone of its MenteeBot humanoid, enabling the robot to interpret visual information and execute learned behaviors in real time. A shared software foundation across NVIDIA Jetson Orin and Jetson Thor platforms helps Mentee extend its innovations from existing robots to next-generation systems.
Universal Robots has built NVIDIA Isaac ROS into its AI Accelerator software development kit to help integrators deploy advanced perception and motion capabilities faster, without developing complex robotics software from scratch. Powered by NVIDIA Jetson at the edge, the solution enables robots to adapt to parts that are not precisely positioned, reducing reliance on costly fixtures and making manufacturing cells more flexible.
ROBOTIS, which builds the developer-friendly ROS-based TurtleBot3, is integrating Isaac ROS into its AI Worker robot, using GPU-accelerated object perception to enable vision-guided manipulation tasks including picking, placing and alignment.
ROBOTIS performs object manipulation tasks using NVIDIA Isaac ROS CuMotion.
FieldAI’s robot foundation models, which can run entirely on robots without relying on cloud connectivity, are integrating Isaac ROS on Jetson devices to take greater advantage of GPU acceleration and improve the efficiency of the on-robot AI stack.
Noble Machines is using NVIDIA Isaac ROS on Jetson to accelerate the development of general-purpose robots for industrial applications, building on ready-to-use AI and perception capabilities rather than creating them from scratch.
By combining an open robotics ecosystem, accelerated computing and new agentic development workflows, Isaac ROS 5.0 helps developers address both sides of the physical AI challenge: building increasingly capable robot applications and efficiently running them in the physical world.
Available now, Isaac ROS 5.0 is free and open source. Developers can learn more and get started with NVIDIA Isaac ROS on GitHub.
Starting with CodeQL CLI 2.27.0, the all-platform CodeQL bundle (i.e., codeql-bundle.tar.gz and codeql-bundle.tar.zst), which includes the binaries for all supported platforms up to this release, is marked as deprecated.
In mid-March 2027, we will remove the all-platform CodeQL bundle. Download the platform-specific bundle for your supported operating system and architecture instead. Linux ARM64 binaries are available only through platform-specific downloads and will not be included in the all-platform bundle. To learn more, see the documentation about supported platforms.
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Antoine du Hamel
c0a42d23e5] - (SEMVER-MINOR) crypto: add crypto.parsePKCS12() (Brian Muenzenmeyer) #65627eb4fabe81e] - doc: add araujogui to collaborators (Guilherme Araújo) #6609064fb33d791] - (SEMVER-MINOR) ffi: load libraries from a mounted VFS (Matteo Collina) #659092b1701f810] - (SEMVER-MINOR) fs: add openAsBlobSync (greenhead) #65644080e76b3d7] - (SEMVER-MINOR) net: support sending net.BoundSocket to threads and child processes (Guy Bedford) #6472513e61f6ae6] - (SEMVER-MINOR) perf_hooks: implement SlidingWindowHistogram (James M Snell) #65825a326546094] - (SEMVER-MINOR) perf_hooks: implement qrde analysis support in Histogram (James M Snell) #658060306b0a71e] - (SEMVER-MINOR) sqlite: bind undefined to NULL (Trevor Burnham) #657093c999edef7] - (SEMVER-MINOR) src,lib: add util.markPromiseAsHandled (James M Snell) #65805f7d18ec360] - (SEMVER-MINOR) test: expand histogram test coverage (James M Snell) #658253ce4d23bbb] - (SEMVER-MINOR) util: implement util.throttle (James M Snell) #65899336f33ccc1] - (SEMVER-MINOR) util: implement debounce (James M Snell) #658992bf082453b] - assert: fix TypeError on deepStrictEqual with null Map key or Set member (Sergey Sannikov) #64449c518831d00] - benchmark: add --csv option to compare.js with --analyze (James M Snell) #6592289bae46e68] - buffer: fix unaligned UTF-16LE decoding (Matteo Collina) #6590501cee9dcef] - build: do not use bundled simdutf when built with --shared-simdutf (Antoine du Hamel) #6589120606a4d19] - build: suppress OpenSSL asm warnings with clang (Richard Lau) #66023a76e97b5c5] - build: derive V8_LOGGING_LEVEL from dcheck_always_on (Joyee Cheung) #65744ac7fcf0cef] - build: sync cargo/rustc version warnings (Richard Lau) #65912f24a495b36] - build: fix quiet default for make builds (Shelley Vohr) #65826c4381765fe] - child_process: clear timeout timer on spawn-time error too (kishore280) #655068955781eeb] - crypto: remove redundent std::move call (Antoine du Hamel) #661110b88aa5a15] - crypto: read RSA-PSS restrictions from provider (Filip Skokan) #66108f90d3d3cb0] - crypto: skip private RSA parameters in key details (Filip Skokan) #6610897828d91db] - crypto: use provider EC group names (Filip Skokan) #661083de05da569] - crypto: fetch ciphers for private-key encoding (Filip Skokan) #66108ca8ee04594] - crypto: decode PKCS#1 keys through providers (Filip Skokan) #6610854a625e953] - crypto: derive keys through EVP_KDF (Filip Skokan) #66108971adece6a] - crypto: use names for asymmetric key algorithms (Filip Skokan) #65966bfb544eb00] - crypto: optimize and benchmark key preparation (Filip Skokan) #65892c0a42d23e5] - (SEMVER-MINOR) crypto: add crypto.parsePKCS12() (Brian Muenzenmeyer) #65627c58a4e0ba7] - crypto: add Hybrid KEMs to Web Cryptography (Filip Skokan) #6575970edf90851] - crypto: avoid EC reconstruction for signature sizing (Filip Skokan) #65908fab5dddd77] - crypto: avoid EC raw export reconstruction (Filip Skokan) #65908220a499614] - crypto: export EC JWK coordinates directly (Filip Skokan) #65908f1295e11db] - crypto: read EC curve metadata directly (Filip Skokan) #65908775173a5db] - crypto: optimize private EC JWK import (Filip Skokan) #65908e44669bb7d] - crypto: validate the limit of PBKDF2 iterations (Filip Skokan) #65704cd5a7611e9] - crypto: use primordials in HKDF info validation (Filip Skokan) #6570494affce749] - Revert "deps: V8: override depot_tools version" (Richard Lau) #66110870a366c49] - deps: V8: cherry-pick 95efbaf92a0d (Igor Sheludko) #66020d62a452738] - deps: V8: backport a0607c5006b8 (Antoine du Hamel) #65891e8edeffe2d] - deps: update googletest to 8eff9e336692fc95961e096564f1044c600b881d (Node.js GitHub Bot) #66009eb4fabe81e] - doc: add araujogui to collaborators (Guilherme Araújo) #660902fffb3872d] - doc: fix duplicate 'the' typo in node_platform.cc (Muhammad Al-Muzahid) #66058317abb8131] - doc: clarify sub-1000ms behavior in socket.setKeepAlive (Haram Jeong) #658691c12f815c2] - doc: remove obsolete mentioning of cl.exe on windows (Chengzhong Wu) #660382cc17dd67c] - doc: note that default signal handling resets the signal mask (Shelley Vohr) #658773a69cc1bf4] - doc: clarify QUIC async write backpressure (John Finnerty) #65947549694349d] - doc: clarify permission model scope for output paths (Rafael Gonzaga) #660043325ded3a8] - doc: fix sign-off format in AGENTS.md (Filip Skokan) #6595889aa2bd10c] - doc: fill in missing zstd docs (James M Snell) #658678d1ce4d477] - doc: add inoway46 as triager (Yuya Inoue) #655659a3697c5c7] - doc: document windowsHide for child_process.fork (Christopher Buss) #6588769911bf6b5] - doc: qualify directory read ordering for native fs (Trivikram Kamat) #658682d0c3fed12] - doc: add DOMException section to errors API reference (Avocado) #65206f744023cce] - doc: expand revert commit collaborator instructions (Mike McCready) #65848412399efd3] - doc: clarify supported Python releases (Mike McCready) #6585079331f5779] - doc: note that FreeEnvironment() runs a shared event loop (Shelley Vohr) #656916ddbb9982f] - doc,test: account for OpenSSL 4.1 behaviours (Filip Skokan) #6595664fb33d791] - (SEMVER-MINOR) ffi: load libraries from a mounted VFS (Matteo Collina) #6590907fcf1ed02] - ffi: throw ERR_INVALID_ARG_TYPE for wrong-typed pointer and size (Soul Lee) #65842b7406b0a9b] - fs: coerce FileHandle.read length like fs.read (Xia Chao) #65521cb9995be4d] - fs: honor dereference for symlinks nested in cpSync trees (Christian Aurich) #657312b1701f810] - (SEMVER-MINOR) fs: add openAsBlobSync (greenhead) #656449b3f1aa03f] - fs: throw on existing dir in cpSync with errorOnExist (Daijiro Wachi) #641242b502e0798] - fs: support removing read-only files in rmSync on Windows (Sparsh :)) #6445377d8f17ab1] - http: don't destroy socket after request completes (Barath Raj) #6595246f76ed57b] - http2: settle pending write callbacks on destroy (Matteo Collina) #66016205443721d] - http2: fix onread assert when destroying session from stream handler (Sankalp Thakur) #651167e1db4724a] - inspector: report an error when DOM storage is unavailable (Avocado) #6597395110712b8] - inspector: fix abort when two Environments own the inspector (Shelley Vohr) #658779fd3e6caa4] - inspector: fix crash when the IsolateData has no platform (Shelley Vohr) #65818cce2795ad7] - lib: fix AbortSignal.any() abort propagation (Yuya Inoue) #660142c0ccc0067] - lib: fix for FileHandle.readableWebStream (Patrick Dähne) #5884228acafe6a0] - lib: avoid repeat internal receiver checks (Filip Skokan) #65910b9b0e33923] - lib: avoid unsafe array iteration in cli table (Donghoon Kang) #658387f514d3310] - lib: fix shared buffer growability validation (Filip Skokan) #658457534117e38] - lib: optimize internal Web IDL dictionaries (Filip Skokan) #65857bdacc08fbe] - lib: validate sequence iterator objects (Filip Skokan) #658445b6ac71fb2] - lib: use Web IDL interface brand checks (Filip Skokan) #65846da341e2571] - lib,src: apply multiple updates to dtls implementation (James M Snell) #6551113eb4843d5] - meta: bump step-security/harden-runner from 2.21.0 to 2.21.1 (dependabot[bot]) #660440f759174af] - meta: bump github/codeql-action/upload-sarif from 4.37.9 to 4.38.0 (dependabot[bot]) #66046c75b830082] - meta: bump cachix/cachix-action (dependabot[bot]) #660471ada7055a4] - meta: bump github/codeql-action/init from 4.37.9 to 4.38.0 (dependabot[bot]) #660481ed8b05742] - meta: bump github/codeql-action/analyze from 4.37.9 to 4.38.0 (dependabot[bot]) #660499165abe840] - meta: bump github/codeql-action/autobuild from 4.37.9 to 4.38.0 (dependabot[bot]) #66050d03e7313cf] - meta: add joyeecheung as v8 currency strategic initiative champion (Joyee Cheung) #6596553d7592378] - meta: expand on collaborator restoration process (Chengzhong Wu) #65962fa972d02b2] - meta: add web-standards as webidl owners (Filip Skokan) #65856080e76b3d7] - (SEMVER-MINOR) net: support sending net.BoundSocket to threads and child processes (Guy Bedford) #647250d14c0e507] - path: remove StringPrototypeCharCodeAt from some methods of posix (Wiyeong Seo) #5466813e61f6ae6] - (SEMVER-MINOR) perf_hooks: implement SlidingWindowHistogram (James M Snell) #65825a326546094] - (SEMVER-MINOR) perf_hooks: implement qrde analysis support in Histogram (James M Snell) #6580632401c2229] - perf_hooks: reuse buffer for uv metrics (Donghoon Kang) #659855e2fbfe0e5] - perf_hooks: validate import normalization offset (Matteo Collina) #659504743cd8d33] - quic: fix two small bugs in HTTP/3 stream internals (Tim Perry) #659700df54fb38f] - quic: improve stream cleanup & lookup (Tim Perry) #659447cf448889c] - quic: fix timeout regression from 11ed32572b3 (James M Snell) #6602861a98e6512] - quic: fix readable stream truncation on stop-sending, abort & timeout (Tim Perry) #63967975cac593c] - quic: add promise to QuicStream for pending strms (Marten Richter) #65862ae29cc92ea] - quic: split headers out from src/quic/stream.{h/cc} (James M Snell) #65863833c58d9dc] - quic: fix crash in onStreamClose (Marten Richter) #6586184ecb21343] - quic: reject zero addressLRUSize (Christian Aurich) #65827293e69d065] - sqlite: throw on invalid URL path instead of abort (Guilherme Araújo) #660266f5fb76d0e] - sqlite: track registered user-defined functions (Trivikram Kamat) #65896baf112b639] - sqlite: always copy changeset before applying (Trivikram Kamat) #658700306b0a71e] - (SEMVER-MINOR) sqlite: bind undefined to NULL (Trevor Burnham) #657091f24901c0c] - src: fix -Wextra warning in WriteFileSync (Antoine du Hamel) #66020b98787fe2d] - src: reuse crypto GetCipherInfo in DTLS session (Ilyas Shabi) #66022410093dbf7] - src: avoid union type-punning in trace values (Khaidi Chu) #659339ec030b1e7] - src: print exception thrown during primordial initialization (Joyee Cheung) #65991a0f99f1d6c] - src: support building with the V8 sandbox (Shelley Vohr) #6223727c62235d0] - src: ffi: create fast-call metadata Symbols lazily (Matteo Collina) #660155a5abd8fd0] - src: avoid copying SEA snapshot data (Colin McDonnell) #65876a05023f331] - src: fix crash on empty, foreign or truncated --snapshot-blob files (Shelley Vohr) #659554141e22606] - src: don't kill own process group on failed spawn (Lazizbek Ergashev) #6505405f4e54eda] - src: fix external reference list race between concurrent isolates (Shelley Vohr) #657791518b7d67f] - src: keep the first snapshot blob alive for later isolates (Shelley Vohr) #6577962e2bf025c] - src: detach cppgc wrappers from their Realm before it is freed (Shelley Vohr) #65778afc3e559d2] - src: fix Stop() terminating the next Environment on the isolate (Shelley Vohr) #65819f245e53b29] - src: seed V8 from the OS CSPRNG instead of OpenSSL's DRBG (Colin McDonnell) #65796830ca7df7a] - src: fix null pointer call when running without a startup snapshot (Shelley Vohr) #6582048158fba8c] - src: stop leaking a CppHeap in CommonEnvironmentSetup (Shelley Vohr) #657923c999edef7] - (SEMVER-MINOR) src,lib: add util.markPromiseAsHandled (James M Snell) #65805e5d2a336bd] - stream: destroy half-open sockets after iteration (Matteo Collina) #659864075161405] - stream: destroy Duplex.from async function on early return (Aman Chadha(IVIXMMI)) #659637d245cfd8d] - stream: fixup stream/iter to drop at most one entry per share call (James M Snell) #6602830e1f7651c] - stream: reject unbounded at SyncShare construction (James M Snell) #660280372056cd8] - stream: make share budget failures detach before throwing (James M Snell) #6602836cc238dce] - stream: make Broadcast.from abort its background pump (James M Snell) #660283134ca6f8e] - stream: update broadcast to retain buffered data with zero consumers (James M Snell) #6602804e5c282a1] - stream: fix async iteration of undefined chunks (Caleb Everett) #65969f81a1483b3] - stream: avoid promise allocation for parked transform writes (Matteo Collina) #65625aec01c5ae1] - stream: keep webstream stream states in fast-mode objects (Matteo Collina) #65625898cd55bdf] - stream: reject closed only after sink abort settles (Lazizbek Ergashev) #657275661526006] - stream: fix ERR_INVALID_STATE when cancelling Readable.toWeb() (Richard Scarrott) #62773fb8a97f46b] - stream: improve handling of falsy errors in stream/iter (James M Snell) #65864cfee7fa3d9] - stream: amortize writable buffer compaction (Gürgün Dayıoğlu) #6584715605210f8] - stream: create write request objects lazily (Matteo Collina) #64455619470a9ae] - stream: allocate stream read buffers from a slab (Matteo Collina) #644557e79c33354] - test: consolidate crypto provider cache coverage (Filip Skokan) #66108dcc9c3c02a] - test: avoid call to chmodSync in test-fs-cp-async-file-modes (Antoine du Hamel) #66104dda22c06d7] - test: deflake test-run-watch-cwd-isolation-none-* (Antoine du Hamel) #66035d63928dda0] - test: move permission FFI test to native suite (Yuya Inoue) #6605980c1bf7fb5] - test: deflake user timing WPT assertions (Filip Skokan) #6603612116d1d70] - test: unskip test-watch-create-isolation-none (Antoine du Hamel) #66041e90575cb4d] - test: deflake util.throttle tests (Filip Skokan) #66034f7d18ec360] - (SEMVER-MINOR) test: expand histogram test coverage (James M Snell) #65825bfa6d49b5c] - test: implement low-risk test optimizations (James M Snell) #65926c3307ebddf] - test: update WPT for WebCryptoAPI to 55ce71bb9d (Node.js GitHub Bot) #65813ee29c56993] - test: prevent parser reuse across close scenarios (Filip Skokan) #660178a27474044] - test: cover cpSync fast path timestamp preservation (Abhinandan Kumar) #6567889297305c0] - test: fix stderr Buffer assertion in exec encoding test (greenhead) #66008d4cb916624] - test: skip test-vfs-real-provider-watch.js on IBM i (SRAVANI GUNDEPALLI) #659876515db8ab6] - test: fix RSA/DSA wrong-passphrase flake (Filip Skokan) #65983fa19ecad9d] - test: improve sequential test performance (James M Snell) #659283666d6a238] - test: schedule WPT variants individually (Filip Skokan) #65984f2aa27d82d] - test: overlap SLH-DSA signature checks (Filip Skokan) #659802170207253] - test: unref cancelled broadcast source timer (Filip Skokan) #6598071a54aaf5f] - test: collect timeout signals explicitly (Filip Skokan) #659801a15aff897] - test: skip retries in DNS timeout coverage (Filip Skokan) #659805c7e6b4a6e] - test: synchronize ordered runner events (Filip Skokan) #6598037f24eddc9] - test: reuse fixed primes in DH tests (Filip Skokan) #65980831ec42bcc] - test: avoid idle HTTP/HTTPS connections (Filip Skokan) #65980f523342bcd] - test: close WebAssembly test HTTP servers (Filip Skokan) #65980f23847a122] - test: use named parameters in DH stress test (Filip Skokan) #659807131e3b437] - test: reduce ZIP64 stress test I/O (Filip Skokan) #65980e4ca72a984] - test: avoid allocations in external memory test (Filip Skokan) #659757fe64727a2] - test: cover experimental stream iterator builtins (Jungwon Sohn) #6596488ba17bae2] - test: deflake node-api test-free-called (Christian Aurich) #659486eee01fbae] - test: fix flaky common WPT inspector test (Yuya Inoue) #65937858702e51c] - test: skip C++ symbols in tick-processor-arguments (Philipp Dunkel) #65906472215ff9b] - test: try fixing windows build replacing WMIC (James M Snell) #659497f5168149c] - test: fix flaky test-bench-stream (Matteo Collina) #658749f2d544576] - test: move sqlite length validation out of the reentry test (Trevor Burnham) #6576975a9fa65ac] - test: fix flaky cleanup in http2 test (Tim Perry) #65701f8cdf05586] - test,benchmark: use OpenSSL feature helpers (Filip Skokan) #6576288b12c03cd] - test_runner: avoid reusing v8 serializers (Yuya Inoue) #659519b3085e660] - test_runner: fix quote escaping in JUnit (Jihwan) #6597102c2302c31] - tls: propagate singleUse to the secure context (Carlos Vinicius) #660252a8b5d9269] - tls: load all CRLs from a PEM bundle (Lazizbek Ergashev) #65577d141ddb8dc] - tls: defer re-entrant calls to SSL state machine from JS (Tim Perry) #65105b7fe779632] - tools: update tools/v8 for Python 3.13 (Richard Lau) #661096105b37a3d] - tools: bump eslint-plugin-jsdoc in /tools/eslint in the eslint group (dependabot[bot]) #66102060ed69df9] - tools: disable fortify warnings in test-shared (Antoine du Hamel) #66020f8b7a2ff41] - tools: clean up handling of shared libs in shell.nix (Antoine du Hamel) #658913c9586c1fc] - tools: group CodeQL GHA updates (Antoine du Hamel) #66057aa531a34f7] - tools: summarize auto-start-ci failures (Filip Skokan) #65979b7eef15f59] - tools: bump the eslint group in /tools/eslint with 6 updates (dependabot[bot]) #660458914947d7c] - tools: update pgo build doc for linux (Chengzhong Wu) #66024a5b1659d5b] - tools: avoid workflow shell interpolation (Filip Skokan) #66013caf0ee0d7b] - tools: use self-repository references (Filip Skokan) #66013d5880dcb05] - tools: correct Slack action version comments (Filip Skokan) #6601366f182f565] - tools: make checkout credential use explicit (Filip Skokan) #66013287ece3f22] - tools: pass author to commit message validator (Filip Skokan) #660126314ccf93b] - tools: reduce test runner timing overhead (Filip Skokan) #65980441350a867] - tools: do not download build tools when linting Nix files (Antoine du Hamel) #6596115f2549f74] - tools: bump js-yaml from 4.3.1 to 4.3.2 in /tools/eslint (dependabot[bot]) #659310e449d02a6] - tools: bump js-yaml from 4.3.1 to 4.3.2 in /tools/lint-md (dependabot[bot]) #6593281eba460b5] - tools: fix commit queue error summary matching (Filip Skokan) #65913e5bbcfdd78] - tools: lint PR commit messages without approval (Filip Skokan) #6587507ae602005] - tools: unlabel author ready on base branch conflicts (Filip Skokan) #65872e3ad68289a] - tools: improve benchmark build cache reuse (Filip Skokan) #6585946ddc0220d] - tools: apply feedback to and simplify contributor guidance workflow (Filip Skokan) #6578566fd78803d] - trace_events: fix abort when Node.js does not own the V8 platform (Shelley Vohr) #659543cb3c23a19] - typings: add task_queue internal binding types (Seongeun Lee) #65662c5c7036e7b] - typings: add timeoutInfo to TimersBinding (greenhead) #6581162fe96c166] - typings: add missing sea binding properties (이혜미) #65815e51673b4ed] - url: add Symbol.toStringTag to URLPattern (Khaidi Chu) #659253ce4d23bbb] - (SEMVER-MINOR) util: implement util.throttle (James M Snell) #65899336f33ccc1] - (SEMVER-MINOR) util: implement debounce (James M Snell) #6589916e3e7eff3] - vfs: add --vfs-mount and --vfs-load startup flags (Philipp Dunkel) #65748c50cb9a553] - vfs: write RealFSProvider files to open fd (Christian Aurich) #658851bbc5488a5] - vfs: close the fs hook gaps for mounted paths (Philipp Dunkel) #65852816790e0e5] - vfs: resolve symlinks when checking rename descendants (Trivikram Kamat) #65904a9149093df] - vfs: support renaming implicit ZIP directories (Trivikram Kamat) #65752cf5d4a8fe6] - vfs: reject statfs for missing paths (Trivikram Kamat) #65693de552bd044] - vfs: return FileHandle from fs.promises.open (Trivikram Kamat) #6573008f96f142e] - vfs: give ZipProvider option bags a null prototype (Philipp Dunkel) #658535c4d319028] - vfs: commit ZipProvider handles the way open(2) does (Philipp Dunkel) #6585329d3406b6c] - vfs: apply open(2) effects to ZipProvider handles (Philipp Dunkel) #65853e379c26a92] - vfs: align virtual file handles with open(2) (Philipp Dunkel) #658547cdf5f014a] - vfs: answer for unowned paths under reserved root (Philipp Dunkel) #658145e55085bc1] - zlib: reject invalid zstd dictionaries (James M Snell) #658674cdcf7f6a4] - zlib: fix zstd reset (James M Snell) #65867678ff3561a] - zlib: improve zstd decoding across chunk boundaries (James M Snell) #65865Windows 64-bit Installer: https://nodejs.org/dist/v26.10.0/node-v26.10.0-x64.msi
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Source Code: https://nodejs.org/dist/v26.10.0/node-v26.10.0.tar.gz
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Documentation: https://nodejs.org/docs/v26.10.0/api/
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AI can produce code. Organizations still have to produce software. Agentic development is changing how software gets made, but it hasn’t changed what it costs to be wrong.
Six months ago, we began publicly experimenting with agentic development environments. Around the same time, we introduced JetBrains Central as an open control and execution system for agent-driven development. We subsequently began rolling out JetBrains Central CLI, shared context, cloud agents, automations, governance, and AI cost controls for teams and organizations.
Today, we are bringing this work together as JetBrains Air: an open, coherent system of products for developers, teams, and organizations, inside and beyond JetBrains IDEs. It is multi-surface and multi-service. Each product solves a distinct problem, but the products work better together.
JetBrains Air marks a significant expansion in what JetBrains is building for. For 26 years, we have focused primarily on the individual developer workbench. Now, we are building for the wider system through which agentic work is initiated, executed, coordinated, reviewed, and governed.
The IDE remains important to JetBrains’ future. The era in which the whole software development system can be contained in one window is ending. As part of our continued investment, we are now bringing the foundational agentic experience into JetBrains IDEs, giving professional developers an environment where they can work effectively with agents while understanding, changing, and verifying the resulting code. JetBrains Air connects the wider system developing around it.
That system is based on a core belief that the future of agentic development will be multi-vendor. No single model, agent, or service will be right for every developer, team, or task.
This strategic shift has a practical consequence: JetBrains Air cannot be just another agent or development environment. It must connect products for individual work, team coordination, organizational control, context, and process automation – and remain open to the tools and agents developers choose, including those JetBrains does not build.
JetBrains Air includes products that are available today alongside others that will be introduced as the system develops:
Junie is JetBrains’ coding agent for professional software development. It will be supported across all Air surfaces.
Air in JetBrains IDEs gives developers the environment to direct agents and verify their output using JetBrains’ code intelligence. Air Teams turns individual agent activity into coordinated team workflows. Air Governance makes that activity visible, governable, and accountable across the organization.
But an open system cannot stop at JetBrains’ own products. The Agent Client Protocol (ACP) standardizes the connection between an IDE and an agent’s full harness, including its planning, logic, tools, model routing, and observability. Through the ACP Registry, developers can discover and run a growing range of compatible agents while continuing to work inside JetBrains IDEs.
Air Governance is designed to extend visibility and cost governance across providers and the different tools through which agentic work takes place. This means developers can choose the agent, model, or service suited to the task without forcing the organization to give up context, visibility, or control.
Together, the Air products allow work to move between developers, agents, tools, and environments without losing the context and controls surrounding it.
Since March, our products have progressed significantly, but so has our understanding of what agentic development requires.
Developers have been adopting agents faster than organizations can build the infrastructure around them. Agent capabilities have advanced, and different models and agents have proven useful for different tasks. However, the context, coordination, governance, and cost management surrounding them have not kept pace.
For many developers, agents are already delivering practical value. At the organizational level, the economics are much harder to prove. The costs surface elsewhere – in review, rework, security, infrastructure, and spend.
Which agents can access company code? Where can data go? Which output requires human review? What happened while an agent was working remotely? Who approved the resulting change, and how was it verified?
Fragmentation at this level isn’t just irritating. It makes software development harder to understand, measure, and govern at exactly the point when more of the work is being delegated.
Code that’s obviously wrong gets caught quickly. That part of the system still works. The harder problem is code that’s almost right: plausible, capable of passing a superficial check, but quietly carrying a bad assumption or architectural inconsistency that won’t surface until it’s expensive.
As agents take on more of the execution, the bottleneck shifts from producing change to understanding, verifying, and owning it. Code becomes cheaper to generate but more expensive to verify. Agent activity becomes easier to start but harder to coordinate, audit, and explain.
And while the work can be delegated, accountability cannot. An agent will not get the call at 3:00 am when something breaks. The responsibility for what ships still belongs to the people and organizations that ship it.
This is why control becomes harder, not easier, as AI improves. A more capable model may produce better output. It does not establish organizational policy, preserve provenance, provide cost visibility, or decide who accepts responsibility for the resulting change.
Multi-vendor support is a foundational design principle of JetBrains Air, shaping how the system is being built from the outset.
We don’t believe this market will consolidate any time soon. Models vary in what they’re good at, and rankings change every few months. Teams inside the same company already make different choices, and they are often right to do so. Standardizing on one AI vendor today means making a multi-year commitment in a market that won’t look the same next quarter.
Keeping the options open is the reasonable thing to do. The problem is what openness currently costs. Every new model, agent, or service an organization adds takes away a little more visibility into its own development work. Context doesn’t carry over between tools. Spend can’t be attributed. Policies have to be rebuilt for each service.
Organizations should not have to choose between using the best available tools and understanding what is happening inside their own engineering. That trade-off exists because nothing in the current stack was built to sit above several vendors at once.
This is the work JetBrains has taken on. We build our own agent, and we intend to make it excellent. But JetBrains Air does not require customers to use ours, and our strategy does not depend on which model provider leads the rankings this quarter. We have no reason to make the ecosystem smaller than it is.
What we can offer instead is one place to run, see, govern, and account for agentic development across every model, agent, and service – for the developer, the team, and the organization.
Supporting multiple models and agents is the floor, not the ceiling. The part that matters is what sits above them: shared context, one set of policies, a single cost view, and a record of what happened, regardless of which vendor produced the change.
Multi-vendor choice solves only part of the problem. Agents also need reliable software intelligence.
JetBrains brings 26 years of engineering intelligence to the problem, helping developers understand the structure and behavior of complex software, not simply generate more of it. That deterministic code intelligence provides a foundation for making agentic work more reliable, efficient, and understandable across different models and agents. We are seeing promising results from giving AI agents access to deterministic code intelligence.
This is an economic advantage as well as a technical one. Agents spend time and money rediscovering information the codebase already contains. An agent that can retrieve that knowledge is cheaper and more accurate than one that has to reconstruct it. Because intelligence does not belong to one model, the benefit can extend across supported agents and services.
We are also going through the same transition as the organizations we build for, adopting agents internally, redesigning workflows, and learning where individual productivity gains translate into better software delivery and where they simply move work elsewhere.
JetBrains Air will develop through a rolling series of releases. We will be explicit about what customers can use now, what is entering preview, and what remains part of our longer-term direction.
Over time, JetBrains Air will extend further into mobile and remote experiences, allowing people to initiate, monitor, review, and continue agentic work as it moves between environments. The goal is not to reproduce the IDE on every surface. We are making the right context and controls available wherever decisions need to be made.
We will also bring JetBrains’ intelligence into more agentic workflows. This includes richer context drawn from code, architecture, repositories, runtime behavior, and organizational knowledge, as well as better ways to route work between developers, models, agents, and services.
More work will be triggered by repository events, schedules, and delivery processes rather than by a developer opening an editor and issuing a prompt. JetBrains Air will provide the intelligence, oversight, and human control these workflows require across surfaces and services.
We will not name future products before their scope and availability are ready to be confirmed. With each release, we will explain what works, how it connects, and what’s still in progress.
The companies that succeed in adopting AI will not necessarily be those that generate the most code or deploy the most agents. They will be those that can expand experimentation without losing quality, context, cost discipline, or human understanding.
JetBrains Air is our commitment to building for that reality. It expands JetBrains from the developer workbench into a system of products connecting developers, agents, teams, and organizations.
The goal is not more code. It is software that developers, teams, and organizations can understand, verify, and stand behind.
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The new repository pull requests page is now generally available to all GitHub users.
The new page makes it easier to find and act on pull requests in a repository, with new filtering and search options to help you find exactly what you’re looking for:
AND and OR keywords as well as nested searches.Since the public preview, your feedback has helped us make several improvements. Now you can:
To learn more, see About pull requests.
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The EvalEval Coalition is thrilled to share that the UK AI Security Institute (AISI) is using EvalEval's infrastructure to openly share evaluation results, supporting more reproducible and verifiable evaluation science.
AISI and EvalEval have previously collaborated on research that began at a joint workshop alongside NeurIPS 2025, and feedback from the Institute has helped shape the Every Eval Ever (EEE) schema. This next phase of the collaboration puts that shared infrastructure into practice.
As AI deployment accelerates, evaluations are becoming increasingly important sources of evidence about model and system performance. Yet results are reported across many formats, platforms, and outlets, often without enough information to reproduce them. Running the evaluations again may itself be prohibitively expensive.
EvalEval's mission is to improve this ecosystem through a shared reporting schema, Every Eval Ever, and an open platform, Evaluation Cards, that brings evaluation results and the information needed to interpret them into a common structure.
This builds naturally on AISI's work to make evaluation more efficient through OptStop, more statistically rigorous through HiBayES, and more standardised in areas including transcript analysis and capability elicitation. Together, AISI and EvalEval are working to diagnose gaps in evaluation reporting and build shared infrastructure to close them.
Transcript-level transparency matters not only for reproducibility, but also for analysis and diagnosis. In this new phase of the collaboration, AISI is making publicly reported evaluation methods and findings available through Evaluation Cards where appropriate. The release includes verified results, context, and configuration information for the five benchmarks in the paper's main experiment:
These results cover six frontier models: Claude Opus 4, Claude Opus 4.5, Claude Opus 4.6, GPT-5, GPT-5.2, and GPT-5.4. The release also includes results from two related cyber evaluations—Cyber CTFs and The Last Ones—which use a different, partially overlapping set of models. The data accompany AISI's paper, How Inference Compute Shapes Frontier LLM Evaluation, which studies how benchmark performance depends on inference-time compute and evaluation protocol.
Performance on Humanity's Last Exam changes with evaluation protocol and inference compute. Each curve shows the cumulative share of attempted tasks solved within a given token count, using the earliest observed success per task. When models received correctness feedback from an oracle after each attempt, they continued to solve additional tasks as token use increased.
When results are openly released with setup information, researchers and practitioners can examine individual studies more closely and compare findings across the wider ecosystem. Where other reports lack these details, releases like AISI's provide verified reference points for interpreting evaluations in context—for example, by helping researchers understand how setup choices may influence reported performance. As more evaluators adopt EEE, open comparisons like these can support broader and more reliable meta-research.
AISI's Terminal-Bench 2.0 results alongside other reported evaluations for the same models, under different evaluation setups.
We are excited about this adoption and look forward to further standardising and sharing evaluations with AISI and other AI evaluation organisations.
The EvalEval Coalition is a research community developing scientifically grounded research and robust deployment infrastructure for the evaluation ecosystem. Its goal is to improve evaluation science, address the lack of consensus around documenting evaluation applicability and utility, and broaden coverage of the impacts that matter for scientific research and policy analysis.
The coalition's flagship projects include Every Eval Ever, a shared schema and repository for evaluation results, and Evaluation Cards, which combines benchmark metadata, evaluation-run data, and model metadata into interpretable records. Together, they make it easier to understand when apparently similar scores were produced under meaningfully different conditions.
The UK AI Security Institute is a research organisation within the UK government's Department for Science, Innovation and Technology. Its mission is to equip governments with a scientific understanding of the risks posed by advanced AI. AISI conducts research and builds infrastructure to understand advanced AI capabilities and impacts, develop and test mitigations, and inform policy.
We're adding support for running GGUF models efficiently in transformers, so you can use checkpoints sized for your laptop's memory through the familiar transformers APIs. Pick a GGUF from the Hub, load it with from_pretrained, and start generating on your own machine.
Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX, it has helped make local inference a practical option for everyday use.
A recent example of what local AI can feel like:
— Julien Chaumond (@julien_c) April 24, 2026This is where we are right now. And i’m not gonna lie it feels pretty magical 🧙♀️
Qwen3.6 27B running inside of Pi coding agent via Llama.cpp on the MacBook Pro
For non-trivial tasks on the @huggingface codebases, this feels very, very close to hitting the latest Opus in Claude… pic.twitter.com/lsIxLoUneU
GGUF, developed by the llama.cpp team, is a widely used format for local inference. The team also shares quantized checkpoints under ggml-org on the Hub. Publishers such as Unsloth, LM Studio Community, and bartowski also provide ready-to-use GGUF checkpoints in a range of quantizations, so users can pick the version that fits their machine. GGUF models have been downloaded millions of times.
We want to make it easier to run these models locally with transformers, too. Compatibility is only useful if the model is pleasant to run. To bring performance close to llama.cpp, we're reusing its underlying ggml kernels through the kernels library, and reducing overhead in generate. Our initial focus is local inference on Apple Silicon, starting with the Qwen3.5 architecture.
GGUF packages model weights and metadata, including tokenizer information and an optional chat template, in one file. It supports different quantization levels, letting you trade some precision for a smaller memory footprint. Variants such as Q4_K_M mix tensor precisions, using mostly 4-bit weights while keeping sensitive tensors at higher precision.
Here's how quantization changes the file size of Unsloth's Qwen3.5-4B:
| GGUF variant | File size | Tradeoff |
|---|---|---|
BF16 |
8.42 GB | Unquantized reference |
Q6_K |
3.53 GB | More precision than the smaller variants |
Q5_K_M |
3.14 GB | A middle ground between size and precision |
Q4_K_M |
2.74 GB | A practical starting point for local inference |
We suggest starting with Q4_K_M, then trying Q5_K_M or Q6_K if you have more memory available. More aggressive quantization can help larger models fit, but the quality tradeoff depends on the model and the task. Evaluate it on the work you actually want the model to do. The Hub's GGUF documentation describes the available quantization types.
To get started, you need:
kernels.pip install -U "git+https://github.com/huggingface/transformers.git" kernels
To load a GGUF model, pass its Hub model_id and filename as gguf_file to from_pretrained.
No extra configuration is needed: when the weights stay packed on Metal, transformers automatically loads the compatible ggml/Metal layer kernels and uses ggml-org/ggml-attn as the attention implementation. If that kernel cannot be fetched, the model falls back to "sdpa" with a warning, and you can always force "sdpa" by passing attn_implementation="sdpa" explicitly. See the GGUF documentation for more loading options.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "unsloth/Qwen3.5-4B-GGUF"
filename = "Qwen3.5-4B-Q4_K_M.gguf"
tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model = AutoModelForCausalLM.from_pretrained(
model_id,
gguf_file=filename
)
That is the only GGUF-specific step. Everything after it is the standard transformers API:
messages = [{"role": "user", "content": "Explain why the sky is blue in a few sentences."}]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
with torch.inference_mode():
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Without a compatible quantization kernel, the loader falls back to dequantizing the model and uses more memory.
You can also use the same checkpoint with transformers serve, which exposes an OpenAI-compatible API:
pip install -U "transformers[serving] @ git+https://github.com/huggingface/transformers.git" kernels
transformers serve "unsloth/Qwen3.5-4B-GGUF:Qwen3.5-4B-Q4_K_M.gguf"
The model argument uses <model_id>:<filename>.gguf: before the colon is the Hub repository (unsloth/Qwen3.5-4B-GGUF), and after it is the file to load (Qwen3.5-4B-Q4_K_M.gguf). This selects a specific quantization from a repository that may contain several.
For models whose chat template supports thinking, add --reasoning off to skip it or --reasoning on to enable it. The default, --reasoning auto, follows the chat template’s default. See the reasoning options for details.
You can connect a client such as Jan or Pi by adding a custom OpenAI-compatible provider with these settings:
| Setting | Value |
|---|---|
| Base URL | http://localhost:8000/v1 |
| Model ID | unsloth/Qwen3.5-4B-GGUF:Qwen3.5-4B-Q4_K_M.gguf |
transformers runs the model on your Mac, while the client provides the conversation interface. The same endpoint can be used by other clients that support this API.
Our reference for local inference performance is llama.cpp. The comparison below focuses on three GGUF checkpoints: a small dense model, a larger dense model, and a mixture-of-experts model.
The llama.cpp column comes from the llama-bench tool (build 5f55650a7, release b10200, Metal backend from ggml 0.18.0), run as llama-bench -m <file> -p 0 -n 128 -r 3, which reports tg128: the token-generation rate over 128 decoded tokens, averaged across three repetitions, with prompt processing excluded. The transformers column is generate producing the same 128 tokens from a 12-token prompt, best of three warmed runs, and it includes prefill.
Measured on a MacBook Pro M2 Max, 32 GB unified memory, macOS 26.6, PyTorch 2.12.1, kernels 0.17.0, plugged in.
The benchmark scriptimport time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id, filename = "unsloth/Qwen3.5-4B-GGUF", "Qwen3.5-4B-Q4_K_M.gguf"
model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)
tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
inputs = tokenizer("The capital of France is Paris. The capital of Germany is", return_tensors="pt")
inputs = inputs.to(model.device)
with torch.inference_mode():
model.generate(**inputs, max_new_tokens=8, min_new_tokens=8, do_sample=False) # warm up
torch.mps.synchronize()
for _ in range(3):
time.sleep(90) # let the machine cool: back-to-back runs decay by 10% or more
start = time.perf_counter()
model.generate(**inputs, max_new_tokens=128, min_new_tokens=128, do_sample=False)
torch.mps.synchronize()
print(f"{128 / (time.perf_counter() - start):.1f} tok/s")
For the other column:
llama-bench -hf unsloth/Qwen3.5-4B-GGUF:Q4_K_M -p 0 -n 128 -r 3
Transformers is close to llama.cpp across all three checkpoints. The chart uses the same measurements described above; it does not imply identical benchmark conditions, since the Transformers measurement includes prefill while llama-bench reports decode-only throughput.
When GGML and llama.cpp joined Hugging Face, we described their complementary roles: llama.cpp provides a foundation for local inference, while transformers provides a foundation for model definition. GGUF support brings those two closer together.
llama.cpp remains our recommended engine when your priority is efficient local inference. Its dedicated runtime, memory management, and broad hardware support are built around that goal. This integration gives developers a convenient way to work with the same GGUF checkpoints inside transformers:
generate, or write your own generation loop in Python.For that last case, use GgufConfig(dequantize=True):
import torch
from transformers import AutoModelForCausalLM, GgufConfig
model = AutoModelForCausalLM.from_pretrained(
"unsloth/Qwen3.5-4B-GGUF",
gguf_file="Qwen3.5-4B-Q4_K_M.gguf",
quantization_config=GgufConfig(dequantize=True),
dtype=torch.bfloat16,
)
The bigger opportunity is bringing ggml's performance to models that llama.cpp does not support.
transformers already provides the PyTorch implementations of these architectures. With ggml kernels and quantization schemes available in PyTorch, we can work toward accelerating their supported operations without first implementing the entire model in llama.cpp. This is especially useful for new architectures, research models, and custom variants that may never receive a dedicated llama.cpp implementation.
That opportunity extends beyond the GGUF format itself. A kernel operates on tensors; it does not require the whole model to come from a GGUF file. The same building blocks can be integrated into other transformers models and loading workflows. This also opens a path to other modalities: computer vision models, audio models, and multimodal models could reuse compatible attention, normalization, and matrix multiplication kernels without first having a full implementation in llama.cpp. Each architecture still needs integration and validation; the initial GGUF examples here cover text generation.
We also wanted to show how far we can get while keeping the model and generation loop in Python. With the right kernels and an efficient generation loop, Python and PyTorch can deliver strong local inference performance. The kernels handle the heavy computation, while the generation loop keeps the GPU busy by avoiding unnecessary synchronization.
Our focus was to make eager execution fast without requiring torch.compile. For interactive use, we wanted a quick start and a steady stream of tokens, without compilation pauses or recompilation when input shapes change. The two main pieces of that work are the kernels and generate itself.
A kernel is a small program that performs an operation on the GPU. PyTorch supplies general-purpose implementations; a specialized kernel can do less work, combine several operations, or read quantized weights directly in their stored format.
The kernels library lets us distribute compatible builds of ggml's Metal kernels on the Hub and call them from transformers. That brings ggml's work into the PyTorch model without replacing the model with a separate inference runtime.
| Kernel | What it does |
|---|---|
ggml-quantization |
Reads packed quantized weights for matrix operations, including the selected experts in an MoE model. It avoids expanding the whole weight matrix before each decode operation. |
ggml-norm |
Fuses normalization operations, including the zero-centered RMSNorm used by Qwen3.5 and Qwen3.8. |
ggml-attn |
Provides ggml's Metal flash attention for prompt processing and token decoding. |
ggml-gated-delta-net |
Accelerates the gated delta network used in the linear-attention layers of the Qwen3.5 and Qwen3.8 hybrid architectures. |
topk |
Selects the experts for each token in an MoE model, combining softmax and top-k routing. This is our own Metal implementation. |
The first four packages build on ggml's kernels; the top-k kernel addresses a separate bottleneck in MoE routing. Together they reduce the GPU work needed for each generated token.
To show the contribution of the layer kernels, we compare the same packed GGUF checkpoints with and without them. The quantization kernel stays enabled in both configurations: disabling it would also change how weights are represented and would measure a different tradeoff.
Faster kernels only help if the GPU has work to do. During generation, the CPU schedules GPU operations and controls the loop that produces the next token. Reading a result back from the GPU can force the CPU to wait until queued operations finish. Repeating even a small wait for every token can noticeably reduce throughput.
Two changes address this in generate, which results in improvements for all transformers models (not just when running GGUF files):
generate copies the stopping decision asynchronously and consumes it on the following step. The CPU can keep scheduling work while the GPU runs. Streaming tokens use the same approach, and any extra step past the stopping condition is removed from the result.These changes improve the generation loop around the model, so their usefulness extends beyond GGUF. They complement the kernel work: kernels reduce the cost of an operation, while fewer synchronization points let CPU scheduling and GPU execution overlap.
These measurements keep all layer kernels enabled; the bars isolate the changes to the generation loop.
The initial target is a single interactive conversation on Apple Silicon. There are a few boundaries to keep in mind:
generate_batch on MPS.If you have a GGUF model you would like to use in transformers, open an issue with the checkpoint and your use case. That will help us prioritize support for the models people are running locally.
We would like to thank Arthur Zucker for initiating this work and reviewing all of my PRs, and Cyril Vallez for the generate PRs. We are grateful to Sayak Paul, the llama.cpp team, and Bertrand Chevalier for their help integrating the kernels. We also thank Aritra Roy Gosthipaty and Pedro Cuenca for reviewing this blog post, and Lysandre Debut for overseeing the project.
We are super excited to welcome Jun as our newest team member 🔥. We are completely invested in local AI, and MLX is a central piece of the ecosystem. We are delighted that Jun chose us to set up home and continue contributing to MLX.
MLX is Apple's framework for local AI, especially optimized for Apple Silicon. We are big MLX supporters since it was the Christmas present from Awni and Angelos in 2023, and proud that Hugging Face is the Hub where people find MLX models and contribute their own. Usage of open, local AI is accelerating, and we believe in a healthy ecosystem where people can find the tools that work for them.
Stability, and hopefully faster development! Graduating from a side job to a fully maintained and funded project will allow Jun to better guide the contributors and build for the long-term. oMLX stays Apache 2.0, and Jun keeps leading it as before.
Our end goal is to unblock the community to run local AI in any shape or form, and provide the tools and building blocks to make that happen. We expect oMLX to serve as a testbed for new ideas, while leveraging the foundational work of the dependencies it already relies upon, such as mlx-lm or mlx-vlm. We believe that strong modeling and inference libraries help the community, so we'd love to upstream work to wherever it makes sense. We have been collaborating with many projects mlx-lm, mlx-vlm, LMStudio, and we hope we can strengthen the relationship with Cheng, Prince, Yagil, and their teams to better serve the community together.
Concretely, one focus area is the quick transition from a transformers model definition to a reference MLX implementation that can be consumed by different engines, so each one can focus on the unique features they provide. The transformers library has become the reference for ML model definitions, we want to streamline the process to make new transformers models run on MLX.
We are incredibly excited about the future.
Welcome, Jun! 🙌
If you run a model in production, you already know the need to swap in a new checkpoint or a new model family: the open model ecosystem moves fast, and the candidate usually looks great in evals or promises better throughput. You want it in front of every user without hiccups, and a way to rollback if it disappoints. The usual options force a tradeoff:
Both of these options put a human in the loop as the safety mechanism. Rollouts move that mechanism into the platform: you describe the source, the target, the steps, and what "healthy" means, and the platform works against this plan at every stage.
A rollout migrates traffic between two deployments on the same endpoint: a source (what's serving today) and a target (what you want to serve tomorrow). You pick one of three strategies:
Here's what happens inside every canary step:
We chose this ordering deliberately; each item prevents a class of incidents:
Through the API or the console, a rollout is created in a PENDING state and does nothing until you explicitly start it (the CLI's rollout command creates and starts in one step). This two-step create/start is intentional because you can create the rollout, review it (or have a teammate review it), and start it when you're actually watching.
Two states in the diagram above deserve a note:
PAUSED means you pressed pause. The rollout holds exactly where it is and resumes from the same step.SYSTEM_PAUSED means the platform found something that went wrong, such as a failed metric gate, a capacity shortfall or missing metrics, and stopped to wait for human approval. It pauses, notifies you and waits; canceling is always your call.There is no FAILED end state that leaves traffic in limbo: a rollout ends COMPLETED (the target serves) or CANCELED (the split is frozen where it was, and you run the rollout in reverse to go back).
The following is a breakdown of what happens in a single canary step, measured on the run at the end of this post (Qwen2.5-7B → Qwen3.5-9B on one H100 each).
The propagation wait is what keeps stale global routing caches from sending requests to a shrinking source. The wait period is grown to the metric window plus ingestion lag. The cold start dominates the first step; later steps add replicas to a target that is already serving and warm.
All three strategies run through the same engine and the same health gates; they differ in how traffic moves, how much extra capacity the overlap costs, and whether there is a wait window for a metric gate.
| Canary | Blue-green | Rolling | |
|---|---|---|---|
| Traffic pattern | Steps through shares you choose (default 5% → 25% → 50% → 100%), each held for a wait window | One cutover, 0% → 100%, once the target is healthy | Replica by replica, traffic following the replica ratio |
| Extra capacity | Near source size; the target grows one step before the source drains that share | Both deployments at full size until the source drains | Source's replica count at each step; one extra replica mid-step |
| Typical duration | One cold start plus a wait per step (at least 390 s each with a metric gate) | One cold start plus 30 s propagation; a few minutes | One cold start per replica; slowest on large deployments |
| Metric gates | Yes, after every step | No (no wait window) | No |
| How to go back | Cancel freezes the current share, then run the rollout in reverse | Run the rollout in reverse; --final-source-replicas 1 keeps the old model warm for an instant return | Run the rollout in reverse |
| Best for | Measuring on live traffic before taking 100% | The fastest switch, when you can briefly afford double capacity | Same-model engine or config changes at a constant GPU footprint |
Here's a three-step canary from a deployment serving your current model to one serving the candidate, with a latency regression gate. The CLI ships as tg in the together Python package (2.34.0 or newer). You pass the target deployment; the source is inferred when exactly one deployment is receiving traffic, otherwise pass --source:
# 1. Create AND start the rollout in one command.
# Intervals and windows are seconds with an "s" suffix ("600s", not "10m").
tg beta endpoints rollout $TARGET_DEPLOYMENT_ID \
--source $SOURCE_DEPLOYMENT_ID \
--canary \
--steps 10,50,100 \
--interval 600s \
--metric router_latency --metric-stat p95 \
--metric-max-regression 10 --metric-direction higher-is-worse \
--metric-window 300s
# 2. Watch it move: pass the rollout ID printed under "Active Rollout",
# or the endpoint ID for the endpoint summary
tg beta endpoints get $ROLLOUT_ID
tg beta endpoints get $ENDPOINT_ID
# 3. Control it: pass the endpoint ID plus exactly one control flag
tg beta endpoints rollout $ENDPOINT_ID --pause --reason "holding for review"
tg beta endpoints rollout $ENDPOINT_ID --resume
tg beta endpoints rollout $ENDPOINT_ID --promote
tg beta endpoints rollout $ENDPOINT_ID --cancel --reason "latency regression on target"
The source drains to zero replicas and stops when the rollout completes (--final-source-replicas defaults to 0), and the target lands with the source's replica count as its floor (--final-target-replicas). The CLI attaches one metric gate per rollout; for several rules use the console or the API.
The same via the REST API, where create and start are separate calls and a rollout can carry several metric rules:
# 1. Create the rollout. It comes back in state PENDING; save its "id" (rol_...) as $ROLLOUT_ID.
curl -s -X POST \
"https://api.together.ai/v2/projects/$PROJECT_ID/endpoints/$ENDPOINT_ID/rollouts" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"sourceDeploymentId": "'$SOURCE_DEPLOYMENT_ID'",
"targetDeploymentId": "'$TARGET_DEPLOYMENT_ID'",
"canary": {
"steps": [{"traffic": 10}, {"traffic": 50}, {"traffic": 100}],
"stepInterval": "600s"
},
"metrics": [{
"name": "router_latency",
"stat": "METRIC_STAT_TYPE_PERCENTILE",
"percentile": 95,
"regressionCheck": {
"direction": "REGRESSION_DIRECTION_HIGHER_IS_WORSE",
"maxRegressionPercent": 10
},
"window": "300s"
}]
}'
# 2. Start it: POST .../rollouts/$ROLLOUT_ID/start -d '{}'
# 3. Watch it: GET .../rollouts/$ROLLOUT_ID
A few things the API is strict about: percentile is an integer (95, not "p95"), enum values carry their full prefix (METRIC_STAT_TYPE_*, REGRESSION_DIRECTION_*, THRESHOLD_OPERATOR_*), durations are protobuf strings like "600s", and a metric name outside the catalog is rejected with a 400 that lists the supported names. Draining the source is the default, so there is nothing to pass for it.
The regression check can be understood as: at each gate, compare the target's p95 router latency (the per-request duration measured at the router, in milliseconds) over the last 5 minutes against the source's. If the target is more than 10% worse, don't proceed.
The same rollout from Python, with the together package (2.34.0 or newer). Field names are snake_case here and camelCase on the wire; the SDK translates.
from together import Together
client = Together() # reads TOGETHER_API_KEY
rollout = client.beta.endpoints.rollouts.create(
endpoint_id=ENDPOINT_ID,
project_id=PROJECT_ID,
source_deployment_id=SOURCE_DEPLOYMENT_ID,
target_deployment_id=TARGET_DEPLOYMENT_ID,
canary={"steps": [{"traffic": 10}, {"traffic": 50}, {"traffic": 100}], "step_interval": "600s"},
metrics=[{
"name": "router_latency", "stat": "METRIC_STAT_TYPE_PERCENTILE", "percentile": 95,
"regression_check": {"direction": "REGRESSION_DIRECTION_HIGHER_IS_WORSE", "max_regression_percent": 10},
"window": "300s",
}],
) # state: PENDING
client.beta.endpoints.rollouts.start(rollout.id, project_id=PROJECT_ID, endpoint_id=ENDPOINT_ID)
# later: .retrieve / .pause / .resume / .promote / .cancel take the same (id, project_id, endpoint_id)
Every rollout accepts the same four controls. An endpoint has at most one active rollout, so the CLI takes the endpoint ID and you rarely need the rollout ID. Each control returns as soon as it is accepted; poll tg beta endpoints get (or the GET endpoint) until the rollout reaches the state you expect. While a rollout is active, including while paused, the endpoint's traffic split is locked and its source and target cannot be stopped or deleted.
The rollout goes PAUSING, lets any step activity in flight finish, then holds at the current traffic split and replica counts as PAUSED. Both deployments keep serving. A pause can last for days; the platform never auto-resumes an operator pause.
CLI
tg beta endpoints rollout $ENDPOINT_ID --pause --reason "holding for review"
REST
POST …/rollouts/$ROLLOUT_ID/pause
{"reason": "holding for review"}
Continues from the same step, for both PAUSED and SYSTEM_PAUSED. If a gate tripped, it re-evaluates against fresh data; the step is not skipped.
CLI
tg beta endpoints rollout $ENDPOINT_ID --resume
REST
POST …/rollouts/$ROLLOUT_ID/resume
{}
Skips the remaining canary steps and runs the final 100% step in full: the target scales to its landing size, traffic shifts, the propagation wait and soak run, then the source drains. Skipped steps are recorded as SKIPPED. Not instantaneous: in a test run with a 10-minute step interval, a promote at step 0 still sat through the final step's full soak.
CLI
tg beta endpoints rollout $ENDPOINT_ID --promote
REST
POST …/rollouts/$ROLLOUT_ID/promote
{}
Freezes the current traffic split into the endpoint's standing weights and ends the rollout as CANCELED. Nothing scales down; both deployments keep serving their frozen shares until you edit the split or run a reverse rollout. A target canceled at 0% is left running with no traffic; scale it to zero or delete it if you no longer need it.
CLI
tg beta endpoints rollout $ENDPOINT_ID --cancel --reason "latency regression on target"
REST
POST …/rollouts/$ROLLOUT_ID/cancel
{"reason": "latency regression on target"}
There is no rollback verb. To move traffic back, after a cancel or after a completion, create a new rollout with source and target swapped, then start it. Any strategy works and the same gates apply. After a cancel the default canary ladder skips the steps the new target has already passed, and the default final replica count is the pair's combined count.
CLI
tg beta endpoints rollout $OLD_SOURCE_ID --source $OLD_TARGET_ID --canary
REST
POST …/rollouts (with the two IDs swapped) POST …/rollouts/$NEW_ROLLOUT_ID/start
Controls on a finished rollout (COMPLETED or CANCELED) are refused. Delete a finished or never-started rollout from the history with tg beta endpoints rm $ROLLOUT_ID; deleting the record does not change the traffic split it left behind.
Metric gates are a canary feature: blue-green and rolling still run health gates, but the staged metric comparison needs canary's step structure to be meaningful. Gates evaluate over a closed catalog of three router-side metrics, measured identically for source and target (any other metric name is rejected at create time):
router_error_rate: router 5xx responses divided by all inference responses, as a 0-1 ratio (0.02 means 2%)router_latency: per-request duration measured at the router, in milliseconds. It is bimodal (the median attempt is often a fast reject), so gate on p95 or higher rather than the meaninflight_requests: concurrent requests per ready replica, averaged over the window (size thresholds per replica, not fleet-wide)Each rule uses one of two checks:
regressionCheck (relative): "the target must not be more than N% worse than the source." This is the right default for latency, because it self-calibrates: you don't need to know your absolute p95, only that the new model shouldn't degrade it. Set direction so the platform knows which way is better/worse.thresholdCheck (absolute): "the target must satisfy operator, value" (e.g. error rate < 0.01). Use this when you have a hard SLO, or when the source itself might be unhealthy and relative comparison would grade on a curve.Two recipes cover most services:
// Latency-sensitive service: don't ship anything meaningfully slower
{
"name": "router_latency",
"stat": "METRIC_STAT_TYPE_PERCENTILE",
"percentile": 95,
"regressionCheck": {
"direction": "REGRESSION_DIRECTION_HIGHER_IS_WORSE",
"maxRegressionPercent": 10
},
"window": "300s"
},
// Error-budget shop: hard ceiling regardless of what the source is doing
{
"name": "router_error_rate",
"stat": "METRIC_STAT_TYPE_AVG",
"thresholdCheck": {
"operator": "THRESHOLD_OPERATOR_LT",
"value": 0.01
},
"window": "300s"
}
Three durations interact, so keep all of them in mind:
window (default 5m): how far back the gate looks when comparing metrics.stepInterval (default 3m): how long each step waits at its traffic level before the gate runs.You must wait for a period of at least window + ingestion lag, so that the gate's entire lookback period lands inside the current step's steady state. If you wait for a shorter period than your window, the gate would be comparing metrics that partially describe the previous traffic split. The platform enforces this for you: if you request a wait period that's too short for your window, it increments it automatically. It is still better to design with it in mind: a 5m window needs a 6.5m wait period. With the default 5m window the platform grows the default 3m interval to 390s (6m 30s); if you set your own stepInterval, make it at least window + 90s.
By default, a tripped gate routes to SYSTEM_PAUSED, which means the system pauses for review. The rollout holds at its current split (the blast radius stays at whatever your canary percentage was), and you decide: resume (the gate re-evaluates), promote, or cancel.
There is no automatic abort: a confirmed regression always parks the rollout for a human, because moving traffic back is itself a change someone should be watching. Recoverable causes such as a capacity shortfall or a metrics-pipeline gap are different: the platform retries those every 15 minutes for up to 3 hours before leaving the rollout paused for you. The platform also guards against false alarms: before it pauses on a regression the system re-queries several times over ~90 seconds to make sure it isn’t looking at ingestion lag or transient blips, and a gate that cannot get trustworthy data pauses with METRICS_UNAVAILABLE rather than counting as a regression.
All three strategies run through the same step engine, so these hold for canary, blue-green and rolling alike.
1. Capacity is never rounded down. Target replicas round up and the source drain rounds down, so a same-size swap never has fewer replicas than it started with. Rolling adds one replica mid-step; blue-green briefly runs both deployments at full size. Replicas your autoscaler added above the plan are kept.
2. Traffic never lands on capacity that is not ready. Every step runs in one order: scale the target, check health, shift traffic, wait 30 s for routing to converge, drain the source, wait, evaluate the gate, record the step. A step that regressed during its wait is never recorded as passed.
3. Each side always has the replicas its share needs. Traffic moves only once the target has enough ready replicas for the new share, and the source is never drained below its remaining share. If a replica dies and the split can no longer be served, the rollout holds the largest split it can and pauses as UNDER_SERVED.
4. The rollout raises floors; it does not fight your autoscaler. Each step writes each deployment's minimum replicas and nothing else, with one exception: the target's maximum is lifted once so it can carry the whole endpoint, and stays lifted. The source's maximum shrinks with its share during the drain. Lower a maximum below what the step needs and the rollout pauses as POLICY_INFEASIBLE instead of overriding you.
5. The gate always returns a verdict. A regression check passes when the target is within your percentage budget of the source. No source data passes; a zero source against a non-zero target on a higher-is-worse metric fails; any other zero source passes. A threshold check ignores the source and compares the target with your value.
6. Gates read only the current step's traffic, and only enough of it. The wait period is at least the metric window plus about 90 s of ingestion lag, so a 300 s window means a 390 s wait. p95 needs 20 requests in the window and p99 needs 100; with fewer the rollout pauses as METRICS_UNAVAILABLE. Error rate and in-flight requests need one.
1. What if there's no GPU capacity for the target?
The rollout checks feasibility for the entire journey up front, before touching anything, and again at each scale-up. A shortfall pauses the rollout in SYSTEM_PAUSED with a CAPACITY_EXHAUSTED category. At this point nothing has moved and your source is untouched. Resume re-checks capacity and continues if it's freed up. Capacity problems are usually transient, so pausing beats failing.
2. Can I pause indefinitely?
Yes. Pause is not a held connection but rather a first-class state. Rollouts are designed to survive multi-day pauses and resume exactly where they left off.
When the platform pauses a rollout, status.condition carries a typed failure category plus a human-readable message. These include:
| Category | What it means | You should |
|---|---|---|
METRIC_REGRESSION | A gate tripped on real data | Inspect the step's metric readings; if the target is at fault, cancel and run the rollout in reverse; if the cause was external, resume |
METRICS_UNAVAILABLE | Gate couldn't get trustworthy data | Check metric names/windows; resume re-evaluates |
CAPACITY_EXHAUSTED | Not enough GPUs for the next step | Wait/free capacity, then resume |
UNDER_SERVED | Ready capacity on one side fell below what the current split needs | Restore capacity (auto-retried); resume if the pause persists |
POLICY_INFEASIBLE | Autoscaling limits can't accommodate the plan | Raise the target's max replicas, then resume |
The docs list the remaining categories and what to do about each: Troubleshooting rollouts.
Everything above is easier to trust after watching it in action once, so here is a run on the current platform (September 2026). We upgraded a live endpoint from Qwen2.5-7B-Instruct to Qwen3.5-9B, each on a single H100, while a steady 5 requests per second of chat completions flowed through the endpoint the whole time and every response code was logged. The newer model is the one we wanted; the question a rollout answers is whether it fits the latency budget the old one set. We gave it a 25% p95 budget.
Setup. The 7B was already serving. We added the 9B as a second deployment on the same endpoint with no traffic and no replicas; the rollout starts it when it needs it.
# $MODEL_ID is Qwen/Qwen3.5-9B-FP8. --config is optional when the model has exactly one serving config.
tg beta endpoints deploy $MODEL_ID --endpoint $ENDPOINT_ID --config $CONFIG_ID \
--min-replicas 0 --max-replicas 0 --traffic-weight 0
Start. One command creates and starts the canary: 10% → 50% → 100%, with a gate that compares the target's p95 router latency against the source's over a 5-minute window after each step.
tg beta endpoints rollout $TARGET_DEPLOYMENT_ID --source $SOURCE_DEPLOYMENT_ID \
--canary --steps 10,50,100 \
--metric router_latency --metric-stat p95 \
--metric-max-regression 25 --metric-direction higher-is-worse \
--metric-window 300s
What happened, by the clock (time since the rollout started):
SYSTEM_PAUSED with 10% of traffic still on the target and nothing torn down. This is what tg beta endpoints get $ROLLOUT_ID --json returned (values in milliseconds):
"state": "ROLLOUT_STATE_SYSTEM_PAUSED",
"currentTrafficPercent": 10,
"pauseInfo": {
"reason": "metrics-regression: regression check router_latency failed: target=1740.38 vs source=734.20, regression=137.05% exceeds max=25.00% (direction=REGRESSION_DIRECTION_HIGHER_IS_WORSE)",
"pausedAt": "2026-09-16T20:50:45Z"
},
"status": {
"condition": {
"category": "ROLLOUT_FAILURE_CATEGORY_METRIC_REGRESSION",
"atStep": 0,
"metrics": [
{
"name": "router_latency",
"stat": "METRIC_STAT_TYPE_PERCENTILE",
"percentile": 95,
"check": "METRIC_CHECK_TYPE_REGRESSION",
"sourceValue": 734.20,
"targetValue": 1740.38,
"maxRegressionPercent": 25,
"direction": "REGRESSION_DIRECTION_HIGHER_IS_WORSE",
"verdict": "METRIC_VERDICT_BREACHED"
}
]
}
}
Decide. The regression is real, not a blip: the 9B is a reasoning model and, at the same max_tokens, generates more per request. That is a product decision rather than something to resume past, so we canceled and went back.
tg beta endpoints rollout $ENDPOINT_ID --cancel --reason "p95 latency regression on the Qwen3.5 target"
# split frozen at 90% / 10%; both deployments keep serving
tg beta endpoints rollout $SOURCE_DEPLOYMENT_ID --source $TARGET_DEPLOYMENT_ID --blue-green
# the 7B takes 100% back, the 9B drains to zero
CANCELED. The split froze at 90/10 within 0.2 s of the command.The probe's verdict across the whole run, including the shift, the pause, the cancel and the reverse: 6,800 requests, 0 non-200 responses.
Audit trail. Every step above is in the endpoint's event feed, filterable by rollout ID:
tg beta endpoints events $ENDPOINT_ID --subject-id $ROLLOUT_ID --json
20:39:45 rollout.created canary rollout created: dep_src → dep_tgt, 3 step(s) to 100%
20:39:45 rollout.started rollout started: step 1 of 3 targets 10% traffic
20:43:42 rollout.traffic_shifted 0% → 10% target traffic: 0% → 10%
20:50:45 rollout.system_paused paused automatically at step 1 of 3: a metric check failed
20:50:54 rollout.canceled cancel requested; traffic will be frozen at the current split
20:50:54 rollout.canceled_complete canceled: traffic frozen at 90%/10% (source/target)An earlier run in July, with a deliberately impossible threshold gate, produced the same shape: a trip at 10% of traffic and 1,198 probe requests with zero errors through the recovery.
1. Two deployments on one endpoint. Keep your current deployment as the source and add the candidate as a target with zero traffic. pip install -U together (2.34.0 or newer) gives you the tg CLI:
# A stopped, zero-traffic target; the rollout restarts it when it scales it up.
# Add --config cr_... to pin a specific config revision.
tg beta endpoints deploy $MODEL --endpoint $ENDPOINT_ID \
--min-replicas 0 --max-replicas 0 --traffic-weight 0
2. Create and start a canary with the default ladder (5% → 25% → 50% → 100%) and one router_latency regression gate:
tg beta endpoints rollout $TARGET_DEPLOYMENT_ID --canary \
--metric router_latency --metric-stat p95 \
--metric-max-regression 10 --metric-direction higher-is-worse
3. Watch it with tg beta endpoints get $ENDPOINT_ID (or the endpoint's Rollouts tab in the console) as it progresses through the steps. Pause, promote or cancel it with tg beta endpoints rollout $ENDPOINT_ID --pause | --promote | --cancel.
Throughout, the endpoint URL and your clients stay unchanged; only the model behind them moves.
📚 Docs: Start a rollout · Gate rollouts with metrics · CLI reference · API reference: Create a rollout
When Cursor became part of SpaceXAI on August 14, our two customer support teams began coming together around a much broader product portfolio.
At the same time, we were preparing to launch Grok Bot, an AI teammate you can give real work to. We expected the product to grow quickly, bringing another wave of users and support demand.
We decided to use Grok Bot itself to help meet that demand, putting it to work throughout the support operation. It signed into the same tools our team used and its role stretched from resolving individual tickets to helping us understand and improve the operation as a whole.
Our new combined team has seen a 175% increase in support tickets, but we have not had to hire any new people thanks to Grok Bot. We might have hired 200 additional people otherwise.
We are also doing it at a fraction of the usual cost. Traditional AI support tools charge a flat $1 to $4 per resolution. With Grok Bot, you only pay for your actual usage, which is already included in your plan. With minor optimizations, we've been able to resolve tickets for as low as $0.20 to $0.30.
We took a crawl, walk, run approach to setting up Grok Bot. We started by connecting it to a few core systems, including Plain for ticketing and Linear for issue tracking. We then had it act as though it owned tickets, while limiting it to internal notes and requiring human approval for every write action. This let us check whether it understood each issue and proposed the right next step without affecting the customer experience.
As the results became more reliable, we added traces and evaluations to every run. When something went wrong, we could see where Grok Bot had gone off course, make an adjustment, and try again. Grok Bot could also analyze these runs itself. This feedback loop allowed us to move quickly while keeping the process controlled.
Once that foundation was in place, we began rolling Grok Bot out on the least complex tickets. During the first day, we manually reviewed its interpretation and proposed response for accuracy, tone, and whether it had followed our instructions. By the end of the day, we had enough confidence to let it begin responding directly to customers. From there, we gradually expanded the range of tickets it could handle.
If you consider the end-to-end time that it takes to resolve a ticket, the majority of the clock happens during discovery, investigation, and troubleshooting. We began applying Grok Bot to every ticket as a pre-investigation step the moment it entered into our system. This could get expensive, so we've looked at common tickets and classified common issues to reduce the amount of tokens we needed to spend. We also don't exhaust a significant amount of troubleshooting capacity when a simple help center check does the trick.
Whenever we run into a known issue (it connects to our Linear instance), or if we hit a common error in our backend (it's connected to Datadog), we've trained Grok Bot to either add to the existing issue or to create a new one. Grok Bot also reproduces the issue with a video, which helps the engineering team quickly resolve it.
Of course, we also need to ensure that our customers are getting a clear response from us. We've trained Grok Bot on over one million customer interactions so that it's learned our tone and voice directly from our humans. Grok Bot is trained to not only respond, but always push the ticket towards resolution. It does this by asking relevant questions (i.e., it won't ask a question where the answer is already found in our logs).
Grok Bot can also take action on behalf of our customers. For example, we've provided it with clear refund instructions where 99% of all refund requests are resolved without human intervention.
Resolving individual tickets is only part of the job. We also need to understand what is happening across the queue. Grok Bot watches inbound volume continuously and adjusts the queue based on what needs attention. It can reprioritize tickets, reassign ownership based on urgency, and alert the organization when we are getting close to breaching a response-time SLA.
Grok Bot also looks across tickets for patterns. When the volume around a particular issue reaches a set threshold, it can declare an incident automatically. It monitors X for changes in sentiment and recurring reports of the same problem, giving us a view beyond the customers who contact support directly. Together, these signals help us spot emerging problems early.
At our current scale, raw volume alerts would create a lot of noise. Grok Bot assesses whether a spike reflects a real support issue and begins investigating before it alerts the team. That gives us more context about what requires action while preserving the team's time and capacity.
As Grok Bot took on more of our customer support work, it also gave us a new way to improve the operation itself. It reviews customer interactions handled by both people and Bots, provides specific feedback on what could be improved, and surfaces coaching opportunities for individual team members and Bots.
Every week, Grok Bot sends our leadership team a summary of where our AI responses are falling short. Sometimes the answer is more training or better documentation. Other times, the summary confirms that the guardrails we put in place are working. This gives us a regularly updated view of quality and helps us address patterns early.
As more users ask Grok for support, our help center increasingly serves as source material for its answers. To keep those answers accurate, Grok Bot reviews changes to our codebase and suggests corresponding updates to the help center.
We have now reached the point where Grok Bots can coach other Grok Bots. They identify gaps in the knowledge system, fill those gaps, and feed what they learn back into the system. We are scaling this loop across the portfolio so that it covers every product surface.
Grok Bot has become our default data analyst, turning what it sees across customer support into daily reports for our Slack channels. When it detects early signs of a poor customer experience, it flags the situation so the team can step in while there is still time to change the outcome.
An example of this is whenever a ticket goes back and forth more than three times between a customer and one of our team members (human or Bot). When that happens, Grok Bot flags the interaction for management review and gives leadership an opportunity to lean in and save the experience. This interaction helps train Grok Bot, and as Grok Bot learns which signals are useful to the team, the reporting becomes more relevant over time.
The same analysis helps us improve product quality. Grok Bot synthesizes more than 20,000 points of product feedback from customer support tickets each day and turns them into clear themes we can bring to engineering. This gives the product team a broader view of where customers are struggling and where the product needs work.
Grok Bot is still a new way of working, but it has already changed how our team operates. Instead of spending most of the day on repetitive support work, we can focus on setting guardrails, handling cases that require judgment, and deciding how the operation should improve. That makes the work more engaging and gives people more room to apply their experience to harder problems.
We are still learning what this model makes possible. As the role of customer support continues to change, we will keep sharing what we find.
The Specialized Intelligence Index (SII) is your one-stop destination to explore the performance of open, closed, and specialized models on domain-specific benchmarks. Each benchmark reflects real-world tasks designed by practitioners. Today, we are launching benchmarks in seven initial domains: healthcare, legal, cybersecurity, finance, customer support, productivity, and software. More are coming soon.
Public benchmarks provide common reference points for tracking progress and comparing models, but they are not a good measure of real work. These evals use bounded tasks, fixed datasets, and standardized scoring. Real work is messier. It involves incomplete information, changing scope, business constraints, complex judgment calls, multi-step workflows, and collaboration.
This distinction matters for organizations seeking to determine if a model is good enough to automate human tasks. An acceptable result must satisfy the standards of real people responsible for real outcomes. Earlier this year, METR quantified the difference. In their work, 4 maintainers reviewed 296 AI-generated pull requests (PRs) from 3 SWE-bench Verified repositories. Maintainer acceptance scores averaged 24.2 percentage points below automated benchmark scores. In their own words: “many SWE-bench-passing PRs would not be merged into main.”
To apply benchmarks to real work, we need to establish what a score measures, how closely the evaluation reflects the intended work, and whether better performance produces a useful operational result.
Does the test measure the capability it claims?
This is a question of construct validity: whether the evaluation supports the interpretation attached to its score. Bean and colleagues examined 445 LLM benchmarks and identified recurring gaps between the phenomena researchers intended to measure, their tasks, and their scoring methods. For example, a task intended to measure reasoning may also depend on memorized knowledge. This makes it difficult to determine whether a high score reflects reasoning, recall, or both.
Does the test represent the work we care about?
Representativeness concerns the coverage and composition of the task set. Wang and colleagues studied 43 agent benchmarks and found a concentration in computer and mathematical work, a category accounting for 7.6% of U.S. employment in their analysis. Management and legal work were underrepresented, as were interpersonal skills common across occupations. Real-work benchmarks must represent their intended domain.
Evaluating models on real work then follows a logical progression, with each step requiring more evidence:
Benchmark score → Capability claim → Business outcome
The score records performance on a defined task set under a specified protocol. A capability claim requires evidence that the system can perform the relevant class of work reliably, including on unfamiliar cases. A business outcome requires evidence that this performance delivers the desired result at acceptable quality and cost.
Three benchmarks across Software, Customer Support, and Finance. Visit the SII to explore the scores.
Real-work evals are defined by practitioners who help outline the work, the constraints, and the conditions for acceptance. Designing real-work evals generally involves these steps:
1. Define the job and its value. Specify the task, intended users, and level of human oversight. Set quality thresholds and time and cost limits. Establish a baseline for the current workflow, then test whether score improvements predict better outcomes in a pilot or controlled deployment.
2. Reflect the work. Sample routine tasks and difficult cases from the intended setting. Include realistic information, tools, permissions, and policy constraints. Add stress tests for consequential failures, but report them separately when their frequency differs from normal usage. A deliberately difficult test set should not be presented as an estimate of everyday performance.
3. Set acceptance criteria with practitioners. Translate professional standards into observable outcomes and explicit rubrics. Distinguish minor defects from failures that make an output unacceptable. Evaluate both the final result and any actions that matter, such as seeking approval before changing a protected resource.
4. Validate the grader. Compare automated judgments with expert review. Examine false acceptances, false rejections, and disagreements among reviewers. Refine the rubric or grading method where those differences reveal ambiguity. Continue sampling outputs for expert review as the system changes.
5. Test generalization and reliability. Keep development and held-out cases separate, check for contamination, and refresh the evaluation as the work changes. Repeat runs and report uncertainty, performance by task category, and critical failure rates. Record the model, prompts, agent harness, tools, resource limits, and grader version so comparisons remain interpretable.
Doximity’s BedsideBench v0.2.0 evaluates frontier AI models across 500 physician-validated clinical cases spanning medical reasoning, calculations, drug safety, guideline adherence, hallucination, diagnostic safety, and treatment planning.
Mercor’s APEX-1: General Practitioner (MD) Benchmark measures how well frontier AI models perform on real primary care physician tasks in diagnosis, workup, and safe escalation.
HealthBench Professional evaluates whether frontier AI models can provide accurate, useful, and safe responses to challenging clinician-authored tasks.
Harvey's Legal Agent Benchmark (LAB) measures how well frontier AI models perform on real legal work across 24 practice areas, requiring them to navigate files and produce work products graded against expert rubrics for factual accuracy, legal analysis, and format.
Harvey’s LAB Contracts tests whether AI agents can move contract negotiations forward across 500 drafting, review, and negotiation tasks. To successfully complete each task, agents must address all changes and open issues to advance the contract within the constraints of the business and deal.
Mercor’s APEX Agents: Corporate Law assesses multi-step corporate-law assignments that encompass chain tool use, retrieval, and document drafting. Practicing corporate attorneys grade the output against the work product a firm would accept.
RedlineBench evaluates realistic, multi-turn contract redlining by an AI agent acting as in-house counsel.
Rogo’s Big Finance Bench assesses AI agents on questions spanning valuation models, financial-statement analysis, forecasting, and other critical finance workflows, with practitioner-written rubrics grading how agents find information, apply financial definitions and citations, and perform calculations.
depthfirst dfbench v1 targets defensive security across vulnerability detection, validation, and differential analysis. depthfirst's own dfs-large1 model, post-trained with Fireworks on a GLM 5.2 base using RL, achieved a new Pareto frontier in its evaluations. The model’s improvements are attributed to RL reward shaping with an effort penalty, a soft finding-budget penalty, and joint training on vulnerability detection and validation. This result is reported by depthfirst.
Novee’s PWNBench-v0.1 evaluates frontier AI models on agentic greybox pentesting of live web applications, covering the full discover–exploit–report workflow. It measures recall, precision, F0.5, and API cost under a shared thin harness.
Decagon’s DuetBench-Diagnosis replays real Duet customer-support investigations and rates model responses head to head across outcome, investigation, tool use, and communication.
Sierra’s τ-Banking evaluates customer-support agents on banking tasks that require searching a 698-document knowledge base across 21 product categories, applying policies, and executing multi-step tool calls while managing an ongoing customer conversation.
Sierra’s τ-Voice evaluates whether voice agents can complete customer service tasks across retail, airlines, and telecom while handling interruptions, background noise, and diverse accents.
Genspark Slides Benchmark evaluates AI-generated presentations on de-identified real user tasks, scoring the finished deck on task completion, content quality, visual design, and process quality, with penalties for layout defects, fabricated content, and ignored instructions.
Traversal’s ORCA-Bench is a site reliability engineering benchmark that evaluates production-style root-cause analysis (RCA) from ambiguous reports, telemetry, and source code. Hard RCA accuracy is the headline score; Medium RCA and incident hallucination remain separate native metrics.
Proximal’s FrontierSWE V2 is a code generation benchmark that evaluates 34 software-engineering tasks at the edge of what an expert human can do: writing a flight-sim renderer in OpenGL, porting Git to Zig, driving a racing bot from vision alone. Each model gets 5 trials per task and up to 20 hours per trial. Every trial earns a graded reward rather than a pass or a fail, so a run that gets most of the way there still counts.
Mercor’s APEX-SWE evaluates AI models on 200 software-engineering tasks that require integrating cloud services and business applications or debugging production failures using logs, dashboards, and incomplete context.
Datacurve’s DeepSWE v1.1 evaluates coding agents on 113 original, long-horizon engineering tasks across 91 repositories and five languages, testing their committed code for correct behavior in an isolated environment.
Macroscope's MacroscopeBench evaluates models’ performance at code review, measuring the reviewer’s ability to detect real known bugs while not posting incorrect comments. It runs over 195 commits from open-source repositories, 144 that introduced a real bug maintainers later had to fix, and 51 clean controls.
Training an open model can deliver better results including: higher pass rates, lower cost per task, and/or lower task duration. Results from Doximity, depthfirst, and Genspark on their specialized intelligence.
No artificial rollup. SII does not average ranks, weight quality against cost or duration, or produce a cross-domain composite. Results remain at the benchmark and domain level, with coverage matrices and score-versus-cost and score-versus-duration views so users can apply their own tradeoffs.
Source. Benchmark results may be reported across models by a partner, Fireworks, or a combination of both; implementation is defined or linked accordingly. Publication follows the benchmark owner’s policy; scores are published, while eval sets, prompts, grading logic, trajectories, and raw partner outputs remain private unless the owner chooses otherwise.
Reproducibility. Each benchmark is labeled by who can reproduce it: anyone, Fireworks and the benchmark owner, or the partner only. Reproducibility comes from the versioned methodology and pinned execution snapshot, which record the harness, sampling parameters, timeouts, snapshot IDs, executor, and any open issues.
Model selection. Models are selected based on whether an organization could plausibly deploy them at production scale, with price as a key consideration. New frontier models automatically enter the qualification pipeline and appear on the Index only after they pass this bar.
For further details on the harness, inference, sandbox, run protocol, confidence, reliability, and cost and duration metrics, visit Fireworks Methodology.
If you run a production eval for a specific domain, it may belong on the Index. Fireworks Lab helps organizations design their own benchmarks and specialized models.
To submit to the Index, partners provide tasks and data in a Harbor-compatible format. Fireworks reviews task diversity and calibration, requests and runs the eval across a model roster at no cost, and publishes scores with the partner’s approval.
GPT-6 Sol and GPT-6 Luna from OpenAI are now available on AI Gateway.
Both models bring GPT-6 improvements in professional work, coding, computer use, factuality, and communication at a lower price than GPT-6 Astra.
GPT-6 Sol (openai/gpt-6-sol) is suited to complex professional workflows and sustained coding tasks where quality and room to iterate both matter.
GPT-6 Luna (openai/gpt-6-luna) is the lower-cost option for high-volume agentic workflows, coding, and everyday tasks.
Both Sol and Luna communicate more directly than their GPT-5.6 counterparts, with less jargon and fewer low-value details. They also improve factual reliability and are less likely to make misleading claims about work completed during coding tasks.
Use openai/gpt-6-sol and openai/gpt-6-luna across the AI SDK, OpenAI-compatible Chat Completions API, OpenAI-compatible Responses API, and coding agents connected to AI Gateway.
Install the latest Vercel CLI and connect your supported coding agents to AI Gateway:
Then select openai/gpt-6-sol for longer or more demanding coding work, or openai/gpt-6-luna when throughput is the priority.
AI Gateway provides a unified API for calling models, tracking usage, and configuring retries, failover, and routing.
Try GPT-6 Sol or GPT-6 Luna in the model playground, or view all language models available on AI Gateway.
Drives for Vercel Sandbox are now available in public beta on Hobby, Pro, and Enterprise.
A Drive is persistent storage that you mount as a directory in a Vercel Sandbox. It isn’t tied to a single sandbox, so you can reuse the same Drive across runs and different sandbox instances.
Use Drives to preserve an agent's workspace or on-disk memory, or to reuse datasets, models, and dependency trees.
Create or retrieve a Drive and mount it at a path when starting a sandbox. Read and write files through the sandbox filesystem at that path.
Anything stored under /data remains on the Drive after the sandbox stops.
A Drive supports one read-write mount at a time. After the Drive has been written to, multiple sandboxes can read from it concurrently by mounting point-in-time, read-only snapshots.
Each snapshot reflects the Drive at the moment it's mounted. Later writes aren’t included; mount a new snapshot to access them.
Each sandbox can mount up to four Drives at separate paths. Drives default to a maximum size of 1 TiB (1 GiB on Hobby) and can be configured up to 16 TiB, with higher limits available by request.
Drives are available in every Sandbox region. Each Drive stays in the region where it was created. Sandboxes that mount it must run in that region and can’t use failover regions.
Drive pricing is based on storage, reads, and writes, with rates varying by region. In iad1, storage costs $0.05 per GB-month, reads $0.0015 per GB, and writes $0.004 per GB. Hobby includes 15 GB of Drive storage and 30 GB each of reads and writes per month. See Sandbox pricing for regional rates and plan details.
Learn more in the Drives documentation.
Claude Opus 5.5 from Anthropic is now available on AI Gateway. It is a step-change improvement over Opus 5, with its biggest gains in agentic coding, long-running agent tasks, and knowledge work. Anthropic cites that Opus 5.5 performs at the level of Fable 5.1, but ~30% faster and ~40% cheaper than Opus 5 per task.
Opus 5.5 is also a better collaborator over long runs. It reports back in plain language on what it did, what it found, and what it needs next, making it easier to supervise work that spans many steps or takes place over a longer period.
Opus 5.5 includes two API changes that can turn previously valid requests into HTTP 400 errors:
Thinking is always adaptive. Requests that disable thinking or set a fixed thinking budget are rejected. The model decides how much to think for each request. Use effort and prompting to steer its thinking behavior.
Forced tool use is retired. Requests cannot require a tool call or force a specific tool. Prompt the model toward the tool, then catch and retry misses in your harness. If you previously forced a tool call to return JSON, use structured outputs instead.
Use anthropic/claude-opus-5.5 across the AI SDK, OpenAI-compatible Chat Completions API, Anthropic Messages API, and coding agents connected to AI Gateway. You can also enable fast mode with the gateway speed option r anthropic/claude-opus-5.5-fast. The model has a 1M-token context window, returns up to 128K tokens, and has a June 2026 knowledge cutoff.
Regional inference and Zero Data Retention are opt-in request controls. This example pins inference to the US and ZDR:
Install the latest Vercel CLI and connect your supported coding agents to AI Gateway:
Then select anthropic/claude-opus-5.5 in the agent. In Claude Code, use /fast to toggle fast mode for the session. See the coding agents guide for other agent-specific instructions.
AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, budgets for API keys, routing rules, and more.
AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests.
Try Claude Opus 5.5 in the model playground, or view all language models available on AI Gateway.
The Next.js team has disclosed a critical severity vulnerability in an upstream dependency that can lead to remote code execution when ImageResponse renders untrusted input. It is patched in 15.5.26 and 16.3.6. Applications that do not pass untrusted input into ImageResponse are not expected to be affected. Here’s what Netlify customers need to know.
next/og ImageResponse. Critical. Patched in 15.5.26 and 16.3.6.Netlify sites are affected only if they use ImageResponse and the image it generates includes untrusted input — text, or an image loaded from the request. Sites that don’t use ImageResponse, or that only render trusted content through it, are not affected.
For sites that do, the impact is limited to a crashed function invocation, not code execution. On Netlify, this has minimal impact: our autoscaling serverless architecture means that a malicious request resulting in a crashed function does not affect other requests. However, active exploitation could increase your function costs.
We strongly recommend upgrading as soon as possible to patched releases:
next 15.5.26 or later, or 16.3.6 or later, then redeploy.Until you can upgrade, do not place untrusted input inside elements passed to ImageResponse. Escape it as XML before rendering, or keep it out of the generated image entirely.
Note that any publicly available deploy previews and branch deploys may remain vulnerable until they are automatically deleted. Consider deleting these deploys manually.
Anthropic’s Claude Opus 5.5 model is now available through Netlify’s AI Gateway and Agent Runners with zero configuration required.
Use the Anthropic SDK directly in your Netlify Functions without managing API keys or authentication. AI Gateway handles everything automatically. Here’s an example using Claude Opus 5.5:
import Anthropic from '@anthropic-ai/sdk';
export default async () => {
const anthropic = new Anthropic();
const response = await anthropic.messages.create({
model: 'claude-opus-5-5',
max_tokens: 4096,
output_config: { effort: 'medium' },
messages: [
{
role: 'user',
content: 'How can AI improve my coding?',
},
],
});
return new Response(JSON.stringify(response), {
headers: { 'Content-Type': 'application/json' },
});
};
Claude Opus 5.5 is also available across Background Functions, Scheduled Functions, and Edge Functions. You get automatic access to Netlify’s caching, rate limiting, and authentication infrastructure.
Learn more in the AI Gateway documentation and Agent Runners documentation.
OpenAI’s GPT-6 Sol and GPT-6 Luna models are now available through Netlify’s AI Gateway and Agent Runners with zero configuration required.
Use the OpenAI SDK directly in your Netlify Functions without managing API keys or authentication. AI Gateway handles everything automatically. Here’s an example using GPT-6 Sol with the Responses API:
import OpenAI from 'openai';
export default async () => {
const openai = new OpenAI();
const response = await openai.responses.create({
model: 'gpt-6-sol',
input: 'Give a concise explanation of how AI works.',
});
return Response.json(response);
};
GPT-6 Sol and GPT-6 Luna are also available across Scheduled Functions, Background Functions, and Edge Functions. You get automatic access to Netlify’s caching, rate limiting, and authentication infrastructure.
Learn more in the AI Gateway documentation and Agent Runners documentation.
Posted on 2026-09-22 by EMS Software Development
Related Proprietary
This is a major release, and the headline feature is our new AI Assistant. We're starting to roll out AI capabilities across our database tools, and SQL Manager for PostgreSQL is the first to get them. It's also available in SQL Management Studio for PostgreSQL, which ships with SQL Manager.
You'll find all the AI Assistant features you'd expect: write a query from a plain-language description, explain existing SQL, fix errors, optimize, make sense of an EXPLAIN plan.
But you can also take on bigger jobs:
Here's the key part: you decide how much of the schema the model sees, down to a single table. You stay in full control of exactly what context goes to the model.
Connect as many providers as you like — all it takes is an API key (stored encrypted), and you can switch models right in the chat. Need to stay completely self-contained? It works with Ollama, so your data never leaves your machine.
Posted on 2026-09-22 by Constructive
Related Open Source
Application code has fast testing loops: runners, fixtures, and red-green feedback in JavaScript, TypeScript, and Python. Postgres can be tested too, but database logic often sits outside those loops. Developers have to provision state, manage transactions, or fall back to a pasted query, a browser refresh, or a manual check. That gap shapes architecture. When rules are easier to test in application code than in Postgres, they tend to end up there, even when Postgres is the better place to enforce them.
Mocks do not close the gap. They test how application code handles a result, not whether Postgres will produce it. A mock does not exercise a foreign key, fire a trigger, evaluate a row-level security policy, or verify the database role under which a query actually runs.
pgsql-test is an MIT-licensed harness that puts a real PostgreSQL database inside those loops. It spins up an ephemeral PostgreSQL database, seeds it once, and rolls every test back to that seeded state. Assertions run in the project’s existing test runner, and Postgres executes the constraints, functions, and policies under test. It is not the first way to test Postgres—pgTAP has long done it in pure SQL. pgsql-test targets the application layer instead, where most developers already work.
Row-level security (RLS) makes access rules enforceable by Postgres. Policies are pure database logic, invisible to mocks, and a wrong one can leak rows. Testing them means checking both what a user can access and what they cannot.
The pgsql-test harness provides an administrative client, pg, for setup and an application client, db, for testing grants and policies. Superusers bypass RLS, so tests need to exercise the database roles and permissions the application actually uses.
Suppose a project defines app.documents with an ownership policy, and its fixtures insert document 101 owned by Alice and document 202 owned by Bob. Each test runs inside a transaction that is rolled back afterwards, so every test starts from the seeded state:
import { getConnections } from 'pgsql-test';
let db, teardown;
beforeAll(async () => {
// create a fresh database and deploy the project's schema
({ db, teardown } = await getConnections());
});
afterAll(() => teardown());
beforeEach(() => db.beforeEach());
afterEach(() => db.afterEach());
test('Alice sees her document and not Bobs', async () => {
db.setContext({
role: 'authenticated',
'jwt.claims.user_id': 'aaaaaaaa-aaaa-aaaa-aaaa-aaaaaaaaaaaa'
});
const result = await db.query(
'SELECT id FROM app.documents ORDER BY id'
);
expect(result.rows).toEqual([{ id: 101 }]);
});
The query has no ownership filter. The policy must make Alice’s document visible and keep Bob’s out of the result.
setContext() applies the role and identity settings through SET LOCAL and set_config(..., true), scoping them to the transaction. They supply the identity the policies read. Nothing validates a token. Test identities must match what the application’s policies expect. The RLS tutorial covers the setup.
The harness seeds through pgsql-seed, which loads SQL files, programmatic fixtures, CSV, JSON, or migrations.
The same harness runs under other stacks:
supabase-test supplies Supabase roles, schemas, and authentication defaults.
drizzle-orm-test runs Drizzle queries within the managed transaction.
pglite-test uses in-process PGlite, with no external database service when the schema and required extensions are supported.
graphile-test runs GraphQL against a PostGraphile schema backed by the test database. graphile-realtime-test adds subscription testing.
In one supabase-test run, noted by Supabase CEO Paul Copplestone, 246 tests across 44 databases completed in four seconds.
pgpm, Constructive’s package manager for modular PostgreSQL, scaffolds workspaces with pgsql-test, Jest, and GitHub Actions; getConnections() deploys the module’s plan by default. Start with pgpm init workspace, then add a schema change and a test.
Each change ships deploy, verify, and revert scripts. verify checks that the schema landed. The tests check that it behaves.
The scaffolded workspace extends the same feedback loop into CI with safegres. Tests verify that database behavior is correct, and safegres checks the deployed schema for security and performance regressions. Its CI job enforces a security threshold and compares performance findings against a committed baseline, so a change cannot lower the schema’s security grade or add performance findings.
Postgres enforces the rules. pgsql-test brings verification of those rules into the application development loop.
pgsql-test and its integrations are MIT licensed and available on npm and PyPI.
Get started: Tutorials · End-to-end testing course · npm · Source code
Posted on 2026-09-22 by PostgresCompare
Related Proprietary
PostgresCompare is pleased to announce the release of version 2.2.0, the first public release since 1.2.2 and the largest change in the product's history. The application has been rebuilt on a new foundation, and the 2.x series adds database pipelines, ad-hoc comparison, data-change scripting, an MCP server for AI agents, and a fully translated interface.
PostgresCompare connects to two live PostgreSQL databases, detects schema differences across tables, views, functions, indexes, types, and 30+ other object types, and generates a ready-to-run SQL deployment script to synchronise them. It runs on Windows, macOS, and Linux.
Rebuilt from the ground up
New ways to compare
Data comparison and scripting
Data comparison gained a scripting engine. Differences between table contents can be turned into a SQL script, with row-level selection so only the chosen rows are included, and the script panel highlights the row under the cursor as you work through it. Comparison results can also carry notes, so the reason for a decision stays with the comparison.
Automation and AI agents
Under the hood
Availability
PostgresCompare 2.2.0 is available for Windows, macOS (Apple Silicon and Intel) and Linux (AppImage and .deb). The pgc command-line tool ships for all three platforms. Users on 1.2.2 upgrade by downloading 2.2.0 directly; from 2.x onward the application updates itself. A 14-day free trial is available with no credit card required, and when a trial ends, quick compare and connection management remain available without a licence. PostgreSQL versions 9.2 through 18 are supported.
Thirty-six percent of businesses on Stripe now have customers in more than one country, and the number of companies selling into more than 100 countries has quadrupled in five years.
This growth can also come with additional risk. Expanding into more markets means operating in new fraud environments, where fraud patterns, cultural norms around authentication, and regulatory requirements vary by region.
The differences can be substantial. In 2025, businesses in Latin America saw 160% higher card fraud rates compared to businesses in Europe, the Middle East, and Africa, and 151% higher than businesses in Asia Pacific. Managing that variability often involves separate risk strategies, with dedicated teams, for each market a business operates in.
We analyzed billions of transactions on Stripe from January 2022 to March 2026 to understand how card fraud patterns differ by region and country, what's driving those differences, and how businesses can respond. Here's what we found.
Of all the regions we analyzed, businesses in Asia Pacific saw the most consistent decline in card fraud rates from 2022 to 2025—and by 2026, had the lowest card fraud rate of any region on Stripe for the first time in our studied time frame.
Markets across Asia Pacific have introduced 3D Secure (3DS) requirements for online card transactions, adding an extra security layer by verifying that the person making a purchase is the legitimate cardholder. The data suggests the mandates are working.
Take Malaysia, where businesses saw a 74% decrease in card fraud rates from 2022 to 2025—the biggest decrease across countries in the Asia Pacific region. Malaysia's central bank applies a relatively strict approach with its 3DS mandate. Financial institutions are required to implement multifactor authentication, migrate from SMS-based one-time passwords to secure app-based authorization, and let customers immediately freeze their account—all of which leave fewer opportunities for unauthenticated transactions to get through compared to other markets.
Businesses in Japan also saw consistently lower fraud rates each year from 2022 to 2025. This is, in part, thanks to Japan’s April 2025 3DS mandate. Our analysis of disputes shows that the mandate is working to reduce fraud: dispute rates—which correlate with fraud, as customers dispute charges once they identify unauthorized transactions—were more than 30% lower in 2025 than the same period in 2024.
Card fraud rates for businesses in Europe have declined 21% from 2022 to 2025, though there is considerable variation among countries within the region. France and Great Britain—two of the larger European markets—have both seen consistent decreases in card fraud rates from 2022 through 2025, with France decreasing 40% and Great Britain decreasing 27%.
This reflects the maturity of the payments ecosystems in each market. Strong Customer Authentication (SCA) regulation, which requires businesses to support two-factor authentication on their checkout page to reduce fraud, has given issuers the framework and time to invest in more sophisticated fraud infrastructure. In France, that maturity also has historical roots. France was one of the earliest adopters of chip-and-PIN authentication, normalizing two-factor authentication years before most other countries. French cardholders are familiar with the process and more likely to complete authentication flows successfully as a result, which can help lower fraud rates.
On the other hand, Iberia was the only named European region in which businesses’ card fraud rate increased every year from 2022 to 2025. Spain and Portugal rely more heavily on one-time passwords as temporary security codes for authentication than other European markets, which can be more susceptible to fraud than biometrics or app-based verification. Spain and Portugal are also both heavily targeted by “smishing” attacks, SMS phishing scams where fraudulent actors impersonate trusted institutions to steal card credentials.
Businesses in Latin America on Stripe have had the highest card fraud rates among the regions analyzed on Stripe since January 2022. The gap remained significant in 2025: card fraud rates for businesses in Latin America were 65% higher than businesses in North America; 151% higher than businesses in Asia Pacific; and 160% higher than businesses in Europe, the Middle East, and Africa.
Some markets are improving. Businesses in Ecuador, Panama, and Brazil saw card fraud rates decrease from 2022 to 2025. But across the region, several structural factors keep overall rates elevated.
To help businesses better manage fraud as they enter new markets, we recently expanded Stripe Radar, our AI-powered fraud prevention product, to protect all supported payment volume globally. That expansion includes bank debits, stablecoin payments, digital wallets, real-time payments, and cash vouchers.
Stripe can also help businesses in SCA regions reduce fraud and meet regulatory requirements through 3DS authentication. On average, businesses in SCA regions can benefit from a 1.20% uplift in conversion while reducing fraud on all transactions by 7.67% with our AI-powered optimizations. Businesses can run 3DS authentication using Stripe while authorizing the payment with any payment processor and intelligently trigger 3DS to optimize for payments, fraud, or conversion use cases.
To learn more about how Radar can help your business prevent fraud as you expand into new countries, contact us or sign up for an account.
At enterprise scale, even small architecture choices can have outsized consequences. A deployment that works for a handful of teams can become a constraint once thousands of developers, repositories, and pipelines depend on it.
That makes each decision made before rollout especially consequential. For example, your:
Together, those factors determine how well the platform can absorb growth without creating new operational constraints.
In this guide:
Your GitLab deployment model determines which parts of the platform your team must size, secure, monitor, upgrade, and recover. For enterprise deployments, the three core options are:
GitLab.com: GitLab’s multi-tenant software-as-a-service (SaaS) offering. GitLab operates the application and underlying infrastructure, while your organization manages its GitLab configuration, integrations, and any self-managed runners.
GitLab Dedicated: A fully managed, single-tenant SaaS offering hosted on Amazon Web Services (AWS). GitLab operates the underlying infrastructure, including updates, high availability, and disaster recovery; your organization controls user and data access through application-level controls.
GitLab Self-Managed: Your organization installs, administers, and maintains its own GitLab instance. You manage the infrastructure and assume responsibility for operating, scaling, securing, and recovering the environment.
Choose the model based on the control your organization requires and the infrastructure responsibility it can sustain. For example, you may want to choose:
Before you decide, document any requirements that could rule an option in or out. Pay particular attention to data residency, network isolation, recovery objectives, and infrastructure control. Then map the operational work each model leaves with your team, including upgrades, monitoring, capacity planning, backups, and incident response.
That exercise should clarify the central tradeoff: how much infrastructure responsibility your organization needs and can realistically own.
Once you define that operating boundary, it’s time to plan the compute layer that will execute your CI/CD workloads.
GitLab Runner executes CI/CD jobs, and the GitLab application coordinates the pipelines behind them. That separation matters at enterprise scale because application capacity and runner capacity respond to different types of demand. The application handles Git, web, API, and automation traffic; the runner fleet absorbs the volume and concurrency of CI/CD work.
For this reason, you should size the runner fleet based on the workloads themselves rather than on developer headcount. Start by documenting:
Use those inputs to estimate how many jobs must run at once to meet your queued-duration target. Workload data matters more than team size because two organizations with the same number of developers can generate very different CI/CD demand based on pipeline frequency, automation, and job requirements.
Runner scope determines how broadly teams can use each pool. Instance runners can serve projects across the GitLab instance, while group runners limit access to projects and subgroups within a defined group. Project runners provide the narrowest scope and fit workloads that need dedicated credentials, specialized infrastructure, or stronger isolation. Because that capacity is reserved for fewer workloads, project runners may also sit idle when job volume is intermittent, so factor utilization into the decision.
Use the broadest scope that meets the workload’s trust and compute requirements. Broader pools generally improve utilization, while sensitive deployment jobs or specialized workloads may justify dedicated infrastructure.
Autoscaling lets runner capacity expand or contract with demand, but operating that infrastructure also takes platform engineering time. If you manage your own runner fleet, account for how quickly new resources become usable: instance provisioning, cloud quotas, image downloads, and cache availability can all affect queued duration during a spike. Keep enough ready capacity to absorb short-term demand while additional compute comes online.
You can also shift that operational work to GitLab. GitLab-hosted runners are available for GitLab.com and GitLab Dedicated, with GitLab managing the underlying runner infrastructure and autoscaling. For teams that want to reduce the time spent provisioning, patching, and scaling runner machines, that changes the runner strategy from an infrastructure-management decision to more of a capacity and workload-placement decision.
Executor choice determines where CI/CD jobs run and what infrastructure your team must operate. For cloud-native environments, the Kubernetes executor uses an existing Kubernetes cluster. For autoscaled workloads on public-cloud virtual machines, GitLab provides the Docker Autoscaler and Instance executors.
The right choice depends on the environment your jobs need and the infrastructure your team is prepared to manage. With the Kubernetes executor, each CI/CD job runs in its own pod, making cluster behavior part of runner performance. Scheduling delays, resource requests and limits, node capacity, and autoscaling can all affect how quickly jobs start and complete.
Account for those constraints in your capacity plan so the executor does not become a bottleneck as CI/CD demand grows.
Availability planning should begin with the business impact of downtime and data loss. Define:
Together, these targets define the redundancy and recovery capacity the architecture needs.
How much of that work falls to your platform team depends on the deployment model. If you use GitLab Self-Managed, your team owns those architecture decisions. Use the GitLab reference architectures as a production-ready starting point, then adapt the topology to your availability and recovery requirements.
With GitLab Dedicated, GitLab manages the underlying disaster recovery infrastructure and failover process. Customers can choose a secondary AWS region for geo-based disaster recovery, while GitLab maintains replication between the primary and secondary regions and manages failover when required.
For Self-Managed deployments, your recovery design should treat high availability, disaster recovery, and backups as distinct but complementary layers:
In addition, for Self-Managed deployments, turn your recovery targets into architecture requirements. Decide where redundancy is needed, how data will replicate, and how backups will protect critical data. Include dependencies such as identity and networking services in the recovery plan.
GitLab Geo provides an active-passive disaster recovery architecture with secondary sites that synchronize from the primary. For Self-Managed, failover requires customer-managed operational steps, so rehearse the process under realistic conditions and measure the results against your RTO and RPO.
Record any failed dependencies or manual steps that could slow recovery, then use those findings to strengthen the design.
As GitLab adoption grows, performance planning shifts from sizing for expected demand to validating the platform’s behavior under real-world load. The first step is to identify where time is being lost.
GitLab separates queued duration from execution duration, which gives you a useful starting point for diagnosis. A job’s queued duration shows how long it waited to start, while job duration captures execution time. Pipeline duration measures the time spent running the pipeline and excludes pending queue time.
Those metrics point to different constraints. A high queued duration may indicate insufficient runner capacity. Longer execution times, by contrast, can stem from pipeline design, test suites, dependency downloads, or repository transfers.
Test representative projects under both normal and peak demand to see where performance starts to degrade. Include conditions such as release windows, scheduled security scans, and periods of heavy commit activity, then track the signals that surface the bottleneck:
Break the results down by runner pool and workload type so organization-wide averages don’t hide bottlenecks affecting specific teams or workloads. From there, use what you learn to set thresholds for expanding runner capacity, optimizing pipelines, or scaling the GitLab application.
Large repositories and monorepos place distinct demands on GitLab and runner infrastructure. Frequent clones and fetches can increase CPU, memory, disk, and network usage, especially when many pipelines access the same repository simultaneously.
Look beyond repository size when estimating that impact. Clone frequency, concurrent CI/CD activity, branch patterns, and the amount of data each job transfers can all shape platform load.
Pipeline design can reduce demand on the platform itself. Run independent jobs in parallel, avoid unnecessary pipelines, cache frequently downloaded dependencies, and limit artifact retention. For monorepos, trigger jobs only when relevant paths change and reduce the amount of repository data each job needs to transfer.
Continue measuring after rollout as usage evolves. For Self-Managed environments, actual resource utilization and workload patterns provide the clearest signal for when the architecture needs to scale.
Kubernetes can play two different roles in a GitLab architecture. The Kubernetes executor can run CI/CD jobs as pods in an existing cluster, while GitLab Self-Managed can run in a cloud-native architecture on Kubernetes.
These choices affect different parts of the platform and should be evaluated separately. Using Kubernetes for runners changes how CI/CD compute is provisioned and scaled. Running GitLab on Kubernetes changes how your team operates the application and its supporting infrastructure.
With the Kubernetes executor, a runner manager calls the Kubernetes API and creates a pod for each CI/CD job. That makes the cluster itself part of your runner architecture.
Plan the Kubernetes resources and controls those jobs will rely on, including namespaces, service accounts, resource requests and limits, and workload isolation. Sensitive deployment jobs may also require stronger separation from less-trusted build workloads.
Capacity matters just as much as configuration. Test whether cluster autoscaling can add nodes quickly enough to meet your queued-duration targets. Even when the cluster eventually provides enough compute, slow node provisioning can leave jobs waiting during demand spikes.
Running GitLab itself on Kubernetes requires a broader architecture decision. GitLab recommends its Cloud Native reference architecture for new Self-Managed deployments. In this model, GitLab components run in Kubernetes, while PostgreSQL, Redis, and object storage remain external.
Cloud Native Hybrid remains an option when specific components need to stay outside Kubernetes. Teams that require a Gitaly Cluster for repository-level high availability, for example, should evaluate a hybrid or VM-based reference architecture because the standard Cloud Native architecture runs Gitaly in a non-clustered configuration.
Whichever model you choose, include Kubernetes in the operating plan for the wider GitLab platform. Your team will need observability across the cluster and external services, along with an upgrade process that accounts for GitLab and its infrastructure dependencies. Capacity and recovery testing should cover the cluster as part of the production environment.
The GitLab cloud-native overview provides more context on this deployment model. Choose Kubernetes when its operating model fits your infrastructure requirements, and your team has the skills to run it reliably.
Use this checklist before rollout to validate the major architecture decisions across deployment, sizing, runners, recovery, and performance. For each item, document the evidence that supports the decision or assign an owner to close the gap.
| What to validate | Evidence or owner |
|---|---|
| Deployment model | |
| The selected deployment model meets data residency, isolation, networking, and customization requirements. | |
| Responsibilities are clearly divided among GitLab, your platform team, and infrastructure providers. | |
| Upgrades, maintenance, support, and capacity management have named owners and documented procedures. | |
| Application sizing | |
| Expected RPS drives the baseline architecture size for Self-Managed deployments. | |
| Sizing reflects the mix of API, web, and Git traffic. | |
| The design accounts for atypical workloads such as large monorepos or heavy automation. | |
| Runner strategy | |
| Runner sizing reflects job volume, duration, peak concurrency, and compute requirements. | |
| Runner scopes match trust boundaries, privileged access, and workload-isolation requirements. | |
| Autoscaling limits, cloud quotas, startup time, and ready capacity have been tested under peak demand. | |
| Queued-duration and pipeline-duration targets are defined and monitored separately. | |
| Runner-manager architecture avoids a single point of failure for critical workloads. | |
| Availability and recovery | |
| Business and technical owners have approved SLO, RTO, and RPO targets. | |
| Redundancy, backups, replication, and failover procedures address required failure scenarios. | |
| Recovery tests include identity, DNS, secrets, networking, and external integrations. | |
| The latest recovery exercise met its objectives or has assigned remediation work. | |
| Performance and growth | |
| Representative projects, monorepos, security jobs, and release workloads have been tested under expected peak demand. | |
| Dashboards track queued duration, job and pipeline duration, errors, infrastructure saturation, and runner utilization. | |
| Scaling thresholds define when to add capacity or optimize workloads. | |
| The architecture has a defined review cadence for changing usage patterns and organizational requirements. |
Enterprise scale puts every early architecture decision under pressure. The strongest GitLab environments reflect how the organization actually operates and leave enough room for demand to change.
Those conditions will evolve as adoption expands. Keep measuring, revisit the architecture as demand shifts, and let evidence drive the next decision. That discipline turns GitLab from a platform that simply supports more users into one that can keep pace with the organization around it.
GitLab Duo Agent Platform orchestrates and automates complex tasks through agentic flows. A key part of the platform is the Flow Registry, a declarative configuration framework, built from reusable components, that compiles YAML into fully functional LangGraph flows. By using Flow Registry, agent builders — both our GitLab engineers and our customers can use declarative YAML configurations instead of repetitive, ad-hoc Python implementations. Flow Registry turns bespoke state management and agent wiring duplicated across agents into a set of reusable components and primitives available to agent builders.
Using Flow Registry has reduced our own code-per-agentic-flow by 45%. What is this translating to?
All of these benefits are available to customers orchestrating and building agents on GitLab Duo Agent Platform.
In this article, we share the architectural principles and lessons from this effort and how to apply them in your environment.
After releasing GitLab Duo Code Suggestions and Duo Chat, we dug into a then-novel technology, autonomous agents. We researched available AI frameworks and selected LangGraph, an agent runtime and low-level orchestration framework from LangChain, as the foundation for GitLab Duo Agent Platform.
LangGraph's rich feature set, which includes a broad range of model adapters, durable execution, and traceability, combined with an excellent level of engineering autonomy, brought all the necessary building blocks we looked for to start GitLab Duo Agent Platform development.
During the initial months, GitLab engineers, empowered by LangGraph, swiftly built the foundations of Duo Agent Platform, and before long the team shipped four agentic flows:
We also quickly realized a critical gap that low-level frameworks such as LangGraph do not address: a lack of structure to support consistent development at scale.
With just four flows present, and a small engineering team working on GitLab Duo Agent Platform at that time, the codebase was growing rapidly. Every flow was implemented as an ad-hoc directed graph, turning into a web of interconnected nodes and edges. The early Duo Agent Platform codebase had no reusability, no composability, and little in the way of shared standards. It became very difficult to develop new features, and any horizontal platform-wide change seemed like an impossible task.
With every flow taking at least 450 lines of ad-hoc Python code and looking like this example, the team's velocity slowed down as engineers struggled to introduce changes, overwhelmed by complexity and coupling.
The graph's complexity spilled into the test suite, as well. Each test case depended on an execution propagating through a whole graph, which changed tests from a quality assurance safety net into a boogeyman that nobody wanted to look at.
It became clear to us that graphs used as an atomic building block at this low abstraction level are not a good match for a platform implementation. To support the scale we envisioned, it was necessary to introduce smaller units, that break down the complexity and reduce cognitive load put on platform engineers maintaining the project.
Furthermore, graphs with low-level nodes managing model API calls or executing function calls produced by said models, were not the right abstraction for AI engineers either, as they are more accustomed to terms like agents and agent orchestration.
Looking for a way out of that maze, we decided to separate those two concerns — AI engineering from platform development — with the introduction of a new layer of abstraction. To do so, we reviewed existing graphs and identified and extracted repeated structures (for example, cycles going between large language model (LLM) calls and tool execution, implementing agent loops). The refactor brought some relief, as the most complex files had been broken down into smaller pieces that formed the new abstraction layer.
However, the platform was still far from a scalable state. The extracted graph pieces unfortunately operated with their own state structures, tightly coupled with the flow from which they originated. This prevented us from reusing extracted entities between different flows, and we were concerned that at that point every new flow would be more likely to create its own set of pieces, rather than be composed from ones that already existed. The system was neither collaborative nor efficient, and it was not sustainable in that state for a longer period of time.
That realization made it apparent — we had to put more effort in, continue to evolve the architecture, and provide clear development guidelines. At that time we already knew that Duo Agent Platform flows wouldn't be exclusively built by other product teams, but that a wider GitLab community would be invited to contribute as well.
Equipped with the past experience, and inspired by ambitious goals, my teammate Alexander Chueshev and I went back to the drawing board, and rethought the system. We set out to introduce a solution that is highly collaborative, composable, and optimized for AI development efficiency.
We wanted this new iteration to hide low-level LangGraph implementation details, and to stop bothering developers with nodes or edges. The system ought to speak their language — the language of AI engineering — with agents being a central component.
It was clear to us that AI development reasons in terms of agents, rather than nodes that invoke models, execute tools, etc. Drawing lessons from the past iteration, we decided to base the new framework on three pillars:
We were convinced that if we were able to design them well, the new framework would be flexible enough to support any AI flow that users might want to build.
Components are the central and most important pillar of Flow Registry. They model common primitives such as agents, human-in-the-loop checkpoints, and fixed-logic steps. This pillar lifts the abstraction level to match terminology used within the AI engineering domain. Thanks to components agent builders no longer need to reimplement those primitives from scratch, but can declare them with YAML snippets that look like this example:
- type: AgentComponent
name: "developer_agent"
prompt_id: "developer_agent_prompt"
inputs:
- from: "context:goal"
as: "goal"
- from: "context:project_id"
as: "project_id"
toolset:
- "read_file"
- "find_files"
- "edit_file"
- "run_command"
- "create_merge_request"
Under the hood, the AgentComponent is still a piece of a LangGraph’s graph, whose simplified structure is shown in the diagram below. However, now its implementation complexity is hidden from agent builders, who operate with a more familiar primitive. The same architectural boundaries also benefit framework maintainers, giving them more freedom to modify and extend the underlying implementation, with changes propagating to flows transparently.
flowchart LR
%% External input/output
input((inputs<br>from<br>shared state)) --> LLMCall
End --> output((outputs<br>to shared state))
%% Prompts
Prompt["You are expert<br>software<br>engineer ..."] --> LLMCall
subgraph Prompts
direction TB
style Prompts stroke-dasharray: 4 4, stroke:#3CB371
Prompt
end
%% LLM and internal component
LLMCall --> End
LLMCall --> RunTools
RunTools --> LLMCall
subgraph Component
direction LR
LLMCall[LLM Call]
RunTools[Run Tools]
End[END]
end
%% Tools
EditFile --> RunTools
ReadFile --> RunTools
subgraph Tools
direction LR
style Tools stroke-dasharray: 4 4, stroke:#1E90FF
EditFile[Edit file]
ReadFile[Read file]
end
The agent as a component, with the ability to delegate work to subagents, is already a powerful base delivered by Pillar 1 alone. Many contemporary agent platforms consider it a complete and sufficient offering. However, GitLab has larger ambitions for Duo Agent Platform, which Flow Registry realizes with the next two pillars.
Flow Registry Routers enable agent builders to orchestrate multiple specialized agents, or even agentic teams, into a flow to model highly complex business, or software development processes. Even though the largest contemporary models are powerful enough to drive complex assignments on their own, a recent rise in popularity of subagent architecture shows that there are many benefits of assembling multiple agents to collaborate over a single task.
To demonstrate a practical example, let’s take a look at GitLab’s foundational flow: Fix pipeline. This flow is configured with an automated trigger to triage, and fix failing CI pipelines. Because CI pipelines can be very complex, not every failure requires any code change to be resolved, for example sometimes a dependency service might be not responsive, and a plain retry is enough to fix a failure. To acknowledge that dual approach, the flow branches early based on an agent that acts as a judge’s decision. The judge agent's ruling on whether a failure is actionable is then used by Flow Registry Routers to navigate flow execution into the correct branch.
routers:
- from: "fix_pipeline_context"
condition:
input: "context:fix_pipeline_context.final_answer.decision"
routes:
"add_comment": "fix_pipeline_add_comment"
"create_plan": "fix_pipeline_checkout_existing_branch"
"direct_code_suggestions": "fix_pipeline_code_suggestions"
"no_action": "end"
"default_route": "end"
It is true that state-of-the-art models should be able to make similar decisions and act on them simultaneously. However, thanks to multi-agent architecture, agent builders can capitalize on the following benefits:
Pillar 2 gives agent builders a choice: Use a simple flow architecture with powerful models, or offload complexity from models prompts into explicit flow structure — catering to a broad range of possible use cases, cost targets, and risk profiles.
The third and final pillar of Flow Registry is a shared state structure that acts as a communication protocol between components. Without it, the previous two pillars could not function, because components would lack a reliable way to communicate. Referring back to the Fix pipeline example: The judge agent's ruling would be of little value if it could not be reliably forwarded to a Flow Registry Router. More broadly, data produced by one agent is often required by subsequent ones, making a well-defined communication contract essential.
Flow Registry state structure includes a special catchall attribute called context, which behaves like a nested key-value store (or a JSON object) granting components a versatile storage space. To further complement context attribute flexibility, Flow Registry introduced a dot-notation declarative access to context, a convention familiar from other domains (such as GitLab CI Functions), where access to shared key-value storage must be expressed within static configurations.
To complete the third pillar, convention is required: Flow Registry supports flexible read operations from shared state via said dot-notation, however all writes follow strict rules, providing a set of stable, predictable outputs on which agent builders can rely. To see that in practice, let’s take a look again at a piece of Flow Registry config for another foundational flow: Code review.
# …
# Step 5: Fetch lightweight MR metadata (file paths + custom instructions)
- name: "fetch_mr_metadata"
type: DeterministicStepComponent
tool_name: "build_review_merge_request_context"
# ….
# Step 6: Transform prescan results into structured JSON for review consumption
- name: "analyze_prescan_results"
type: AgentComponent
prompt_id: "analyze_prescan_codebase_results"
prompt_version: "^1.0.0"
inputs:
- from: "context:fetch_mr_metadata.tool_responses"
as: "mr_context"
- from: "context:prescan_codebase.tool_responses"
as: "prescan_tool_responses"
optional: True
toolset: []
ui_log_events:
- "on_agent_final_answer"
# …..
routers:
# ….
- from: "fetch_mr_metadata"
to: "analyze_prescan_results"
Code Review’s agent analyze_prescan_results requires data pulled by a preceding fixed step action fetch_mr_metadata, that dependency is expressed via inputs declared for analyze_prescan_results agent
inputs:
- from: "context:fetch_mr_metadata.tool_responses"
as: "mr_context"
Here, dot-notation and strict output conventions work in tandem, giving agent builders a stable and predictable protocol for moving data between components within a flow.
Even though Flow Registry uses declarative YAML configurations, we started the design and rearchitecture in Python, and deferred any declarative configuration API to future iterations. However, once all three Flow Registry pillars came together within a single Python block, it became obvious to us that converting those declarations into a YAML config was just a step away, so we took it.
That change completely decoupled the Flow Registry framework from Python and LangGraph, offering a high-level abstraction syntax for declarative AI flow creation. It established a clean boundary between the platform still implemented on LangGraph foundations, and the external framework's declarative interface, which enabled AI engineers to operate with concepts more familiar to them.
Introduction of Flow Registry framework as a basis for GitLab Duo Agent Platform propelled the whole system from vanilla LangGraph per-use-case implementations into declarative YAML configs like this one behind GitLab Duo Developer Flow, which is currently operating in production.
With the platform decoupled from the framework API designed for AI engineers, the underlying Python codebase becomes shareable across all flows, and any improvements introduced to the engine itself are brought to all flows, further emphasizing the efficiency gains from the clear separation.
In addition, the per-flow code cost drops with every new flow added. At the time of writing, the ratio of Python source code per flow has been reduced by 45% in favor of Flow Registry — and it will keep improving with every new flow being built.
Finally, AI engineers and domain experts are no longer required to understand any of the underlying platform implementation details, nor do they need to implement any repetitive boilerplate Python code that would require its own test suite and maintenance. This was proven by almost 7,000 developers who signed up for the GitLab AI Hackathon earlier this year and submitted 600+ agents and flows.
A key observation we made is that modern AI engineering is still a very young branch of software development, in which common architectural patterns and paradigms haven’t fully been formed yet. However, it does not mean that already established good software engineering practices can’t be applied to AI engineering. In fact, as Flow Registry's story shows, reaching back to existing software engineering paradigms and practices, such as identifying repeated code, extracting it into named entities with clear roles within a system, and forming abstraction layers from them, can yield powerful results.
Beyond that, we would also like to share a few other takeaways that apply broadly to any team building agentic systems, while others are practical starting points for teams working with low-level frameworks like LangGraph.
To get a feel for how it all works in practice, visit the AI Catalog where GitLab exposes the resulting Flow Registry orchestration framework for anyone to build custom flows.
You can also try GitLab Duo Agent Platform for free.
At the end of August, we announced our first Maintainers in Residence, Rust Project contributors who are funded for their upstream contributions and maintenance work from the Rust Foundation Maintainers Fund (RFMF). Since then, the Rust Leadership Council has dedicated more funds from its Project Priorities budget to RFMF, and together with AWS also providing additional funds, this allowed us to open a new full-time Maintainer in Residence (MiR) position to support the Cargo team. We would like to thank the Rust Leadership Council, AWS, and also the Rust Foundation for providing us with this opportunity! If you would like to help us hire more maintainers to improve Rust, consider donating to RFMF.
This post explains why we chose to support the Cargo team specifically, and introduces Scott Schafer, the new Cargo Maintainer in Residence.
The new MiR full-time position is dedicated to helping with the maintenance of Cargo, our build system and package manager. The Cargo project is deeply involved in many new Rust features, improvements, and Project Goals. Combined with its cross-cutting nature, where it has to support many different use-cases and integrate with several other tools, it takes a lot of work just to keep up with its maintenance needs, let alone support so many feature requests and proposed changes.
Because of that, the Cargo team has sometimes struggled with meeting its maintenance demands. You might remember that for several years, it actually held a feature freeze, to reduce Cargo's internal tech debt, perform necessary refactorings, go through the issue and pull request backlog, and come up with scalable internal development and design processes, so that they could eventually go back to even thinking about adding new features.
Recently, some changes occurred within the team, which made it more difficult for them to meet their maintenance baseline. Some members of the team left, while others lost their dedicated funding for working on Cargo maintenance and had to scale down their involvement. The Funding team thus considered it very important to support this team, given that we had an opportunity to do so. And thus we decided to hire a full-time maintainer to work on Cargo for (at least) the next 12 months.
Even though we know that a single full-time maintainer will not completely solve the maintenance struggles of the Cargo team, we hope that it will improve the situation, and provide a bit of a relief for the team.
We are very happy to welcome Scott Schafer (@muscraft) into the Maintainer in Residence role! Scott has joined the Cargo team three years ago, and apart from working on Cargo, he is also the lead of the Rust Docker team, which prepares official Docker images for every Rust version.
Apart from working on general maintenance of Cargo, Scott has implemented Cargo's Workspace inheritance feature, and has also spearheaded a complex multi-year effort to switch the rendering of diagnostics in the Rust compiler to use the annotate-snippets crate. This effort has been completed in the Rust 1.93.0 release. Thanks to it, the same diagnostics interface can now be shared between the compiler and Cargo (and also other tools), which amongst other things unblocked further development of the Cargo linting system, which has now been stabilized and will ship in the Rust 1.100.0 release.
Everyone we talked about was very excited about Scott becoming a Cargo Maintainer in Residence, and we share that feeling. We wish Scott all the best in his new role, and we are very happy that we can support his maintenance work.
Here is what Scott thinks about it:
I am incredibly excited to work on Cargo full-time! There have been so many things that I wish I could've worked on over the years, that I will now be able to get to. I hope that my efforts will bring Cargo into a more maintainable state.
We are incredibly happy that we keep getting more funds for the Rust Foundation Maintainers Fund, which allows us to support Rust Project contributors. The funding team will be working with the supported maintainers, and also the funders, to ensure that they are all happy with the arrangement, so that we can secure stable funding for Rust maintenance for years to come.
If you would like to help us support more Rust maintainers, consider donating to RFMF!
Enterprise owners can now export a complete inventory of every credential that can access their enterprise (e.g., SSH keys, classic and fine-grained personal access tokens, OAuth App access tokens, and GitHub App user-to-server and installation tokens).
This enables enterprise owners with a single plane view into all the credentials owned by members and apps across their enterprise. During a security incident, this enables their security teams to quickly assess the risk surface area and plan remediation and communications based on the identified set of compromised tokens.
With this release, enterprise owners and members with the fine-grained permission View enterprise credentials can:
If you’re an enterprise owner, you can find the “Export CSV” setting in your enterprise Settings->Authentication Security ->Credentials, next to the “Overview” section. Alternatively you could programmatically call the new REST API endpoints to build your own reporting and automation.
This release is available now for GitHub Enterprise Cloud and will be supported in upcoming releases of GitHub Enterprise Server.
To learn more, see our documentation on responding to security incidents in your enterprise by reviewing credentials in your enterprise and the new Rest API to export credential inventory.
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Kubernetes v1.37 promotes the PersistentVolumeClaimUnusedSinceTime feature gate to Beta (enabled by
default). With this feature, the PersistentVolumeClaim (PVC) protection controller adds an Unused
condition to each PVC, telling you whether any running pod currently references it — no custom
tooling or cross-referencing required.
For the API definition of PVC conditions, see the
PersistentVolumeClaim API reference.
Read on to learn how the Unused condition works and how to use it.
In large-scale Kubernetes clusters, it is common for users to create PVCs and then delete the associated pods without cleaning up the storage, because Kubernetes does not automatically delete PVCs when their pods are removed (to protect against accidental data loss). Over time, these orphaned PVCs may accumulate, silently consuming storage capacity and driving up cloud costs.
Before Kubernetes v1.37, it was easy to identify an unused PersistentVolume, but much harder to determine whether a PVC was still being used. Doing so required cross-referencing pods, PersistentVolumes, and PVCs over a potentially large window of time. Administrators often resorted to custom monitoring pipelines or scripts to answer a seemingly simple question: "Is anything actually using this volume?"
The PersistentVolumeClaimUnusedSinceTime feature solves this by making the answer available
natively in the PVC status. Once the feature is enabled, every PVC gets an Unused condition managed
by the PVC protection controller.
Unused condition set to True so I
can automate cleanup in development environments."The PVC protection controller — which already watches pods to enforce the
storage object in use protection
— now also manages a new Unused condition on PVCs.
The condition works as follows:
| Scenario | Condition status | Reason |
|---|---|---|
| No non-terminal pods reference the PVC | Unused=True | NoPodsUsingPVC |
| At least one running or pending pod references the PVC | Unused=False | PodUsingPVC |
A few details worth noting:
Succeeded or Failed) does not
keep the PVC marked as in use. This means batch jobs with restartPolicy: Never won't prevent
the PVC from becoming Unused=True after they finish.Unused=True only after the *last" non-terminated pod is removed or terminates.lastTransitionTime to find when a PVC became idleLike every Kubernetes condition, the Unused condition carries a standard lastTransitionTime
field. This means you get a useful bonus for free: when the condition transitions from False to
True, the lastTransitionTime records exactly when the PVC became idle. You can use this
timestamp to answer questions like "how long has this PVC been sitting unused?" — for example,
to find PVCs that have been idle for more than 30 days (see the
example query below).
Kubernetes v1.36 introduced this feature as Alpha, where you had to enable the
PersistentVolumeClaimUnusedSinceTime feature gate explicitly. For Beta in v1.37, the feature gate
is enabled by default, and the feature has full end-to-end test coverage.
Since the feature is Beta and enabled by default in Kubernetes v1.37, the Unused condition will
appear on PVCs automatically. Here is a walkthrough to see it in action:
Create a PVC:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: my-data
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi
After a short time, inspect the PVC conditions:
kubectl get pvc my-data -o jsonpath='{.status.conditions[*]}' | jq .
You should see an Unused condition with status True and reason NoPodsUsingPVC:
{
"lastProbeTime": null,
"lastTransitionTime": "2026-09-14T12:03:11Z",
"message": "No pods are currently referencing this PVC",
"reason": "NoPodsUsingPVC",
"status": "True",
"type": "Unused"
}
Create a pod that uses the PVC:
apiVersion: v1
kind: Pod
metadata:
name: my-app
spec:
containers:
- name: app
image: busybox
command: ["sleep", "3600"]
volumeMounts:
- name: data
mountPath: /data
volumes:
- name: data
persistentVolumeClaim:
claimName: my-data
Check the condition again — it should now show Unused=False:
kubectl get pvc my-data -o jsonpath='{.status.conditions[?(@.type=="Unused")].status}'
Output:
False
Delete the pod and wait for the condition to transition back to Unused=True:
kubectl delete pod my-app
kubectl get pvc my-data -o jsonpath='{.status.conditions[?(@.type=="Unused")]}'
The condition should show Unused=True with reason NoPodsUsingPVC again.
To list all PVCs that have been unused for more than 30 days, you can use a command like:
This command uses jq, a command-line JSON processor.
kubectl get pvc -A -o json | jq -r '
.items[]
| select(.status.conditions[]? | select(.type=="Unused" and .status=="True"))
| select(
(.status.conditions[] | select(.type=="Unused") | .lastTransitionTime) as $t
| (now - ($t | fromdateiso8601)) > (30 * 86400)
)
| "\(.metadata.namespace)/\(.metadata.name) unused since \(.status.conditions[] | select(.type=="Unused") | .lastTransitionTime)"
'
Depending on feedback and adoption, the Kubernetes project intends to graduate this feature to General Availability (GA) in a future release. If you have feedback on this feature, please open an issue in the kubernetes/kubernetes repository.
To learn more about this enhancement, refer to KEP-5541: PersistentVolumeClaim last used time.
The Kubernetes project always welcomes new contributors. If you would like to get involved, you can join us at SIG Storage.
If you would like to share feedback, you can do so on our public Slack channel (visit https://slack.k8s.io/ for an invitation if you need one).
Special thanks to the contributors who helped design and implement this feature (alphabetical order):
Every AI factory needs power and cooling that fit its computing architecture. As AI infrastructure expands, power, cooling, water, site and grid constraints are shaping what builders can deploy. Choosing products that fit the complete factory design helps builders turn computing capacity into useful AI output.
To help builders make those decisions, NVIDIA is introducing NVIDIA DSX Ready, a qualification program for partner products and solutions that meet applicable NVIDIA DSX AI factory reference design requirements.
The program launches with two initial categories: battery energy storage systems (BESS) and cooling distribution units (CDUs). Category-specific requirements and review through the program help builders evaluate offerings with greater confidence, reduce integration risk and move toward deployment.
The NVIDIA DSX AI factory platform unifies AI factory design and operations across compute, networking, power, cooling, facilities and software. It helps partners design and operate the factory as one system to produce more useful AI output within available power, cooling, water and grid constraints.
That system view matters now because optimizing one part of an AI factory can shift the bottleneck elsewhere. Power and cooling suppliers need a clear path from reference designs to qualified offerings, and builders need a clearer way to discover and evaluate those offerings.
DSX Ready connects that selection process to applicable NVIDIA DSX requirements. It gives builders a clear qualification to look for and gives partners a defined way to demonstrate that a specific offering meets the requirements for its category.
At launch, DSX Ready includes qualified BESS solutions Hitachi Energy, LG Energy Solution and Tesla, and qualified CDU solutions from LG Electronics, LiquidStack and Vertiv. These initial categories bring power systems and liquid-cooling infrastructure into a common program, with requirements tailored to each technology. Additional categories across infrastructure and software will be rolled out over time.
For BESS providers, partners run the required qualification tests and submit supporting data for NVIDIA review and approval within a defined qualification boundary. Passing qualification does not replace site-level engineering or imply site-level stability.
For CDU providers, the path uses the CDU self-qualification suite to determine whether a specific offering meets applicable NVIDIA functional requirements.
Teams can then focus on how qualified offerings fit their site, configuration and operating needs. A qualified CDU may meet the relevant cooling criteria, for example, while the builder still evaluates how it will fit the planned facility.
The value of a reference design grows when builders can connect it to specific products and informed engineering decisions. DSX Ready makes that connection, bringing partner innovation into the infrastructure choices behind NVIDIA DSX AI factories.
Explore NVIDIA DSX Ready qualification categories and learn how to participate. Connect with the right NVIDIA team to begin qualification for a product or solution.
Monoclonal antibodies are one of the workhorses of biopharmaceutical development, with over 100 FDA-approved drugs and well-established manufacturing, regulatory, and clinical-development pathways. Yet conventional antibody discovery remains hampered by mounting costs and long timelines, typically six to twelve months to get from a target to a lead candidate. By designing and characterizing therapeutic antibodies computationally, AI promises to make development cheaper, faster, and more flexible. But scientific questions abound. Development of an antibody-based drug hinges on three factors: the best binding site on the target, which candidates bind to it most tightly, and whether any of them can survive manufacturing and the clinic. For each, the field has predictive models that do well on familiar targets and assays but considerably worse on unfamiliar ones. Benchmarks built around in-distribution accuracy have made that gap difficult to measure — and to close. Three papers from our science team at Amazon Bio Discovery, an AI-powered application that gives scientists access to biological AI models and integrated lab services to design and test novel drug candidates, tackle research questions about each of these three factors. Two are peer-reviewed journal papers on prediction: ranking candidates by binding strength and flexibly predicting developability. The third brings prediction into an end-to-end design process, navigates the selection of binding sites with an agent, and delivers experimentally validated antibody hits against a novel cancer target. Ranking binders from sequence alone One of the biggest questions in antibody design is which candidates bind the best. In "A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation", published in iScience, we propose a new framework to assess binding affinity predictors and train a new sequence-based predictor, MochiBind. Most affinity predictors are evaluated on their ability to predict the absolute binding affinity, on antigens that appear in their training data, against test sets that contain few or no nonbinders. Each of these characteristics makes the evaluation easier than the intended application. Absolute affinity values are not comparable across assays, and performance degrades for antigens the model has not seen. The practical use case, meanwhile, involves ranking a pool of thousands of candidates, most of which don’t bind to the target at all, to pick the ones worth testing in the lab. Surveying seven prior studies, we found that none satisfied all the conditions necessary to train a reliable universal predictor. We therefore reframed the task. Rather than predicting an absolute number, MochiBind predicts which of two antibodies against the same antigen binds more tightly. We begin by using a pretrained protein language model (ESM-2) to embed residues of antibody-antigen complexes in a representational space. We then compute the mean of each complex’s residue embeddings, to give it a single embedding. A specially trained network layer projects these embeddings into a lower-dimensional space, and predicts relative binding strength from the difference between the two projections.[HL2] Pairwise comparisons are then aggregated into a global ranking over the candidate pool using TrueSkill, a Bayesian rating algorithm originally developed for ranking video game players based on match outcomes. No structural input is required at any stage. This formulation has two practical advantages: relative orderings are more consistent across assays than absolute values, so the training signal is less sensitive to measurement noise, and the output is the ranked list the discovery process needs. Our paper also presents a novel evaluation framework. We used the AlphaBind dataset, which covers four antigen systems (targeting TIGIT, PD-1, HER2, and theSARS-CoV-1 RBD) with roughly 30,000 experimentally characterized variants for each and pairwise sequence similarity between antigens that’s close to zero. The protocol is strictly cross-antigen: train on two antigens, validate on a third, and test on the fourth, rotating so that each serves as the held-out system once. We then standardized two metrics: (1) pairwise accuracy and (2) retrieval accuracy and precision at top K, which measure how many of a model's K recommendations are experimentally confirmed strong binders. MochiBind achieved higher pairwise accuracy than every structure-based baseline on all four held-out antigens, outperforming the closest competitor by almost 10% on average. In terms of ranking performance, MochiBind also achieved the highest retrieval accuracy on all four antigens and the highest retrieval precision (lowest false-positive rate) on three out of four. It also scored 200,000 antibody pairs in roughly 13 seconds on a CPU, a more than 100-fold inference speedup over competing methods that should enable the screening of very large design libraries. Learning to predict antibody properties in context Proteins that bind tightly to their targets but clump together or degrade in the bloodstream or provoke an immune response are not effective or safe as drugs. Most attempts to predict such properties from biological data encounter the same problem: batch effects, or systematic differences in the way different labs handle samples or conduct experiments that lead to predictable deviations in measurement — deviations known as batch offsets. A model fine-tuned on one lab's data quietly inherits its offsets. In "Context-aware multi-property antibody predictor: A novel framework integrating text and protein language models", in npj Systems Biology and Applications, we address batch effects during inference. Our model — the context-aware multiproperty antibody predictor, or CA-MAP — takes a prompt containing a variable number of example antibodies with their measured properties, followed by a query antibody and the name of the property to predict. When the examples come from the same lab as the query, their measured properties capture the batch offset. The model’s input — its context — thus includes the information it needs to adjust for batch effects without retraining. Getting a model to use that context, however, is not straightforward. A model trained on data from a single source can learn to ignore the examples — whose measurements are systematically skewed, after all — and rely on the query sequence alone. Our training strategy, AB-context-aware, prevents this by applying a hidden random transformation to both the context properties and the expected answer, resampled for every prompt. Under this scheme, the transformation can be recovered only from the context, so the model must use it. We measured the effect on a fine-tuned domain-specific multimodal LLM, TxGemma, predicting hydrophobicity. Without batch effects, standard fine-tuning and AB-context-aware training perform comparably, a correlation with ground truth of 0.99 (according to Spearman’s rank correlation coefficient, where 1 is perfect correlation). With a simulated additive batch effect in the 0–0.3 range, standard fine tuning falls to a 0.58 correlation, while the context-aware model remains at 0.99. CA-MAP has a relatively small multimodal architecture combining text and proteins. Sequences (encoded with ESM-2), property names (encoded with sentence embeddings), and numerical values each have dedicated encoders and projectors, and a state space model based on the sequence-modeling architecture MAMBA composes them. Trained on a synthetic dataset of 876,898 antibody-heavy chains covering six developability properties, CA-MAP achieves a Spearman correlation (denoted ρ) greater than 0.8 on several of them and outperforms the fine-tuned TxGemma baseline across all four properties tested jointly. The architecture is also considerably cheaper to train and run, with roughly 182,000 trainable parameters to TxGemma’s 40 million, and it’s about 200 times as fast per prompt at inference. Because properties are specified as text, CA-MAP can also be queried for properties absent from its training data. In one set of experiments, we trained CA-MAP on only four of the dataset’s six developability properties and tested it on the other two (positive-charge heterogeneity, or PosCh, and immunogenicity). When we used only the two target properties as context, immunogenicity prediction reached ρ = 0.25; with all six correlated properties in the context, ρ = 0.73. PosCh improved from ρ = 0.08 to ρ = 0.73 under the same comparison. These gains indicate that the model is drawing on correlations between developability properties, which suggests that expensive assays could be estimated in part from cheaper ones. Designing antibodies with AI, validating them in the lab In our third paper, "Agent-guided de novo design of nanobody binders against a novel cancer target", which was presented as a Spotlight at the ICML 2026 Workshop on Generative and Agentic AI for Biology and received the Best Paper Runner-Up Award, we bring predictive and generative antibody models together to design therapeutic nanobodies from scratch in a real drug discovery project. The target antigen for the design project — or “campaign”, as it’s known in the industry — was chosen to reflect real clinical need: a cell surface target for desmoplastic small round-cell tumors, a rare and aggressive pediatric cancer. Our collaborators at the Dr. Nai-Kong V. Cheung’s Lab at Memorial Sloan Kettering Cancer Center in New York identified it by sequencing patient tumor specimens for proteins that (1) sit on the tumor cell surface, (2) are driven by a specific genetic error, and (3) are largely absent from healthy tissue. The target has no experimental structure and no public antibody information, so there was no template to graft, no prior campaign to affinity-mature from, and no possibility that the design models encountered this antigen during training. One of the key decisions at the outset of a de novo design campaign is which specific regions on the antigen surface, known as epitopes or hotspots, to target. We designed a hotspot recommendation agent that orchestrates seven bioinformatics tools, which do things like determine solvent-accessible surface area, secondary structure, hydrophobicity, and sequence uniqueness against user-specified negative targets; match epitopes against 500,000 entries in NIAID’s Immune Epitope Database; and annotate domains according to the categories in the protein families (Pfam) database. Our model synthesizes these tools’ outputs into hotspot recommendations with an explicit biophysical rationale for each. Grounding the recommendations in deterministic tool outputs focuses the search on evidence-supported regions rather than relying on the model's parametric knowledge of protein biology. Evaluated on antibody-antigen complexes from the SAbDab benchmark, the agent recovered at least one true epitope residue within its top five proposed regions about 80% of the time on a diverse holdout set. For the target antigen in our design campaign, it proposed eight hotspot regions. We then used three generative models with different design principles — RFantibody (diffusion over protein backbones), IgGM (joint sequence-structure diffusion), and mBER (backpropagation through a structure prediction model) — to generate antibody designs that target those hotspots. Each model produced 96,000 designs, and each design was scored on properties like folding confidence (how likely the antibody is to fold into the shape necessary to bind to the target), complex quality (how likely the antibody is to form the correct binding interface with the target), and sequence liabilities (how likely the antibody sequence is to cause development or manufacturing problems), and MochiBind's sequence-based affinity estimate. Our candidate selection agent applied multi-objective Pareto filtering to ensure the retention of designs excelling on different metric combinations, and it prioritized 100,000 candidates for experimental screening. Each candidate was synthesized and displayed on the surface of a yeast cell to be screened for whether it stuck to the target, and the designs that stuck most strongly were carried forward through two rounds of sorting and filtering. None of the 116 candidates that survived these rounds bound to an unrelated control protein, indicating that they bind specifically to the intended target, rather than being generally sticky. All 116 were then individually measured to determine how tightly they bind to the target antigen, and 46 were identified as strong binders. These 46 binders, along with the binder and nonbinder labels from the full screen, become training data for the next design cycle: a lab-in-the-loop workflow where each round of experiments sharpens the models that propose the following round. Amazon is uniquely well positioned to run that loop , with the scientific expertise to build foundational ML for biology, the computational capacity to design and score hundreds of thousands of candidates, and a path to deliver these methods, including those like MochiBind and CA-MAP that aren’t available today, to customers through Amazon Bio Discovery, an AI-powered application that connects these biological AI models with integrated lab services so scientists can move from design to experimental validation in a single workflow.Jev is now available as a judge for evaluations in LangSmith. Jev gives teams a fast, low-cost way to evaluate open-ended agent behavior and turn the results into structured feedback they can track in LangSmith.
Below, we explain why a System One model like Jev is useful for agent evals, share what we found when we tested it, and walk through setting up a Jev-as-a-judge evaluator for online evals.
Try Jev-as-a-judge in LangSmith today by visiting the Evaluators tab in any tracing project.
Back in 2023 when we first started building agents (which we mostly called LLM apps at the time), the primary approach to evals was code-based. Later that year, researchers introduced LLM-as-a-judge, and since then, code-based and LLM-as-a-judge have been the two main ways to evaluate agents.
Code-based evaluators check for specific, deterministic conditions: Did the agent call a tool? Does the output match a pattern? Is a field present? That's fast and reliable, but it only covers the narrow slice of agent behavior you can fully specify before the agent runs. Since agents are non-deterministic, an agent that solves the same problem three different valid ways will fail a code-based check that only expects one of them.
LLM-as-a-judge evaluators fill that gap. You give an LLM judge an agent trace, along with instructions and a rubric on how to grade it, and it reasons through the trace in free text before returning a verdict. However, that flexibility comes at a cost. LLM-as-a-judge evaluators are slower and more expensive to run than a function call, and because they're non-deterministic, the same input can produce a different verdict from one run to the next. On top of that, the step that turns free text into a structured output is itself a source of error, independent of whether the judge's reasoning was correct.
Now, System One models like Jev introduce a third type of agent eval, one that trades some of code-based evaluation's speed for the flexibility to evaluate open-ended agent behavior, at a fraction of the cost of an LLM judge.
Jev isn't a traditional LLM and doesn't generate text. The TypeSafe AI team calls it a System One model:
📖 System One models are a class of AI models built to make fast, structured decisions that software can use directly. A System One model evaluates a state and returns typed answers and probabilities.
For evals, the state can be an agent trace, a single message, or any other context you want evaluated. Questions define the criteria you want to evaluate the state against, like whether a response leaked PII, what the user's intent was, or how frustrated the user seemed. Jev can answer three types of questions: (1) a noul returns a yes/no probability, (2) a choice picks one option from a set, and (3) a score rates the state on an ordered scale. Each answer comes back typed, instead of a block of generated text that gets converted into structured output.
The three question types Jev can answer, using the feedback keys from this post: PII leakage (noul), user intent (choice), and user frustration (score).
Three things about Jev map directly onto pain points in agent evals. According to TypeSafe AI, Jev is up to ~450x cheaper and ~200x faster than comparable LLMs on classification tasks, and it can evaluate multiple questions about the same state in parallel.
Cost is a common reason teams evaluate their agents less than they would like to. Every eval carries a trade-off: score more agent runs, evaluate more criteria, or test more changes, and the cost of testing grows proportionally. With multiple agents and high-volume usage, an LLM judge that costs a few cents per eval gets expensive fast across production traffic, large datasets, and regression tests against every model or prompt change. Teams end up running fewer evals to manage costs, which slows down the feedback loop that building great agents depends on. At a fraction of that cost, a Jev judge can remove that trade-off.
With Jev-as-a-judge, you can score every trace instead of a sample of them, check more criteria per trace, and run the same judgment repeatedly to see how consistent the judge is. The cheaper the judge, the more of your agent's behavior you can afford to evaluate, and the tighter that agent improvement loop becomes.
Speed matters a great deal for online evals, where a judge is scoring live traffic. Being up to ~200x faster than an LLM judge, a Jev judge is better at keeping pace with traffic as it arrives. That matters most for feedback keys that flag security or safety risks, like PII leakage, prompt injection, or toxicity, where you can set an alert on the feedback key that triggers a webhook to automate a response. The faster the judge, the smaller the window between something going wrong and something being done about it.
Parallelization changes how many criteria you can evaluate against a single agent trace. Jev evaluates every question in a request together, so scoring a trace against multiple feedback keys, such as PII leakage, user intent, and user frustration, costs only marginally more than scoring it against one.
💡 System One models evaluate every question in a request in parallel. Adding questions barely changes the response time and costs only the tokens for the extra questions, which are cheap.
An LLM judge, by contrast, either needs a separate call per criterion or has to reason through all of them sequentially in one prompt with output tokens scaling with the number of criteria.
System One models map well onto these pain points, but none of this makes LLM judges obsolete. Fine-tuned and open models can be effective judges at much lower cost than a frontier model, and for open-ended criteria where you want written reasoning alongside a verdict, an LLM judge is still the better tool. Jev is a good fit when the decision you need is narrow and typed and you are making it at volume.
We put Jev to the test in Jev-as-a-Judge for Agent Evals, comparing it against GPT-5.6 Luna, GPT-5.6 Terra, and Claude Sonnet 4.6 on accuracy, consistency, speed, and cost. Jev was more accurate, dramatically more consistent, and both faster and cheaper than the LLM judges.
Jev matched a human reviewer on every decision, with 92-913x lower variance than the LLM judges. It averaged 0.44 seconds per call, compared to 2.16-2.83 seconds for the LLM judges. At $0.00035 per call, running the full set of judgments cost $0.34 with Jev, versus $0.39 with GPT-5.6 Luna, $2.90 with GPT-5.6 Terra, and $28.17 with Claude Sonnet 4.6.
This was one test on one agent, but the results are a promising early sign that Jev-as-a-judge is a viable third type of agent eval, alongside code-based and LLM-as-a-judge.
TypeSafe is now a model provider in LangSmith, with Jev available as a model. Setting up a Jev-as-a-judge evaluator follows the same path as an LLM-as-a-judge evaluator. The key difference is that a Jev-as-a-judge evaluator defines a state and a set of typed questions instead of a prompt and evaluation criteria.
Jev-as-a-judge is available in LangSmith today.
Sign in or sign up for LangSmith, then open the Evaluators tab in any tracing project, add an LLM-as-a-Judge evaluator, and select TypeSafe as the provider to try it out. For more details on online evals, including filters and advanced options, see the online evaluators guide.
If you try Jev as a judge on your agents, we want to hear how it holds up, especially against the LLM judges you use today. Share what you find on the forum or tag us on X.
To see how Jev fits into the agent loop beyond evals, including model routing and tool-risk gating, read Building a harness with Jev.
If you want to learn more about building agents with Jev, we're hosting a livestream with the TypeSafe AI team on Tuesday, Sep 22nd: https://events.langchain.com/webinar/building-a-harness-with-jev/
Vercel Connect now includes a managed connector for Microsoft Teams. Creating one gives your organization a Teams bot that your apps and agents run. People can @mention it in channels or message it directly, and your code receives the message and replies as the bot.
As a Vercel Managed Connector, Vercel registers the Entra app and Azure Bot resource in your tenant, so there's no client secret to store. A tenant administrator with an Azure subscription completes setup once. Incoming Teams activities are verified and forwarded to your project as Connect trigger.
Create a connector from the dashboard or Vercel CLI:
Once the connector is set up, your code requests a token only when it needs one. Use it with the @vercel/connect SDK, eve channel or Chat SDK adapter:
Each token is scoped to what you request and refreshed automatically, so there's nothing to rotate by hand. Connectors only work in the environments you attach them to, and you can revoke access at any time with vc connect revoke-tokens.
Read the Vercel Connect documentation, view details about the Teams connector in the Vercel Connect catalog, or create a Teams connector to get started.
Living in the Netherlands, I spend a fair amount of time on trains, and that is usually where I catch up on what the builder community is writing. Until now, that meant opening a laptop or squinting at a browser tab on my phone. This week I found myself scrolling through trending articles and checking a workshop from the AWS Builder Center mobile app while waiting for a delayed train, and it made those spare twenty minutes very useful. That is why I am glad to open this week with the Builder Center mobile app.
AWS Builder Center is now available as a mobile app on iOS and Android, extending the experience beyond desktop and web. Using your AWS Builder ID, you stay signed in across sessions and can browse trending articles, access 600+ AWS Skill Builder courses, and manage hands-on workshops with free sandbox environments from your mobile device. You can follow AWS Heroes, Community Builders, and User Group Leaders, check Builder Loft event calendars on the go, and receive push notifications for subscribed topics and communities. The app also supports the Wishlist feature for submitting product feedback directly to AWS teams. It is available worldwide on the Apple App Store and Google Play Store.
Builder Center also added two features this week. Polls give you a way to ask the community a question from the Home feed: write a question, add 2 to 5 answer options, set a deadline, and people vote, with results updating live and discussion happening in the comments. Votes are anonymous, and creators see aggregate counts and percentages only. Separately, the Zero to Shipped hackathon is open from September 18 to October 2. You connect your coding agent to AWS, build a real application, and ship it live on AWS for a chance to win a share of a $28,000 prize pool. Five winning projects each receive $5,000 in AWS credits and an AWS Builder swag bundle.
Last week’s launches
Here is what else happened this week.
For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.
Other AWS news
Here are some additional posts you may find useful:
For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.
Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:
Summer has officially given way to September, but the weather where I am has not quite caught up. The days are still unusually warm, and I suspect these are the last mild afternoons before autumn settles in for good. I am making the most of them while they last. Come back next week for more!
— EsraPhysical AI is moving rapidly from research to large-scale deployment. By 2035, ABI Research projects an installed base of 49 million level 3-5 autonomous vehicles (AVs), while Omdia estimates that roughly 60 million industrial robots will be deployed between 2026 and 2035. As these machines enter roads, factories, warehouses and other environments shared with people, safety must scale with them.
Physical AI safety means proving that AI-driven machines — AVs, humanoid robots, industrial robots and more — behave safely when their decisions turn into physical action. That requires safety across the hardware, software, AI, operating environment and deployment lifecycle — not a one-time check before deployment.
After years of testing and benchmarking, AVs continue to expand commercially. That progress has required developers to demonstrate how automated systems address potential hardware and software failures, limitations in intended functionality and AI-specific risks.
Robotics is approaching a similar inflection point as autonomous machines move into factories, warehouses and other environments shared with people.
Across physical AI, manufacturers, regulators, insurers and workplace safety teams need evidence that hardware, software, AI behavior and operating environments can work together safely without human intervention.
Four shifts define new safety standards:
Together, these shifts require safety to be operationalized across design, deployment and validation, from the underlying hardware to AI behavior and the operating environment.
Physical AI safety requires specialized engineering, data, processes and validation that few companies can reproduce alone. NVIDIA’s safety foundation draws on more than a decade of development in AV safety, building expertise in functional safety, sensor fusion, AI behavior assurance, vision AI, simulation and real-world validation.
NVIDIA Halos is the first and only full-stack safety system for physical AI, helping developers engineer safety across every layer of design, validation and deployment. The principles are shared across AVs and robotics, while the platforms, standards and evidence remain specific to each domain.
For AV development, Halos spans:
Together, these elements connect cloud-based AI development and simulation with in-vehicle deployment so safety evidence can remain traceable across the vehicle lifecycle.
For robotics, Halos spans:
Across both AV and robotics, the NVIDIA Halos AI Systems Inspection Lab turns safety, cybersecurity and AI safety requirements into repeatable inspections and helps prepare Halos integrations for final system-level certification by third-party agencies.
NVIDIA Halos connects the companies that build, integrate, assess and deploy physical AI solutions, including product developers, software and embedded-system providers, sensor and silicon companies, safety solution developers and certification bodies.
In autonomous vehicles, Geely, Isuzu, Nissan (powered by Wayve software) and Einride are building level 4-ready vehicles on NVIDIA Hyperion, supported by Halos OS.
Uber, Grab, Lyft and other mobility providers are also using Hyperion to scale robotaxi development and deployment. Members of the NVIDIA Halos AI Systems Inspection Lab include AUMOVIO, Bosch, Gatik, Hesai, Lucid, MIRA, onsemi, PlusAI, Sony, Valeo and Wayve, spanning autonomous-driving development, ADAS, sensors, silicon, systems integration, validation and safety assurance.
In robotics, acontis and QNX provide the embedded software needed to run safety functions predictably, while Advantech and NexCOBOT build safety-designed NVIDIA IGX systems. Infineon, NXP, STMicroelectronics and Texas Instruments contribute sensor, safety-microcontroller and other semiconductor technologies. KION Group is developing functional safety agents for autonomous forklifts. Agility is integrating NVIDIA IGX Thor and Halos Core into the safety system for its Digit 5 humanoid.
For AVs, TÜV SÜD certified NVIDIA’s Automotive Product Lifecycle software process and DriveOS 6.0 to ISO 26262 ASIL D, as well as NVIDIA’s automotive engineering processes to ISO/SAE 21434. TÜV Rheinland also performed an independent UNECE safety assessment of NVIDIA DRIVE AV.
For robotics, TÜV Rheinland is inspecting NVIDIA IGX Thor, Halos OS and Holoscan Sensor Bridge for functional-safety certification readiness, building on TÜV SÜD’s inspection of the Thor SoC and Halos Core for ISO 26262.
Across physical AI, ANAB has accredited the NVIDIA Halos AI Systems Inspection Lab as an ISO/IEC 17020 inspection body. The lab inspects scoped Halos integrations and helps companies prepare for final certification by independent third-party bodies.
The companies that scale physical AI will not simply build the most capable systems. They will build systems that can be assessed, certified, deployed and trusted in the real world. Designing functional safety from the start is what separates a prototype from a scalable solution.
Learn more about NVIDIA Halos for AVs and robotics, and explore the full-stack safety architecture for physical AI.
Given a set of objects and a set of bins, how do we assign objects to bins in a way that optimizes specific objectives while meeting certain constraints?
This question arises at all layers of Meta’s infrastructure stack including in
The main challenges to designing a reusable framework for solving problems like these are its usability and scalability. Usability is impeded by practitioners struggling to translate real-life policies into the precise mathematical formulas required by formal optimization methods, while scalability is hampered by NP-hard problems that cannot be solved efficiently by commercial solvers.
Rebalancer addresses both of these challenges by separating a problem’s specification from its solution. Rebalancer provides a language for describing problems using objects, bins, constraints, and objectives, as in the examples above. Once a problem is described in this way, Rebalancer transforms the problem into a directed-acyclic graph called an expression graph. Rebalancer’s solving algorithm uses the expression graph to either design a local search heuristic or to build a mixed integer program (MIP) solvable with either a commercial (FICO Xpress or Gurobi) or open source solver (HiGHS).
Rebalancer’s specification language employs a three-step approach to incrementally elevate the level of abstraction for ease of use.
In the example above, tasks are modeled as objects and servers are modeled as bins into which tasks are to be placed. Servers are physically situated in racks; this grouping is modeled as a scope. Tasks take a certain amount of CPU and storage and servers have a limited amount of each. CPU and storage are modeled as dimensions. The CPU and storage utilization of a server corresponds to the sum of all of the tasks assigned to that server and the server’s utilization limits are modeled using a CapacitySpec. The expression API could be used to change how the utilization is calculated if a simple sum isn’t appropriate.
Further, we model tasks as belonging to jobs. A grouping of objects like this is called a partition and we use a GroupCountSpec to ensure that each rack has only a single job type (partition) assigned to it. A BalanceSpec ensures that each server’s utilization is balanced across both its CPU and storage dimensions.
This example demonstrates how complex assignment problems can be easily and naturally constructed using Rebalancer and how specs provide a way of expressing constraints and goals that can be re-used in many different ways by varying dimensions, scopes, or partitions.
Please check out an exhaustive list of Rebalancer specs in the docs.
Once a problem is specified using the API described above, Rebalancer translates it into an expression graph. The leaf nodes in this graph represent utilization expressions; for example, the memory utilization of server A, as obtained by summing the memory contribution of tasks assigned to server A. These utilization values are then recursively composed using aggregation nodes such as Max and Sum, or transformation nodes such as Square and Abs. Note that the value of each node in the expression graph depends on the current assignment and needs to be updated every time the assignment changes.
Along with the problem objectives and constraints, modelers also provide Rebalancer with an initial assignment and a stopping condition, such as a time limit. Rebalancer will compute an optimized assignment that minimizes the objective value and does not violate any new constraints. The constraints that were violated by the initial assignment become high priority goals and their violation is minimized, ideally to zero.
Rebalancer offers two distinct techniques to solve the assignment problem.
Optimal Solver. In this mode, Rebalancer translates the expression graph into a set of expressions that can be fed into MIP solvers such as FICO Xpress, Gurobi, or HiGHS. During this translation, Rebalancer needs to represent utilization of a bin by a weighted sum of binary decision variables (one per object) that indicate if the object is assigned to the bin; this can lead to very large MIP models! Rebalancer automatically uses techniques such as variable aggregation (compacting similar objects into a single integer variable), interchangeability, and symmetry breaking to reduce model sizes, but the worst case size of the generated MIP model can still be quadratic, i.e. O(|objects| * |bins|). The largest problems we consider are too big for any MIP solver.
Local Search Solver overcomes this limitation by working directly on the expression graph, exploring the local neighborhood around the current assignment by moving some objects to another bin. This neighborhood has a worst-case size of O(|objects|+|bins|), which allows Rebalancer to model even very large problems without hitting memory limitations. Each move creates a new candidate assignment for which Rebalancer evaluates the new values of the objectives and constraints. After all candidates have been evaluated, Rebalancer applies the best candidate assignment; that is, the one that does not violate a constraint and improves the objective by the maximum amount. This process of evaluating and applying moves is repeated until no progress can be made or a stopping condition is reached. Rebalancer’s local search algorithm is heavily optimized and parallelized so that each evaluation is relatively inexpensive (millions of evaluations per second are possible), allowing us to quickly explore the search space. In addition, Rebalancer knows how to prune the search space, cutting down the number of evaluations needed in the first place.
The right solution technique will depend on your needs. At Meta, almost all large-scale problems use local search. Small- to mid-size problems that have moderate solve time requirements often use the optimal solver. It is also common to prototype with the optimal solver and then migrate to local search after a high-quality baseline solution has been identified. Offline, the optimal solver can be used to tune local search.
For the last decade, Rebalancer has been continuously used and improved at Meta. It is used to solve a wide range of infrastructure optimization problems including assigning shards to servers (Shard Manager), servers to services (RAS), routing traffic from globally distributed edge datacenters to main datacenters (Taiji), grouping serverless functions to improve locality, balancing online ML training workloads across regions while considering the priority of ML workloads, and so on. At the time of this writing, Rebalancer is used to solve roughly 40 million assignment problems every day with more than 30 unique problem formulations. The P99 solve time is 12 seconds on a problem with 265k objects and 3.2k bins. For problems with more than 1 million objects and 5k bins, the average solve time is 171 seconds and there are more than 3.4k such runs.
Unsurprisingly, Rebalancer has also been used to solve non-infrastructure problems such as assigning meetings to meeting rooms to minimize travel time, assigning support tickets to engineers, and optimizing desk placements. Beyond Meta, assignment problems arise in many domains such as healthcare, energy and utilities, transportation and logistics, education, and emergency response, and, while we don’t have the expertise to apply Rebalancer to these areas ourselves, we hope that others do and will.
With Rebalancer making it easy to formulate and solve problems, we found that the majority of engineering time for modelers shifted to debugging the solver’s behavior. Without proper tools, such debugging required a deep understanding of the solver’s internals.
Over time we identified common questions and pain points among modelers and built a specialized UI tool for answering them: Rebalancer Explorer.
Explorer accompanies Rebalancer in this open-source release as a Dockerized web UI that facilitates rapid debugging and iteration when solving problems with both local search and optimal solvers. It helps answer questions such as which constraints are binding, what would happen if a constraint were relaxed, and why an object was placed in one bin and not another.
We are always looking to optimize Rebalancer’s performance, add new capabilities, and extend it to support a wider range of assignment problems. Rebalancer is proud to be open-source (Apache 2.0 license) and we invite both systems and optimization experts to try Rebalancer and contribute to the project by identifying performance bottlenecks, adding new solve techniques, extending it to support new sorts of problems, or just by fixing bugs. We look forward to seeing how the systems and optimization communities adopt, build, and contribute to Rebalancer.
Rebalancer was developed by past and present members of the Algorithmic Optimization team at Meta: Pol Mauri Ruiz, Igor Kabiljo, Neeraj Kumar, Vijay Menon, Mayank Pundir, Andrew Newell, Liyuan Wang, Richard Barnes, Sahil Deshpande, Karthik Velakur, Yang Liu, Leart Gjoni, Ravi Surulikamu, Tony Zhang, Raj Rajendran, Aravind Narayanan, Lakshmi Ganesh, and Saranyan Vigraham.
The post Open-Sourcing Rebalancer: A Generic, High-Performance Library for Solving Assignment Problems appeared first on Engineering at Meta.
Today, Egypt’s AI builders gathered in the Grand Egyptian Museum for a reception that highlighted the nation’s rapidly growing AI ecosystem — spanning AI natives, developers, researchers, startups and enterprises — building applications across industries.
The event included a keynote from Paolo Guglielmini, vice president of EMEA at NVIDIA. Ahmed Mostafa, regional AI adoption lead for the Middle East and Africa at NVIDIA, delivered a session on “Why Accelerating Every Layer Matters,” exploring NVIDIA’s full-stack approach to AI development and deployment.
The event also featured a panel moderated by Basil Fateen, head of startups for the Middle East and Africa at NVIDIA, with participation from startups across smart spaces, healthcare, cybersecurity and robotics.
In Egypt, the NVIDIA Deep Learning Institute learner base grew more than tenfold in a single year. In June, the National Telecommunications Regulatory Authority of Egypt licensed Hassan Allam Data Centers to build and operate data centers in the country. Under that license, Hassan Allam Utilities and investment firm A15 agreed to develop a new data center, an estimated $400 million investment.
In addition, NVIDIA and A15 in July hosted an event connecting Egypt’s leading founders with NVIDIA’s global ecosystem. This has been complemented by recent startup and investor events in Egypt, including engagements with A15 and Plug and Play at the Creativa Innovation Hub at Sultan Hussein Kamel Palace, an affiliate of the Ministry of Communications and Information Technology.
Spanning a range of industries, members of the NVIDIA Inception program in Egypt include:
The NVIDIA VC Alliance has also expanded its presence in Egypt, with A15 and M Empire joining the program, connecting more local investors with NVIDIA’s global startup ecosystem. This momentum has been complemented by startup and investor events, including collaborations with RiseUp and Plug and Play, as well as Flat6Labs, a regional entrepreneurship platform supporting startups and innovation across emerging markets, and Algebra Ventures, a Cairo-based venture capital firm backing technology startups.
The Egypt ecosystem event showed just a piece of Africa’s larger, growing AI ecosystem.
For most of the past decade, conversations about Africa’s AI ecosystem revolved around “potential.” They centered on what the technology might do for the continent, rather than what the continent could build with it.
Africa is home to roughly 18% of the world’s population but less than 1% of the world’s data center capacity. This means African developers have often relied on cloud-based compute hosted outside the continent for large-scale AI training.
This is rapidly changing. Within a year, four AI factories have been announced or brought online across Africa, with another 656 megawatts of new capacity in the pipeline. These AI factories will provide African developers and enterprises with the accelerated computing infrastructure needed to train, fine-tune and deploy AI models closer to where data is created.
Cassava Technologies, Africa’s first NVIDIA Cloud Partner, is expanding access to NVIDIA accelerated computing through its AI factory in South Africa and planned deployments across Egypt, Kenya, Nigeria and Morocco — an investment that could reach $720 million. In Egypt, Cassava and Vodafone Egypt recently announced an AI factory initiative to provide businesses and government organizations with locally hosted AI infrastructure through GPU-as-a-service, supporting the development and deployment of AI applications while keeping data in-country.
Building on Cassava’s first AI factory in South Africa, the rollout aims to expand access to local accelerated computing, helping developers, enterprises and researchers train, customize and deploy AI models closer to where their data is created.
Similarly, Stratos Lab, a South African AI neocloud provider, is partnering with ECOBLOX and Digital Parks Africa to launch an AI cloud powered by more than 50 NVIDIA HGX B300 systems — delivering over 7 exaflops of performance. The deployment gives African enterprises and developers access to high-performance GPU compute locally, enabling model training, inference and agentic AI workloads with lower latency and at reduced costs.
And at GITEX Africa in Marrakech in April, Nexus Core Systems announced a collaboration with Morocco’s Ministry of Digital Transition, its Ministry of Investment and the investment agency AMDIE to build the Nexus AI factory outside Casablanca: a $1.2 billion initial investment with 500 megawatts planned, NVIDIA Blackwell accelerated computing and renewable power from TAQA Morocco.
Infrastructure of this kind is not based on mere potential — it’s based on confidence that local developers and enterprises are ready to put the infrastructure to productive use.
In 2024, NVIDIA set a target of training 100,000 developers across Africa through the NVIDIA Deep Learning Institute within three years. To date, NVIDIA’s trained over 85,000 developers in Africa, and the continent is among NVIDIA’s fastest-growing developer regions — Nigeria is now the institute’s second-largest market globally.
InstaDeep joined NVIDIA Inception in 2017 as a small team in Tunis using its first NVIDIA DGX system. Since then, it’s developed drug discovery, protein design and logistics optimization applications, and was acquired by BioNTech for about $680 million.
Africa accounts for roughly a third of the world’s languages, often invisible to frontier models. To help advance African language models, language model lab Lelapa AI built InkubaLM and the Vulavula speech service for South African languages including isiZulu and Sesotho.
MeetKai, for example, an NVIDIA Inception member, is expanding its work in Egypt with Smart Africa, building its AI stack on NVIDIA technology as part of its plans for the market. The company is among a growing group of AI innovators using NVIDIA technology to build and scale across Africa.
And there’s still much room to grow. McKinsey puts African data center demand at roughly 0.4 gigawatts today, and estimates it will rise to between 1.5 and 2.2 gigawatts by 2030, which requires between $10 to $20 billion in construction.
Learn more about the NVIDIA Developer Program, NVIDIA Inception and NVIDIA Deep Learning Institute.
Demand for AI infrastructure is at an all-time high. Global accelerator shortages mean engineering teams can rarely get all the compute they need from just one data center — capacity comes a cluster here, a cluster there, often an ocean apart. At the same time, workloads are getting hungrier: Today’s long-running agentic workloads often have context windows of 100k to 800k+ tokens, which consume accelerator memory faster than any previous generation of AI traffic.
In this environment, the goal is to maximize "intelligence per dollar." Fragmented, poorly balanced infrastructure is rarely up to the task though, allowing expensive accelerators to sit idle, while requests queue up somewhere else.
To close that gap, we built a layered routing architecture that makes globally scattered capacity behave like a single pool behind a single entry point. At the edge, the multi-cluster GKE Inference Gateway focuses on global, multi-region traffic distribution and high availability. Beneath that, the LLM-d router handles the complex, memory-aware scheduling algorithms that keep utilization high.
This architecture is deliberately runtime-, model-, and accelerator-agnostic — it works across serving frameworks, model families, and GPU or TPU hardware. To make the results concrete rather than abstract, we recently benchmarked managing production-level global request routing at scale across a multi-region GKE deployment of 17,000 compute nodes spread across the US and Europe. The deployment served a leading Mixture of Experts (MoE) foundation model using SGLang.
The results: Scaling to three clusters achieved a near-linear throughput boost while maintaining a 99.9% success rate under heavy multi-client concurrency. Additionally, routing traffic through the multi-cluster GKE Inference Gateway added less than 1% overhead, delivering 99.5% of the throughput of a direct, local cluster call.
Read on to learn how it works, more on the benchmark results, and what it means for your own distributed inference deployment.
The deployment spanned three GKE clusters in three geographic regions: us-east5 (the config cluster), us-west8, and europe-west4. However, from the client’s perspective, none of that geography exists. Requests hit a single global virtual IP, and the gateway decides — in real time — which cluster should serve each one.
What makes that decision smart rather than blind is telemetry. Instead of traditional round-robin routing at the network layer, the multi-cluster load balancer is configured to route traffic based on live application signals. Specifically, the Endpoint Picker Proxy (EPP) reads the KV-cache token utilization natively exposed by the underlying inference engines and emits it as a metric for the load balancer. When the load balancer sees a region running hot based on this emitted metric, it spills traffic to the next healthy region.
Multi-cluster GKE Inference Gateway topology. The config cluster holds routing configuration but sits outside the request path; each target cluster runs its own EPP and reports KV-cache utilization back to the load balancer.
Distributed LLM engines also operate differently than standard web apps. In a typical inference engine's distributed mode (such as tensor parallelism across multiple nodes), only the master (rank-0) pod serves the API. GKE already handles local routing using standard Service selectors and LeaderWorkerSet (LWS) to direct traffic exclusively to leader pods. The multi-cluster Inference Gateway also integrates with this foundation: It routes global traffic to the correct regional services, helping your cross-region load balancing respects your underlying multi-node topologies out of the box.
The net effect: Three isolated regional data centers start behaving like one cohesive global accelerator fleet, with failover and load balancing driven by what the models are actually doing.
The first question every team asks about a global routing tier is almost always, ‘How much throughput am I giving up for cross-region capability?’
The benchmarks answer this directly: Deploying the multi-cluster GKE Inference Gateway to maximize your accelerator fleet doesn't have to come at the cost of throughput. Routing traffic through the Gateway added less than 1% overhead, delivering 99.5% of the throughput of a direct, local cluster call.
That’s the whole trade-off. All the benefits of global load balancing, essentially for free.
A bigger test is scale. In our test, growing the fleet from one cluster to three, spanning the US and Europe, while every client request originated from a single region (us-east5), put real pressure on the Gateway: If it couldn’t distribute load efficiently across those distances, throughput would flatten as hardware was added.
Instead, throughput multiplied almost exactly in line with capacity:
|
Fleet topology |
Request throughput |
Token throughput |
Success rate |
|
1 cluster (us-east5-a) |
0.72 req/s |
2,898 tok/s |
99.87% |
|
2 clusters (+ us-west8-a) |
1.40 req/s |
6,380 tok/s |
99.95% |
|
3 clusters (+ europe-west4- b) |
2.10 req/s |
8,457 tok/s |
99.90% |
Scaling to three clusters achieved a near-linear throughput boost while maintaining a 99.9% success rate under heavy multi-client concurrency.
Round-robin load balancing is inadequate for serving LLMs because it treats every request as equal. They aren’t. Heavy prompts saturate GPU compute cores, long generations stress memory bandwidth, and long-context conversations quietly eat VRAM until the engine can’t schedule anything new.
Here, the pressure on memory bandwidth came from the routing signal chosen for this deployment. By mapping Inference Engine's native token-usage metric onto the Gateway’s KV-cache signal, the routing plane gained a real-time view of memory pressure across the entire 17,000 fleet. (Depending on the workload, the Gateway can route on other signals too, like queue depth or running concurrency.)
Under live production loads, as the primary region climbed toward its high-bandwidth memory (HBM) limits, the Gateway detected the saturation the moment the cluster crossed its 40% KV-cache utilization threshold; it then automatically began routing the overflow to the next healthy region. No operator intervention was needed. The complexity of running in multiple regions simply never reached the user.
By routing traffic based on live KV-cache utilization, this GKE Inference Gateway setup effectively pools globally scattered compute capacity into one unified engine. For this deployment, the result was a near-linear throughput boost across three global regions, with virtually zero routing overhead.
This translates directly into maximizing 'intelligence per dollar,' extracting near-perfect proportional performance out of every accelerator you add to your fleet, rather than letting capital go to waste.
If you’re planning your own distributed inference deployment, five lessons from this work stand out:
Smarter load balancing pays for itself. Round-robin routing wastes expensive GPU capacity because it can’t see memory or compute pressure. Routing on real-time application signals turns fragmented regional clusters into one efficient fleet — the difference between stranded hardware and 90%+ utilization of scarce compute.
Agentic workloads change the bottleneck. Long-running agents with extreme context windows exhaust memory long before there’s no more compute. If your routing layer can’t see memory pressure, your compute will strand compute behind full VRAM. Make KV-cache utilization a first-class routing signal.
AI traffic breaks web-era assumptions. Traditional load balancers are tuned for sub-second transactions; LLM requests can run for minutes. Plan connection limits and timeouts for AI-scale latency early, or expect aborted connections in production.
Your routing layer must integrate with native serving patterns. Distributed LLM engines have master-worker topologies where only certain pods can serve traffic. By pairing your Gateway with native Kubernetes constructs like LeaderWorkerSet (LWS), your global routing respects local pod topologies out of the box, saving your team from building custom proxy infrastructure.
For large foundation model builders, bet on an open, portable stack. Teams operating at frontier scale face the most acute capacity fragmentation, forcing them to hunt for compute resources across whichever regions have availability capacity. An open, portable inference stack such as LLM-d on GKE lets you absorb that capacity wherever it lands, rather than hard-wiring your serving architecture to any single cluster, region, or bespoke infrastructure.
Ready to maximize your distributed accelerator efficiency and set up global cross-region load balancing with multi-cluster GKE Inference Gateway?
Deploy it yourself: Set up the multi-cluster GKE Inference Gateway.
Understand the architecture: About multi-cluster GKE Inference Gateway.
Learn about the cross-region spillover behavior featured in this post: About elastic cross-region high availability and Configure elastic cross-region high availability.
The surge in AI development has created unprecedented demand for compute capacity around the globe. This can have negative implications for data processing and pipelines with Apache Spark. Whether you are managing your own Spark infrastructure or using a managed service, you can face availability constraints. However, a significant advantage of using Google’s Managed Service for Apache Spark is the availability of flexible VMs, which provide a targeted mechanism to adopt a dynamic, resource-agnostic philosophy and ensure your pipelines remain operational, even during regional or zonal capacity stockouts.
Capacity stockouts occur when demand for a specific machine family (such as N2 or N2D) exceeds available capacity in a target zone or region. For time-sensitive analytics pipelines, rigid single-VM requirements transform standard provisioning into a single point of failure which can result in cluster creation delays, failed executions, and potentially compromised business SLAs.
Flexible VMs fundamentally overhaul how a Managed Spark cluster requests compute resources. Rather than binding a cluster to a rigid instance type, flexible VMs allow teams to establish an ordered list of acceptable machine families for master, primary worker, and secondary worker nodes.
Multi-family blending: Mix nodes across diverse machine types and generations, combining Gen2 families (e.g., N2, N2D) with Gen4 families (e.g., N4, C4) in a single configuration.
Mixed storage support: Broaden available capacity pools by allowing storage options to dynamically adapt to the underlying host family's supported disk types.
Comprehensive cluster coverage: Apply flexible rules to primary workers, secondary (preemptible/spot) workers, and master nodes to guarantee cluster provisioning end-to-end.
A successful flexible VM implementation relies on intentional ranking. By defining a clear hierarchy of options, Managed Spark clusters automatically attempt provisioning, systematically mitigating stockout risks without requiring manual intervention. To improve the availability of suitable VMs, we recommend specifying at least two machine families in the highest priority (Rank 0) flexible VM list.
As an example, for production pipelines standardizing on n2d-standard-16 shapes, the following tiering strategy provides robust resilience against capacity constraints:
|
Rank |
Machine family examples |
Storage recommendation |
|---|---|---|
|
Rank 0 (Primary) |
n2d-standard-16, n2-standard-16 |
Standard Local SSD or PD |
|
Rank 1 |
n4-standard-16, n4d-standard-16 |
Hyperdisk Balanced |
|
Rank 2 |
c4-standard-16, c3-standard-22 |
Hyperdisk Balanced |
|
Rank 3 |
e2-standard-16 |
Standard PD |
For pipelines standardizing on legacy n1-standard-16 shapes, the following tiering strategy helps transition workloads toward newer, more available architectures while preserving operational stability:
|
Rank |
Machine family examples |
Storage recommendation |
|---|---|---|
|
Rank 0 (Primary) |
n1-standard-16 n2-standard-16 |
Standard Local SSD or PD |
|
Rank 1 |
n2d-standard-16 |
Standard Local SSD or PD |
|
Rank 2 |
n4-standard-16 n4d-standard-16 |
Hyperdisk Balanced |
|
Rank 3 |
e2-standard-16 |
Standard PD |
Unlocking maximum availability with flexible VMs often requires adopting modern storage architectures like Hyperdisk Balanced. Newer instance families (including N4 and C4) rely on Hyperdisk to deliver predictable performance across variable VM sizes. Starting with default IOPS and throughput settings typically provides a reliable baseline for the majority of distributed Spark jobs.
While flexible VMs dramatically improve cluster provisioning success, aligning them with enterprise requirements involves evaluating several architectural and financial factors:
It is no longer enough to have one specific machine (e.g., N2) quota. You need to ensure you have sufficient compute and disk quotas allocated for all specific machine types and disks (including Hyperdisk) defined in their flexible VM lists.
Traditional, resource-based CUDs are tied to specific machine families, which limits flexibility. Adopt Compute flexible Committed Use Discounts (CUDs) to apply savings across multiple VM families and regions.
Performance can vary between machine generations, as well as between Local SSD and Hyperdisk. While the Managed Spark team maintains internal benchmarks for these comparisons, actual outcomes are workload-dependent. Testing your specific Spark jobs across these families is essential for understanding SLA impacts.
In addition to implementing flexible VMs, there are several other key architectural and scheduling strategies to improve resource availability and workload stability:
AutoZone: Implement AutoZone routing to allow Managed Spark to automatically select the zone best suited to execute the job based on current capacity.
Smaller machine shapes: Avoid high in demand, large-core shapes. Design workloads and YARN containers to utilize smaller machine shapes (such as 4, 8, or 16 cores). These smaller shapes are much easier to fulfill from the available GCE on-demand pool.
Autoscaling: Deploy cluster autoscaling with reasonable maxInstances to manage capacity effectively for bursty or unpredictable workloads without relying on rigid, massive upfront provisioning.
Partial cluster creation: Configure a minimum acceptable number of primary workers. This allows clusters to spin up under resource constraints and begin executing, while autoscaling can dynamically add remaining workers as resources become available.
Establish regional fallbacks: Some regions, such as us-central1, can experience high demand. Setting up fallbacks to other regions reduces capacity stockout risks.
Managing your own Apache Spark infrastructure can be complex, especially when capacity stockouts disrupt your data processing. Utilizing a managed service like Managed Service for Apache Spark provides unique advantages — including built-in platform resilience and access to flexible VMs. By adopting a prioritized fallback strategy with flexible VMs, you can protect your workloads from regional hardware shortages and keep your critical pipelines running.
Ready to improve your Spark workload resilience? Start configuring flexible VMs for your Managed Spark clusters today.
When running modern AI workloads, there’s often a conflict between performance and cost. Workloads like large language models (LLMs) load massive files, and may serve thousands of AI agents that need to execute code instantly. If each component is starting “cold” with a full data-load process, all this provisioning takes time, often forcing organizations to overprovision their infrastructure just to meet scaling requirements.
To solve this, we introduced Google Kubernetes Engine (GKE) Pod snapshots, a new feature that lets you save the running state of your workload, including CPU and GPU memory, and restore it on demand.
GKE Pod snapshots reduce AI inference start-up by as much as 89%, loading 70B parameter models in just 37 seconds and 8B parameters models in just 15 seconds. This speed allows your infrastructure to scale as fast as your demand, significantly reducing the need for overprovisioning.
The cold start problem isn't unique to AI; it’s a challenge for any application that requires significant initialization time — from game servers to complex Java monoliths. However, the cold start problem is particularly acute in AI workloads. Inference servers must initialize, then download and load gigabytes of model weights into GPU memory — a process that can take several minutes. Further, many agentic AI workloads, including code execution and computer use tools, require isolated sandboxes for each request, and they need to be started quickly and suspended when idle.
In both scenarios, startup latency degrades the user experience and prevents rapid auto-scaling during traffic spikes. Consequently, engineers often resort to overprovisioning expensive infrastructure, or building sophisticated, custom systems to quickly restore state at the application level.
For generative AI, GKE Pod snapshots solves the linear scaling penalty of model loading. Typically, every new replica you add to a cluster must independently download model weights and load them into accelerator memory. For models with tens of billions of parameters, this step alone often accounts for the majority of the startup time.
With Pod snapshots, you perform this initialization once to create the initial snapshot. GKE captures the fully loaded state including the CPU and GPU memory and persists it in high-throughput Cloud Storage. When the workload needs to scale up, new replicas restore directly from this state, bypassing the initialization phase entirely. In our benchmarks this approach reduced startup latency by as much as 89% for large models like llama3-70b. This speed allows platform teams to shift from expensive overprovisioning strategies to on-demand autoscaling, to help you meet service level objectives while significantly reducing idle GPU costs.
GKE Pod snapshots also provide distinct advantages for agentic workflows where agents delegate code execution and computer use to isolated sandboxes. Isolating untrusted, LLM-generated code and commands means one sandbox per user or discrete workflow. In these scenarios, both startup latency and idle sandboxes can result in significant overprovisioning and underutilization.
Pod snapshots addresses both of these challenges:
To improve startup latency, a snapshot can be captured once of the initial agent sandbox environment, and later used to quickly initialize new sandboxes.
To reduce idle sandboxes, a sandbox can be suspended when idle, capturing its entire compute resources. Later it can be resumed nearly instantly when the environment is needed.
This approach is showing significant success by our customers. For instance, Retake, an AI-powered photo editing platform built by Codeway, faced a significant performance bottleneck with its GPU workloads. By adopting Pod snapshots, they were able to replace a complex custom caching layer and reduce startup time to seconds.
"At Retake, serving personalized models to millions of users requires a massive, unified pipeline for both fine-tuning training and real-time inference on A3 H100 GPUs. We initially engineered a complex custom caching layer for compiled artifacts, which reduced startup time to 1 minute. However, this solution added significant maintenance overhead and still limited our ability to autoscale aggressively. We resolved this by replacing that complexity with GKE Pod snapshots, slashing startup latency to just 8 seconds. By eliminating the initialization penalty, we can now dynamically spin up H100s for specific fine-tuning or inference jobs instantly and shut them down immediately after, drastically reducing idle GPU costs and simplifying our codebase." - Ahmet Furkan Çomak, Lead DevOps Engineer, Codeway
We designed Pod snapshots to improve startup performance and fit naturally into existing Kubernetes workflows. Adopting Pod snapshots to your workload is easy: just define a new declarative policy using Pod snapshot CRDs. The policy allows you to define which Pods to snapshot and where to store the data, and handles the end-to-end storage lifecycle and management.
You can take snapshots at any stage of the workload — either at workload startup using a workload signal, or during the lifecycle of the Pod using an on-demand trigger. You can further control storage and restore behavior, setting snapshots retention for cost optimization, choosing between the default behaviour of restoring from the last taken snapshot, or specifying an explicit snapshot during a new Pod deployment.
While the primary use cases for GKE Pod snapshots are AI inference and agent sandboxes, this feature is workload-agnostic. You can use it to speed up any application with a long initialization phase, such as complex Java applications, game servers, or legacy monoliths.
You can begin optimizing your startup latency today with GKE Pod snapshots. Check out the documentation to learn how to get started and we look forward to your feedback.
A resilient software supply chain is the foundation of modern delivery, and securing your continuous integration and continuous delivery (CI/CD) pipeline is what keeps innovation moving safely.
Notable supply chain attacks more than doubled in the first half of 2026 compared to the second half of 2025, according to Wiz’s recent Cloud Threat Highlights report.
It’s crucial that your source code not be the weakest link in your private cloud. To help you better address software supply chain threats, Google Cloud Secure Source Manager (SSM) lets you manage your source and CI/CD systems with unified authentication and authorization mechanisms.
We now offer two new capabilities, both generally available, that can simplify and secure your development and CI/CD workflows:
Unauthorized access to CI/CD systems: Attackers only need to alter a single deployment script to turn your CI/CD pipeline into a vehicle for malware. To help mitigate this risk, from the version control system to the build and artifact systems, to deployment tools, SSM can now block unauthorized access to your CI/CD systems even if your corporate network has been compromised.
Unauthorized changes to code by authorized users: The new Code Owners system manages pull request approver sets at a per-file and per-branch level to help provide more granular identity and access management (IAM). Code Owners helps engineers who need to write, edit, and review code. It adds additional guards to files and directories in your repository at a per-file or per-branch level.
Beginning with source code changes to your CI/CD pipeline, the new code owners feature gives you granular merge guards: Check in CODEOWNERS files to your repository to specify required approvers highly granularly:
Per-path approver sets: Using flexible glob-style path specifiers, you can require that changes to matching files be approved by one or more of given sets of users.
Branch-specific governance: Manage security and deployment rules across branches without friction. You can define different owners for main or dev in the same file, eliminating the merge conflicts that occur with existing CODEOWNERS solutions. See our documentation for more details.
Nestable multi-file ownership: You aren't limited to one giant, 5,000-line root file. You can nest CODEOWNERS files in sub-directories. SSM uses a "more local wins" logic, allowing sub-teams to own their folders while the root admin maintains veto power over the entire repo.
Independent approval sections: Using the [SectionName][count] syntax (e.g., [Security Team][2]), a single pull request (PR) can require independent sign-offs from multiple departments. A PR might be reviewed by a peer, but it won't merge until two members of the security team also approve.
With your source code ready, SSM’s new Developer Connect integration makes it easy to connect your CI/CD system and runtimes securely, even when they are in different private networks.
The private CI/CD blueprint architecture follows a secure path: Secure Source Manager connects to Private Service Connect, which connects to Cloud Build. The repository, the build pools, and the artifact storage all reside in a private network, with VPC Service Controls (VPC-SC) providing defense-in-depth to limit access to proxy endpoints.
To secure your network, follow our new Private Network Integrations guide to connect SSM to Cloud Build with Developer Connect.
To secure your pull request approvals, create a root CODEOWNERS file to replace blunt IAM "Approver" roles with file-specific ownership.
Developing new medicines and materials requires making new molecules but planning how to make them is still largely manual, time-consuming, and costly. RetroChimera automatically proposes high-quality synthesis routes. The model combines two strong models with complementary strengths, learning how to rank their proposals to produce better predictions than either alone. In blind tests, PhD-level chemists prefer RetroChimera’s individual reaction predictions over preceding models and recorded literature reactions.
Custom-made molecules are unlocking advances in modern medicine, smart materials, and sustainable agriculture. Yet, progress is slowed by chemical synthesis—the time-consuming process of making new molecules from simpler building blocks in the lab. In addition, synthesis is a significant driver of drug development costs. So even as computational methods make it possible to explore large numbers of novel molecules, finding practical ways to synthesize them remains a critical challenge.
Retrosynthesis approaches this problem by working backwards from a target molecule, breaking it down step by step into simpler precursors (Figure 1). This process is comparable to playing strategic board games like chess and Go. It involves contemplating a wide range of possible immediate moves, or individual disconnections, while also requiring high-level strategic thinking to reach the end-to-end synthesis plan. However, the number of possible moves in retrosynthesis is much larger than in board games, and it is not obvious which moves would be available for a given molecule. Existing systems face major challenges, including recalling rare but strategically important reactions, robustness beyond the training distribution, and aligning with chemists’ expectations. As a result, retrosynthesis often requires highly specialized expertise, which hinders scaling and automation of scientific discovery.
In a paper recently published in the journal Nature (opens in new tab), we present RetroChimera (opens in new tab), a new framework for retrosynthesis prediction. It is built around two models (Figure 2). R-SMILES 2, a Transformer-based de-novo model, predicts precursor molecules directly from the input molecule. This gives it the flexibility to learn reaction patterns directly from data. However, its unconstrained generation can also make it prone to hallucination.
NeuralLoc, in contrast, is a graph neural network- (GNN) based model that encodes both the target molecule and reaction templates as graphs. It selects reaction templates and predicts where they should be applied to the target molecule. Its predictions are grounded in reaction patterns extracted from the training data, so it tends to produce more accurate and reliable outputs. But it’s more constrained when encountering reactions not covered by the template library.
These differences actually turn out to be a strength. Rather than making the same kinds of predictions, the two models capture complementary patterns in chemistry and specialize in different reaction types. R-SMILES 2 performs particularly well on reactions that involve large changes over the course of the reaction, while NeuralLoc excels in reactions of low precedence and those involving more localized changes.
RetroChimera combines the ranked predictions of both sub-models using a learned ensembling strategy. Each model assigns a learned, rank-dependent vote to each predicted reactant set, and votes are added when both models propose the same reaction. By learning how much to trust each model at different ranks, RetroChimera can leverage their complementary strengths, approximately matching the better-performing sub-model across reaction classes.
As a result, RetroChimera performs strongly across both common and rare reaction classes and produces retrosynthesis predictions that better align with chemists’ judgment (Figure 3). In blind tests, expert chemists preferred disconnections of complex molecules suggested by RetroChimera over those obtained from its constituent sub-models, as well as those from more established approaches, and even from the test set itself.
We believe RetroChimera could help researchers identify promising synthesis route more efficiently, supporting faster design-make-test cycle across molecular science applications, including drug discovery and design of smart materials. RetroChimera could enable chemists to assess more—and more complex—candidate molecules at large scale. Paired with increasing levels of laboratory automation, we expect further acceleration toward closed-loop, self-improving systems for synthesis planning and execution.
RetroChimera is available on GitHub (opens in new tab) (MIT license) and accessible via Microsoft Foundry (opens in new tab). For instructions on how to access the checkpoint, we refer to the GitHub repository.
We invite the broader chemistry community to experiment with RetroChimera, helping us identify its strengths and shortcomings so we can enhance it in the future. We are looking forward to hearing how it performs on various targets you care about!
For a deeper look at the findings, including evaluation experiments with our external research collaborators, see the full Nature publication (opens in new tab) and the accompanying Microsoft Source article (opens in new tab).
Opens in a new tabThe post Improving synthesis prediction of small molecules at scale with RetroChimera appeared first on Microsoft Research.
Today, agent evals come in two flavors: code-based and LLM-as-judge. Both have their own limitations: code-based evaluators can only be used for a narrow set of problems with set inputs, while LLM judges can be slow, expensive, and unreliable. With the popular release of TypeSafe AI’s Jev, we wanted to see whether the “System One” model might be a new third form of agent evaluator, and the impact it could have on agent engineering.
Jev is a new model released by TypeSafe AI. Jev is actually not a traditional LLM; it doesn’t generate text. It’s what the TypeSafe AI team calls a “System One” model:
📖 System One models are a class of AI models built to make fast, structured decisions that software can use directly. A System One model evaluates a state and returns typed answers and probabilities.
According to TypeSafe AI, this makes Jev faster and cheaper than LLMs, up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks.
If you want to learn more about building agents with Jev, we're hosting a livestream with the TypeSafe AI team on Tuesday, Sep 22nd: https://events.langchain.com/webinar/building-a-harness-with-jev/
Agent evals today are either code-based or LLM-as-a-judge, each with its own set of benefits, limitations, and tradeoffs.
Code-based evaluation has existed for as long as code has. Cheap, quick, and reliable, its main disadvantage is in its narrower abilities. Given a traditional function’s need for set deterministic inputs, its ability to evaluate the stochastic world of agent behavior is limited. For example, while a traditional function could evaluate whether an agent called a tool in its first run, it would have a harder time evaluating if the agent then used the tool result to successfully answer the user’s question. In an open-ended task, there can be several valid ways to use the same tool result, so encoding every acceptable answer as deterministic logic quickly runs into the narrow-scope limitation of code-based evaluation.
Enter LLM-as-a-judge, which uses an LLM to reason through the unstructured input of an agent’s trace and score it. An LLM judge can accept the question, trace, and evidence as unstructured input, then use a prompt to evaluate whether the response addressed the user’s request.
As any agent engineer will attest though, the LLM judge is not a perfect solution. They are inherently non-deterministic systems, which are not a solid foundation for a trustworthy testing apparatus. They are also slower and more expensive to run than traditional code-based evaluation.
Agent evaluation is a decision task: given an agent’s state and behavior, assign a score that provides feedback. Jev is designed for this pattern. It evaluates typed questions against structured state and returns typed answers with probabilities. Autoregressive models, on the other hand, reach a judgment through token-by-token generation. In our experiment, that decision-first design coincided with lower latency, lower cost, and lower variance.
Jev supports three types of questions:
Choice selects one option and returns probabilities and confidencesearched_appropriately, searched_unnecessarily, or failed_to_search, plus probabilities and confidenceScore rates an answer against an ordered rubric and returns probabilities and confidenceExample: “How useful is the answer?”1 (unhelpful) to 5 (highly useful), plus probabilities and confidenceNoul returns the probability that a yes/no judgment is truefloat from 0.0 to 1.0, where 1.0 means fully groundedMultiple atomic questions can be evaluated in parallel against the same state.
Comparing judges is difficult when the agent behavior, retrieved data, or trace context changes between runs. Deep Agents and LangSmith let us capture a single agent run as a dataset and replay it across each model.
In order to put Jev to the test, we needed an agent to score. We built a target agent with Deep Agents, our open source agent harness. We then defined a test set as a LangSmith dataset so each evaluator ran against the same questions and expected behavior. The test set consists of five weather requests:
For each example in the dataset, we captured the weather agent’s response and stored the full output as a fixed example in LangSmith. Each judge evaluated the five captured runs with two signals: quality, a continuous score; and does_pass, a binary decision.
To measure correctness separately from repeatability, we had a human reviewer label each fixed response against the same rubric. Using the human reviewers labels as the oracle score enabled a richer analysis on the affects of precision and correctness on overall evaluator effectiveness.
Accuracy measures agreement with the human oracle. Variance measures whether a judge reaches the same judgment consistently on identical agent behavior. Lower variance does not automatically mean higher accuracy: a judge can still be consistently wrong. But when a judge is accurate, lower variance makes that accuracy more dependable in production.
We compared Jev with GPT-5.6 Luna, GPT-5.6 Terra, and Claude Sonnet 4.6, calculating per-case variance across 100 repetitions and agreement with the human oracle.
Using the human reviewer’s labels as the oracle for this comparison, we calculated accuracy for the binary pass/fail decision.
For the binary does_pass score, Jev matched the oracle on all 500 repeated decisions. Terra matched on 99.8% of decisions, Luna on 96.4%, and Claude on 80.0%.
Accuracy tells us whether a judge agreed with the human oracle. Precision asks whether it produces the same quality score when the agent behavior is unchanged. We measured precision with the observed variance of each judge’s scores.
Jev had the lowest observed mean per-case variance: 0.0000149. Luna was 433× higher, Terra was 913× higher, and Claude was 92× higher.
This experiment cannot tell us why Jev’s scores varied less. One hypothesis is that the models are optimized for different kinds of output. TypeSafe describes Jev as a decision model trained to return calibrated probabilities and typed answers, while an autoregressive LLM judge generates text before the evaluator maps that output into a score. That difference may make Jev a better fit for this bounded evaluation task, but the result is observational, not evidence that its training objective caused the lower variance.
Low cost means running agent evaluations at scale can be practical. When evaluator calls are expensive, teams have to decide between coverage and their budget. At $0.00035 per call in this experiment, Jev makes that tradeoff less severe. Teams can afford more repeated judgments and more frequent regression checks. This matters even more for online evaluation, where lower per call cost lets teams run more judges across a larger share of production traces, producing a denser feedback signal.
For a production agent that produces 10,000 traces per day, the observed per-call costs translate into a meaningful operating difference.
To account for whether a low-cost call is useful, we define signal value as binary oracle agreement multiplied by binary repeatability. Repeatability is the chance that two independent calls on the same trace return the same verdict. This rewards judges that are both accurate and stable, while penalizing a judge that is consistently wrong.
A high-signal, low cost judge like Jev could unlock better value in online evaluators. Teams could generate feedback on more production traces, spot changes in quality sooner, and set alerts when that feedback starts to trend in the wrong direction.
Today, every agent eval carries a tradeoff. Score more agent runs, evaluate more dimensions, or test more changes, and the cost of your testing grows. That pushes teams to evaluate less that they would like.
In our experiment, a Jev judgment cost $0.00035. In addition to its low cost, Jev offered high accuracy and low variance, meaning the judge results were reliable and high-signal. A quality judge at that price means builders can evaluate each agent run against several focused criteria, measure every agent change, and repeat judgments when confidence matters.
This matters because building great agents requires substantial testing and monitoring. The more often you evaluate an agent, the more useful feedback enters the development cycle.
We still need to see whether the results in this experiment carry over to other agents and production workflows. Additionally, low cost can amplify mistakes - a consistently wrong evaluator can produce bad feedback at scale. Engineers still need to incorporate human review and judge alignment into their workflows.
The new System One style of models could make high quality evaluation abundant. That can speed up the entire agent development lifecycle. Agent engineers can turn more traces into feedback, catch regressions sooner, and move faster as they build, test, monitor, and deploy agents. The unlock is not just cheaper evals, but a tighter feedback loop for building reliable agents.
This project’s GitHub repository is available here.
We ran the LLM judges through LangSmith Gateway: GPT-5.6 Luna, GPT-5.6 Terra, and Claude Sonnet 4.6. We accessed Jev through langchain-typesafe==0.0.1a2.
For reproducibility, the run used Deep Agents 0.7.15, LangChain OpenAI 1.6.2, LangSmith 0.12.6, and Tavily Python 0.8.3. We did not set temperature, top-p, seed, or max tokens for the LLM judges, so each provider’s defaults applied. The Jev service version was not available in the experiment metadata.
If you want to learn more about building agents with Jev, LangChain is hosting a livestream with the TypeSafe AI team on Tuesday, Sep 22nd.
Deployments now show their billable duration and CPU minutes in the dashboard, vc inspect, and the REST API. Use it to understand how each build contributes to your usage.
Billable duration is the build and post-build time combined, rounded up to the next whole minute. CPU minutes are that figure multiplied by the machine's vCPUs.
To see the same breakdown from the CLI, update to version 59.23.1 or later with npm i -g vercel@latest, then run vc inspect:
vc inspect <deployment>
Duration
build duration 5m 9s
post-build duration 3m 21s
billable duration 9m
CPU Minutes Usage 270 minutes (9m x 30vCPU)
Learn more about build machines and CPU minutes in the docs.
Menu. Currently selected: Availability in GitHub Copilot
Grok 4.7, xAI’s latest reasoning model, is now rolling out in GitHub Copilot. Building on Grok 4.6, it is designed for agentic coding and complex, multistep workflows.
This model is billed at provider list pricing under usage-based billing. See Models and pricing for GitHub Copilot for details.
Grok 4.7 will be available to Copilot Pro, Pro+, Max, Business, and Enterprise SKUs.
You’ll be able to select the model in the model picker in:
Rollout will be gradual. Check back soon if you don’t see it yet.
Copilot Enterprise and Copilot Business plan administrators can manage access to Grok 4.7 through the model policy in Copilot settings. Under default model enablement, new models are automatically enabled unless an administrator has turned off the global default or explicitly disables this model.
To explore all models available in GitHub Copilot, see our documentation on models and get started with Copilot.
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AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work.
As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what works faster.
The internet and cloud computing changed how software operates, while core security responsibilities endured: establish identity, control access, limit exposure and verify that protections work.
AI agents introduce new capabilities — reasoning, using tools and adapting actions based on the data they encounter. Those capabilities require applying established principles to new operating conditions.
This pace creates pressure. Organizations want the productivity benefits of AI while the practices to govern and secure these systems are still developing.
Applications depend on code, data, identities, services and infrastructure. Security depends on how those components work together — and AI agents extend that system.
Models provide capabilities; harnesses organize context, tools and workflows; and runtime environments provide the infrastructure within which actions execute. Each part of that stack carries security responsibilities, and proper protection requires controls across each layer, as data, instructions and actions move through the system.
Consider an agent updating a customer record. Say it encounters malicious instructions in an attached document and attempts to export customer data to an unauthorized destination.
A network policy should block the transfer, and protected logs should capture the attempted tool call, authorization decision and outcome so the security team can identify the tool used and the destination it attempted to reach.
Permission to update a customer record should not automatically extend to exporting that data. An agent can request additional access, but it cannot authorize that access itself.
A security boundary has to hold even when an agent makes the wrong decision. The environment where an agent runs determines what it’s allowed to do, and must therefore install limits on files, network destinations and processes independently of the agent’s reasoning.
Instructions and safeguards can help guide behavior, but security also requires enforceable boundaries.
Each agent needs a traceable identity and credentials limited to its assigned task. Organizations need clear policies defining what information agents can access, which systems they can change and which actions require approval. Within those boundaries, consequential actions and permission changes still require human approval.
Teams also need to verify the source and integrity of the tools, skills and dependencies agents use. If something goes wrong, protected records of tool calls, authorization decisions and outcomes help investigators reconstruct what happened. Clear procedures for revoking access and containing incidents make that evidence actionable.
NVIDIA OpenShell is an open source, secure runtime that enforces policies outside of the agent’s reach and provides sandboxed execution while governing how agents access, data, network and system resources. Open Secure AI Alliance partners are building on OpenShell: Cisco’s DefenseClaw adds a governance layer, and JFrog integrates with OpenShell to scan and verify agent skills and enforce policies on which skills agents can access.
Before deployment, teams need evidence that proper controls block attempts to obtain credentials beyond an agent’s scope or send sensitive data to an unauthorized destination.
Testing should also cover attempts to change permissions or interfere with monitoring, and be repeated after material changes to models, tools or workflows.
A named owner must use those results to decide whether the system is ready for deployment and ensure failed tests lead to corrective action. Failures discovered in testing or operation should be reproduced, investigated and addressed. Each finding can then become a repeatable test, allowing teams to check that the fix continues to work in future releases.
Examples include CrowdStrike’s SafeMind for testing and strengthening defenses through repeated attack simulations, and Palo Alto Networks Prisma AIRS for continuous red teaming as models and applications change.
Investigating failures requires capable tools suited to the task, data and environment. Open and closed models serve complimentary needs.
Closed models offer managed capabilities and services, while open models give defenders options to inspect relevant components, adapt strategies and work on infrastructure they control.
During an incident, that control can help a team reproduce a failure and test a fix against its own systems while keeping sensitive evidence within its environment.
Capable AI can support this work by helping find vulnerabilities, validate fixes and investigate attacks. Its value should be assessed through reproducible findings, verifiable fixes and accelerated response time.
Examples include Capital One’s VulnHunter for AI-powered code security, and ReversingLabs’ Spectra Assure for AI-powered analysis of software packages to detect malware and tampering.
Sharing evidence of what failed, which controls worked and how fixes were verified helps other teams strengthen their own systems.
NVIDIA’s security research and the Open Secure AI Alliance support that exchange by bringing research, practical tools and expertise into the broader security community.
AI security is an engineering problem. Every agent deployment needs enforceable boundaries, an accountable owner and evidence that its protections work. Open research and shared tools help more defenders meet that standard and improve it as capabilities advance.
Learn more about NVIDIA’s security research and join the Open Secure AI Alliance.
One of the cheapest ways to make a large language model faster is also one of the bluntest: delete whole transformer blocks. Because the model literally gets shorter, block removal (also called depth pruning) buys predictable inference speedups on top of the memory savings, and it stacks cleanly with quantization, low-rank compression, and other techniques. The hard part is deciding which blocks to cut. Remove the wrong ones and the model collapses; and the effect of removing any one block depends on which others you remove alongside it, so the choices interact. That makes it a combinatorial problem, not a ranking problem, and combinatorial problems with interacting binary variables are exactly what the physics of spin systems was built to describe.
Our latest paper, LLM Compression by Block Removal with Constrained Binary Optimization, takes that correspondence literally. We reformulate block selection as a constrained binary optimization (CBO) problem that maps directly onto an Ising glass, a disordered spin system with all-to-all interactions and a fixed number of "up" spins. The energy of that spin system turns out to be a strong, cheap proxy for how well the pruned model will actually score on benchmarks, which means we can rank a huge number of candidate configurations without benchmarking any of them, and hand the hard instances to the same classical and quantum-inspired solvers we use elsewhere at Multiverse. The payoff in the deep-compression regime is large: at 50% compression of Llama-3.3-70B-Instruct, we gain almost 23 percentage points on MMLU over the best competing block-removal method.
Most existing block-removal methods score each block on its own, then remove the ones that look least important, using magnitude, sensitivity, or "block influence" heuristics. In physics terms these are mean-field methods: they treat each block as if its contribution were independent of the others, the way mean-field theory replaces a spin's neighbors with a single averaged field. A related shortcut is to only ever remove a single consecutive run of blocks, which keeps the problem small but throws away most of the search space.
The trouble is that blocks are not independent, any more than spins in a real magnet are. Whether removing block 20 hurts the model depends on whether you also removed block 19 or block 24, an interaction, or coupling, between the two decisions. As models get deeper and more heterogeneous, ignoring those couplings leaves quality on the table, especially when you want to remove a lot of blocks at once. What you really want is to search over combinations of blocks while accounting for how they interact, but the number of combinations grows exponentially, so brute force looks hopeless. This is precisely the regime, exponentially large configuration spaces with pairwise couplings, where the tools of statistical physics earn their keep.
We attach a binary variable to each transformer block: 0 means keep it, 1 means remove it, just like a spin that can point down or up. Then we do a second-order Taylor expansion of the model's loss with respect to those variables, which produces an (approximate) Hessian matrix. The diagonal of that Hessian is how much each block matters on its own; the off-diagonal entries are exactly the pairwise couplings between blocks, the many-body physics that mean-field methods throw away.
That reformulation turns "which blocks should I remove?" into a clean optimization: find the set of M blocks whose removal minimizes the energy xᵀH⁰x, subject to removing exactly M of the N blocks. Mathematically this is a constrained binary optimization problem; physically it is an Ising glass, an all-to-all coupled spin system with conserved magnetization (the fixed number of removed blocks plays the role of a fixed total spin). The key property we establish is that this energy is a strong proxy for downstream quality: low-energy states of the spin system correspond to high-performing pruned models. Minimizing energy and maximizing benchmark score become the same search.
Block selection becomes a constrained binary optimization problem, equivalent to finding low-energy states of an Ising glass; each solution says which M of N blocks to delete. Right: the coupling variable α we insert into each block's residual path to build the Hessian. Source: paper Figure 1.
The reason this is practical is cost. The Hessian, i.e. the full set of couplings, is computed just once, from forward and backward passes on a small calibration dataset. After that, evaluating any candidate configuration is a single cheap energy calculation, no need to run the actual model, let alone benchmark it. And because the couplings don't depend on the compression target, the same Hessian can be reused to solve for many different values of M.
For most models the configuration space is large but still checkable. Because computing one energy is so cheap, we brute-force it on a single GPU, checking up to tens of billions of spin configurations. A few million take seconds; the hardest tractable case here, removing 8 of Llama-3.3-70B's 80 blocks (about 29 billion configurations), took roughly two days.
Beyond that the exact approach breaks down, and this is where casting the problem as an Ising glass pays off a second time. In its equivalent QUBO form (the constraint absorbed into a penalty term), the exact same task can be handed to the highly optimized classical, quantum, and quantum-inspired solvers built for this class of Hamiltonian, the machinery of quantum annealing, QAOA, tabu search, and specialized branch-and-bound. We find that an open-source tabu solver reliably reaches the lowest-energy states in seconds, even on the hardest cases we can verify against brute force. So the method scales to models where enumerating configurations is out of the question, using solvers that are squarely in Multiverse's domain.
There's a subtle but important point here, and it runs against the usual grain of optimization. Normally a CBO or annealing solver is judged by whether it finds the true ground state. We don't actually need the ground state. What we need is a fast way to generate a handful of good low-energy states, and that is a far easier bar, which is why lightweight solvers work so well for us and why we can afford to run several of them.
The energy is a strong proxy for quality, but not a perfect one, so the single lowest-energy state isn't always the best model. This turns out to be a feature, not a bug: once the Hamiltonian is set up, reading off the ground state and the low-lying excited states is essentially free, giving a spectrum of high-quality candidate prunings to try rather than one fragile answer. Exploring excited states, not just the ground state, is itself an area of active physics research, and it maps neatly onto what practitioners actually need here.
A concrete example: for Llama-3.1-8B-Instruct at 16/32 blocks removed, most of the top states cut blocks toward the end of the model, as prior work would expect. But the 17th excited state is the first to propose removing a block near the beginning of the model, and after light retraining that configuration outperforms the ground state across several benchmarks. That directly disproves the common assumption that the best pruning is one consecutive chunk of middle-or-late blocks, and it shows why respecting the full many-body structure of the problem pays off.
Left: which blocks each of the 20 lowest-energy states removes (red = removed). Right: the 17th excited state, which removes an early block, beats the ground state on several benchmarks after retraining. The best model is an excited state, not the ground state. Source: paper Figure 2.
Across Llama-3.1-8B-Instruct, Qwen3-14B, and Llama-3.3-70B-Instruct, our method (CBO) is on par with or better than state-of-the-art block-removal baselines, and the gap widens as compression gets more aggressive.
The clearest win is deep compression of Llama-3.3-70B-Instruct, evaluated without retraining. Up to 24 of 80 blocks removed, CBO is roughly on par with block influence. But at 32/80 and 40/80, it pulls decisively ahead, with an almost 23-point MMLU advantage at the deepest setting, where it beats the baseline on every benchmark we tested. For Qwen3-14B at 12/40 removed, CBO leads MMLU by about 10 points. At lighter compression the methods are comparable, which is expected: the couplings matter most when you're cutting deep.
| Llama-3.3-70B-Instruct, no retraining | Blocks removed | MMLU |
|---|---|---|
| Original | 0 | 82.2 |
| CBO (ours) | 32 / 80 | 76.6 |
| Block influence | 32 / 80 | 59.3 |
| CBO (ours) | 40 / 80 | 76.9 |
| Block influence | 40 / 80 | 54.0 |
At 40/80 (50% depth), CBO holds MMLU near 77 while the strongest baseline falls to the mid-50s. Source: paper Table 2.
Block removal gets much harder on modern heterogeneous architectures, where different block types are interleaved, and the Ising formulation doesn't care: a coupling is a coupling regardless of what kind of block sits at each site. To stress-test that, we applied the method to NVIDIA-Nemotron-3-Nano-30B-A3B-FP8, a hybrid model that interleaves Mamba2, attention, and mixture-of-experts (MoE) layers in a non-uniform pattern, without any retraining.
Nothing about our formulation assumes a homogeneous stack, so it transfers directly. Removing 2–3 MoE layers or 2 attention layers, CBO finds configurations that beat block influence on AIME25 and GPQA. The results also confirm that redundancy in these hybrid models is real but unevenly distributed: some expert layers are far more disposable than others, and the method's ability to search the coupled configuration space is what locates the good cuts. Even here, the pattern from the dense models holds, the best configuration is often an excited state rather than the ground state.
Reframing a messy machine-learning problem as an Ising Hamiltonian, then solving it with the classical and quantum-inspired optimization machinery built for physics, is squarely in Multiverse's wheelhouse, it's the same instinct that runs through our compression stack. And block removal composes with the rest of that stack, quantization, low-rank/SVD compression, width pruning, and knowledge-distillation-based healing, so it slots into a larger pipeline rather than competing with it.
Want the full technical details, including the Taylor-expansion derivation, the QUBO mapping, the solver benchmarks, the calibration-dataset ablations, and the complete results tables? Read the full paper on Hugging Face, or get in touch with our team to talk about applying this to your own models. The code is open-sourced at github.com/CompactifAI/Block_removal_through_constrained_binary_optimization.
We introduced Python Workers two years ago, providing a way to run Python applications in the Cloudflare Workers runtime. Our goal was to make it as simple to write Workers in Python as it is in TypeScript, and to make the ecosystem of Python packages and frameworks “just work”.
Today, Python Workers are now generally available (GA).
What does GA mean? It means Python is now a first-class, fully supported language on the Cloudflare Developer Platform. You can bring the Python code, libraries, and design patterns you already know and connect them seamlessly to Workers AI, R2, D1, Hyperdrive, Durable Objects, Queues, Workflows, and the rest of the Cloudflare platform. You can also run popular Python frameworks like FastAPI, Django, and Flask inside Python Workers. You can even create a Python Worker inside another Worker using Dynamic Workers.
Bringing Python to Cloudflare Workers was a natural choice. Because Workers has supported WebAssembly since 2018, it gave us the perfect environment to run a Wasm-compiled Python interpreter. By using Pyodide, we were able to quickly support a wide range of Python applications in Cloudflare Workers.
Our goal was to create the first platform for infinitely scalable Python apps, while making it as easy and performant as developing Python apps anywhere else.
The features we are highlighting today are the result of this multi-year effort. Many developers are already building applications within Python Workers; today, we are making these capabilities production-ready for everyone.
Python Workers now natively support Cloudflare Developer Platform bindings. Previously, using these Cloudflare bindings in Python Workers required converting Python objects into TypeScript objects explicitly at the RPC boundary. For example, sending a Python dictionary into a Cloudflare Queue required the following glue code to work:
This required Python developers to keep the JavaScript environment and code in mind while writing Python Workers, and it was a common source of error for both humans and AI agents. To address this, we have encapsulated the entire type conversion process within the Workers runtime and the Python SDK. This allows you to utilize all Cloudflare bindings in a Pythonic way without writing a single line of JavaScript code, making the following just work:
You can now run your favorite Python framework, such as FastAPI, Django, or Flask, to build an API server in Python Workers. We implemented a built-in connector that you can use to easily connect your web application to Python Workers.
Let’s say you have a simple FastAPI web application:
In native environments, you would use a web server such as uvicorn to run this application.
In Python Workers, you can run the same application using the workers.asgi package we provide, just by adding this snippet to your code:
Similarly, you can use workers.wsgi package to run synchronous web applications such as Django.
Python has a standard contract for how web applications should communicate with web servers, known as the Web Server Gateway Interface (WSGI), or its modern asynchronous counterpart, ASGI. This standard allows developers to build applications that are completely server-agnostic. In a traditional deployment, web servers like Uvicorn or Gunicorn are responsible for handling multiple concurrent client connections and threads to scale traffic, while web frameworks like FastAPI can focus purely on the application logic.
In Cloudflare Workers, the Workers platform itself serves as the web server. Since our global network already seamlessly handles load balancing and infinite scaling, we don't need to reinvent the wheel by running a server inside Python Workers.
Instead, our workers.asgi and workers.wsgi connectors act as a thin, optimized bridge. They translate the incoming native JavaScript request into the standard WSGI/ASGI structures that Python applications expect, and seamlessly pipe the response back out with minimal overhead. By doing this, Python developers get the best of both worlds: you can write and organize code using your favorite web frameworks, while letting the Cloudflare Workers platform instantly scale your API across the globe, without ever configuring a server.
These connectors can be used not only with FastAPI, Django, or Flask, but with any Python web framework that uses the WSGI or ASGI interface.
You can find more information about using each web framework in the Python Workers documentation.
If you are building a Python application using relational databases such as PostgreSQL or MySQL, you can now integrate Hyperdrive into Python Workers.
Previously, Python Workers didn’t support TCP sockets, making database drivers unavailable. To understand why this was a blocker, you need to look at how WebAssembly operates. Python database drivers like aiomysql or asyncpg rely on the standard library's socket module to establish connections. In a standard environment, this module makes POSIX system calls to the underlying operating system. Inside a WebAssembly sandbox, those POSIX networking syscalls are normally stubs that always fail. Any attempt to open a standard socket would immediately fail. To solve this problem, we implemented socket system calls using the Workers connect API.
When a database driver attempts to open a TCP connection, it goes through our custom socket syscall implementation. It translates standard Python socket operations like opening a connection and reading bytes into the corresponding JavaScript calls used by the Workers runtime. Because this translation happens at the system call level, your database drivers don't have to know about the underlying implementation at all.
This socket bridge is what makes our Hyperdrive integration possible. To use Hyperdrive in Python Workers, first connect your database with Hyperdrive and set up the binding in the Wrangler config:
Then, connect to Hyperdrive using the database drivers you are familiar with:
You can refer to the Hyperdrive Python Workers documentation to find out how you can use Hyperdrive in Python Workers, and which packages are currently supported.
Because Python Workers run inside a WebAssembly sandbox, any packages with native C/C++/Rust extensions must be cross-compiled to WebAssembly to run in Python Workers. However, previously, there was no standard way to cross-compile any Python packages to WebAssembly. That meant our team had to manually compile and host custom WebAssembly packages. This greatly limited the number of packages you could actually use in Python Workers.
We wanted to fix this and allow users to use a wider variety of packages. However, we didn’t want to merely build packages usable only in Python Workers, which wouldn’t benefit the community. Since Python Workers are built on top of Pyodide, we wanted the ecosystem to evolve in a way that benefits Pyodide and the entire Python-on-WebAssembly community.
To this end, we proposed PEP 783, which standardizes a platform for running Python in the browser runtimes called PyEmscripten. After over a year of discussion and refinement, this proposal was accepted, enabling package maintainers to build and publish packages for the PyEmscripten platform and make them available across all environments that implement PyEmscripten.
We also stabilized the existing Pyodide build toolchain and evolved it into a form that is accessible to all package maintainers, enabling developers to easily build packages for the PyEmscripten platform. Furthermore, we added PyEmscripten platform support to cibuildwheel, to make it easier for others to adopt support for the PyEmscripten platform.
While the ecosystem is still adopting this standard, we hope every Python package will have a wheel that works with WebAssembly in the future. We are also actively working with major package maintainers to add PyEmscripten builds. If you encounter a package that isn’t supported yet, let us know on Discord or GitHub, and our team will work to get it built.
You can also check out our EuroPython 2026 talk: “Python Everywhere: The State of Python on WebAssembly” to see how we made this possible.
The large ecosystem of data science and machine learning packages makes Python the natural choice for building intelligent agents and AI pipelines. But bringing these to Python Workers historically presented a challenge: libraries such as openai and langchain rely on HTTP clients like requests or httpx to communicate with external APIs. However, because of missing low-level socket operations support in Python Workers, these HTTP clients didn’t work properly.
To solve this, we contributed upstream to ensure these HTTP clients can route requests directly through the JavaScript fetch API in WebAssembly environments. Combined with our new support for low-level socket operations as explained in the previous section, this makes the entire networking stack work seamlessly inside Python Workers.
As a result, you can now run AI libraries like openai, langchain, and mcp natively in Python Workers. You can also combine them with Workers AI to run serverless inference on GPUs in Cloudflare’s network, or proxy requests through Cloudflare AI Gateway.
The example below shows a way to run Worker AI models in langchain, using the langchain-cloudflare package:
We have assembled a collection of production-ready patterns in our python-workers-examples repository. Here are some ways you can combine Python Workers with the Cloudflare ecosystem.
Building a full-stack AI application often means connecting multiple services such as storage, queuing, and inference. This example shows how to build an AI-driven image-to-image generator purely in Python Workers. It accepts user requests, drops them into a Cloudflare Queue, and uses Workflows to orchestrate the image generation step via Workers AI, and stores the image to an R2 bucket.
Consuming a firehose of real-time events usually requires a dedicated server to maintain the connection. In this example, we use a Python Worker to connect to the ATProto/Bluesky Jetstream WebSocket. By backing this connection with a Durable Object, the Python Worker can maintain long-lived state, ensuring that the WebSocket connection stays alive.
Build and deploy an MCP server using the official Python MCP package to give your AI assistants access to edge data.
Building a RAG system using Workers AI and Vectorize, Cloudflare’s vector database.
We’ve updated our docs across Cloudflare products to include Python example code. Nearly everywhere where there is a code example showing how to do something in TypeScript, there’s also a code example in Python. We’re committed to continuing to include Python examples across all of our products. You can toggle code snippets between JavaScript, TypeScript, and Python throughout our developer documentation.
Reaching GA is just the start. We have many plans to make Python Workers better, including making Python Workers more performant and memory efficient, as well as supporting more packages.
Keep telling us what you want to build on Python Workers, and we’ll keep pushing the bounds of what is possible. Check out Python Workers documentation and start building your first Python Worker!
Today, we’re announcing Petal, the first transoceanic subsea cable at petabit capacity, and the first to deploy multi-core fiber at scale. Spanning 7,000 km between France and the United States, Petal will deliver 1 Pbps (1 petabit per second or 1,000 terabits per second), doubling what today’s most advanced subsea cables carry at this distance.
That’s roughly the network capacity required for 75% of the world’s population to stream music at the same time.*
Petal is a key piece of Meta’s subsea cable investments bringing greater capacity, stronger resiliency, and future-proof infrastructure to Europe as demand for communications and reliable connectivity continues to increase.
The road to this point has taken years of collaborative engineering with our partners and a complete rethinking of the subsea industry’s approach to cable design.
A subsea cable is the least visible, yet one of the most critical layers of the internet. Approximately 99% of intercontinental data traffic – nearly every message, phone, or video call between continents – travels through glass strands on the ocean floor.
Since the introduction of the erbium-doped fiber amplifier (EDFA) in the 1980s, there have been several transformational shifts in subsea cable capacity. In the 2010s, coherent optical transmission technology and dispersion-uncompensated cable designs launched the industry into a decade of dramatic fiber capacity increases of 10x and more until the ever-looming Shannon Limit finally pushed back.
To overcome this, the industry pivoted to spatial division multiplexing (SDM) to increase the number of fibers within a subsea cable. Meta scaled its subsea cable approach from Marea’s eight fiber pairs, to Amitié’s 16 fiber pairs, and recently to Anjana’s 24 fiber pairs – the first 0.5 Pbps transatlantic cable system.
Three innovations could double capacity again:
With Petal, we’ve opted for 2-core fiber technology in a 24 fiber-pair system, equivalent to 48 fiber pairs, to make the leap to 1 Pbps at transatlantic distances. This is double Anjana’s capacity and makes Petal the single largest generational increase in cable capacity of any repeatered subsea system, ever.
Carrying a petabit through one cable significantly reduces materials, resources, and carbon footprint compared to building two 0.5 Pbps systems. However, transitioning to a 2-core fiber ecosystem comes with challenges that affect the fiber and subsea repeaters.
There are two main challenges to enabling 2-core fiber for Petal. First is ensuring low attenuation while maintaining the physical dimensions of the outer fiber, including the 125 μm width. Second is minimizing crosstalk between the cores to maximize optical performance and capacity.
The former is achieved by using ultra-pure synthetic silica during the manufacture of the preform. The latter is achieved by carefully controlling for high refractive indexes in the cores against lower indexes within the surrounding medium and counter-propagating the optical signals, resulting in nearly immeasurable crosstalk.
A 7,000 km subsea cable typically needs about a hundred repeaters to amplify the digital signals along the length of the cable. Petal’s single-body 96 amp repeater uses single-core fiber amplification with a Fan-In/Fan-Out (FIFO) interface to transition 2-core fiber into two single-core fibers within each repeater and then back to 2-core fiber following amplification. This design allows Petal to retain the highest efficiency and reliability of single-core amplification with an SDM pump-sharing architecture.
FIFO, combined with highly efficient amplification and high-quality, low-loss fiber, will enable Petal to double capacity without a proportional increase to required power. Petal will remain within existing power feeding equipment limits, rated up to 18 kV, which avoids triggering a requalification of the subsea ecosystem necessary at higher equipment voltages.
Meta’s vision for Petal wouldn’t be possible without the engineering capabilities of our partners at NEC, Sumitomo Electronic Industries, and Orange.
NEC, our turnkey system supplier, engineered and qualified the world’s first petabit transoceanic system around the next generation SDM foundation – cable with 2-core fiber, repeaters, FIFO systems, system powering – and is responsible for manufacturing and installing the final product. NEC has made deep investments in their manufacturing facilities to produce petabit-class SDM repeaters, multicore fiber cable as well as associated technologies.
“Achieving petabit-per-second capacity across a transoceanic submarine system represents a major technological milestone in the history of global telecommunications. This achievement reflects NEC’s sustained investment in research and development, combined with decades of experience delivering some of the world’s most advanced, reliable, and secure submarine networks that interconnect the globe,” – Eduardo Mateo, Chief Strategy Officer, Submarine Network Division, NEC Corporation
Sumitomo Electric Industries, NEC’s fiber supplier, developed and manufactured the 2-core fiber with ultra low losses and practically immeasurable crosstalk for counterpropagating signals, resulting in optical performance nearly identical to single-core fiber.
“We are thrilled that Sumitomo Electric’s innovative submarine multi-core fiber, “2C Z-PLUS ULL Fiber” will contribute to “Petal”, an epoch-making Pb-class transatlantic cable system. As a pioneer with nearly four decades of experience in ultra-low loss submarine fiber manufacturing, we are committed to supporting the global network expansion essential to realizing a highly digital future.” -Takehiko OKADA, General Manager of Optical Fiber & Cable Division, Sumitomo Electric Industries, Ltd.
Orange and Meta are working together on plans to land Petal ashore France’s Atlantic coast including the terrestrial interconnection into the European network.
“Reaching one petabit on a transatlantic link, 25 years after the terabit milestone, represents a significant breakthrough to meet the exponential traffic growth while optimizing network capacity. This makes us very proud to welcome this new generation petabit subsea cable with dual-core fiber technology in our infrastructure, as the landing party in France. This new project reinforces our commitment with Meta and demonstrates our leading expertise in landing subsea systems, and extending connectivity to other European countries. It underlines our dedication to developing reliable infrastructure that guarantees the security and resilience of the terrestrial segment of those connections.” – Jean-Louis Le Roux, EVP, Orange International Networks
Meta has been one of the world’s largest investors in subsea cable infrastructure, building the digital backbone that connects continents and strengthens the global internet, enabling a future that is for everyone.
We’re moving multi-core fiber from experimental to mainstream, making it a practical design for building subsea systems at scale – a shift the entire industry can benefit from. Our aim is to set a new standard for what undersea infrastructure can deliver and invite the ecosystem to invest alongside us.
*Calculation based on a total capacity of 1 Pbps with an audio stream bitrate of ~0.16 Mbps (160 kbps) for ≈ 6.25 billion simultaneous streams.
The post Inside Petal: Building the World’s First Petabit-Class Transoceanic Subsea Cable appeared first on Engineering at Meta.
Clean energy isn’t hard to come by, but the pace of large-scale adoption has historically been slow due to bottlenecks — including out-of-date infrastructure, elongated research and development timelines, and upfront cost barriers.
At New York Climate Week, NVIDIA is highlighting five companies pioneering clean energy projects with AI baked into their foundation, accelerating research-to-inception pipelines and ultimately helping build a low carbon grid.
ThinkLabs AI is curating digital twins and agents — using the NVIDIA CUDA platform — to speed up interconnection timelines, seamlessly integrate clean energy sources and optimize the grid to handle ongoing variability.
“The grid is getting less and less certain, so the ability to see things not statically, but as a probability — hence all the utility actions — should be risk informed,” said Josh Wong, founder and CEO of ThinkLabs, a member of the NVIDIA Inception program for cutting-edge startups. “That hits not just reliability objectives, but also affordability, so we know how to maximize and optimize investments.”
Southern California Edison used ThinkLabs software to reduce the time required to evaluate each grid interconnection application from 30-45 days to just two minutes.
The speedup came from a ThinkLabs agent that runs grid simulations and identifies solutions to interconnection barriers.
Atomic Canyon, another NVIDIA Inception member, is bringing AI-powered knowledge management and assistance to reactor operators with its NVIDIA accelerated compute-powered Neutron and NIVA platforms to streamline nuclear power plant operations.
Neutron acts as an AI workbench built for nuclear professionals. It turns all of their procedures, regulatory guidance, operating experience, licensing records, design calculations, and structured and unstructured data into a knowledge layer.
NIVA, short for Nuclear Industry Virtual Assistant, was built with the Institute of Nuclear Power Operations, Electric Power Research Institute and the Nuclear Energy Institute as a unified platform bringing AI-powered capabilities to the national nuclear fleet.
“Nuclear is a known technology we’ve been doing for 50 to 60 years, but the way we’ve been doing things simply will not scale to meet the moment that’s in front of us; it needs to be reinvigorated by artificial intelligence,” said Trey Lauderdale, founder and CEO of Atomic Canyon.
Electricity demand from AI is accelerating faster than new grid infrastructure can be built, leading to a bottleneck in the industry’s growth.
Redwood Materials is closing that gap by harnessing its expertise in battery engineering, power electronics and systems design to build large-scale, off-grid power solutions for AI factories — enabling new capacity to come online in months rather than years.
These 100%-recycled electric vehicle batteries are firming up power suppliers by acting as onsite energy storage to create a secure, flexible energy system for AI factories and the national grid at large.
The batteries are integrated into existing clean energy infrastructure with an AI intelligence layer — running on the NVIDIA Blackwell platform — that makes the system hypervigilant and adaptable to the real-time energy needs of the data center it’s supplying power to.
This approach also lowers the cost of power. Redwood’s simplified architecture — built on in-house power electronics — requires far fewer transformers and inverters, eliminates the need for an uninterruptible power supply, and can deploy repurposed or new electric vehicle batteries.
“Using these batteries with our power electronics and systems control software, you can make a highly responsive power source that can deal with the novel fluctuations of AI training,” said Colin Campbell, chief technology officer of Redwood Materials, another NVIDIA Inception member.
Nuclear energy currently powers 20% of the nation’s electricity. TerraPower is working to make sure that more power is safe, sustainable and quickly harnessable through its emission-free Natrium reactor.
“Twenty years ago, our founders, including Bill Gates, realized that emissions avoidance should also be a part of this energy solution,” said Chris Levesque, president and CEO of TerraPower.
TerraPower is connecting an NVIDIA Omniverse-powered platform to create digital twin software that will support its efforts to accelerate the siting and delivery of future plants from years to months.
Natrium reactors have a unique design that separates them from traditional nuclear power; they’re cooled with liquid metal sodium instead of water — allowing them to operate at a lower pressure, equivalent to atmospheric pressure. This reactor doesn’t require offsite water or electricity to keep it stable — in the event of an emergency, it can keep itself cool without any intervention.
Fusion energy is poised to join the clean energy stack in the 2030’s, thanks to Commonwealth Fusion Systems (CFS).
With the help of NVIDIA Omniverse libraries and OpenUSD, CFS is compressing years of experimentation into weeks for its SPARC tokamak demonstration fusion machine, which can successfully replicate the sun’s power source on Earth.
Fusion energy is a carbon-free, safe source of power.
“With fusion, there’s no running out of control,” said Brandon Sorbom, cofounder and chief science officer of CFS. “It is passively safe, since the default mechanism is shutting itself down.”
Using high-temperature superconductors, CFS enables stronger magnetic fields than previous fusion systems.
These magnets allow SPARC’s design to be 40x smaller and, by extension, cheaper to build and operate. The company’s ARC power plant — the first of which will be built in Chesterfield County, Virginia, and will connect to the grid in the 2030s — is more than 10x smaller.
“We will be able to build a first-of-its-kind plant that will be cost-competitive with both renewable and nonrenewable sources of energy,” said Sorbom.
Explore more NVIDIA-powered sustainability use cases.
The tokenizer has not historically been the bottleneck within ML workflows. Compute-wise, tokenization is light compared to the heavy modeling happening in the rest of the pipeline. Yet, in some cases, it has rapidly become key to accelerating (or slowing down) your machine learning work.
As models become faster and workloads scale, that balance begins to shift. Training on massive datasets, serving many concurrent requests, or repeatedly processing long inputs can put enough pressure on the tokenizer that it starves the model of data.
This is why we have chosen to heavily focus on performance for the upcoming version 1 of tokenizers. Tokenization should be light and should scale with your workflow. Your GPUs should never sit idle waiting for the CPU to complete its tokenization.
In this article, we look at what makes v1 faster than v0.23, often by tens of times.
This work was entirely possible thanks to the rest of the ecosystem. Tokenization is a very active area of open source work, and libraries such as gigatoken, tiktoken, kitoken, tokie, fastokens, wordchipper and ai-tokenizer, as well as many others, have each pushed on what a fast tokenizer can be. We read that work, and several of the ideas below reached us because another project showed they were worth trying.
Before this refactor, tokenizers was nowhere near the performance it could have had, so contributing to it may not have seemed worth it. With this refactor, we hope to make clear that we intend tokenizers to be a library worth contributing to.
We also thank IBM, NVIDIA, and the ExecuTorch team for contributing patches and helping us test across a wide range of hardware to broaden platform support.
We showcase results for the release candidate of tokenizers v1 against other widely used alternatives. We go over single-threaded, multi-threaded, scaling across threads, per-model comparison, per-language comparison, latency, decoding throughput, memory heap, as well as crate size.
We run this from the tokbench repository, and add a command to rerun the benchmarks on your hardware if you would like to do so.
v1 will produce the same token IDs as v0.23. The goal was to preserve the output, the API, the vocabulary and the merge ranks, and improve everything that can be improved. That includes breadth. The library stays general across tokenizer families rather than specialising on BPE, so v1 loads everything v0.23 loaded.
A tokenizer converts text into the list of integers a model reads. tokenizers runs that conversion in four stages. Normalization applies operations such as lowercasing or Unicode normalization to the raw text. Pre-tokenization splits the text into smaller pieces called pre-tokens. The model turns each pre-token into tokens and maps them to IDs in its vocabulary. Post-processing adds any special tokens the model expects.
The model stage is where most of the work described here happens. Eight of the ten model families measured in this article use byte pair encoding, or BPE. BPE starts from the bytes of a pre-token and repeatedly joins the highest ranked adjacent pair until no ranked pair remains. The ranking is learned when the tokenizer is trained and ships with it, so the same text always produces the same IDs. A merge never crosses a pre-token boundary. The other two families use WordPiece and Unigram, the two other model types the library supports.
The tokenization pipeline page documents the four stages. Tokenization algorithms documents BPE, WordPiece and Unigram.
Each stage was worked on. These are the changes that mattered:
| change | what it does |
|---|---|
| workspace split | one crate became a workspace: tk-encode is the required runtime, and tk-serialize, tk-convert and tk-train are linked only when an application needs them |
| no-alloc model | the merge working set lives in a caller-owned scratch buffer; the loop never touches the allocator |
| bitcannon | the split pattern becomes Boolean operations over bitstreams, using SIMD instructions to find splits instead of a regex engine |
| merge-loop rewrite | the pieces being merged form an intrusive doubly-linked list inside one preallocated buffer, so a merge updates two indices instead of moving data |
| word cache | a thread-local memo from pre-token bytes to finished ids, so a repeated word is merged once |
| native parallelism | one shared tokenizer encodes from many threads at once; each thread draws its scratch buffer and word cache from its own sub-pool, so threads no longer queue on a single lock (#2365) |
BPE models use a regular expression to split the input text into smaller, easier to process chunks called pre-tokens. Merges happen inside a pre-token and never across the boundary between two of them, so this split decides what the rest of the pipeline sees.
That regular expression is a fixed parameter of the model. It ships with the tokenizer and never changes at runtime, so there is no need for a general-purpose regex engine to interpret it on every encode. An equivalent splitting function can be written by hand, once, for the pattern a given model actually uses.
A hand-written function can then use the SIMD instructions (single instruction, multiple data) of a modern CPU, which apply one operation to many bytes at once and suit UTF-8 text well. bitcannon views the input's bytes as parallel streams of bits, so boundaries fall out of boolean operations across whole registers instead of a scan that advances one character at a time. It decides 64 bytes per register operation. The same idea drives Parabix for text processing and simdjson for JSON.
This depends on recognising the pattern. A handful of grammars cover most byte-level BPE models, and a tokenizer whose pattern is not among them keeps the regex path and none of this speed-up. That is why the gains above vary as much as they do.
Real text contains many repeated words. Because BPE always produces the same token IDs for a given pre-token, v1 can save the result after processing it once. A thread-local cache maps each pre-token's bytes to its token IDs, allowing later occurrences to skip the merge process.
Naturally, as the input grows, the number of unique words can grow more slowly than the total number of words. Repeated words then account for an increasing share of the input. New words still appear, which accounts for the occasional misses in the animation below.
Reproduce the shared-prefix result with:
tokbench measure prefix-sharing \
--engine pipeline \
--engine hf-tokenizers \
--compare-to pipeline-no-cache \
--corpus agentic_swe
Caching works best when the input contains repeated pre-tokens. Input with few repeated pre-tokens can pay for lookups without receiving many hits.
The next major cost comes from the BPE merge loop. For each pre-token, the loop repeatedly finds the highest-priority adjacent pair and merges it. The previous implementation allocated new memory for every call and built a new priority queue for every pre-token.
v1 reuses a scratch buffer owned by the caller, removing those repeated allocations. It stores symbols in a flat array and links adjacent symbols by their positions in that array, which makes updates during merging cheaper. It also processes a batch of pre-tokens in a single model call.
Each candidate pair is also packed into a single 64-bit value, with the merge rank in the high bits. Comparing two candidates is then just comparing two integers, and "no merge here" is the largest possible value, so the loop finds its next merge without a branch.
Small differences in benchmark design can produce large differences in tokenizer performance. We used the following rules to keep the comparison consistent across engines.
| rule | why |
|---|---|
| one timing loop | every engine runs the identical loop; no per-engine fast path |
| load excluded | vocabulary load is timed separately, never inside encode |
| id-hash verified | FNV-1a over the output ids must match the baseline exactly |
| common cells only | medians are over cells every engine ran and verified |
| complete sweep per process | each repeat starts in a new process and retains every cell |
| physical-core pinning | workers are pinned to eight distinct physical cores, never sibling SMT threads |
| independent Jobs | separate Jobs measure host-to-host variation |
Repeatedly encoding one document can be faster than encoding a stream of distinct documents on the same build. The first approach measures performance when the entire document is already represented in the cache. The second measures performance on new input while allowing previously seen pre-tokens to remain cached.
Both conditions are sometimes described as "warm," even though they measure different workloads. Our headline results use distinct documents, and the complete corpus is too large to fit in the cache. Tokenizer benchmarks should identify which workload they use because the choice can dominate the result.
Across the ten model families v1's encode path covers, it encodes text 3 to 30 times faster than v0.23 with one thread on an Apple M4 Max. The low end is t5-base, the high end gpt2. It scales at 76% of linear across eight workers. Throughout these changes, v1 produces exactly the same token IDs as the released library.
The overall improvement comes from several changes working together: a hand-written splitter in place of a regex engine, a cache that answers a repeated word without merging it again, a merge loop that never touches the allocator, and one model call per batch of pre-tokens instead of one per pre-token. Each reduces the work done at a different point in the pipeline.
The next priority is support for more model families. We will move additional models onto the new merge loop before 1.0.0. Once the release candidates stabilize, the next step will be bringing about the improvements within the transformers library and the rest of the ecosystem which depend on the tokenizers library.
This post is generated from tokbench results and will be updated as support expands.
A release candidate for v1 is on crates.io. The API you call is the one you already call, so the only thing that changes is which build you install.
It is the ordinary install:
cargo add tokenizers --pre
Training is behind a default-on feature that pulls a C++ dependency with it. If you only need to encode, turn it off to exclude the training implementation:
cargo add tokenizers --pre --no-default-features --features http
Encoding is unchanged: same call, same ids.
use tokenizers::tokenizer::{Result, Tokenizer};
fn main() -> Result<()> {
let tokenizer = Tokenizer::from_pretrained("deepseek-ai/DeepSeek-V4-Flash", None)?;
let encoding = tokenizer.encode("The tokenizer is no longer the bottleneck.", false)?; println!("{:?}", encoding.get_ids()); // [671, 17840, 9160, 344, 1119, 5827, 270, 111127, 16] println!("{:?}", encoding.get_tokens()); // ["The", "Ġtoken", "izer", "Ġis", "Ġno", "Ġlonger", "Ġthe", "Ġbottleneck", "."]
Ok(()) } ```
For a batch, `encode_batch` is what scales across cores. It is the call the scaling view above measures.
```rust
let encodings = tokenizer.encode_batch(documents, false)?;
Every figure in this post was measured against this crate. The Python bindings wrap the same code and are built from bindings/python, but they add per-call overhead that none of these measurements include.
The benchmarks in this post cover the completed release-candidate work listed first. The remaining sections show what is still required for 1.0.0 and what we plan to explore afterward.
This work is in the Rust pre-release on crates.io:
cargo add tokenizers --pre
tk-encode, tk-serialize, tk-convert and tk-train, so an application links only what it usesaf5a3e3STAGE_POST pipeline stage #2182role_to_token support #2343tk-encode during training validation so training and inference cannot produce different tokenization resultsGrok 4.7 is our most capable model for coding and knowledge work. It works longer on difficult tasks, checks its own work more carefully, and comes with our best-calibrated safeguards to date. Served at the same price and speed as Grok 4.6, it is highly competitive in its class.
On CursorBench 4.0, which stresses longer-running coding tasks, Grok 4.7 is at the frontier in price-performance.
Grok 4.7 uses a new, larger base model compared to Grok 4.6. It was trained with a longer reinforcement learning run on a harder mix of tasks, weighted toward problems that take many hours to complete. The model is better at verifying its own work and managing longer context. We also trained Grok 4.7 to natively understand the Grok Bot harness, making it better at conversational tasks and general knowledge work.
Grok 4.7 xHigh
Grok 4.6 High
GPT-5.6 Sol Max
Fable 5.1 Max
Input token price$ per million
$2
$2
$4
$10
Output token price$ per million
$6
$6
$20
$50
Software engineeringCursorBench 4.0
46.3%
40.4%
41.7%
51.8%
Software engineeringDeepSWE v1.1
71.0%*
65.2%
72.7%
70.0%
Electrical engineeringEEBench
64.0%
53.0%
39.4%
56.4%
Multi-hour office workAA Briefcase v1.1
1,657
1,546
1,487
1,678
Multi-hour terminal workTerminal-Bench 4.0
37.6%
20.3%
37.3%
57.9%
Legal workHarvey Legal Agent Benchmark
19.6%
15.8%
2.5%
6.7%
Clinical reasoningHealthBench Professional
56.7%
48.5%
60.5%
62.1%
* high effort
Grok 4.7 is better at creating documents and presentations. In GDPval and AA Briefcase, AI is asked to work on tasks done by professionals such as lawyers, nurses, and financial analysts. Grok 4.7 improves upon Grok 4.6 on both benchmarks and performs comparably to other frontier models.
Professional knowledge work
GDPval
Grok 4.7 was built with an entirely new safeguard stack. It is the strongest model we’ve tested on refusals and jailbreak resistance. In dual-use domains like cybersecurity and biological work, it leads on both utility for benign tasks and safe refusal on dangerous ones, topping LatchBio’s biosafety benchmark at 62.4%.
Grok 4.7 balances strong cyber defense capabilities with low refusal rates for legitimate use. It shows the highest safety on HackerBench v0.3, our benchmark for risky and malicious cyber tasks, allowing only 3.3% of risky dual-use prompts through while rarely blocking legitimate security work. We’ve also started giving select cybersecurity partners invite-only access to Grok 4.7’s red-team capabilities for defense research.
Grok 4.7 is available today in Cursor and Grok Build. It is also available through the Grok API, third-party coding harnesses, and model routers and cloud platforms.
The model is priced starting at $2 per million input tokens and $6 per million output tokens. We also serve a fast variant with twice the output speed at twice the price.
Get started today at x.ai/build.
How much better could a coding agent perform if it used the best model for each task?
The best single model, GPT-6 Astra, gets 74.1% of DeepSWE tasks at $6.52 each. Pick the right model for each task and the same eighteen models get 97.6% at $1.88. 23 points better, at under a third of the cost.
That number comes from hindsight. We ran all eighteen models on every task first and picked the winner for each one. What it measures is the capability already sitting in the pool, but it's split across models that nobody uses together.
Putting them together is a router's job. It picks which model handles each task before the work starts, and before is the hard part. Looking back, it's easy to point at a task and name the model that would have done it better. A router has to choose before it sees the outcome, and a wrong choice costs far more than the few dollars it saved.
We analyzed DeepSWE v1.1, an agentic coding benchmark where the unit of work is an engineering task: the agent has to understand an issue, inspect a repository, use tools, edit code, execute it, and get the task to pass.
The policy is deliberately simple. Pick one model at the start of a task and keep it for the whole run, with no switching mid-session.
Then we name the winner for each task by measured pass rate, breaking ties on cost. That's the oracle router. the same method we used in our Kimi K3 and Fable analysis.
The oracle scores on the same 113 tasks it picks from, using four rollouts per model-task pair, and taking a maximum over 18 noisy estimates biases it upward.
The best models score around 70% and spend $6.46 to $13.41 a task getting there:
That's the best a fixed-model policy does. Now pick per task:
The oracle router across all eighteen models reaches 97.6% at $1.88 a task. That is 23 points above GPT-6 Astra, at under a third of its cost. Restrict it to open-weight models only (DeepSeek V4 Flash and Pro, GLM-5.3 and GLM-5.3 Flash, Kimi K3, Qwen3.8 Max), and it still reaches 90.3% at $1.45 a task, which beats every closed model here by 16 points while spending under a quarter of what Astra does.
These results make "open versus closed" a less interesting debate. The emerging race is to move from the theoretical oracle router to building a system of models with collectively better intelligence than any single model. A system of open models can in principle already far surpass the closed frontier.
There is substantially more capability in the pool than any individual model exposes.
In our prior work, we found different models are sufficient (even exceptional) on different tasks. The DeepSWE analysis makes the cost implications concrete. At that 97.6% point, the oracle still sends 94 of the 113 tasks to a model costing under $3.
The three most expensive models in the field, all above $11.50 a task, are the sole best choice on only three tasks.
On 79 of the 113 tasks, at least one of those expensive models ties the top score and loses the task on price alone. A strong general-purpose model can be excellent across a broad distribution without being uniquely necessary on most individual tasks.
A fixed-model policy pays for broad capability on every task. A system can ask a narrower question:
What capability does this task actually require?
How many models does it take to capture the effect?
The best pair adds 13.1 points over the best single model, and the best trio reaches 91.2%. Expanding from three models to all eighteen adds another 6.4 percentage points. The useful object is not a catalog of hundreds of nearly interchangeable models. It is a portfolio with complementary coverage.
The value is capability coverage, not model count.
LLMRouterBench evaluates routing across 33 models and more than 400,000 instances. It finds that a handful of models covers most of what the full set can do, and that bigger pools add little without careful curation.
An oracle is easy to love because it never gets to be wrong. A production router does. We measured it strictly: we use pass@1, the probability that a single attempt passes, rather than a "did this model ever succeed across four attempts" rule. That second rule would make the ceiling look far more impressive while meaning much less.
The gap is a product problem and the literature is blunt about it. LLMRouterBench finds that several recent routing approaches, including commercial ones, fail to reliably beat simple baselines, and traces much of that to model recall: even when a model with the right capability exists in the pool, the router has to recognize when to reach for it.
So sticking with one model you know isn't conservative, it's rational: a stable error distribution beats a router that unpredictably picks the wrong specialist. The bar for a routing system is to make model specialization predictable enough that changing models improves the system without making its behavior less trustworthy.
Routing is usually introduced as a cost optimization: send easy work to a more cost-optimized model, reserve the expensive one for hard work, and keep the difference. At Fireworks, we take a broader view.
If different models are genuinely complementary, then selecting among them moves you up the capability curve, not merely left along the cost curve.
That's what FireRouter is built for. It routes at the task level across both open and closed models, and it's cache aware, so switching models doesn't silently throw away the context you already paid for.
Over four weeks of our own production coding traffic, sessions routed through FireRouter cost $7.42 against $15.81 for Opus 5 alone, a 53% reduction across 2,334 sessions.
The useful unit of AI work is already larger than the single model call. A coding agent is a model inside a harness that supplies context, tools, execution, tests, state, and feedback.
Once several models have complementary strengths, the selection policy becomes a component of the system, alongside context, tools, and tests. Choosing and composing those components is the job. That's what AI engineering is.
Our experiment measures only the simplest version of that system: pick one model at the start of a task and leave it there. The selection policy is the part we can actually build.
We serve every frontier open model in production, which is where a real understanding of each model's strengths comes from. You do not learn what a model is uniquely good at from benchmark averages. You learn it by running all of them, on real work, at scale. That is where FireRouter's model choices come from, and that bar is the one we intend to clear. We will go into our own router and how to hill-climb on your own specialized intelligence in future posts.
Define your frontier on FireRouter.
On costs. All cost figures in this analysis come from the DeepSWE leaderboard's published per-model numbers. The raw cost_usd in the public trials file does not match what the board displays, and for the DeepSeek family it differs by several times over, so each model’s per-task costs are scaled so its mean matches the published figure. Accuracy comes from the four raw rollouts of each task-model pair, cost from the board.
Source: DeepSWE v1.1 trials, refreshed 17 September 2026. 113 tasks, 18 models each at its best available configuration, 2,034 model-task cells.
MiMo V2.6 Pro, MiMo V2.6 Flash, and MiMo V2.6 Pro UltraSpeed from Xiaomi are now available on AI Gateway.
MiMo V2.6 combines coding, reasoning, and tool use with native text, image, audio, and video understanding. Its 1M token context supports long repositories, tool traces, and multi-session agent work, with structured outputs and up to 128K output tokens.
Choose a model based on the workload:
xiaomi/mimo-v2.6-pro is the larger sparse mixture-of-experts checkpoint, with 1.02T total parameters and 42B activated per token, for complex software engineering and long-running agent work.
xiaomi/mimo-v2.6-flash uses 309B total parameters and activates 15B per token, making it the more efficient option for multimodal automation and everyday agent workflows.
xiaomi/mimo-v2.6-pro-ultraspeed serves Pro at up to 20 times its output speed for interactive and latency-sensitive workflows, with the same capabilities.
To use MiMo V2.6 in a coding agent, install the latest Vercel CLI and run:
Then select xiaomi/mimo-v2.6-pro, xiaomi/mimo-v2.6-flash, or xiaomi/mimo-v2.6-pro-ultraspeed in your agent.
Try MiMo V2.6 Pro, Flash, or Pro UltraSpeed in the model playground.
AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, budgets for API keys, routing rules, and more.
You can now call Jev from TypeSafe AI through AI Gateway using an existing TypeSafe client or the HTTP API, in addition to the AI SDK.
TypeSafe client: Point an existing TypeSafe client at AI Gateway without changing its evaluation calls.
HTTP API: Call Jev directly from any language or framework.
AI SDK: Call Jev from a TypeScript application using AI SDK.
Jev is a probabilistic decision model for software. State goes in, and typed answers come out with probabilities attached, so there's no generated text to parse. Requests are billed through AI Gateway on all three paths, so they appear alongside your other model calls in usage and observability.
Change the base URL and API key. Your systemOne calls, noul questions, and response shapes stay exactly as they are.
New integrations name the model as typesafe-ai/jev and ask one of three question types: boolean returns a probability from 0 to 1, choice picks one option from a set you name, and score rates against a scale you define. This example asks whether an agent should keep working after fixing a bug and passing its tests.
With the HTTP API, POST to /v1/evaluate:
With the AI SDK, run the same evaluation through evaluate:
You can also use Jev through eve, a framework for building and deploying agents with sandboxed compute, human approvals, and evaluations already built in. eve uses Jev as the default evaluation model for automatic model selection, typed evaluations, and automated tool approvals.
See the TypeSafe-compatible API guide to migrate a client, or the evaluation guide for the HTTP API or AI SDK.
Grok 4.7 from SpaceXAI is now available on AI Gateway and 40% off through September 27. The discount applies automatically when you call spacexai/grok-4.7.
Grok 4.7 has a 500K token context window and supports low, medium, high, and xhigh reasoning levels, giving you control over the tradeoff between latency and depth.
Use spacexai/grok-4.7 everywhere you call the model. The same ID works with the AI SDK, the OpenAI-compatible Chat Completions API, and coding agents connected to AI Gateway:
To use Grok 4.7 in a coding agent, install the latest Vercel CLI and run setup:
Then select spacexai/grok-4.7 in fx, Cursor, Codex, Amp, OpenCode, or another supported agent. See the coding agents guide for agent-specific instructions.
To create a new eve agent with Grok 4.7 and xhigh reasoning, pass the same model ID to the initializer:
Grok 4.7 supports Zero Data Retention and disallow prompt training. Requests appear in AI Gateway logs, custom reporting, and budgets alongside your other models.
AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, budgets for API keys, routing rules, and more.
AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests.
Try Grok 4.7 in the model playground, or view all language models or check out more promotions on AI Gateway.
September 21, 2026
What's left for us to build?
Five years into the AI-assisted coding experiment, I rarely find myself saying "wow" anymore. We, as an industry, have honed the prompt-to-code pipeline to just about the finest point imaginable. That's not to say we haven't made a huge leap – I recently used a first generation Retrieval-Augmented Generation (RAG) chat coding assistant for the first time in years, and it felt like I was trying to code by writing in the dirt with a rock.
The progress has been so fast, and so massive, I've come to expect the world. However, when a new model drops these days, I can rarely detect a difference in the code. The harness wars just don't feel that exciting anymore, and the fact that they're all competing on new battlegrounds (cloud infrastructure, multi-agent orchestration, extensibility) makes it clear that we've pretty well nailed the prompt to PR (or issue to PR, or plan file to PR, pick your favorite jumping off point) problem.
That doesn't mean the developers of the world can pack up and start their own farms. I still find myself groaning in pain whenever I need to update something non-trivial in our massive Sourcegraph monorepo.
I talk to engineering leaders at large, enterprise companies every week that tell me the same story. I don't know if it's just a context problem anymore; if it's context availability, or context window exhaustion, or low quality retrieval and wasted effort, or a simple mismatch between the coding agent paradigm and the sheer scale of these codebases. Maintaining existing, "brownfield" code remains completely unsolved.
What's more, as the quality of code generated by new models has begun to plateau (at pretty damn good code), I can confidently say that a new model drop isn't going to solve this problem.
It's part context, part infrastructure, part interaction model. It requires a paradigm that looks absolutely nothing like "prompt to PR."
The simplest version of an autonomous agent is a cron job.
"Every Monday morning at 8am analyze our logs and o11y stack for anomalies and let me know what you find."
"Every evening send me a recap of progress against our Q3 roadmap in Linear."
As groundbreaking as a tool built in 1975 can be, these sorts of autonomous workflows have changed the way I work more than any coding agent harness has in the last couple of years. You can still vaguely see that same "prompt to PR" shape in these agents, but the jump they take from human initiated to self-driven clearly sets them apart.
At a high level, I don't want to be an engineering manager. I don't want to have to tell an agent what to do every single time a change is needed. The promised land is a self-maintaining codebase.
The simplest primitives for the system I'm picturing are:
This system would be autonomous, composable, and fully agentic. Yet, it is still more deterministic than what many thought leaders are proposing; it's a simple, directed graph workflow, with purpose-built agents deployed to solve enterprise codebase problems. The system could be recursive, or even self-modifying, but that's not required. The agent harnesses you choose determine how much rope you give it.
I should be clear that this is not a new concept. Every enterprise I talk to is thinking about agentic Software Development Life Cycle (SDLC) automation. Agent-to-Agent (A2A) was defined partly to enable this sort of workflow. Billions of GitHub Actions run per year, a large portion of which likely have a large language model (LLM) step in them! Yet, massive, unsolved problems like identity, authorization, and budget controls remain outstanding.
My belief is that many of these issues are our own creations, and are solvable at the harness level. We've spent four years generalizing harnesses in pursuit of prompt-to-PR perfection: an agent that can take any human instruction and execute against it!
In the coming years, inside of enterprises, we will move in the opposite direction, and see more narrowly scoped and narrowly authorized agents composed into trigger/function workflows that automate codebase maintenance work safely.
That is the promise of the autonomous codebase.
The latest trend in large enterprise agent rollouts is "enterprise knowledge bases." Let me tell you, it's a great time to be a context shovel seller.
However, I want to be clear that this is a very, very positive development in the cycle. Thousands of enterprise dev teams have moved mountains and spent millions of dollars in token contracts to roll out coding agents to every corner of their engineering orgs, in many cases rewarding and even mandating tokenmaxxing.
The result is a tidal wave of absolutely terrible code that then needs to be reviewed, tested, fixed, instrumented, and ultimately trashed or deployed. Agents can do all of that, too (the Anthropic and Cursor sales reps say)!
What they can't do is tell you, before the merge, that the service or library you changed is used by another part of the organization in a different repo, on a different code host. Or that the blast radius of your agent's work was completely underestimated.
I can't blame those sales reps though. Their products are revolutionary, and can turn any prompt into a PR. In the real world, they're being asked to guess what number you have behind your back. Context, as they say (or in this case, retrieval), remains absolutely essential for agents to do good work.
The autonomous codebase system I describe above is beautiful in its simplicity, but deployed against a two-thousand-repo codebase, it simply won't be capable of doing much of anything right. How can an agent investigate a CVE if it literally can't clone and grep every single repo before its sandbox times out, before it goes into context window exhaustion psychosis, or before the LLM just decides "I've done enough, this should be good?"
Everything worth doing in an enterprise codebase starts with universal code visibility and code understanding. Some things never change: context is king.
With Sourcegraph, the code understanding platform for enterprise.
The Rust Security Response Team was notified that Miri stores all environment variables to target/, allowing secrets to persist in caches.
While not necessary a vulnerability in and of itself, when paired with GitHub Actions caching behavior, it is possible for this to expose secrets to PRs.
GitHub Actions makes it possible to cache directories between runs. Typical setups allow CI runs on main (and other branches) to write to cache, and PRs can only read from cache (preventing cache poisoning). Rust projects tend to speed up CI by caching binaries built by cargo install and sometimes the contents of target/.
PR CI can be triggered by anyone who can open PRs on your repository. GitHub requires maintainer approval for the first PR, but future PRs will rerun CI on every push. Anyone who has previously landed a change can trigger a CI run extracting information from cached target/ and then cover their tracks by pushing a second commit to the PR.
GitHub sometimes hides overwritten commits in its UI, making this kind of attack harder to detect. CI run logs and overwritten commits are also deleted after a few months.
When cargo miri is invoked, Miri needs to retain build-relevant environment variables between runs1. The current code to do so achieves this by storing all environment variables to target/. This, of course, persists when target/ is cached.
If your environment contained secrets, these can now be accessed by PRs via the cache.
Our short term fix for this is to make Miri only preserve CARGO_* environment variables (excepting CARGO_*_TOKEN) and OUT_DIR. In the longer term, Miri and cargo may figure out better ways to inform Miri of the relevant list of environment variables. Note that this patch may not be available on nightly yet.
We also performed an ecosystem scan of GitHub repositories and identified 1 repository with this issue and 7 repositories that do not appear to be vulnerable but should be cautious anyway. We have reached out to those maintainers.
It is likely that our scan was imperfect, so we recommend you check your own GitHub Actions setups if you run Miri.
You are vulnerable if:
cargo miri in CIcargo miri has access to secrets as an environment variable:
env for the workflowtarget directory, usually done via actions/cache or swatinem/rust-cachePossible quick fixes include:
Once done, please clear the cache. Consider rotating any secrets that might have leaked.
The Miri release in the upcoming nightly (2026-09-22) will no longer have this problem.
Even if you do not run Miri, ensure jobs that can write to public caches do not have access to secrets. Many tools do not have special handling for secrets, and assume the entire environment can be written to the filesystem.
We consider it bad practice to have a cache that can easily be tainted by secrets.
If caching target/, it is worth making sure that the inputs to processes that create target/ (anything invoking cargo) do not have secrets available. It is generally rare for standard cargo build/test subcommands to need any secrets or tokens2, so this is mostly a matter of being careful about having secrets exposed as environment variables to the entire job.
Cargo/Miri/Rust does not guarantee that environment variables will be safe from being copied into target/. While we are treating this as a security issue and patching it out of an abundance of caution, this is not something you should rely on in general. Beyond official Rust tooling, it is possible for build scripts to be doing things that lead to the environment being stored in compilation artifacts.
Thanks to Predrag Gruevski of OpenAI for reporting this issue to us. Furthermore, the ecosystem scan was performed using Codex access and credits donated by OpenAI, which we also thank them for.
Issue triage and remediation was performed by Manish Goregaokar, Ralf Jung, Ben Kimock, Weihang Lo, Jacob Finkelman, Walter Pearce, Josh Stone, and Mark Rousskov.
Miri is invoked multiple times by cargo miri for complicated reasons ↩
In theory it could come up with build scripts reading from the network ↩
Some of the most damaging outages are the ones your monitoring never flags: a slice of your customers quietly fails while every health check still reads normal. These "gray failures" leak users and revenue for hours before anyone connects the dots. This post is about catching them early with anomaly detection — how we do it at Databricks with a system called RADAR, and how you can build the same thing for whatever metric matters most to your business. It's written for the people who own service reliability: SREs, platform and data engineers, on-call responders, and the engineering leaders they answer to.
Picture a normal Wednesday. Keeping a customer-facing service reliable is your job, and every dashboard on your wall is green — CPU healthy, latency fine, servers up, database connected. By every signal your team watches, the system looks perfect.
It isn’t.
For nearly seven hours, your monitoring insisted everything was fine while customers walked and revenue leaked.
That Wednesday is a textbook gray failure. On the surface everything looks healthy; underneath, one specific piece has quietly stopped working — and it hurts customers without ever tripping an alert.
Two things make gray failures so sneaky:
Think of it as smoke behind the wall. From the outside the house looks fine, but inside the damage is spreading — and the longer you wait, the bigger the blast radius. Researchers have a name for the underlying problem, too: Microsoft’s Gray Failure: The Achilles’ Heel of Cloud-Scale Systems calls it differential observability — your failure detectors don’t notice a problem even while your users clearly do.
Most teams handle gray failures exactly the way that Wednesday played out: they wait for customers to tell them. Customer reports matter — they’re real human pain — but your customers shouldn’t be your monitoring system. Leaning on reports alone has three problems:
The fix isn’t to stop reading tickets — keep doing that. It’s to add automatic detection that runs all the time and catches what people miss. Concretely, you want something that fires the moment a lot more customers than usual start hitting the same issue at the same time.
| Customer reports alone | Add automatic detection |
|---|---|
| Manual — easy to miss | Catches what people miss |
| Delayed — noticed in days | Fast — noticed in real time |
| Customers suffer in silence | Flags the spike — many users at once |
That’s the idea behind RADAR — Reliability Anomaly Detection, Alerting, and Root-cause analysis. We built it at Databricks to catch gray failures in minutes instead of hours. The name fits: when visibility is low, you don’t wait until something hits you — you scan for weak signals early.
Here’s how we point it at one especially useful signal: user errors.
A gray failure often shows up as a sudden spike in errors that look like the user’s fault. Picture a bunch of users in one region who suddenly can’t spin up a certain type of cluster. Each request fails with INVALID_ARGUMENT - an error that is politely saying, “this one’s on you.”
But when many users hit the same “your fault” error at the same moment, it stops being their fault. It’s ours. That spike is exactly the pattern RADAR is built to catch.
RADAR turns that instinct into a pipeline with four stages:
Running RADAR on ourselves changed the shape of these incidents. Before, we waited on customer tickets to discover such incidents, leading to days of delay. With RADAR, we achieved a 95% reduction in incident-discovery time, at over 90% precision, with no human needed to spot the pattern. As a result, we are able to keep the blast radius of gray failures contained.
Here’s the part that matters most for you: RADAR doesn’t care what the metric is. We happen to point it at user errors, but the same pattern works anywhere a number can quietly go wrong:
It’s the same pattern under different settings. Anywhere you have something that could quietly go wrong, RADAR applies.
The best news: every piece you need is already on Databricks. Map the four stages to the platform and it looks like this:
And the whole thing deploys as a single unit through a Declarative Asset Bundle (DAB).
Wiring all those parts together by hand is the annoying bit — so we removed it. We distilled the entire internal RADAR system into a single scaffold: one markdown file that works like a recipe, mapping each part of RADAR to a specific Databricks component (collect and store → a Delta table; detect the anomaly → a job; alert and dedupe → a ticket; visualize → a dashboard).
Then comes the payoff. You bring your own metric — wherever your signal lives — and hand the metric, the scaffold, and a short prompt to an AI agent. It builds the whole RADAR system for you, live on Databricks. You can follow the Github instructions on how to build one from a single prompt.
Two things to walk away with:
Get the RADAR scaffold on GitHub
Because the best outcome isn’t a faster response to angry customers — it’s that your customers never have to discover your incidents for you.
Enterprise teams on Flexible Commitment plans can now use Spend Management, already available on Pro, at no additional cost.
You can set a budget at any time in Spend Management settings.
Set a budget per billing cycle, and when your team's metered usage approaches or crosses it, Spend Management can send email notifications, trigger a webhook, or pause the production deployments of all projects. The amount governs the metered usage that draws down from your prepaid balance. Setting an amount does not stop usage on its own; pausing is opt-in, and it does not stop AI Gateway or v0 usage.
Learn more in the Spend Management docs.
mcp-handler now has experimental support for WebMCP, the proposed web standard for exposing tools to in-browser agents. Add a single script tag to your site, and your existing MCP tools become available there too.
Opt tools in by adding them to the experimental_webMcp object:
Then load the script from your MCP endpoint with the ?webmcp-script parameter:
The script registers those tools with the page and proxies each call back to your MCP server as the signed-in user, so authenticated tools work without a browser-side OAuth flow.
Upgrade to mcp-handler@2.2.0 and read the documentation to get started.
How can a Dutch poffert arrive at your door, 450 miles (700 km) away, the very next day? It’s thanks to careful logistics optimization — especially the middle-mile segment. This part of the journey covers the longest distance, represents a huge portion of the overall costs, and most importantly dictates whether your poffert arrives fresh or stale.
Logistics research has historically focused on the first mile (moving goods from producers to initial consolidation points) and the last mile (delivering to the consumer). Both stages are typically modeled as variants of the vehicle routing problem (VRP). However, the middle mile, which handles the bulk movement of goods between distribution centers at a regional or continental scale, has received significantly less attention in operational research despite representing a sizable portion of total logistics expenditure. Academic progress in middle-mile optimization has been hindered by a lack of public, high-quality data. Indeed, most logistics companies treat their network topologies and demand volumes as highly sensitive proprietary information.
Middle-mile logistics has many applications in the supply chain. These range from moving goods from factories to consumers in e-commerce and retailers in city centers, to carrying the right parts from individual plants and central storage to car manufacturers and shops. It also includes time-sensitive movements, like transporting temperature-controlled pharmaceuticals between storage facilities and hospitals.
To address the lack of standardized data for this domain, in “A Novel Instance Generator for Simulating Middle-Mile Logistics Networks”, we introduce MilleMiglia, a C++ instance generator designed to create realistic benchmarks for middle-mile delivery problems. This work serves as a foundational building block to enable future research results. In this post, we explore the unique constraints of the middle mile and how MilleMiglia successfully captures them to generate realistic, privacy-preserving data. The source code and documentation are available on GitHub.
The distinction between first-, middle- and last-mile logistics lies in the journey of an individual shipment. Throughout this journey, the primary operational goal is to efficiently use a fleet of vehicles to visit multiple locations. Consider the example of a manufacturer that sells goods on a typical online marketplace to reach individual consumers.
In first- and last-mile logistics, a specific shipment remains in a single vehicle from its origin (the factory in the first mile, the distribution center in the last mile) to its destination (the distribution center in the first mile, the customer in the last mile). These VRPs involve optimizing a fleet of several vehicles over a limited time span, usually a single day. The optimization challenge is essentially one of assignment and sequencing: determining which vehicle handles which set of shipments, and in what order.
In our example, the first mile corresponds to the collection of the items that have been sold by the manufacturer (e.g., pofferts) while the last mile covers the final delivery to the consumers (some of them being quite hungry!). In both cases, a single truck transports goods to or from the regional distribution center. However, if the manufacturer and the consumer are in different regions, middle-mile logistics bridge the gap between far-away distribution centers. For instance, goods from a manufacturer in Groningen (Netherlands) would first move to the regional distribution center in Utrecht, travel to another center in Paris (France) before being delivered to a consumer in Versailles.
In contrast to the first and last mile, the middle mile functions as a relay race. A single shipment may be transported by several different vehicles across a continental network before reaching its final destination, maybe a week after departing. At intermediate distribution centers, the shipment may be unloaded, sorted by destination, and consolidated with other freight before being loaded onto the next vehicle. This creates a complex synchronization problem: the shipment must arrive at a distribution center within a specific time window to catch its scheduled outgoing truck. If it misses its scheduled connection, it will have to sit at the distribution center until the next cycle, leading to significant delays.
In our example, once the manufacturer’s goods arrive in the Utrecht regional center, they are loaded onto the first truck for Antwerp (Belgium) to arrive the same day. Because the most immediate truck to Paris is full, and let’s say the customer opted for standard shipping, the goods take the second truck the following day from Antwerp to Paris. The parcel arrives in Paris on the night of the second day, where it enters the last-mile network for the final delivery to the customer the next day.
The mathematical structure of middle-mile delivery differs from the standard VRP in several key ways.
In a traditional VRP, such as those solved by open-source tools like OR-Tools or specialized APIs like Google Maps Platform Route Optimization (GMPRO), the goal is typically to optimize tours for a fleet. The focus is on vehicle routing and sequencing of stops to meet tight customer deadlines. Unlike last-mile delivery, middle-mile logistics has the added flexibility of moving between trucks. We model this added dimension as a multi-commodity flow problem on a space-time graph. In these models:
While many academic VRPs are defined with few constraints, middle-mile operational constraints are difficult to relax without distorting the structure of the operational problem at hand:
Because of these dependencies, existing VRP solvers cannot apply to the middle mile. The problem requires a sequence of intermediate distribution centers and assignments across multiple vehicles, often over a multi-day time horizon.
MilleMiglia uses a variety of statistical distributions to ensure that the synthetic networks look like actual distribution networks without revealing any private information:
The distributions interpolate between publicly available information from industrial actors and privately disclosed data.
MilleMiglia is written in C++. It uses Protocol Buffers for data serialization, so that the data in its diversity can be stored in a single file for each instance. Thus, the generated instances are compact and can be easily consumed by solvers written in different programming languages.
Unlike VRP instances, with many variants such as the CVRP (with capacities), VRPTW (with time windows), or PDPTW (pickup and delivery with time windows) to capture diverse operational requirements, the structure of our middle-mile data format embeds all interesting constraints in the same file format: fixed vehicle schedules, distribution-center throughput limits, and complex synchronization prerequisites are all fundamental elements of the problem structure.
The intent is to provide the community with a range of instances:
The generator also enables learning scenarios, as it can create huge data sets to train ML algorithms.
MilleMiglia is the first step toward a standardized benchmarking suite for middle-mile logistics, similar to what CVRPLIB (Capacitated Vehicle Routing Problem Library) provides for the VRP community.
This project comes from an ongoing collaboration between Google and academic partners at UniBrescia and ENPC Paris. Beyond instance generation, we are currently working on a specialized solver and API designed specifically for middle-mile operational problems. This solver aims to leverage the unique structure of middle-mile flows.
By open-sourcing our instance generator, we hope to encourage the broader research community to focus on the operational challenges of the middle mile, leading to more robust and efficient global supply chains. We hope to start a challenge on middle-mile problems to increase the interest from academics and industrial solver developers in this underlooked-but-in-need-of-optimization venue. Anyone interested in the field can start by looking at a sample instance hosted in the GitHub repo.
This research was primarily conducted by Aymane Lotfi during his Student Researcher tenure at Google and by Matteo Petris (now at ENPC Paris), as part of an ongoing collaboration. Thanks to Thibaut Cuvelier and Bruno De Backer for their contributions to this work. Special thanks to Claudia Archetti (now at UniBrescia) for her leadership and support.
At Databricks IT, our vision is to empower people to work from anywhere without putting company data at risk. On mobile devices, the focus has shifted from merely checking email to getting real work done. People use Slack, approve requests, and access internal apps on their personal phones, and they expect it just to “work”. Additionally, the proliferation of AI Agents and tools like Genie, Omnigent, and Claude Code has shifted the way people work, with a growing desire to move desktop sessions to phones on the go to avoid losing deep work and context. Mobile BYOD makes that harder, because work and personal life share the same device. A personal phone is different from a company laptop. We, as in Databricks, do not own it, certain access cannot be restricted, and we have no right to view its contents. The challenge we set out to solve was simple to state and hard to do: protect corporate data on devices we don't own, without ever intruding on user privacy.
This post walks through how we approach mobile security internally. Instead of focusing on just one product, our approach consists of four layers, each serving a specific purpose: device management, authentication, zero trust, and application management.
Before securing a phone, we must establish a trusted method for installing apps, configuration profiles, certificates, and security policies. This is achieved through Mobile Device Management (MDM), the foundational layer on which all other components rely.
The most important decision for personal devices is how to enroll them. We utilize Account-Driven User Enrollment (ADUE) on iOS, tailored for the "bring your own device" scenario. We avoid full device management on personal phones. User enrollment manages only the work-related components, never the device itself, which prevents us from taking control or imposing restrictions we have no business imposing on someone's personal phone. There have been notable security incidents in the wild where the absence of full wipe capabilities is a major benefit and helps build user trust in adopting Mobile Security controls.
During enrollment, the phone establishes a separate, encrypted workspace for work data linked to a managed corporate identity, while personal apps, photos, and messages remain fully private and inaccessible to us. On Android, the Work Profile offers a comparable clear separation.
MDM is often mistaken for the finish line. In reality, it's just the starting point. It allows us to establish a baseline, but it doesn't decide who gets access or check whether the device is trustworthy. Those capabilities lie in the subsequent layers.
Once device management is established, the next step is determining access. Authentication (authN) and context-aware signals act as the gatekeeper for every company resource and are managed through our identity provider.
No request is granted on identity alone. Every request is weighed against a set of signals that together decide whether the gate opens. First is identity, confirming the user is who they claim to be, backed by strong, phishing-resistant, passwordless, multi-factor authentication. Next is the device, confirming the request comes from a known and managed phone rather than an unregistered or unknown one. Finally, the network path allows access only when the request arrives through our trusted tunnel. This is where authentication quietly leans on the next layer. The gate verifies that requests come from our secure network addresses, and those addresses are valid only while zero trust deems the device healthy. If any of these signals are weak or missing, the gate stays shut.
For most organizations, this is the highest-impact control you can turn on, and it's the right thing to enforce first. No trusted signals, no access.
Authentication determines whether access should be granted, whereas implementing a Zero Trust Network Access (ZTNA) solution assesses the device's current health and provides real-time enforcement, not limited to login events. Work-related traffic is routed through a secure tunnel via a per-app VPN, ensuring personal traffic remains separate. Posture is evaluated continuously while the device is in use, not just once at the door. For example, our policy can automatically identify a vulnerable or compromised OS and block that device's traffic immediately, without manual intervention.
The fundamental principle is to deny access by default and permit only when acceptable conditions are met. Rather than granting broad network access, ZTNA grants access only to specific applications, while both the user's identity and the device's health remain valid. If either slips, access drops. We focus on our most critical applications, where continuous verification matters most.
When deploying an app to a mobile device, the first step is installing it as a managed app. This ensures the copy of the app on the phone is controlled by us, not a self-downloaded version. How we then secure company data depends on the app. Sometimes, we push a managed configuration via the MDM, such as settings that restrict data to within the app or pre-configure secure sign-in. Certain apps include their own enterprise management features, while others offer tenant-level controls, such as blocking copy and paste outside the app, managed through the service rather than the device. When effective, corporate data remains within a secure boundary, even on personal devices.
Mobile device management provides the app, while application management determines its functionalities.
We can only reliably enforce the managed version of an app when the app or the service itself is compatible, either by refusing to run without our managed configuration or by accepting traffic only from our secure tunnel. When an app supports neither, our identity policy can confirm the device is managed, but it cannot tell whether the specific copy in use is ours or one downloaded straight from the app store. We solve this by tiering apps by sensitivity, strictly favoring apps that support enterprise mobility controls, steering web apps through an enterprise-managed browser so a single controlled channel covers many services at once, and requiring support for managed configuration or network restrictions when evaluating new mobile applications.
A mobile security program's success depends on employee enrollment. Even the most sophisticated controls are useless if staff perceive the company is secretly monitoring their personal devices, leading to low participation. Therefore, we prioritize employee experience and transparency as crucial components.
Our foundation is complete transparency regarding privacy. We clearly communicate, in plain language, what company staff can and cannot access and what actions they can take on personal devices. We documented this policy, reviewed it with Legal and Privacy teams, and made it easily accessible before enrollment. Trust is built through this high level of transparency.
We use this model to build and secure our own mobile apps - including the Genie mobile app – in which Databricks IT was customer zero.
Databricks IT collaborates closely with Engineering rather than acting as a stakeholder. We work alongside them, recommending additional controls that Genie continues to use to this day. Genie is deployed to our mobile fleet as a managed app, with access limited through identity controls to ensure only authorized users on managed devices can use it. Traffic is sent through our secure tunnel for security and regular posture checks. Since the foundational security layers were already in place, Genie did not need a separate mobile security solution and instead used our existing infrastructure.
As customer zero, Databricks IT had the opportunity to guide product development and produce documentation that helps our customers deploy the app. We provided feedback to Engineering on enrollment, mobile access processes, and the security model needed for mobile. This ongoing input helps shape Databricks offerings like Genie and Omnigent. This partnership enables many future internal and customer-facing applications that deliver a secure, mobile-first experience.
No single control can secure a personal device. Instead, security depends on multiple layers working together. Start with mobile device management, gate access through identity and device status, run continuous health checks on vital signals, and contain data at the app level wherever feasible. Roll these layers out gradually and always respect user privacy, so security feels inherent rather than imposed. On devices the company doesn't own, willing participation is what makes security effective.
Sign up for our session at JAMF Nation User Conference to learn more: https://reg.jnuc.jamf.com/flow/jamf/jnuc2026/home26/page/sessioncatalog/session/1774388531566001paC8
Please visit https://www.databricks.com/trust to learn more about our platform security and compliance capabilities
Cloudflare operates at a scale so big that even after working here for years, it doesn’t seem real. We have thousands of servers all over the world with petabytes of RAM and millions of CPU cores, and all of it is pushed to the max. As vast as those resources feel, they are still finite, and when you need every service to run on every node, it doesn’t leave room for wasted space.
At this scale, small improvements are greatly magnified, so even 1%-at-a-time improvements are worth celebrating. And some tweaks add up to a lot more: in this post, we’ll look at how small changes to a single algorithm reduced the memory footprint of one of our Pingora-based services significantly. That allowed us to reclaim more than 100TB of RAM globally, on top of the 100TB of memory the DNS team was able to shed last month.
Maintaining equitable resource sharing between teams is not easy, especially in large organizations. One of the ways Cloudflare ensures the balance is kept is through the tireless efforts of the wonderful Performance team.
This story starts with a ticket filed by Ivan who found: Excessive memory usage from pingora-ketama in Pingora Backend Router. The finding was that our internal load-balancing service, Pingora Backend Router (yes, PBR), was using significantly more memory than expected — specifically in structures associated with pingora-ketama, which is our open-source library for handling consistent hashing.
In order to talk about how we addressed this seeming overuse of memory, we need to talk about what consistent hashing even is, why we are using it in PBR, and how it became so memory hungry. Along the way, we’ll learn some Rust and even a little math.
Consistent hashing is a widely used method for distributing tasks across multiple servers in a way that does not require large changes when servers are added or removed. Internally we use it to route cacheable requests to servers by URL. This allows us to keep only one copy of a file stored per data center and gives a stable way to find the location of each file. We have mentioned this system before, but let’s take the time to walk through how and why this algorithm is used and how it works.
The key concept of consistent hashing is that while hash functions can accept any kind of input, their output is limited to a single unsigned integer (32, 64, or 128-bit integers depending on which hash function). This allows us to relate tasks and servers to each other in a consistent way. Most discussions of consistent hashing have you think of that output space as a continuous, circular ring that wraps around from its max value to zero. This depiction makes for some nice visualizations, but it can also make the simple concept of integer ranges seem more complicated than it needs to be. For our discussion, we’ll represent the 32-bit output of our hash function as a number line.
Now, let’s say we have a set of servers, A, B, & C, and a set of tasks t-z. We can map each onto the number line based on the hash of their representative values, so something like IP addresses for servers and cache keys for tasks.
Assigning tasks to servers is now just a matter of finding the first server to the left of each task. We can represent this visually by coloring in the region of hashes that will be associated with each server. Notice that the range covered by server C wraps around to the beginning, hence the idea that hashes exist in a ring.
And that’s it. At a base level, consistent hashing is this simple — but it doesn’t take long to see that there is room for improvement. Notice that the range covered by server A in our example is significantly larger than that of either B or C. This is a problem because the fraction of the requests a server handles is going to be proportional to the size of its range on the number line. Ideally we would like to guarantee each server will have an equal size, but because hashes are essentially random numbers, we have to talk about the size of the regions in terms of statistics. 😨
First: don’t panic. I promise I'm not about to lie to you and that we will stay safely within the bounds of a day-one probability lesson. When we talk about statistical distributions, there are two big factors that help us quantify uncertainty in helpful ways: expected value and standard deviation. In (over-)simplified terms, expected value gives us a point where measurements based on a distribution will be centered, and standard deviation tells how close to that central point most measurements are likely to be.
For consistent hashing, we can calculate these factors for the fractional size of the range associated with one of N servers. (Details on where this formula comes from later).
In terms of concrete numbers, let’s say we have 100 servers. The formulas above give:
That tells us that we can expect that the range each server handles will be centered around 0.99% of the total and most of the lengths to fall within 1% of what's expected. This sounds good until we realize that that’s 0.99% of the total length. We need to scale the standard deviation by the expected value to see how big the error is as a fraction of the target size. This value is called the coefficient of variation.
The simplicity of consistent hashing is a double-edged sword. It’s easy to understand and implement because everything is turned into easily-relatable hashes on the same numberline, but any improvements to the system will also need to be relatable to that numberline. That means the solution to any consistent hashing problem can only be more hashes. It’s less like a golden hammer (a tool with which all problems look like nails) and more like a golden nail in that it turns all tools into hammers.
To solve the problem of imbalanced workloads, we can add multiple hashes to represent each server instead of just one. We’ll get to the math behind this momentarily, but it should make some intuitive sense that while each individual range has a large standard deviation, adding a bunch together should make their total size even out. If we take our three-server example from the above diagrams and add two more hashes at random for each server, we see that it helps even out each server’s workload.
This is an admittedly contrived example. The random nature of the system means there’s no guarantee how much improvement you will get from adding 2 additional hashes per server, but it should make some intuitive sense that combining more of these hash segments together produces a more even distribution. Each segment in the sum has a chance of balancing another. Maybe one is too short; maybe one is too long. This is essentially what the law of large numbers tells us should happen… The obvious problem is it only works for large numbers. In NGINX, the baseline number of hashes per server is hardcoded to 160, and Pingora uses the same value as the default. I’ll spare you the math for now, but if we go back to our 100-server example, if we use 160 points per server instead of just one, the coefficient of variation (which we can think of like an error margin) drops from about 99% to about 8%, a significant improvement.
We saw above that increasing the number of hashes per server by a constant amount allows us to improve how evenly workloads are distributed per server, but what if we don’t want to distribute the work evenly? In Cloudflare’s case, we have some servers that have more storage space than others, so it would be better to have the number of requests allotted to a server be proportional to its disk space. One way to accomplish this is with the ketama algorithm. The naming is a little funny because the algorithm is named after the library where it was first implemented, and the library was named … well you can google it 😶🌫️.
For us, since we want workload to be scaled based on storage, we can use the disk space as the weight, which is exactly what the Pingora team has been doing for years. Elsewhere in the company where workloads are more compute-intensive, weights might be based on CPU or GPU count.
The last problem we need to address is that so far we are working under the assumption that any server can handle any request, but in practice that is not the case. Things like compliance requirements or enabled caching features mean only a subset of servers can handle any particular request. Unfortunately, unlike before, we can’t solve this problem by adding more hashes to the same ring. We have to add completely new rings, and not only that — every combination of features potentially needs its own specific ring!
One big improvement came from Zaidoon, who had an insight about our struct for storing hashes in PBR. That struct looks like this:
Unfortunately, Rust doesn’t make it that easy. Changing the size of the index as we did above does nothing to reduce the memory footprint. This is because Rust has alignment rules that require the size of a structure in memory to be a multiple of its largest (or “most aligned”) field. In this case, the hash is the largest with four bytes, so when stored in memory, a Point is required to have size $mN \times 4m$, so the minimum size is eight bytes.
Luckily there are well-known ways around this. You (meaning me) might be tempted to use #[repr(packed)], but that is controversial for good reasons. A safer but less readable solution is to store the hash and index as raw byte array and access them with getters. Both methods compile to the same thing.
This simple (if wordy) change reduces the amount of memory used for consistent hashing by a whopping 25%! In order to do better than that, we’ll need to jump back into the math, so everybody hang on to something; this is the home stretch.
You may have noticed that we gave the formula for the standard deviation for the case where there is only one hash per server. Deriving the formula for the case where there are $m k m$ hashes per server is not easy, and most sources only give you an approximation or an asymptotic limit, but not us. I might not be a statistician, but I grew up with a calculus teacher (Hi, Mom!), and I wanted to know the actual value. The full derivation is in a supplemental post, but here is the payoff.
To see how increasing the hash count improves the accuracy, we need to look again at the coefficient of variation.
The predictions from my beautiful math only work if we think about hashes in a continuous ring, but in practice we use 32-bit numbers for the hashes that have the potential for collisions, and the probability of collisions goes up surprisingly quickly as the number of hashes increases (see the birthday paradox). Collisions matter because in the ideal case, every hash contributes to the volume and distribution of requests handled by the associated server, but a collision means some contributions are randomly dropped, introducing unpredictable error. If we compare some simulated results with 32-bit hashes with the predicted error rate, we can see that for data centers with 2048 servers, the error rate increases: between 10,000 and 100,000 hashes per server.
Ultimately, even though this realization feels kind of bad, it’s great news for our plan to reclaim some RAM! Now that we have some math to back it up, we determined that we could decrease the number of hashes we were generating for each server by 90% without incurring any appreciable error, so that is what we set out to do.
There was one more problem: changing the hash ring changes where some cacheable requests go. Even if the new ring is better, switching the whole network at once would effectively invalidate almost all cached content. It would turn a memory optimization into an apocalyptic increase in origin traffic.
So we did not make this a single global flip. For a while, PBR carried both versions of the cacheable load balancer in memory: the old ketama ring and the new smaller one. Each request used our normal migration framework to decide which ring should select the backend. That meant the rollout decision was stable per request hash, and it also gave us a clean rollback path. If anything looked wrong, we could send new requests back through the old ring without redeploying PBR.
We then rolled the migration out in layers. We started with small validation locations, moved through progressively larger groups of data centers, and only then continued toward the rest of the world.
The important part was that we controlled two dimensions independently: how much traffic used the new ring, and where that traffic was allowed to move. A plain global percentage rollout would have spread cache churn everywhere at once. Data-center-scoped rollout kept the blast radius small and made it much easier to tell whether a change was actually safe.
During the migration, we watched backend-selection traces, ring-version counters, PBR connection errors, process memory, startup time, cache behavior, and origin traffic. Once the migration reached 100%, we removed the temporary old-ring path, and voila!
The chart above shows the comparison of the memory used by PBR the week of the change compared with data from a few weeks before, as well as the result of subtracting one from the other. The sharp drop is the day where the version of PBR with the large (now unused) hash rings was decommissioned forever. Looking at the difference, we get the satisfying result that our changes dropped the used memory by 100TB!
All the changes we talked about in this post are available now in the pingora-ketama crate in the form of a (for now) unadvertised cargo feature. The v2 ring has the compacted storage format, a faster sorting method, and the ability to scale the base number of hashes per node. Our focus in making these changes had to be on stability and control, so the v1 ring is identical to what pingora ketama has always used, and the library makes it possible to run both simultaneously and decide on a request-by-request basis which to use and when.
Beyond trying our literal consistent hashing changes, I would like you to take away from this some inspiration to dig into your own systems to see what “simple” or “obvious” decisions are hiding potential wins, if you’re willing to get into the numbers. You might not be able to solve all your problems with Rust, but math is universal.
v0 now installs private packages from npm and custom registries using credentials stored as shared environment variables on Vercel.
This makes it easier for teams to build with their existing design systems, component libraries, and internal packages directly in v0.
To get started, add one of the following as a shared environment variable on Vercel, scoped to Development and/or Preview:
Use NPM_TOKEN for private packages hosted on registry.npmjs.org.
Use NPM_RC to configure custom or multiple registries.
NPM_RC supports scoped registries and references to other environment variables. For example, configure an organization scope such as @acme for GitHub Packages, or direct package requests through a private JFrog Artifactory registry.
Credentials can be marked sensitive, and v0 never exposes them to the model or writes them to the sandbox filesystem.
You can view the integration status from Settings → Integrations in v0.
Learn more in the private dependencies docs.
Vector search is a critical component of generative AI, retrieval-augmented generation (RAG), and data agent architectures, but sometimes vector search alone isn't enough. While vector embeddings are incredible at understanding conceptual meaning, they stumble on specific alphanumeric IDs and exact product SKU numbers. To build truly robust search and AI applications, you may need the combination of semantic vector search and traditional exact keyword full-text search — what we call hybrid search.
In search, Best Matching 25, or BM25, is a key algorithm used to estimate how relevant a document is to a given query. Until today, if you wanted BM25 ranking with AlloyDB or Cloud SQL, you needed to add an additional full-text search backend. This introduced data silos, sync lags, and operational complexity. Today, we are eliminating the friction of maintaining a separate full-text search backend altogether, with the preview of the native BM25 index in AlloyDB and Cloud SQL for PostgreSQL 17+, made possible through the open-source pg_textsearch extension created by Tiger Data.
Now, with a unified hybrid search backend, you no longer need to provision, manage, or pay for separate systems to get state-of-the-art full-text retrieval. It all happens directly inside your database, where your operational data lives, delivering:
Industry-standard keyword ranking: Powered by Tiger Data's pg_textsearch, bring lightning-fast, C-optimized BM25 scoring directly to your Postgres tables.
No complexity, total consistency: Eliminate the data duplication, ETL pipelines, and synchronization lag that you get when you maintain multiple backends for vector and full-text retrieval.
Supercharged semantic search (AlloyDB exclusive): Get up to 6x and 10x faster vector search queries (when compared to standard PostgreSQL) with ScaNN and HNSW index types.
If you’ve used PostgreSQL's built-in ts_rank for full-text search at any meaningful scale, you already know its limitations. Ranking quality degrades as your corpus grows. There’s no support for inverse document frequency, so common words carry the same weight as rare ones. There’s no term-frequency saturation, so a document that mentions "database" 50 times outranks one that mentions it once.
BM25 is the information retrieval gold standard, providing inverse document frequency (rarer terms matter more), term frequency saturation (repetition doesn't dominate), and document length normalization. You can learn more in this blog post by Tiger Data about how they built a BM25 search engine on PostgreSQL pages.
Here’s how to get started with BM25 full-text search on both AlloyDB and Cloud SQL. Consider a sample table, cymbal_products, that contains the unique identifier uniq_id, a product_name column, a product_description column containing a text description of each product, and a generated product_embedding column. cymbal_products contains information on various retail products, including indoor and outdoor plants.
To use BM25, enable the pg_textsearch extension.
Create the index on the product_description column from the cymbal_products table.
A BM25 full-text search query can be executed using the <@> special operator. In the snippet below, we search for ‘cherry tree’.
Sample output is shown below. A more negative score indicates a stronger relevance match.
Setting up a hybrid search system in AlloyDB is simple. You can create both your vector and keyword indexes on the same table and merge the results seamlessly using the hybrid search user-defined function (UDF).
Here is how to create a ScaNN vector search index:
AlloyDB provides an out-of-the-box hybrid search UDF that makes it very simple to run hybrid search queries. The UDF merges the ranked results from each search component into a single, unified list using the Reciprocal Rank Fusion (RRF) algorithm. This query utilizes the UDF to perform a vector search for ‘trees that grow taller than houses’ and a keyword search for ‘California’ in the product description.
As shown in the sample output below, results are ranked in descending order of their RRF scores.
Here, hybrid search bridges the gap between semantic intuition and exact keyword matching. While vector embeddings excel at grasping conceptual queries, like "trees that grow taller than houses", traditional full-text search provides the pinpoint precision needed for strict identifiers like "California." By fusing the two, AlloyDB helps ensure your application prioritizes highly specific, locally relevant results like ‘California Sycamore’ right at the top of the list.
In Cloud SQL, you can create both your vector and keyword indexes on the same table and merge the results seamlessly using Common Table Expressions (CTEs) and coalescing the RRF score, as shown below.
Here is how to create an HNSW index in Cloud SQL.
Here is the hybrid search query.
The resulting output is identical to the AlloyDB hybrid search results shown above.
Watch how this all comes together in this demo video.
Today, we are excited to announce enhancements to the borderless Lakehouse, our answer to how data engineers, data scientists, and increasingly, AI agents, can query governed data directly where it lives.
To reason accurately and automate complex enterprise workflows, agents and data consumers of all types need fast, unified access to an organization's complete data estate, joining customer records, transaction logs, and operational telemetry across clouds. However, modern enterprise data is rarely confined to a single location; data estates often span Amazon S3, Azure Data Lake Storage (ADLS), Google Cloud Storage, operational databases, and SaaS platforms like Salesforce, SAP, and Workday. Historically, uniting these distributed datasets required brittle ETL pipelines, duplicated storage, and prohibitive cross-cloud data transfer costs.
We introduced the borderless Lakehouse earlier this year to let organizations query and activate data in place across clouds. By adopting the Apache Iceberg REST catalog specification, we federate directly to catalogs such as Databricks Unity Catalog, AWS Glue, and Snowflake Horizon. We also introduced Partner Cross-Cloud Interconnect to establish high-bandwidth, private links to other cloud providers, lowering per-gigabyte transfer costs compared to the public internet.
Today, we are taking multi-cloud efficiency a step further by optimizing how much data needs to be transferred across the wire in the first place.
We are excited to announce two new features to help further reduce costs of querying cross-cloud data. First, the preview of cross-cloud caching for Lakehouse transparently accelerates cross-cloud queries in BigQuery and cuts remote transfer costs by caching frequently accessed data locally in Google Cloud. Combining standard Iceberg columnar compression with cross-cloud caching means you often only need to transfer under 5% of the data you process across clouds, which helps lower the Total Cost of Ownership (TCO) to make cross-cloud analytics and AI viable at enterprise scale. In addition, BigQuery cross-cloud connections are also available in preview to query non-Iceberg data in other clouds and accelerate workloads.
Cross-cloud caching meets enterprise performance and security requirements with no knobs to turn or storage to manage to accelerate your queries. Some of the mechanisms used under the hood are:
Sub-file block granularity: Instead of transferring entire multi-gigabyte files across clouds when a query touches only a few columns, cross-cloud caching operates at the sub-file block level for columnar formats like Apache Parquet. BigQuery caches only the specific column chunks and dictionary pages projected by the query. On a cache miss, BigQuery fetches the needed data from the remote cloud to answer the query, and saves a local copy in the cache for future queries, drastically cutting network transfer and latency on repeated workloads.
Default encryption at rest: Cached data blocks are encrypted at rest by default using Google-managed encryption keys (GMEK) so that temporary cache storage maintains the same enterprise-grade security posture as native BigQuery storage without extra overhead.
Tenant and regional isolation: Cache entries are strictly partitioned by project and catalog boundaries to help prevent cross-tenant data exposure. Lakehouse anchors both the local cache and query execution strictly to the configured Google Cloud region (e.g., us-east4) to support compliance with regional data residency requirements when querying remote clouds.
Freshness checks: Multi-cloud caching often forces a trade-off between speed and freshness. To avoid stale reads, BigQuery fetches remote object metadata before using cached data to ensure the data hasn’t changed and the user still has access. Any upstream table modification prompts BigQuery to fetch new files, while unreferenced cached blocks expire automatically, delivering local query speed with single-source-of-truth accuracy.
For more details on caching mechanics, statistics counters, and regional considerations, see the Lakehouse intelligent caching documentation.
So how does this work in day-to-day operations? Consider an e-commerce team querying a 10 TiB Iceberg sales table (aws_lakehouse_catalog.sales.web_sales) in Amazon S3, federated into Lakehouse from Databricks Unity Catalog. During evening promotional drops (8:00–9:00 PM), analysts query historical transactions to identify which storefronts drive peak volume and revenue among high-intent demographics:
On this initial cold run, the local cache is empty (cacheBytesRead: "0"). BigQuery applies partition pruning and column projection to transfer only the required Parquet byte ranges from Amazon S3 over Partner Cross-Cloud Interconnect:
Logical data processed: BigQuery processes 214.5 GiB across the 10 TiB dataset.
Standard Iceberg compression efficiency: BigQuery reads 24.1 GiB from S3 thanks to standard Iceberg columnar compression with Zstandard (zstd) — an 8.9:1 compression ratio. As these sub-file Parquet blocks arrive in Google Cloud, BigQuery populates the regional cache.
In practice, analysts and agents rarely run the exact same query twice in a row. To drill deeper into fulfillment methods, the analyst modifies the query by adding the shipping method dimension (sm.sm_type):
Job statistics for this follow-on query show:
94.8% cache hit rate: BigQuery serves 24.1 GiB of previously queried columns directly from local cache.
Granular remote retrieval: BigQuery transfers only 1.33 GiB from S3 for the new ws_ship_mode_sk column and ship_mode table.
Sub-file flexibility: Modifying a query reuses cached column chunks and transfers only newly required bytes.
When thinking about TCO of cross-cloud queries, the top two factors to account for are:
Compression ratio: when using default compression algorithms (Zstandard/zstd) on Iceberg, columnar data is highly compressible. If you assume that your data achieves a compression ratio of 8:1, it means every 1 TiB of logical data processed only requires ~128 GiB of data to move over the network.
Cache hit rates: when data is retrieved from cache rather than across the network because it was recently accessed, a network transit is avoided. Assuming 80% of your data results in a cache hit it means for every 100 GiB of physical data accessed only 20 GiB moves over the network.
Taking both factors and assumptions into account, for every 1 TiB of data your organization processes, you only need to transfer ~26 GiB across the network (under 3% of total data processed). Combining this reduction with Partner Cross-Cloud Interconnect lowers TCO enough to make cross-cloud analytics and AI cost-effective at petabyte scale.
Alongside cross-cloud caching, the preview of BigQuery cross-cloud connections lets organizations connect BigQuery directly to open-format data in Amazon S3 and Azure Storage.
Understanding when to use catalog federation versus cross-cloud connections is straightforward:
BigQuery cross-cloud connections (for raw files): For standalone files (CSV, JSON, ad-hoc Parquet) without an Iceberg catalog, cross-cloud connections let you create BigQuery external tables referencing remote bucket paths directly.
Lakehouse catalog federation (for Iceberg): For Iceberg data managed by catalogs like Databricks Unity, AWS Glue, or Snowflake Horizon, Lakehouse automatically synchronizes schemas and table snapshots to simplify the user experience and ensure users are always querying the latest data.
Cross-cloud connections serve as the modern architectural evolution by using standard BigQuery compute workers in Google Cloud regions rather than compute workers in other clouds. This approach helps unlock global region availability and provides full BigQuery feature parity — including with BigQuery AI and Gemini on remote files.
The cross-cloud caching capabilities for Lakehouse applies to data queried from BigQuery cross-cloud connections as well as Lakehouse catalog federation. To learn how to create connections and query external bucket paths, see the BigQuery cross-cloud connections setup documentation.
State and local governments are driven by a shared mission to provide responsive, equitable, and accessible services. However, achieving this goal is often hindered by legacy technical debt, disconnected data, and heavy administrative burdens that slow down mission delivery.
This systemic fragmentation creates costly operational bottlenecks across the public sector, including:
Today, agents can help break down silos, automate routine and manual tasks, and enable agency employees to focus on high value public services, and the deeply human work they were called to do.
Across the public sector, AI has rapidly evolved from an experiment to a core part of the strategy. Reflecting on this shift, the National Association of State Chief Information Officers (NASCIO) State CIO top 10 annual report recently ranked AI as the number one priority for state CIOs for the first time. This reprioritization matters deeply for the future of state and local governance: as state agencies face mounting administrative backlogs, aging infrastructure, and shifting public expectations, CIOs recognize that intelligent automation is the central mechanism to increase staff capacity, streamline caseworker workflows, and deliver more responsive, equitable services to local residents.
As agencies move from AI pilots and experiments to full-scale adoption, the central question for many agencies becomes: How do we leverage AI to bridge the gap between existing legacy investments and modern service delivery?
Google provides an integrated AI stack designed to remove the friction of manual systems integration, with a focus on speed, scale, and cost-efficiency. Let’s take a closer look at some public sector organizations who are partnering with Google Public Sector and putting AI to work:
The agentic era is all about augmenting human capacity and empowering leaders and builders who make public service possible. Organizations across the public sector are leveraging Google Cloud’s integrated AI stack to redefine how they serve their stakeholders, empower their workforce, and advance their mission. At Google Public Sector, we are excited to partner with pioneering organizations as we build a more resilient, responsive, and connected government, together.
Join us at our Google Public Sector Summit on October 20 to hear from public sector leaders who are leveraging AI to re-imagine service delivery in the agentic era.
As enterprises invest in generative AI, tech leaders keep seeing the same pattern: Developers test AI tools for a week, hit setup problems, and then drift back to the backlog. Nothing ships.
The real gap is enablement. In this landmark Harvard Business Review article, Josh Bersin and Marc Zao-Sanders noted that knowledge workers carve out just five minutes a day for formal learning. Most enterprise training programs still lean on week-long classroom bootcamps, multi-week certification tracks, and passive video lectures, none of which fit into the time developers actually have.
With the Build with Gemini event series underway, Google Cloud Consulting is seeing more leaders rethink AI enablement by building quick, daily practice into their teams' routines. In this post, we'll walk through a four-pillar approach and the lessons from our global developer challenges to share what micro-habit upskilling looks like.
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The traditional method… |
…now becomes |
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Multi-week, semi-annual classroom bootcamps |
Five-minute hands-on exercises |
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Local workstation configuration and credential setup |
Pre-configured browser-based sandboxes |
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Mandatory attendance and compliance checks |
Daily streaks, badges, and team challenges |
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Multiple-choice quiz completion |
Deployable agent tools and reusable code |
Rolling out a model like this comes down to keeping each task small and manageable. Here's how we structure that work across engineering teams:
Make micro-learning a habit. Offer short objectives that each cover one skill, like connecting a model to a database schema or validating structured output, in place of full-day training blocks.
Give teams browser-based sandboxes. Setup is where most training stalls, so remove it. With a pre-configured, managed cloud environment, developers open a tab and are writing code within minutes, with no credentials to request and nothing to install or maintain on their own machines.
Build in daily streaks. Milestones, shared wins, and teammates comparing solutions turn practice into a normal part of the workday.
End every session with something that runs. Each exercise should leave behind a working component, and over time those components accumulate into a shared library of code and prompts the whole team can pull from.
When Google Cloud launched Advent of Agents, a daily agent-building program for developers, we wanted to test one question: what happens when you remove setup and scheduling from technical enablement?
Each day, developers got one short, real-world agent exercise they could run right in the browser, with no half-day to block off and no setup guide to read first.
150,000+ developers participated across global teams.
859,000+ hands-on code executions in browser-based environments.
31% of participants returned daily, more than triple the 10% industry average for self-paced tech, and significantly exceeding the standard 5%–15% MOOC benchmark
32,000+ participants built working agent components.
The above data was accessed via Advent of Agents Google Analytics metrics.
Keeping each exercise under five minutes and pre-wiring the sandboxes removed the two things that usually stall workplace training: setup time and scheduling. The numbers suggest developers will make time to learn when the exercise fits into the day they already have.
AI enablement doesn't have to pause your sprints. It takes a consistent habit of practice and the tools that let teams build alongside their regular work.
Experience live building. Bring your engineering teams to a Build with Gemini workshop. The events are complimentary and run different tracks according to technical depth, from no-code for business leaders to code-first for developers, with live hands-on labs supported by Google Cloud experts.
Build skills with GEAR. Enroll your technical and business teams in the Gemini Enterprise Agent Ready (GEAR) program. Membership is free and includes monthly learning credits on Google Skills, hands-on labs, and skill badges, with learning paths for developers, line-of-business leaders, and IT decision-makers.
Developing AI skills starts with a change in routine. Short, daily, hands-on exercises let developers learn by doing, and the working code they produce along the way becomes the team's starting library for production work.
Give your developers a few minutes a day and a sandbox that's ready when they are. Start with one exercise this week and see how small, daily habits can build AI capability across your organization.
Join a Build with Gemini workshop: Sign up today for interactive labs and practical training for developing secure AI agents.
Start building with GEAR: Join GEAR and discover how to deploy enterprise-grade agents with hands-on learning and guidance.
Explore free courses on Google Skills: Build in-demand AI expertise at your own pace.
Sep 18, 2026
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Nobel Laureate Philippe Aghion, Professor Ajay Agrawal, and leading researchers join Google’s AI & Economy program to expand our scientific understanding of AI’s impact on economic activity worldwide.
Scott Strand
Head of StratOps and Special Projects, Technology & Society
Zanna Iscenko
AI & Economy Lead, Chief Economist's Office
We recently launched the AI & Economy ATLAS v1.0 and its interactive open-access site to understand how people are using Google’s AI tools at work and in their daily lives. But tracking adoption patterns is only the beginning. As artificial intelligence reshapes jobs, businesses, and everyday lives, navigating this shift requires a multidisciplinary approach that unites fine-grained data with rigorous economic inquiry.
To further help organizations, workers, researchers, and policymakers make sense of this complex transition, we are expanding our AI & Economy Research Program, and welcoming world-class economists to help build and lead this work.
Technological shifts are rarely instantaneous. Our program measures and analyzes this evolution in real time, focusing on core areas including the future of work, productivity and growth, global technology diffusion, and AI’s impact on scientific discovery.
Executing a research agenda of this scope requires deep collaboration among academia, industry leaders, and policymakers. To expand our scientific capacity and engage more deeply with these diverse stakeholders, we are bringing together leading external advisors, visiting scholars, and dedicated research program leadership. Joining us as Academic Advisors and Visiting Fellows:
To steer empirical projects and integrate insights across our agenda, we are also introducing two renowned researchers as Directors of Google’s AI & Economy Research Program:
Anu Madgavkar and Daniel Rock will lead Google's AI & Economy Research Program alongside Alex Imas, Director of AGI Economics at Google DeepMind, and Zanna Iscenko, AI & Economy Lead, in Google's Chief Economist's Office.
Maximizing AI’s economic opportunity while mitigating disruption requires sustained partnership. This expanded team of experts will serve as a scientific bridge, directly informing future ATLAS updates and empirical research.
Together, we will focus on identifying the organizational practices, public policy frameworks, and training programs needed to ensure AI upskills workers, democratizes expertise, and drives broadly shared prosperity.
To learn more, read our latest research publications, and interact with our latest global data, visit ai.google/economy.
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We are living in genuinely interesting times. AI is disrupting software development at a pace where new models, tools, and practices appear almost daily. Many teams’ natural first instinct is to spend ever more time chasing updates.
After almost two years of AI product and market research at JetBrains, we’ve come to a different conclusion: the speed of change is not a problem, as long as you can see the bigger picture. We deliberately don’t try to track everything that happens. Instead, we try to understand where everything we observe comes from – and where it is ultimately going. That gives us a prism to look through, a filter that separates signal from noise. It’s also what saves us from change fatigue.
This post is about that prism. But before we get to the framework itself, let’s start where the webinar started: with what we actually see on the market today. Because you can’t build a useful model of the future without first building an honest model of the present.
Looking through our research, three things stand out.
First, AI is already a common part of the developer’s life. People know about it and use it not only at home but at their companies – including the big ones, which are traditionally the slowest to adopt anything new. We no longer question whether AI in software development “is a thing.” It’s here, and it’s staying.
Second, agentic coding is gradually becoming the new normal. More and more developers use AI coding agents that go beyond automated code edits – they actually delegate coding to agents. This fundamentally changes the development loop from “code → validate → fix” in an editor, to “plan → execute → review” in an agentic chat. The biggest push here came from Anthropic’s Claude Code, which by our estimates is used by around 8.5 million coding professionals, earns roughly $7B in yearly revenue, and is broadly considered the best AI coding tool across all categories. Its release also kicked off the race of IDE-agnostic CLI coding agents – with similar offerings now from OpenAI, Google, and a wave of niche players.
Third, AI agents have started moving to the cloud. Tools like Devin have existed for a while, but only now is this trend starting to actually mean something. With more capable models, more powerful agents, and developers better aware of what AI can and cannot do, developers are making a more conscious decision to delegate work to cloud agents, which are more autonomous by design. They still have heavy limitations, but they can already handle simple, low-effort “garbage tasks”, like fixing a linting error found during a CI run.
And yet, here is the paradox: even though everybody uses AI, we can’t say AI is used everywhere. In reality, AI is mostly used for just two main development activities: brainstorming and coding. But development is more than just coding. Many parts of the software development lifecycle remain largely untouched, creating enormous room for further adoption. AI use is growing steadily, but unevenly.
The question everyone wants answered is: what’s the next big thing? But before jumping there, let’s take a small step back and look at the past. We have to build a proper model of reality first, and only then look at the future through it..
What has the evolution of AI in software development looked like so far?
This reads less like a list of features and more like a trajectory. We can draw a line through these points and ask ourselves: what does this line actually mean? Why did all these embodiments of AI in developer tools show up in this particular order? And if we extend the line into the future, where does it lead?
In early 2025, we were asked to collect insights to evaluate our AI strategy. While working on this, we were inspired to create a “theory of everything”: one that explains not only the current state of the field, but what is fundamentally possible. That’s how we ultimately arrived at our own theory of everything for AI development tools. We called it the Artificial Intelligent Development Environments Framework, or the AIDEs Framework.
Like any piece of theory, we started with definitions and assumptions. Definitions let us abstract away from current jargon and narrowed thinking; assumptions draw boundaries around the problem, making it possible to reason about it systematically. This is standard practice in any rigorous discipline, and it’s remarkable how rarely it’s applied to thinking about developer tools.
Artificial Intelligent System (AIS): Any computer system created by humans that demonstrates the traits of “intelligence” while helping users achieve their goals (their Jobs-To-Be-Done). The key insight: people want to feel intelligence from their tools – but that intelligence doesn’t have to come from LLMs. Our IDEs were always considered “intelligent,” yet the core of their capabilities is built on deterministic heuristics. So the principle is: target the user experience of intelligence, not “AI everywhere.”
Artificial Intelligent Development Environment (AIDE): Simply put, an AIS for creating software. There is a huge set of tools used to create software, applied at particular stages of the process and at specific levels of work delegation. In other words: there is a big world outside of IDEs, full of opportunities we might not have considered yet.
Principal and Agent: Terms borrowed from economics and sociology to describe the relationship between two parties in a delegation. The principal is the party whose interests or objectives are being served, while the agent is the party entrusted to act on the principal’s behalf. But keep in mind that both the principal and the agent can be either a human or an AIS. That means we can consider scenarios where an AI principal delegates work to an AI agent, and even where an AI principal delegates work to a human agent.
Software Creation: We use this term instead of “software development,” as the latter might suggest that software is mostly about writing code. In reality, software creation involves many different roles. These roles can be understood as relationships of delegation: a product team may delegate implementation to software engineers, frontend developers may delegate UI design to UX designers, and so on.
The direction of delegation depends on your perspective. A software engineer may see a UX designer as someone they depend on for a particular activity, but from the perspective of the broader product team, both may simply be contributors to a larger process. In this sense, organizational responsibility is relative to the level and perspective from which you view the work. Adding AI does not fundamentally change this structure; it introduces another kind of actor that can participate in these relationships.
We started with four foundational assumptions:
1. Whatever the future becomes, people will still have the goal of creating software. We don’t believe demand for software will decrease or that humanity will find a completely different technology to replace it. On the contrary, digitalization will continue to be the primary driver of both productivity gains and personal evolution, so demand for software will actually increase. And at least in the mid-term, the basic principles of software development will remain the same.
2. The primary driver of change on the market will be the gradual delegation of software creation activities to artificial intelligent systems. Let’s be honest – we’re all a little lazy, and we’d gladly hand off the work we see as routine. All of human history supports this, from the division of labor, to automation, to digitalization – all of it was, at its core, delegation. Delegation is already present on today’s market. At higher levels, humans delegate to other humans (the most comprehensive IT solutions are still created collaboratively), and at lower levels we delegate to artificial systems through process automation. As AISs develop further, they will become essential actors in the division of labor itself – and the rising level of delegation to AIS will become the ultimate metric of their real capabilities and impact.
3. AIS will never fully replace humans, who will retain two key jobs: task specification and oversight. (The article “AI as Normal Technology” dives deeply into this subject.) AI will not “kill” the developer profession, but it will transform what the profession means. Today, high-level task specification and oversight among developer roles is typically done by architects, a senior grade earned over years. In the future, we might see the emergence of junior architects – a new category that would require rethinking not just roles, but the entire system of CS education.
4. With higher levels of delegation to AIS, personal “immersion” into specific development activities will decrease. Simply put: if you’re not the one doing the job, you’ll always know less about it than if you’d done it yourself. This is exactly what happens between human principals and human agents today. As developers delegate activities with lower added value (like code authoring) and focus on higher-value ones (like requirements formulation), their awareness shifts to a “higher level” of the project. This does not mean everyone goes full “vibe coding” (after all, current tools don’t offer solutions for high-level context communication and management). Future developers should be aware of their projects the way development leads are aware of the projects their teams deliver. Solving this “loss of immersion” problem is a prerequisite for elevating delegation – and this is why context abstraction and management of uncertainty matter so much in the framework.
Our framework operates in three dimensions: stages of the software creation process, levels of delegation, and organizational context of development.
The first dimension is a reworked take on the traditional software development lifecycle, focused on outcomes rather than process. We map 35 high-level activities grouped into 5 activity groups — from “Ideation and Conceptualization” to “Delivery, Maintenance, and Feedback Collection.” Any developer will recognize these immediately, so we won’t dwell on them here. Explore the interactive figure below.
During this stage software creators ideate on original problem and potential solution, explore and come up with vision and high level concepts of what they want to create, identify a valuable opportunity and decide whether it’s worth pursuing.
During this stage software creators “operationalise” the initial ideas and concepts into the design of “engineering solution” – a more specific definition of what should be done from the perspective of system and software engineering. After this stage the developer (who will write code) should understand well what should be done, how it should be done and what are the acceptance criteria (“definition of done”).
During this stage software creators create a codebase and related artifacts that realize the design and pass initial validation. In addition any activities that are required to create and validate this codebase / artifacts are also performed here (e.g. setting up DB, working with external services and / or creating custom tools).
During this stage the created codebase is getting verified and validated against initial requirements, acceptance criteria and quality standards. The end of this stage means the software has passed QA – all critical defects are fixed, and stakeholders are confident in the product’s correctness and stability.
During this stage the created codebase is getting delivered to the end users either via deployment (web production environment) or distribution (application stores, file storages, package repositories). In addition, this stage covers the “operational” state of the software solution, which includes maintenance (making sure the software is still available to end users) and feedback collection (for future improvements).
Regarding our methodology: The taxonomy is designed to cover all types of development involving any roles within software teams (not just developers), yet is not so granular that we lose homogeneous groups of activities. The stages look like a linear workflow, but in reality developers jump between stages and between activities within a stage. These activities can also serve as a foundation for formulating high-level developer Jobs-To-Be-Done.
This is the more novel dimension. Here we define the distribution of roles between principal and agent, along with 10 attributes of delegation – autonomy, level of planning, proactivity, and others. Different combinations of roles and attribute values define five levels of delegation:
L1 – Tool. Delegation of very limited, scoped actions. Code completion is the canonical example: you let AI finish writing what you’ve already started.
L2 – Assistant. Delegation of a well-defined sequence of actions – a “task” with very specific boundaries. One example might be generating a unit test for a specific function. Simple, well-defined, and minimal context – but it’s a task with a series of steps, not just one action. It’s like having a third hand: it’s doing the work, but it’s still your hand.
L3 – General-purpose Executor. This is where focus starts shifting from the process to the deliverables. An L3 agent can execute any task, but requires expert input from the principal, who acts as a “consultant” on more complex topics. Current agentic coding sits roughly here: we believe agents like Claude Code and Codex are well capable at code writing and low-level solution engineering, but we still don’t trust them with decisions about what should actually be built – that requires deeper knowledge of the business domain. So we fully delegate execution, but retain task setting and review.
L4 – Supervised Executor. Here we move beyond the individual space to the organizational perspective, because the agent is now responsible for an entire development function, like managing the backend implementation of your full-stack web application. It is “supervised” because the principal’s role narrows to approving key decisions; everything else the agent decides itself. This is also where we run out of real-world examples, except perhaps some usage patterns of vibe-coding platforms like Lovable or Replit.
L5 – Competence Center. Imagine you’re the CEO of a startup with an engineering team at your side. You define what the company wants to achieve, how you’ll do it, what the key metrics are, and whether you’re performing well. Your engineering team exists to execute your strategy and make your vision a reality. You don’t care what stack they use, what API structure the app has, or whether it’s hosted in Azure VMs or Docker containers on managed Kubernetes in GCP – you delegate those decisions to the team. That kind of delegation is L5.
You may have noticed something conspicuously missing here: there’s nothing about the raw capability of AI or how “smart” it can be. This omission is deliberate, for two reasons.
First, benchmark performance does not automatically translate into real-world delegation. AI models can achieve remarkable results on standardized tests and still struggle to earn enough trust from people to perform even relatively simple tasks autonomously. Thus we might see an AI model having top-notch benchmark results but surprisingly little economic impact. Conversely, a deterministic system that effectively orchestrates a set of less capable agents can potentially produce more useful work than a single super-smart AGI.
Second – and this is the deeper point – everything we’ve described is not an attribute of the agent, but an attribute of the relationship between the principal and agent. The level of delegation is a decision made by the principal, based on their personal perception of the agent. A developer may delegate code writing to Junie at L3 and let it execute a task end-to-end, but for more critical cases they’ll switch to L2, put Junie “on a leash,” and feed it much narrower tasks. Even if the agent is capable of L3, there will be scenarios where the principal chooses to delegate less. The level of delegation is not an attribute of Junie – it’s an attribute of the “agentic contract” between the two, and the principal is the one who sets its terms.
Even when AI is technically capable of doing the job, it’s humans who decide how much control to let go of.
The third dimension describes the organizational context in which development happens. We differentiate three contexts:
These contexts define different constraint types and different complexity of organizational dynamics – which are later reflected in the complexity of development decisions and, ultimately, the codebase itself. We added this dimension primarily so we never forget this aspect – and we already see certain things becoming relevant specifically at the scale of large organizations.
Now, remember our “timeline” picture from earlier? Through the lens of the framework, it becomes obvious that the line running through it is, at its core, the level of delegation dimension. But since the model is richer than a single line, we can also track how AI penetration grows across SDLC activities and how it differs across organizational contexts.
In our regular surveys on AI usage, we have a dedicated section on exactly this, which lets us build what we call AIDEs maps.
Continued at the source.
We want local coding agents to be smart and fast, with the ability to understand a codebase, do useful work, and finish tasks without long waits. This Junie Local update makes it practical to use a more capable model on your own machine.
In the first release, we had to choose between two versions of the same model. With reasoning disabled, Qwen3.6 was fast enough to be usable on a laptop. Qwen3.8 completed more tasks, but it needed reasoning enabled to work reliably, and that made tasks take roughly four times longer. We picked speed.
This update is our attempt to remove the need to choose. We built Qwen3.8-3.6-27B-blend by merging the two in equal proportions. In our coding evaluation, it completed more tasks than Qwen3.6 while generating 71% fewer output tokens than Qwen3.8.
In this post, we’ll show where the new model improves coding results, how we made it run efficiently, and what we learned while testing it. We’re also bringing Junie Local to more machines with experimental NVIDIA support on Windows.
In our 100-task internal coding benchmark, the new model completed 37 tasks, compared with 34 for Qwen3.6 with reasoning disabled. It came close to Qwen3.8’s 39 solves while generating 71% fewer output tokens.
One possible explanation for the token savings was just that the blend model spends fewer tokens when it gets stuck. To test that hypothesis, we compared token use for the 30 tasks that were completed by both Qwen3.8 and the blend model. On these tasks, the blend generated about 70% fewer tokens – 279K for the blend versus 935K for Qwen3.8. It used fewer tokens on 29 of those 30 tasks, further proving its token efficiency.
We started with a simple experiment. Since Qwen3.8-27B is based on Qwen3.6-27B, and they both share the same architecture, we simply merged their weights in equal proportions. This produces a single 27B model without any additional post-training.
However, this simple blend was already a surprisingly useful improvement. The early results were better than we expected, so we focused on evaluating this model across more benchmarks and tasks. That evaluation gave us enough confidence to make it the model for this release while the other experiments continue.
There are many ways to reduce reasoning times, including distillation, reinforcement learning, and more elaborate model merging methods. We are continuing a wider set of model and runtime experiments, and more of that work will appear in future Junie Local releases.
To see how the new model performs beyond our agentic coding tasks, we evaluated it on multiple public benchmarks. Repeating the evaluation runs lets us see which tasks are consistently completed, how much variance there is between runs, and whether a result depends on one favorable sample.
Across four LiveCodeBench runs, the blend model averaged 85.47% correct answers, compared with 83.29% for Qwen3.8, at a similar output cost. Qwen3.6’s four complete passes averaged 67.87% and used about 24.1 million output tokens per pass, versus approximately 6.14 million for the blend model.
The visual benchmarks expose a different tradeoff. The blend model used substantially fewer tokens than Qwen3.6 with thinking enabled, but more than Qwen3.8. We checked identical questions, images, and generation settings, and we found that the extra tokens were almost entirely due to the blend model spending more time on reasoning.
The blend can still overthink when it struggles to find a solution. If Junie keeps revisiting the same approach without new evidence or useful tool results, we recommend interrupting it and restarting it with a narrower goal.
There is also room to make successful reasoning more efficient. Across four identical benchmark runs, the length of CoT varied significantly. Picking the shorter correct trace would have cut token use by 24.5%, which suggests that shorter successful paths exist, and we could potentially teach the model to take those paths with zero performance loss.
The model determines how much text Junie generates, while the runtime determines how quickly that text reaches you and how much memory it needs. Our goal is to improve both.
Junie Local already uses multi-token prediction (MTP). A small subnetwork called the MTP head proposes multiple tokens that the main model checks in parallel. Correct proposals result in more output tokens per pass. We want to make more correct proposals, but this also adds GPU work, so it does not always mean faster generation.
On the M5 MacBook Pro, proposing two tokens per round made decoding 60% faster than running without MTP. Increasing that to four brought the speedup down to 36%, because the extra GPU work of drafting and checking proposals outweighed the benefit of accepting more tokens.
We compared how a four-bit MTP head (Q4) and an eight-bit one (Q8) performed on real-world coding trajectories at five context sizes, from 16K to 128K, with three seeds each. Q4 accepted 63.0% of proposals, and Q8 accepted 63.6%:
The acceptance rate tells us how often the guesses are useful, while decode speed tells us whether they save time.
We found no consistent speed advantage for the Q8 MTP head, so we kept Q4 to save memory.
To understand why MTP slows down with longer context, we profiled the GPU load during the token verification process. Calculating attention accounted for most of the increase: Its time rose from 8.4 to 40.2 ms per round, while feed-forward and Gated DeltaNet computations stayed nearly flat.
This MTP limitation results in slower responses as Junie works through a long coding session, even when its predictions remain accurate. We are researching how to reduce this verification cost and keep Junie responsive as sessions go on.
During the early stages of development, our internal evaluations showed performance degradations that we were unable to reproduce when actually using Junie Local. The reason was a setting we had introduced to make evals reproducible: Every request received the same random seed. This caused numeric instability, as reusing the seed gave the same tokens the same random advantage each time the sampler generated a token. When the model’s predictions stayed similar, it could be steered back toward an unsuccessful action even after the prompt changed. Notably, Qwen3.8 was more affected by this instability than the other models we tested.
We corrected the setup by advancing the seed with each agent step and reflection attempt, allowing subsequent attempts to take a different path while keeping the tests reproducible.
Apple M5 users can already try the new model via Junie:
junie
Run /local and install Qwen3.8-3.6-27B-blend to switch Junie Local over to it. Make sure Junie is updated to the latest version.
For Windows users the nightly build of Junie now includes experimental RTX support, covering all NVIDIA RTX cards based on Ampere or newer architectures with at least 24 GB of VRAM.
junie --channel=nightly
This early preview lets you try Junie Local on Windows and help shape its development with your feedback.
You can find Qwen3.8-3.6-27B-blend on Hugging Face.
Qwen3.8-3.6-27B-blend is just one result of our broader model and runtime research. We are continuing that work, and you will see more of its results in future Junie Local releases.
Sep 18, 2026
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Two fashion designers created custom tools in Google Flow to help with set design and styling for New York Fashion Week.
Yeawon Choi
UX Designer, Envisioning Studio
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This content is generated by Google AI. Generative AI is experimental
Ask an independent fashion designer how they actually spend their time, and "designing clothes" is rarely the answer. Administrative tasks, factory logistics, and vendor coordination take up the bulk of their days, leaving very little time for design.
Ahead of New York Fashion Week, Google’s Envisioning Studio, with support from Google Labs, set out to streamline the creative workflow process—helping designers bring ambitious runway visions to life with less friction.
Google engineers worked side by side with designers Jane Wade and Sergio Hudson, using Google Flow, our AI creative studio, to build two specialized tools to address their specific challenges. Jane’s Google Flow tool helped her create balanced head-to-toe looks before producing physical samples. Sergio used his tool to stage his runway on a tight studio budget.
The Google Flow tool we co-created with Jane, Styling Suite, mapped every facet of her runway model looks. In-person casting and fittings typically consume up to three full days for a design team. The tool allowed her to curate hair, makeup, accessories, shoes, and garments on digital models, then play with the styling virtually. She was able to balance each look and identify missing elements before cutting and sewing additional pieces.
Sergio’s main challenge was staging his show without breaking his budget. In the past, asking his production crew to change lighting and props added to the overall costs, since a new 3D rendering was needed for every design revision. Our co-developed Google Flow tool, Runway Visualization, eliminated the back-and-forth by simulating his runway. It let him adjust the set-up of his venue and swap the lighting and props with options that worked within his budget. He was also able to refine which paths the models would walk, allowing him to harmonize the show and viewer experience.
The results of these collaborations were visible on the runways at New York Fashion Week — and there’s room for more innovation.
While there’s plenty of excitement around AI in the fashion industry, many projects remain stuck in theoretical testing. AI can make the production process smoother for designers by co-developing tools that work within their existing processes and ensure they’re firmly in the driver’s seat.
Build your own bespoke tools in Google Flow using natural language to describe the tool or workflow you’re looking to create, no coding experience required.
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We're partnering with Accenture on independent evaluation of frontier AI. This is an important step toward the commitment, made in our CEO’s essay “We Must Pace the Frontier,” to embed evaluators within Anthropic.
The partnership will be led by Faculty, Accenture’s specialist AI business, and will include evaluating and red-teaming models, conducting alignment assessments, and testing model safeguards. Accenture helps businesses and governments deploy AI across many industries. Their understanding of how enterprises use AI in practice informs their safety approach, and they will bring that perspective to evaluating our models.
Anthropic and Accenture each expect to invest at least $1 billion in building capacity in this area over the next five years.
Embedded evaluation is new, and many of the details about how it will operate are still being worked out. Unlike today’s external evaluators, embedded evaluators will work inside AI companies, with access comparable to an employee's. That access allows them to watch models take shape in training, follow the decisions that govern how those models are built and deployed, and speak directly to employees. From this vantage point, embedded evaluators can assess how a company operates, verify that it is keeping its safety commitments, and identify blind spots. They can also report incidents and give the public a more informed account of benefits and risks.
To be clear, independent embedded evaluators do not reduce our accountability, but help to make it more verifiable. The safety of our models remains our responsibility.
There are, as yet, no standards for what information embedded evaluators should have access to, or how they should report what they find. There is also no settled system for funding independent evaluation. Long-term, we think funding should come from pooled or government sources, as we called for in our Advanced AI Framework in June. As neither exists today, we plan to work with different evaluators under different funding arrangements.
Given the importance and urgency of this work, Anthropic will fund Accenture's work directly. We are also in dialogue with METR and other nonprofit evaluators to pilot elements of embedded evaluation using their own funding. Ultimately, we believe frontier AI needs an ecosystem of evaluators operating with shared standards.
We expect frontier labs to work with several organizations at once. Our partnership is non-exclusive; Anthropic will work with other evaluators to be announced in the coming weeks, and Accenture will work with other AI developers in similar capacities.
We'll continue to train and release frontier models, and we want independent evaluators working alongside us as we do. We’re sharing these early efforts now so people and other AI developers can see our process. We expect our approach to evolve as the field matures, and we’ll share more as our work begins and as we bring on additional evaluators.
The Life Sciences Verification Program (LSVP) gives life science professionals access to Claude Mythos, Opus, and Sonnet models with a refined set of safeguards more permissive for biology-related work.
On July 30, we reported three incidents in which Claude models gained unauthorized access to real computer systems. We are conducting an in-depth analysis of both incidents, and planning to work with METR for an independent review. In the meantime, we’re sharing some of the changes we’ve made over the past month.
You have the change ready and the tests are green. Now someone has to launch the app, find the right screen, and check the flow. Often, that someone is still you, even when an agent helped write the code.
You should be able to delegate that part too.
Junie /demo is a new mode in Junie CLI. Describe what you want to check, and Junie builds and launches your app, interacts with its UI, and records what happens. You get an HTML report, screenshots, and a video you can review or share.
The useful part is getting the routine clicking off your plate while keeping the result open to inspection. You decide whether the change is ready to ship.
Let’s use a small issue tracker as our example. You have added bulk status updates: select two issues, mark them “Done”, and see the counters change. You also want to check that the update survives a reload.
First time in this repository? Start Docker and ask Junie to set up /demo. It analyzes your project and proposes a build and launch plan. Once you confirm the plan, Junie fills in the configuration for you. Review the generated files, then run:
/demo
Choose the changes from your branch, session, working tree, or last commit. For a specific check, enter a request in the prompt field:
Reset the sample data. Select PB-101 and PB-102 and mark them Done.Check that Open drops from 3 to 1 and Done rises from 1 to 3.Reload the page and verify that both issues are still Done.
Review the prompt and let the agent work:
You can watch the live run as it moves through the UI and inspect what it actually does:
A request with an expected result gives the run a clear target. “Check the feature” leaves more room for interpretation than naming the action, the expected state, and the condition that should survive a reload.
A screen recording is much easier to review when you know what you are looking at. Each demo video starts with a slide introducing the demonstration. If the run covers several scenarios, each gets its own introductory slide. A final slide sums up the results.
A model helps prepare that structure. During post-processing, it examines the captured screenshots, identifies the scenarios, and writes the explanatory slides. These are added to the recording as the final video is assembled.
The video also has explanatory subtitles, which you can turn on or off in the player. Voice-over may follow in a future update.
The HTML report brings together the request, the result, the video, and the screenshots. You can inspect the steps that ran and see which checks passed, failed, or remained incomplete.
That is useful for a reviewer, a QA engineer, or a teammate asking how a feature works. We are also experimenting with this in Junie Live, our Slack agent, to answer suitable feature questions with a demonstration.
A diff explains the code change. A demo adds the behavior you can see: which screen opens, what changes after a click, and whether the flow reaches the expected result.
Inside JetBrains, we connected the demo agent to GitHub Actions. In our agent repository, we have run it for more than 1,500 unique PRs and created over 2,100 demo videos.
The first workflow example follows the same idea. It checks whether a PR contains behavior worth demonstrating, runs the demo when it does, and adds a comment linking to the available artifacts. The prompts are inside the YAML, so you can read and adapt the whole example in one file.
You can read the full demo-pr-changes.yml and adapt it to your repository.
This is most useful when a change has an interface to exercise. A backend change may also be demonstrated through an existing Swagger UI, for example. The value depends on what the run can actually observe.
We also use the demo agent for release smoke tests. Our internal workflow runs 22 scenarios on pushes to release branches and keeps a result and video for each. Across our internal release branches, we have used the agent for more than 1,300 smoke tests.
The second example starts small: two independent scenarios, triggered by a push or a manual run. Replace the prompts with your own steps and expected results. A commented schedule shows how to add regular runs.
You can read the full demo-release-tests.yml and replace the scenarios with your own.
There is one detail worth keeping: a completed agent process does not tell you whether a check passed. In this example, the prompt asks Junie to write an explicit verdict. Only PASS passes the result check. FAIL, PARTIAL, and missing or invalid results fail it. Other scenarios can still finish and upload their evidence.
Both examples use GitHub Artifacts, so there is no separate video hosting service to configure.
The demo environment is a Docker container based on Debian Bookworm. The base image includes Chromium, Node.js, xterm, a virtual desktop provided by Xvfb and a window manager, plus screenshot tools, xdotool, and ffmpeg.
A model with Computer Use support drives the app through clicks, keystrokes, and screenshots. Your Dockerfile adds the project’s dependencies; .junie/demo.md describes its build and launch steps.
A complex repository can have several VM templates. For a monorepo with a backend and several frontends, each environment can have its own Dockerfile under .junie/vms/ and its own launch settings. Describe which template to use, which services it needs, and how to start them in .junie/demo.md. Junie can then choose the right environment for the requested demo.
Junie keeps your active model if it supports Computer Use and is available. Otherwise, it selects the first available model in this order: GPT-5.6 SOL, GPT-6 Astra, GPT-5.5, then GPT-5.4. All models run with High reasoning effort in /demo, regardless of your selected effort level. The run cannot start without a supported model. The Junie /demo documentation covers the environment and configuration in detail.
In CI, the same mode is available through --demo:
junie --auth="$JUNIE_API_KEY" --demo -p . \ --task "Open the app and demonstrate the bulk status update."
In our internal 22-case comparison, GPT-5.6 SOL had the lowest average time and cost among the three models we measured.
The full set cost $19.94 on SOL. In the subscription conversion used for these figures, $1 equals one AI Credit. These are internal measurements on our scenarios, so your app, build steps, and prompts will affect the result. Budget for CI runner usage separately.
The team also found SOL faster in these runs without a noticeable drop in observed quality. That observation comes from our own workloads and helps explain the model preference.
A run still takes minutes. The benefit is that you can hand over the routine interaction and come back to something you can inspect.
Set up /demo once in your repository, check the generated configuration, and start with a small feature or fix. For CI, commit that configuration and add a JUNIE_API_KEY repository secret before copying either workflow.
Pick the change you were about to click through yourself. Ask Junie to demonstrate it, watch the output, and decide what needs a closer look.
Ktor 3.6.0 is here! This release is full of new experimental features, including typed authentication capabilities with specialized support for OpenID Connect and HTTP/3 support for the Netty engine. There are also a few quality-of-life improvements for routing and request handling, more convenient defaults for Kotlin Multiplatform clients, and more. Check out What’s new in Ktor 3.6.0 on our website for the full list of changes, or review the release notes.
Ready to explore Ktor 3.6.0? Start your next project with the interactive project generator at start.ktor.io. Your feedback and contributions are always welcome!
Until now, Ktor’s authentication has relied on implicit typing to bridge configuration to the routes. In this module, you get new types to guarantee full type safety when working with complex authentication. It also supports role-based access and anonymous users. By leveraging context parameters, we were able to ensure even more elegant syntax. Read the type-safe authentication documentation for setup, role checks, and failure handling.
val jwtAuth = jwt<User>("my-jwt") {
verifier(jwkProvider, issuer)
validate { credential ->
val payload = credential.payload
User(
id = payload.subject,
email = payload.getClaim("email").asString()
)
}
}
routing {
authenticateWith(jwtAuth) {
get("/profile") {
val user = call.principal
call.respondText(user)
}
}
}
The new OpenID Connect (Oidc) plugin aims to reduce complexity when securing your service through OpenID Connect Providers. The Oidc plugin allows you to create typed authentication providers that support all OpenID Connect features in a typed way. There is also support for sessions, a browser login interface with auto-refreshing tokens, and more. For the full documentation, check out the Ktor website – here.
suspend fun Application.module() {
val oidc = install(Oidc)
val auth0 = oidc.identityProvider("auth0") {
issuer = "https://my-tenant.auth0.com"
bearer {
audience = setOf("https://api.example.com")
}
}
routing {
authenticateWith(auth0.jwtBearer) {
get("/orders") {
val subject = call.principal.claims.subject
call.respondText("Hello $subject")
}
}
}
}
The Netty server engine now has experimental HTTP/3 support over QUIC. To enable it, configure an SSL connector, then opt in with enableHttp3 { }:
embeddedServer(Netty, environment, {
sslConnector(
keyStore = keyStore,
keyAlias = "server",
keyStorePassword = { "changeit".toCharArray() },
privateKeyPassword = { "changeit".toCharArray() }
) { port = 8443 }
enableHttp3 { quicMaxIdleTimeout = 30.seconds }
}) { /* application */ }.start(wait = true)
The enableHttp3 {} block also lets you tune QUIC-specific settings, such as flow-control limits and UDP socket configuration. It is still experimental, so we would love your feedback if you decide to try it.
A Netty server can now also serve h2c on one connector and HTTP/2 over TLS on another. Enable both with enableH2c = true and enableHttp2 = true.
embeddedServer(Netty, configure = {
connector { port = 8080 }
sslConnector(...) { port = 8443 }
enableHttp2 = true
enableH2c = true
}) { /* application */ }.start(wait = true)
Request-parameter conversion now supports Kotlin’s Uuid, Byte, and unsigned numeric types. ApplicationCall.receive() now also accepts nullable types, making the route contract explicit and deprecating receiveNullable().
put("/users/{id}") {
val id: Uuid by call.parameters
val preferences = call.receive<NotificationPreferences?>()
if (preferences == null) {
preferenceService.clear(id)
} else {
preferenceService.update(id, preferences)
}
call.respond(HttpStatusCode.NoContent)
}
We have also added respondHtmlPartial, which replaces the deprecated respondHtmlFragment. The new function uses TagConsumer<Appendable>, so it can respond with unrestricted partial HTML – with all elements supported by FlowContent.
get("/status") {
call.respondHtmlPartial(HttpStatusCode.OK) {
td { +"Ready" }
}
}
ContentNegotiationThe client ContentNegotiation plugin used to merge its registered content types into every Accept header. That is usually helpful, but not when an API expects the header you set on a request to remain exactly as it is.
With ContentTypeMergeStrategy.SkipIfPresent, an explicit Accept header wins. When a request has no Accept header, the plugin continues to add the registered content types as usual:
install(ContentNegotiation) {
register(ContentType.Application.Json, noOpJsonConverter)
acceptHeaderMergeStrategy = ContentTypeMergeStrategy.SkipIfPresent
}
Ktor 3.6.0 introduces ktor-client-engine-defaults: a curated set of client engines for Kotlin Multiplatform projects. Add it to commonMain, and create an HttpClient() without choosing an engine in shared code. Ktor selects the appropriate available engine for each target.
The HTTP cache has moved in the same direction. File-based cache storage now uses the Path of kotlinx-io, so persistent HttpCache storage is no longer limited to JVM java.io.File APIs. Together, these improvements make setting up a KMP client with a simple cache significantly simpler:
// build.gradle.kts
kotlin {
sourceSets {
commonMain {
dependencies {
api("io.ktor:ktor-client-engine-defaults:3.6.0")
}
}
}
}
// Main.kt
val client = HttpClient() {
install(HttpCache) {
publicStorage(FileStorage(Path("build/cache")))
}
}
This gives Ktor projects a more natural common-code setup while retaining the option to choose and configure a specific engine whenever a platform needs it.
For the full list of 3.6.0 changes, including WebRTC support for JVM, asynchronous DNS resolution for CIO, OpenAPI tag descriptions, duplicate-cookie parsing, and JavaScript fetch() overrides, see What’s New in Ktor 3.6.0.
Thank you to everyone in the community for the feedback, issue reports, and contributions that help make every Ktor release better. A special thank-you to the external contributors whose work is included in the release: kdelay, Rafa Ruiz, and solo.
Start building your next project at start.ktor.io. Your suggestions and contributions are always welcome!
👉 Get Started With Ktor | 💬 Join the community on Slack
Git made isolated development a baseline for software teams. Each developer can create a branch, work independently, and merge changes when they're ready.
Database branching brings this same isolation to the database. It lets developers, continuous integration (CI) jobs, and AI agents create isolated database environments from a shared database state and make changes without affecting the parent database or each other.
That means less time waiting for shared environments, fewer test failures caused by other people's changes, and faster feedback on schema migrations. When something goes wrong, you can throw away the branch instead of repairing or restoring a shared database.
Database branching gives you an isolated database environment based on another database's state at a specific point in time. The branch starts with the parent database's schema and data, but changes you make to the branch do not affect the parent or any sibling branches.
If you're familiar with Git, the basic idea should feel familiar. A code branch gives you a private line of development from a known commit. A database branch gives you an isolated database environment from a known database state.
One important difference is that you usually don't merge changes from a database branch back into the parent database. Instead, migration files remain the durable source of truth. You can test a migration on your branch, make sure it works against realistic data, and then let your deployment pipeline apply that same migration to the target database. In this way, database branching makes long-standing practices such as evolutionary database design, database-per-developer environments, and version-controlled migrations practical even when you're working with production-scale data.
As shown above, a parent database provides the known schema and data for multiple isolated branches. You can use a developer branch to modify and test changes, a pull request branch to run migrations and CI, or an agent branch to explore and evaluate changes. When the work is done, each branch can be reset, deleted, or pruned without affecting the parent database or the other branches. Database branching makes this level of isolation possible through copy-on-write.
Copy-on-write (CoW) makes database branching practical by avoiding an upfront full copy of the database. When you create a branch, it initially shares the parent’s existing data instead of duplicating it. Both can read the same underlying data, while changes made to one remain isolated from the other. But when the branch modifies data, the storage layer creates a new version of the affected data for that branch, while unchanged data remains shared with the parent.
Lakebase uses this copy-on-write approach to create database branches without duplicating the entire parent database. As a result, each branch requires additional storage only for the data that diverges from its parent.
Consider a 40 GB database. With a traditional full copy, creating a developer branch and a pull request branch requires an additional 80 GB of storage. However, with copy-on-write, both branches initially share the parent’s data and consume additional storage only when they diverge.
As shown in the diagram above, if the changes in the developer branch are just 1.6 MB and the changes in the PR branch are just 4 MB, the two branches add only about 5.6 MB of storage. Traditional copies duplicate the entire database for each branch, while copy-on-write branches share unchanged data and store only their changes.
The same principle applies when the parent changes after a branch is created. The branch continues to reference the original version of unchanged data, while the parent writes new versions of the pages it modifies. This allows the two branches to change independently without duplicating unchanged data.
Once you have database branching, several development workflows become much easier to implement:
You can create database branches from a protected production snapshot so every developer and CI job starts from the same known state. That lets you test migrations against realistic data, existing constraints, and production-scale tables instead of an empty local database or stale staging environment.
For example, a migration like ALTER TABLE orders ADD COLUMN customer_id UUID NOT NULL may work on an empty database but fail against millions of existing orders. Testing it on a production-like branch exposes that problem before the migration reaches staging or production. When the branch becomes stale, you can delete it and create a fresh one from the same baseline.
You can give every pull request its own database environment. CI creates the branch when the PR opens, applies the proposed migrations, and runs integration tests against it. When the PR closes, the pipeline deletes the branch.
This means two developers can make conflicting schema changes without affecting each other's tests. A PR that adds a column, changes a constraint, or modifies an index gets its own database state, so CI tests the change in isolation rather than against whatever another developer is doing in staging.
Branches also make it easier to isolate failed migrations and experiments. If a backfill produces unexpected results, a test corrupts data, or a migration leaves a branch in a bad state, you can discard the affected branch and create a fresh one from the parent instead of continuing to work with a contaminated development environment.
For example, you can safely test a destructive operation such as DELETE FROM orders WHERE created_at < ... on a branch, inspect the results, and discard the branch when you're done. The parent database remains untouched throughout.
For developers and DevOps teams, these benefits are already compelling. However, if you're building or running AI agents, database branching becomes important at an entirely different scale.
AI agents may need their own database environments to test different approaches to a task. They can create a branch for each approach, compare the results, and discard the ones they don't need. Across an agent fleet, that can mean hundreds or thousands of short-lived environments running at once.
At that scale, full database copies become expensive and slow to provision. Database branching avoids that overhead, making it practical for agents to create and discard environments as they work.
Branching can also reduce the blast radius of agent mistakes by giving agents an isolated environment for testing changes. Instead of granting an agent write access to a production database, you can give it access to a branch where it can test destructive operations without affecting the parent.
Database branches are easiest to manage when you treat them as disposable environments and automate their lifecycle. A few practices keep that workflow safe and predictable:
With these guardrails in place, teams can use database branches as disposable environments across development, continuous integration and continuous delivery (CI/CD), and agent workflows. Each branch provides an isolated environment for testing changes and can be automatically removed when the work is complete.
Database branching gives you a practical way to create isolated database environments without the cost and overhead of full copies. You can use branches to test migrations against realistic data, give every pull request its own database, recover from failed experiments, and run database-backed workloads in parallel.
Start with a simple workflow, such as one branch per pull request, and automate creation and cleanup. From there, you can extend branching to developer environments and agent workloads as your needs grow. Ready to try it? Explore database branching with Databricks Lakebase or follow a hands-on tutorial for implementing database branching in Postgres.
Database branching creates an isolated database environment from a parent database at a specific point in time. The branch starts with the parent's schema and data, but changes made to the branch remain isolated. With copy-on-write, branches share unchanged data with the parent, which makes them fast to create and inexpensive to discard.
The idea is similar: both let you create an isolated environment from a known state and make changes without affecting the original. Git branches isolate source code, while database branches isolate database schema and data.
The workflows differ after that. Git branches are typically merged back into the main branch, while database branches usually aren't. Instead, you test your migration on the database branch and then apply the reviewed migration to the target database through your deployment process.
Database branching gives developers, CI jobs, and AI agents isolated environments for testing changes without affecting production or other workloads. You can use branches to test migrations against realistic data, create per-PR environments, recover from failed experiments, and run multiple database-backed workloads in parallel.
The two common approaches are full-copy branching and copy-on-write branching. Full-copy branching duplicates the database for each branch, so creation time and storage requirements grow with database size. Copy-on-write branching shares unchanged data with the parent and stores only changes made to each branch.
Branching depends more on the database's storage architecture than on its data model. Relational, document, key-value, and graph databases can all theoretically support branching, but the implementation and capabilities vary by platform.
Implementation depends on your database platform and storage architecture. In general, you need a parent database and a way to create isolated branches from a known database state. Databricks Lakebase provides database branching for development, CI, and agent workflows, with branches that can be created and removed as needed. For a practical implementation, see the Databricks branch-based development tutorial.
Within 24 hours of launching on AI Gateway, Jev from TypeSafe AI reached more than twice as many paid teams as any previous model launch, making it the fastest-adopted model in gateway history.
Jev passed every other comparison model in its first twelve hours and continued to widen its lead for the rest of the day. By hour 24, nearly 13% of paid teams were using it. That's 2x the GPT-5.6 family and more than 6x Fable 5.1's share.
Jev’s launch shows how quickly a specialized model can find a place in production. Its first-day adoption was unmatched among recent launches; the next test is whether that early adoption lasts.
Jev was introduced on September 15 as a probabilistic decision model designed to support structured decision-making within software. An application sends it context and a set of questions. Jev evaluates those questions in parallel and returns typed choices, scores, or true-or-false answers, along with probabilities.
Unlike the text produced by a general-purpose language model, Jev’s answers come in a format the code can use directly. Developers can use it to:
choose an agent’s next tool or subagent
decide whether a workflow should continue, retry, ask the user, or stop
score urgency or risk before taking an action
verify model outputs, enforce guardrails, or send uncertain cases for human review
In its own workflow evaluations, TypeSafe AI reports that Jev was up to 194 times faster and 445 times cheaper than language models.
See the September AI Gateway Production Index for more model usage data, or try Jev through AI Gateway and build with the AI SDK.
AuthorsAlex Ferrando de las Morenas†, Xavier Suau Cuadros, Jordi Gonzàlez Sabaté†, Pau Rodríguez Lopez
Activation steering has emerged as a powerful method for guiding the behavior of generative models towards desired outcomes such as toxicity mitigation. However, most existing methods apply interventions uniformly across all inputs, degrading model performance when steering is unnecessary. We introduce Dynamically Scaled Activation Steering (DSAS), a method-agnostic steering framework that decouples when to steer from how to steer. DSAS adaptively modulates the strength of existing steering transformations across layers and inputs, intervening strongly only when undesired behavior is detected. At generation time, DSAS computes context-dependent scaling factors that selectively adjust the strength of any steering method. We also show how DSAS can be jointly optimized end-to-end together with the steering function. When combined with existing steering methods, DSAS consistently improves the Pareto front with respect to steering alone, achieving a better trade-off between toxicity mitigation and utility preservation. We further demonstrate DSAS’s generality by applying it to a text-to-image diffusion model, showing how adaptive steering allows the modulation of specific concepts. Finally, DSAS introduces minimal computational overhead while improving interpretability, pinpointing which tokens require steering and by how much. The code will be available in Github.
This paper was accepted at the Workshop on Unifying Representations in Neural Models (UniReps) at NeurIPS 2025.
Activation steering methods in large language models (LLMs) have emerged as an effective way to perform targeted updates to enhance generated language without requiring large amounts of adaptation data. We ask whether the features discovered by activation steering methods are interpretable. We identify neurons responsible for specific…
*Equal Contributors
In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose STEER, a steering detection model that predicts whether a follow-up turn is a user’s attempt to steer the previous command. Constructing a training dataset for steering use cases poses challenges due to the cold-start problem. To overcome this, we…
This customer ships financial products to millions of users across dozens of markets, and growth shows no sign of slowing. Sustaining that pace is an engineering problem before anything else, and the company's engineers lean on AI coding agents to do it.
That puts inference on the critical path of how fast the company ships, rather than inside any single customer-facing feature. The workload runs on GLM-5.2, the mixture-of-experts model built for long-horizon coding and agentic work, served on Together. Traffic follows the working day: spiky, concentrated in engineering hours, and it climbs every time another team adopts agents into its workflow.
The customer came to Together after running coding workloads with other inference providers, and first consolidated onto our earlier dedicated offering. That offering worked, but wasn't built for how this workload actually behaves. The coding-assistant traffic isn't steady; it's peak-load and relatively low-TPS, concentrated in engineering hours, with sharp bursts in concurrency and prompt size as more teams put agents into their daily workflow. That shape is precisely why concurrency, not raw throughput, was the design priority when the workload moved to GLM-5.2.
Under the earlier model, absorbing that kind of burst meant someone had to see it coming. Teams ready to move agents into their daily workflow often waited on capacity rather than provisioning it, and the customer's platform team absorbed the coordination for every one of them, filing requests and sizing clusters. The team worked to plan ahead, but planning stopped working once adoption became unpredictable in both timing and size. You can't forecast a burst that's driven by a hundred different engineering teams independently deciding to lean on their coding agent harder this week.
When capacity is provisioned to yesterday's forecast and traffic is genuinely spiky, prefill capacity and KV cache headroom get exhausted exactly during the burst, which is when it matters most, producing exactly the kind of multi-minute request queuing that agentic coding workflows can't tolerate. Fixing that after the fact, versus giving the customer's own teams the ability to see load and scale ahead of it, is the difference between a coordination problem and an infrastructure one.
The customer set requirements for the Together team around autonomy, in addition to raw performance, and the workload's own shape makes clear why. The coding-assistant traffic runs at ISL p50/p90/p95 of roughly 81K/163K/178K tokens and RPS p50/p90/p95 of 3/6/7, a peak-load, relatively-low-TPS pattern where bursts in concurrency matter far more than raw tokens-per-second. That's the backdrop for what the customer asked of Together:
Dedicated Model Inference exposes the full endpoint lifecycle: creation, sizing, scaling policy, and configuration changes. The customer's infrastructure team used exactly this when a migration reshaped their GLM 5.2 endpoint, shifting toward fewer, larger replicas, same total footprint, different ratio of replica count to chips per replica. When that re-shape hit near-100% prefill capacity a few days later, with requests queuing one to three minutes and decode throughput collapsing to roughly 5 tokens per second, the fix wasn't a new deployment or a ticket back to Together. It was a live configuration change: restoring the tuned cache-session-aware routing policy in place of DMI's default cache-aware-by-hash policy, and widening the max-inflight-per-worker threshold. All of it was pushed same day with zero downtime.
Programmatic access to endpoint usage and performance data is how the root cause was found. Together API Support traced a single 192-second slow request end-to-end through the metrics data and found it wasn't compute-bound, and had spent almost the entire span queued behind a 2.3M-token pending-prefill backlog from other requests, not its own 250K-token prompt. That's the specific value of self-serve observability: the customer's own team diagnosed a queuing problem, not a capacity problem, without waiting on Together to pull logs.
Dedicated Model Inference gives users self-serve access to frontier open-source models, as well as performance-aware configurations to help customers opt for any combination of TTFT, TPS, TPM, and other metrics. The customer's team works closely with Together's forward-deployed engineers to continuously optimize these configurations as its coding agent use evolves.
This showed up as the GLM 5.1 to GLM 5.2 and context-length iterations transitioning on the same account and endpoint pattern, with no renegotiation involved. It also showed up as a deliberate configuration trade-off the customer's team made themselves: given their traffic profile, they evaluated a 1M-context configuration and turned it down, because doubling context to 1M would have cut the concurrency headroom their peak-load, low-TPS workload actually depends on. The team chose to stay at 256K/512K instead.
The coding-assistant relationship predates the GLM 5.2 production endpoint by several months. Together's Solutions Architecture team had already built dedicated load-testing infrastructure modeling the customer's actual usage pattern, initially validated against an earlier GLM release.
That groundwork carried straight into GLM 5.1. Early on, the customer's project lead asked over a weekend for a checkbox-style concurrency test of GLM 5.1 across 8 to 16 B200s, explicitly for the coding use case and distinct from earlier tests that had been consumer-facing and latency-focused. Together turned the test endpoint around the same day, and GLM 5.1 passed the bar and moved to production: two dedicated endpoints, split by accessibility, running as the customer's internal developer-facing coding assistant.
Before DMI, changing a config or adding capacity meant routing through Together: filing a request, sizing a cluster, waiting for a redeploy. Every engineering team that wanted to adopt agents added to that same queue, so the customer's platform team ended up coordinating on behalf of the whole organization.
On DMI, that entire flow moved in-house:
The deployment stopped being a single endpoint and started being a surface for innovation. That pattern is now repeating as a pipeline, not a one-off. The customer's team is already scoping a dedicated GLM 5.1 node in a new region, sized against a real production workload. It's a different shape of workload than the original coding assistant: a chat-style customer-support NLP workload rather than long-horizon agentic coding, landing on the same infrastructure and provisioning pattern.
Today we're releasing Grok Voice Transcribe 2.0, our latest speech-to-text model. Across our real-world evaluations, Grok Voice Transcribe 2.0 is one of the most accurate transcription models available today and twice as accurate as Grok Voice Transcribe 1.0, at the same price.
Grok Voice Transcribe 2.0 is built on the audio foundation model behind Grok Voice. Grok Voice already powers tens of thousands of customer-support calls a day, transcribes millions of hours of video narration, and runs voice agents in physical products, including the Grok assistant in Tesla vehicles. It is trained on a unique dataset of live, noisy, multilingual audio recorded across a diverse set of environments and refined with post-training.
The result is one of the most accurate transcription models for speech in real-world settings.
Most transcription models do well on clean, single-speaker audio. Real-world audio is harder: flaky phone lines, competing voices, local accents, and phone numbers or email addresses read aloud. We built Grok Voice Transcribe 2.0 for the hardest audio across conditions and environments.
On the public Artificial Analysis leaderboard, Grok Voice Transcribe 2.0 ranks first for accuracy among 32 streaming models.
In addition to public benchmarks, we measure word error rate on four internal sets drawn from production traffic: telephony audio from customer-support calls, conversations with Grok, spoken credentials such as account codes and email addresses, and short multilingual voice commands. Grok Voice Transcribe 2.0 improves on Grok Voice Transcribe 1.0 across all four, and on telephony it leads every model we tested.
Grok Voice Transcribe 2.0 transcribes dozens of languages, detects the language automatically, and follows mid-recording switches in a single pass. Multilingual accuracy is its largest improvement over Grok Voice Transcribe 1.0.
Short phrases such as in-car commands give the model little context to identify the language. On our short-phrase set, word error rate drops from 20.6% to 6.8%.
Grok Voice Transcribe 2.0 supports advanced configuration and controls. Existing Speech-to-Text API integrations get the accuracy improvement with no code changes:
Atlassian Loom is widely used for recording and sharing screen recordings. Atlassian found Grok Voice Transcribe 2.0 more accurate than their existing solution for transcribing Loom videos. Accurate transcripts open up new AI workflows: record an action plan in Loom, pipe the transcript into Cursor, and it makes the code updates directly.
“We've always believed the best way to move work forward is to capture context once and let it flow everywhere. With Grok powering Loom's speech-to-text and Cursor turning that into code, we're closing the loop from context to code: record what you mean, and the work gets done. It's a glimpse of where AI-assisted development is headed.”
Grok Voice Transcribe 2.0 pricing is identical to Grok Voice Transcribe 1.0. Batch transcription remains $0.10 per hour of audio and streaming $0.20 per hour, with diarization, timestamps, and key terms included.
Grok Voice Transcribe 2.0 will soon be the default in the Speech-to-Text API, and Grok Voice Transcribe 1.0 will be deprecated in the coming weeks. To stay on it during the transition, pin grok-voice-transcribe-1.0.
In August 2026, Hacktron reported what looked like a remote code execution (RCE) vulnerability in Next.js image optimization. Their investigation found that the vulnerable code was not in Next.js itself, but upstream in libheif, an AVIF image decoder used by Next.js, ImageMagick, WordPress, sharp, and much of the web.
Shortly after Hacktron notified us, we worked with them to reproduce the RCE against a current Next.js build and disclose it to the maintainers of sharp, libvips, and libheif. We then deployed a platform-wide mitigation on Vercel and started working with the maintainers on a fix.
Next.js image optimization lets applications resize and optimize images through the <Image> component (next/image). For AVIF images, the image-processing dependency chain is as follows:
<Image> invokes /_next/image,
/_next/image calls sharp
sharp calls libvips
libvips uses libheif to decode the image
That meant the vulnerable code was not in Next.js, but it was still reachable through Next.js image optimization. A malicious AVIF image sent to the image optimization endpoint would invoke libheif through sharp and libvips.
As such, one obvious mitigation was to disable AVIF optimization in Next.js. Malicious AVIF images would then stop at the image optimization endpoint instead of being passed through sharp and libvips to libheif. The exploit would not propagate upstream.
However, only mitigating Next.js, without an upstream fix, posed a disclosure problem.
After we worked with Hacktron to successfully reproduce the issue, we rolled out a platform-wide mitigation on Vercel and reached out to the maintainers of sharp, libvips, and libheif to disclose the vulnerability and begin working on a fix.
Here is the timeline:
August 11-12: Hacktron reported the issue to Vercel; Hacktron and Vercel reproduced the RCE with a working proof of concept.
August 13: Vercel applied a platform mitigation through its Image Optimization Service.
August 19: The Next.js team met with the libvips maintainer and began coordination across sharp, libvips, and libheif.
August 24: Next.js informed its security partners.
August 25: Next.js published a security release that disabled AVIF optimization.
The Vercel security team contacted the maintainers of sharp and libvips by email, and opened coordination with libheif through a GitHub Security Advisory. Hacktron had also submitted vulnerability and exploit details to libheif. On August 19, the Next.js team met with the libvips maintainer and aligned on the path forward across sharp, libvips, and libheif. The libheif maintainer continued remediation through Hacktron’s GitHub Security Advisory.
On August 24, Next.js informed its security partners of the libheif vulnerability and its impact on Next.js (partner notifications are a routine part of Next.js’ security release process).
On August 25, six days after the August 19 meeting, the libheif maintainer released v1.23.2, which remediated the RCE.
Securing Vercel and its customers was straightforward: all Next.js image optimization requests on Vercel go through a central Image Optimization Service. Therefore, we disabled AVIF optimization and resizing in that central service. Any incoming AVIF images were not passed to libheif for decoding and RCE was not possible on Vercel.
Protecting self-hosted applications required a Next.js release. On August 25, Next.js published a security release that had originally been planned to address a separate issue. After coordinating an upstream fix, we bundled the AVIF mitigation into that release and shipped it a day earlier than planned. The release disabled AVIF optimization and resizing in Next.js; given that the patched libheif release was still propagating downstream, this was the most timely option. We also published a security advisory to communicate the issue’s severity.
The volume of OSS vulnerabilities discovered continues to increase, and the numbers are overwhelming:
In 2026, the CVE program has published more than 35,000 CVEs.
Private vulnerability reports on GitHub grew from 500 per week in January to 3,000 per week in May.
GitHub also reported 1,560 reviewed advisories in May 2026, the highest monthly volume in the advisory database’s history.
As LLMs accelerate vulnerability research, we expect to see more upstream vulnerabilities like the libheif RCE surface across the OSS ecosystem. There have been a higher number of Next.js security releases in recent months, and we expect that trend to continue as we mitigate new vulnerabilities that both we and the research community uncover.
We are committed to proactively finding vulnerabilities before attackers, responsibly disclosing everything we find, and collaborating with researchers and maintainers on fixes.
Thanks to Hacktron for responsibly disclosing the AVIF vulnerability, working with us to reproduce the issue, and coordinating with the upstream maintainers through remediation.
We also want to thank the maintainers of sharp, libvips, and libheif. Their work on the upstream fix made coordinated remediation possible across the image processing dependency chain.
We work with a talented set of researchers to secure Next.js and other open source frameworks through Vercel's Open Source Bug Bounty. Anyone interested in contributing to the security of eligible frameworks is encouraged to participate there.
GLM 5.3 FlashX is now available on AI Gateway.
GLM 5.3 FlashX is a high-speed serving option for Z.ai's multimodal coding model, delivering inference at ~200 tokens per second for faster streamed responses.
The higher serving speed is useful for coding agents, tool loops, and interactive applications where users wait on generated output.
Use zai/glm-5.3-flashx across API formats and in coding agents:
To use it in a coding agent, see the coding agents guide, then run vercel ai-gateway setup to create a key and configure your supported agents. Select zai/glm-5.3-flashx inside the agent.
AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, budgets for API keys, routing rules, and more.
AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests.
Try GLM-5.3-FlashX in the model playground, or view all language models available on AI Gateway.
For travel and leisure businesses, combating fraud is a balancing act between speed and security. These businesses—which include not only hotels and travel booking platforms, but also museums, theme parks, and live-event venues—sell offerings that are time-sensitive, easily resold, and often purchased across borders. That makes fraudulent transactions hard to stop and losses hard to recover.
At the same time, travelers booking last minute are rarely willing to wait. Travel and leisure businesses need to approve high-value bookings in seconds or risk losing legitimate customers to competitors. As a result, they have less room to add verification steps, even when fraud risk is high.
That’s giving bad actors an opening. Last year, Stripe data shows that fraud attempts against travel and leisure businesses hit a four-year high. We analyzed payment activity from more than 200,000 active travel and leisure businesses on Stripe to understand where fraud is rising, how effectively it’s being blocked, and what businesses can do in response.
Among Stripe businesses in travel and leisure, fraud attempts rose sharply over the past three years. Scams continue to multiply, from reservation hijacking to WhatsApp-veiled hotel impersonators. Bad actors are also going after travel businesses themselves—even phishing kits are now sold as a service. And with AI making it easier to launch convincing scam campaigns and create synthetic identities at scale, fraud is becoming both more prolific and harder to detect.
Stripe Radar, our AI-powered fraud product, blocked the overwhelming majority of those attempts. Radar also became more effective over time: the share of attempted fraud that made it through to payment fell by more than two-thirds from 2023 to 2025. As a result, the vast majority of attempted fraud activity was intercepted before payment, while the rate of fraud identified after payment remained broadly stable.
Trained on more than $1.9 trillion in transaction volume across millions of businesses, Radar blocked more than $3 billion in suspected fraudulent payment volume among travel and leisure merchants last year alone.
Fraud attempt rates rose across most regions last year for travel and leisure businesses on Stripe. Those in APAC saw the biggest year-over-year increase, followed by EMEA, with both rates up more than fivefold from 2024. The fraud attempt rate also rose 37% year over year among LATAM businesses. North American businesses were the exception, with the fraud attempt rate declining from 2024 to 2025.
Travel is growing fastest in regions where mobile-first and cross-border bookings are also becoming more common, giving fraudulent actors more ways in. In North America, slower travel growth and more established fraud controls may be keeping attempted fraud at bay.
For travel and leisure businesses operating globally, a fraud control that works in one market may not work in another. Breaking out fraud attempts, successful fraud, disputes, acceptance rates, and false declines by region and payment method can help businesses pinpoint what’s driving risk in each market, whether that’s card testing, stolen-card bookings, account takeovers, or post-trip chargeback abuse.
Targeted fraud controls can reduce losses without adding the same checks to every booking. For example, Oasis Hotels, which serves international guests in Mexico, used Radar to apply additional authentication to bookings where the name on the reservation did not match the name on the card. Within six months, its fraudulent dispute rate fell by 90%.
Likewise, SiteMinder, a hotel commerce platform serving properties in 150 countries, implemented Radar to strengthen fraud screening across the payments it processes for hotel partners. Fraudulent payment volume fell 61%, and fraudulent bookings dropped 27%.
Among travel and leisure businesses, fraud often shows up in four areas: bookings, extras and travel credits, promotions and new account offers, and post-trip disputes.
Bookings remain a primary target. Stolen cards are often used by a person who is not the cardholder to book last-minute or high-value reservations. The traveler can then present an ID that matches the name on the ticket or booking, even though the cardholder didn’t authorize the purchase. If the booking has been confirmed, the bad actor often uses the flight, hotel stay, or rental car before fraud is detected, making the loss harder for businesses to recover. Stolen payment details are also sometimes used to buy extras around the booking, including seat upgrades, baggage credits, and lounge access. Because these purchases are usually smaller than the main booking, they’re less likely to trigger review.
Promotions create another common opening for abuse. Bad actors can use bots to create multiple accounts and email addresses, repeatedly claim sign-up discounts or referral offers, and then use those discounts to book travel at a lower price, either for personal use or resale. To prevent this type of abuse, businesses need to be able to identify suspicious behavior when accounts are created and block promotion redemptions at checkout.
Radar can use login-related signals to identify possible multi-account abuse and flag accounts for additional verification or review. It can also help detect fraud patterns associated with unusual account activity and suspicious payment behavior at checkout. Stripe Identity can add an extra verification step before high-risk actions, while 3D Secure can help protect payment methods used for those purchases.
Fraud can also happen after the trip is complete. A customer might stay in a hotel or take a flight, then dispute the charge as fraudulent in an attempt to get a refund. Keeping clear records of bookings, customer approval, service delivery, cancellation terms, and any refund issued can make it easier for travel businesses to respond. Smart Disputes, available for card disputes, can help businesses assemble and submit the most relevant evidence, though the final decision ultimately rests with the card issuer.
Travel fraud becomes much more expensive when it gets past checkout. Once a booking is paid for, the business can end up dealing with both the original financial loss, as well as the follow-up work across fraud, support, and disputes. Early detection gives businesses more time to stop suspicious bookings before they become losses or disputes.
Learn more about how AI is changing the fight against fraud, or get in touch to see how Radar can help protect travel revenue.
I joined GitLab at a moment when the way teams build and secure software has been changing rapidly. GitLab CEO Bill Staples recently framed that shift in When Code Is Abundant. When code is no longer the bottleneck, trust becomes scarce, and that constraint shows up first in what reaches production.
As a CISO accountable for the same decisions as my peers, my operating thesis is simple: Agentic software development stays trustworthy only when security, governance, and guardrails sit in the path from plan to production. Leaders must continuously know the attack surface, constrain execution, and close the loop from discovery to verified fix at machine speed. Instead of the number of scans, tickets, or reviews, the metric that matters is time from detection to verified remediation.
That metric becomes more relevant as the economics of an attack change. The risks themselves are familiar: an open server, an over-scoped credential, or an exposed deployment path. AI models make these conditions faster and cheaper to discover, connect, and exploit. I find that more unsettling than a novel zero-day because the exposure was already in our environment; the difficulty of uncovering it was part of what protected us.
Anthropic and OpenAI have both described this shift publicly, and so has every security team I've talked to this year regardless of industry: advanced models and agentic systems are compressing the time and cost required to find and exploit weaknesses. Open weight models are catching up quickly with the most capable security systems available today, which means capabilities that recently lived inside a small set of labs will become available to a much wider set of threat actors.
I can see that acceleration inside GitLab. We have published 317 CVEs so far in 2026, compared with 181 in all of 2025 and 170 in 2024. Our bug bounty program received just over 3,600 reports in the last 90 days, compared with 1,440 in all of 2024.
We are not an outlier. In April, the National Institute of Standards and Technology (NIST) stopped enriching most CVEs, conceding that a record year of output still wasn't enough. This year's Verizon Data Breach Investigations Report put exploitation of vulnerabilities ahead of credential abuse as the leading initial access vector for the first time in 19 editions, with median time to resolution slipping from 32 days to 43. The Forum of Incident Response and Security Teams (FIRST) made the same point this summer.
Published advisories and incoming reports measure different things, but they create the same operating pressure: Discovery volume is rising faster than teams can verify, prioritize, and remediate what matters.
Severity models still assume a finding stands alone, but agents can chain a low-severity flaw, an overly broad permission, and an exposed path into a material attack. A queue sorted by CVSS increasingly misses that context while the backlog grows faster than teams can clear it using their traditional tools and processes.
The operating model must change with the economics of attack. Security controls must sit in the execution path, with a closed remediation loop behind every material finding. That governed path across your software development lifecycle (SDLC), under your guardrails, context, and workflows, is the foundation for an enterprise software factory.
Security teams have always been outnumbered, and our own intake is running about six times the 2025 rate. But capable models change the math in the defender's favor first.
Defenders have access to the code, infrastructure, deployment paths, configuration, identity systems, and operating context. Point the same capability at the same target and we can see much more, if we use it against our own surface first. Models can turn that broader context into machine-speed discovery, prioritization, and remediation. This has never been true before.
The build process is also becoming observable. For years, much of the work on an issue was not captured in systems that security teams could inspect. Security teams reviewed what remained: the diff, the build, and the running application.
When an agent builds software, construction can become an event stream: file reads, tool calls, commands, credentials issued, systems accessed, and approvals granted. That record lets us govern how software is built.
This architectural advantage exists when agents run somewhere their actions can be identified, constrained, and recorded. Once the necessary infrastructure is in place, every improvement in model capability strengthens the defensive system.
I believe machine-speed defense requires three layers that strengthen as model capability and commit volume rise across the SDLC.
This is the discovery pass for everything that follows. With the assumption that a capable attacker already has the same models you do, you should use those models on your own surface proactively across code, infrastructure, and deployment paths. The goal is a verified picture of where you stand today.
Frontier labs sit closest to the capability curve, which is where new AI model capacity first shows up in both offense and defense. Their contribution raises the defensive posture the rest of the industry can build on: model-assisted discovery pointed at real systems, and remediations drafted by agents with your team approving the change. We are running that with Anthropic on Project Glasswing, using their models across our critical systems and products, and repeating the pass when a stronger model arrives.
We then use GitLab Duo Agent Platform to continuously triage and remediate those findings, reduce the introduction of new issues, and ship software that has already been verified before production. We are customer zero for the bar we hold the software industry to, as we aim to translate that into trust in what you build on our platform.
A baseline tells you where you are. The harder problem is maintaining it while code volume, agent capability, and attacker capability continue to increase.
That foundation must satisfy six requirements.
These requirements work together: continuous coverage so findings have somewhere to go, fixes tested on the path they ship on, policy where the work runs, and agents with their own identity instead of a developer’s access token.
Software and its environment keep changing after production. The artifact you shipped last month can become vulnerable because of a disclosure next month, with exploitation following within days and sometimes preceding an available patch.
As a result, catching up once is not enough: keep scanning after the merge, reassess production as stronger models arrive, and land fixes in the same developer workflow that produced the change. Govern each merge and close each fix so every new finding moves toward remediation.
The operating model is to enforce the security you already have, measure time from detection to verified remediation, and keep customer experience checks in the same build path as security.
WHAT MY PEERS ARE SAYING
Cybersecurity experts and peer CISOs are describing a similar operating approach in an effort to enable governance and remediation at the speed of development.
“Machine scale discovery without an equally fast path to governed remediation is not progress. It is an inventory problem dressed up as security. The organizations that will hold up under agentic development are the ones that treat detection as the start of a closed loop: policy on every change, remediation in the build path, and a baseline they can re-verify as models improve.” Gadi Evron, CISO-in-Residence for AI, Cloud Security Alliance
“A durable security program for agentic software development keeps every agent on lawful rails: an explicit identity, constrained permissions, and a sanctioned path from plan to production. An agent working outside those rails is lawless: no identity, no record, no way to govern what it touched. That discipline has to hold as models improve and agent volume rises.” Bill Shields, CISO, Workday
“Trust in what you ship depends on continuous hardening of the models and development platforms you build on and governance of every change in your software lifecycle. Those layers reinforce each other, and neither substitutes for the other.” Sam Curry, Chief Security Officer, Zscaler
As agents produce a larger share of the code, your SDLC is splitting in two. One path runs through governed repositories, CI/CD systems, identity controls, approvals, and security tooling. The shadow path runs through personal laptops, local credentials, unmanaged tools, and agent sessions outside those controls. It may produce valid code, but without a reliable record of how that code and its related infrastructure changes were made.
A commit shows whose credential was used, but it reveals little about the agent, tools, permissions, commands, and external systems behind the change. Capturing that evidence requires a governed execution environment that connects identity, permissions, tools, policy, and approvals. Without it, security teams inspect artifacts after the important actions have already occurred.
Capturing events is only the beginning. At agentic volume, a complete transcript becomes another backlog. Security systems must turn those events into enforceable policy, attributable decisions, and verified outcomes. Tool calls are evidence, and an agent's explanation of its own reasoning is secondary.
On GitLab, that architecture is becoming concrete. Policy sits in the execution path. Scanning runs where developers and agents already work, early enough that the fix is cheap. Third-party findings converge in one vulnerability system, and agents turn validated findings into tested merge requests carrying the application context needed to fix them. Secrets are short-lived, scoped, and revocable by default. High-risk agent actions stop at an attributable approval boundary. If the pipeline cannot prove it, the pipeline does not ship it.
Authorship capacity is becoming elastic while human review capacity remains constrained. On a recent release, we ran agentic security review across 969 of 997 eligible merge requests, or 97%. A year ago, that level of coverage was inconceivable. Today I treat it as the expectation.
GitLab brings these controls together across the platform. GitLab Duo Agent Platform closes agentic triage and remediation loops on the same governed path as source, security, CI/CD, and merge. If you are a GitLab customer, the opportunity is to put those controls in the execution path and measure whether they shorten your exposure time from detection to verified remediation.
The deeper architectural question is whether agentic work runs through the same governed foundation for your software factory. When it does, teams and agents share a common control plane. When it does not, the shadow software factory persists regardless of how much security tooling surrounds the downstream pipeline.
A credible program can answer these questions from its operating data:
Those answers should become increasingly automatic. A program that is working keeps coverage continuous across the full attack surface, puts an attributable owner and a tested remediation path behind every material finding, and either eliminates exposed and long-lived secrets or time-boxes them with compensating controls you can defend. Agents run through sanctioned identities and constrained permissions, with a recorded approval on high-risk operations, and you measure detection to verified remediation continuously, by severity and by attack path, driving that time toward machine speed.
We are learning what that takes by running the model ourselves. In the coming months, GitLab will publish a blueprint that turns these principles into operational guidance: the controls, architecture, metrics, and practices to establish a baseline. The blueprint will aim to help you keep agentic software development on a governed path, eliminate shadow production work, and continuously move findings through verified remediation.
The goal is practical: Give security and engineering leaders something they can implement and measure, regardless of where they are starting.
I'm writing this as a peer accountable for the same class of decisions you are. When code generation is abundant and trust is scarce, your agentic software development demands machine-speed verification and remediation. Security, governance, and guardrails have to be part of your foundation, not a set of gates around it.
The opportunity right now is unusual. The same models increasing offensive capacity can also expand defensive capacity. As cybersecurity professionals, we have more context than the attacker, greater access to our own systems, and a chance to make the construction of software itself observable and governable. We have to use that advantage.
Expect continuous hardening from the platforms you build on. Ask for evidence of what they find, how quickly they remediate it, and whether the controls survive the next increase in model capability. Apply the same standard to your own software development. GitLab customers can use Duo Agent Platform to bring agentic triage and remediation into the same governed path as source code management, CI/CD, and the rest of the SDLC.
The new standard now is to move “detection to verified remediation” at machine speed.
Join us at Transcend, our livestreamed event on October 6, where we will dive deeper on the topic and share our latest innovations to help you secure your agentic software development.
On September 17, 2026, GitLab 19.4 was released with the following features.
Jimmy contributed across the GitLab codebase, client-go, and the Terraform
provider to ensure that tokens, service accounts, and push mirrors can be
managed end to end through infrastructure as code.
Previously, you could only apply AI agent tool governance rules to internal GitLab Duo Agent Platform tools. Tools available to both GitLab Duo Agent Platform and third-party agents through the GitLab MCP server followed fixed rules that could not be changed.
You can now govern GitLab MCP server tools from the same place as internal GitLab Duo Agent Platform tools. They appear alongside internal tools in your group and project GitLab Duo settings, where you can set a mode for each tool:
You can now restrict access to MCP (Model Context Protocol) servers by allowing or denying access to:
This feature gives you assurance that AI agents within Duo Agent Platform are operating within governed boundaries and can only access MCP tools that are within their scope to perform their activities, sessions, and tasks.
These controls apply consistently wherever AI agents run, including:
This feature is currently in beta and we welcome your feedback in issue #628378.
Use the Vulnerability Context Flow to produce context to triage vulnerabilities more efficiently and intelligently.
The flow produces context in the following three categories:
Advanced SAST now scans Kotlin, Dart, and Scala codebases with the same deep taint analysis that covers Java, Python, and other supported languages, all delivered through the Software Factory architecture with per-language front-ends and framework-aware rule gating.
All three additions are verified using deliberately vulnerable real-code repositories, with findings reported as code flows from source to sink.
GitLab license data now carries SPDX license expressions, including compound declarations
such as MIT OR Apache-2.0 or GPL-2.0-only WITH Classpath-exception-2.0.
Previously these were reported as unknown in the dependency list and were invisible to
license approval policies.
Composite licenses now appear in the dependency list with their operator (AND, OR,
WITH), and license approval policies can allow or deny them the same way they handle
single-license dependencies.
Expressions declared in a CycloneDX SBOM have been supported since GitLab 19.3. This release adds them to the license data GitLab synchronizes. Offline instances receive expressions only after downloading the v3 license data.
GitLab Duo CLI now includes a /goal slash command that delegates open-ended objectives to a
governed, goal-driven flow that runs locally.
You describe a goal and GitLab Duo handles implementation and verification, using an independent judge to decide when you have achieved your goal or reached the iteration limit. You stay in control the whole time: pause, update the goal, or redirect the agent at any time.
The /goal slash command requires GitLab 19.3 and later, and GitLab Duo CLI 9.17.0 and later.
To get started, run /goal <task>.
For example:
/goal Fix the failing tests in spec/models/user_spec.rbYou can now invoke GitLab Duo agent flows directly from Slack, without switching to the GitLab UI.
With the GitLab Duo Slack integration, you can mention GitLab with @GitLab in any Slack channel or thread. Mention GitLab to trigger agent flows, get answers from your codebase, and create GitLab issues from conversations. GitLab Duo streams its progress back into the Slack thread in real time, and includes thumbs-up and thumbs-down feedback buttons so you can rate responses without leaving Slack.
This integration is available as an experiment. To share your feedback, add a comment to issue 624364.
Build custom flows for your GitLab projects with the GitLab flow builder, a new visual editor for AI-native workflows in the GitLab for VS Code extension. Compose a flow visually from components (Agent, Custom tool, and AI task), or edit the underlying YAML directly.
To start, open your flow’s YAML file in VS Code and select Open GitLab Flow Builder. Test your flow with the Run button, which opens an execution console. When your flow is ready, select Publish to publish it to the AI Catalog.
The flow builder is available as a beta feature in GitLab for VS Code 6.87.0 and later. To get started, enable the gitlab.featureFlags.flowBuilder setting in VS Code.
New CI/CD tools let agents trigger, inspect, and control CI/CD from any MCP client:
save_pipeline runs, retries, or cancels a pipeline without switching tools.get_job returns job metadata together with the job trace, so an agent can
read the log of a failed build and diagnose the problem on its own.Previously, agents had no way to trigger or inspect pipelines through MCP.
Merge request tools let agents run the full merge request loop through the GitLab MCP server:
save_merge_request opens and updates an MR.get_merge_request inspects an MR in depth, with new diffs, conflicts, and
approvals facets.list_merge_requests now works at group scope.save_merge_request_review leaves line-level review comments, with batched
diff comments and a summary in a single call.accept_merge_request merges an MR once checks pass, and can also approve
or unapprove it.New project and user tools give agents the context they need to target work correctly through the GitLab MCP server:
get_project and list_projects find and read project details.list_project_members enumerates members and their roles.get_user looks up user details for assignment and mentions.Previously, agents had no way to discover project membership or user information through the GitLab MCP server.
New repository tools let agents browse a project’s structure, read its commit history, and propose changes through the GitLab MCP server:
list_repository_tree explores the file tree.list_branches and list_tags enumerate refs.list_releases inspects published releases.get_commit retrieves a commit’s metadata, diff, or notes.list_commits pages through a branch’s history.add_commit commits one or more file actions in a single call, optionally to a
new branch from a specific starting ref or source project.fork_repository forks a project, so an agent can go from exploring an
upstream repository to proposing changes without leaving its client.semantic_code_search is now semantic_search. The tool finds code by meaning
rather than by exact symbol or filename, which is unchanged from earlier
releases. The rename adds a scope parameter so that additional indexed content
types can fold into the same tool in future releases. Today scope accepts
code only.
The GitLab MCP server now exposes work item tools, so agents and MCP clients can search, read, create, and update issues, epics, tasks, incidents, objectives, and key results.
Use get_work_item to read a single item in depth, list_work_items to search across a group or project, and save_work_item to create or update any work item type.
Because issues and epics are work item types, get_work_item and save_work_item cover what get_issue and create_issue do today.
save_note lets an agent comment on a work item or merge request and reply inside an existing discussion thread. The introduction of this tool renames existing create_merge_request_note and create_workitem_note.
In previous versions of GitLab, the Merge request trigger event type only supported the Approved, Marked ready, and Merge conflict actions. You had no way to run a flow or external agent the moment someone opened a merge request without using a tool outside GitLab.
You can now select Created as a trigger action. When someone opens a merge request in draft or ready state, and GitLab generates the diff, your flow or external agent runs. Use this for a first-pass review, or to add context from related issues.
To configure this trigger, go to AI > Triggers in your project, or select it when you enable a flow.
Finding the details that matter about an agent session used to mean hunting through a cluttered panel. Now, the session details panel surfaces what you need at a glance: status, timestamps, and the triggering user appear in an overview bar, while the right rail organizes identity, execution, and supplemental details into clearly labeled groups.
A new Linked items section separates what started the session from what it produced, including merge requests, work items, jobs, and comments. In the GitLab Duo side panel, session details now live in a collapsible bar pinned to the bottom, so they stay accessible without getting in your way.
The GitLab Duo Agent Platform now supports independent model selection for the Developer Flow. As an administrator, you can select a specific AI model for the Developer Flow separately from other GitLab Duo Agent Platform features, giving teams greater control over model selection.
The GitLab Duo Agent Platform now supports three open-weight models: GLM 5.3, Kimi K3, and MiniMax M3.
In GitLab Duo Agentic Chat, you can select any of these models for your own conversations. Users with the Owner role for a group and administrators can also set them as the default for Agentic Chat and for other agents, flows, and features.
In previous versions of GitLab, the only way to stop a trigger from automatically starting a flow was to delete it entirely. Deleting a trigger meant losing any complex filter configuration you had set up.
Now you can turn a flow trigger off and retain its configuration. Use the new toggle to turn it back on at any time.
To manage triggers, go to AI > Triggers.
In previous versions of GitLab, you turned on SAST false positive detection, GitLab Duo Vulnerability Resolution, secret detection false positive detection, and dependency scanning auto-remediation for each project individually. Now you can apply an Automated Triage and Remediation profile to a group or project, setting severities and run modes in one action. Start with a preset, or configure each flow yourself:
Profiles are available only with the GraphQL API, and require GitLab Duo Agent Platform with foundational flows turned on for the top-level group. Most flows consume GitLab Credits.
When a file is locked, you now see who locked it and what your options are, without leaving the blob viewer.
Previously, only a Locked label appeared, with no way to tell who locked the file or whether you could unlock it yourself. Now, a popover next to the label shows who locked it. If you have permission to unlock the file, the popover includes an unlock action. If you don’t, it explains why. For locked directories, the popover links you directly to the specific file that’s blocking your changes.
Security teams can use the bulkSetVulnerabilityFindingsDueDates
GraphQL mutation to assign, update, or remove due dates for
vulnerability findings in bulk. Each request supports up to
1,000 finding UUIDs and returns counts for assigned,
removed, and skipped updates, along with structured
errors. Teams can use this information to
connect vulnerability remediation
timelines with existing service-level agreement (SLA) and workflow automation.
When you apply filters to the Vulnerability Report, exported CSV reports will respect those filters. Rows that are not included in the Vulnerability Report UI after filtering will not appear in the CSV file export either.
You can now view scanner coverage for an entire group hierarchy from one page. In previous versions of GitLab, the Security Inventory showed coverage per subgroup, but no total for the entire group. A coverage widget now aggregates scanner coverage across every project in the group and its subgroups, and shows the percentage and number of projects where each scanner is enabled, not enabled, failing, or stale. To focus on one scanner, such as SAST or Dependency Scanning, use the scanner dropdown list. Then select a status to filter the project list, and turn on scanners for the projects that aren’t covered.
The Security Inventory also now lets you control which columns are shown. To show or hide the Vulnerabilities, Tool coverage, and Security attributes columns, select Display.
When secret detection finds a leaked GitLab personal access token in a public project, automatic response revokes it. In GitLab versions earlier than 19.4, revocation used only one detection rule and revoked only the legacy token format. Tokens created on GitLab 18.3 and later use the routable or versioned routable format. GitLab detected and reported these tokens without revoking them.
In GitLab 19.4 and later, revocation recognizes all three GitLab personal access token detection rules:
gitlab_personal_access_tokengitlab_personal_access_token_routablegitlab_personal_access_token_routable_versionedRevocation also covers findings from GitLab Secret Scanning for Source Code and the Gitleaks-based analyzer. You do not need to change any configuration. Instances with automatic response enabled get this wider coverage immediately.
Previously, pasting a copied table into a table cell always merged the copied cells into the existing table, which made it difficult to create a nested table.
Now you can choose how a pasted table behaves:
You can also use a keyboard shortcut to paste a table into a cell as a nested table: Command+Option+V on macOS, or Control+Alt+V on Windows and Linux.
Standard paste with Control+V or Command+V works as it did before.
In previous versions of GitLab, Dependency Scanning only surfaced packages with known CVEs. Malicious packages, those crafted to harm through typosquatting, compromised maintainer accounts, or embedded malware, produced no findings.
GitLab 19.4 introduces malicious package detection in beta. Dependency Scanning now checks
your dependencies against GitLab malware advisories,
so threats can surface before they are widely known. Findings appear in your Dependency List
and Vulnerability Report with a red Malware badge, always Critical severity, identified
by a GLAM- ID, not a CVE.
You can also block malicious packages before they merge, using the malware rule in merge request approval policies.
You don’t need any additional setup. Coverage applies to the supported package types: npm, PyPI, Maven, Go, NuGet, Cargo, and RubyGems. The same advisories power continuous vulnerability scanning, and offline instances download them manually.
Share feedback on issue 606036.
Security dashboards are now available at the organization level.
Users can see the following data across all top-level groups in an organization:
In GitLab 19.4, the GitLab MCP server provides the following new tools for vulnerability management:
list_vulnerabilities, which lists security vulnerabilities in a GitLab project with optional filtering
by severity and report type, with cursor pagination.get_vulnerability, which fetches full details for a single vulnerability by numeric ID, converting
it to the gid://gitlab/Vulnerability/<id> global ID format.save_vulnerability, which covers five write operations on GitLab vulnerabilities in a single
consolidated tool:These new vulnerability management tools allow AI agents to run vulnerability triage and remediation actions through the GitLab MCP server.
GitLab 19.3 introduced email notifications for reservation thresholds and for the moment a capped capability is cut off. The spend cap itself had no early warning, so the first email about a cap arrived when usage had already stopped.
GitLab now emails billing account managers when a capability’s on-demand usage reaches 50% or 80% of its monthly spend cap, naming the capability and the cap in credits. Only the highest threshold crossed is sent, at most once per capability per billing period. Caps of less than $10 are skipped, so a small cap does not generate noise.
Geo SSH proxying enabled by default
The following feature flags are enabled by default in GitLab 19.4:
geo_proxy_fetch_ssh_to_primarygeo_proxy_push_ssh_to_primaryGeo SSH proxying provides a more reliable path for SSH fetches and pushes to a Geo secondary site when the operation is proxied to the primary site. It also resolves long-standing bugs where proxied operations failed, such as pushes with push options and fetches from large repositories.
Action required for Cloud Native GitLab deployments
Cloud Native GitLab deployments using the bundled NGINX Ingress must either:
Otherwise, SSH fetches and pushes through Geo secondaries may hang or time out.
See the Geo troubleshooting documentation for SSH proxying for more information.
When a subscription had temporary evaluation credits, all usage drew from that shared pool first. Every user’s included monthly credits sat idle until the evaluation pool ran out, and then reset at the end of the month.
GitLab now consumes each user’s included credits first, and draws from the shared pool of temporary evaluation credits only after a user has used their included amount. The Monthly Commitment Pool, One-Time Charge credits, and On-Demand credits are consumed in the same order as before, so your bill is unaffected.
The credit usage export gave you one row per day, which told you how much a subscription spent but not what it spent on. Attributing credits to a team, a project, or a single automation meant guesswork.
The export now returns a ZIP file with two CSV files: the daily summary you already had, and a per-event file with one row for each billable event. Each row includes the product, flow type, session, user, namespace, project, credits used, and token counts. Exports run in the background, and GitLab emails you a download link when the file is ready.
Credit caps limit how many GitLab Credits each user can consume, but until now you could only configure them through the GraphQL API. Setting a different cap for a handful of users meant writing mutations by hand.
The new Credit caps page lets you set the flat cap that applies to every user by default, and add per-user overrides for individual users through a searchable picker. This page is available in GitLab Credits for group Owners on GitLab.com and administrators on GitLab Self-Managed. The GraphQL mutations still work if you prefer to script cap changes.
You and your agents can now deploy static artifacts to Vercel in under one second through Vercel CLI.
Run vercel deploy to share a prototype, publish an HTML report, or preview a page created by your coding agent.
Vercel automatically detects eligible deployments, and valid artifacts will skip the build step, immediately returning a live URL.
Instant deployments support directories with:
Up to 10 HTML or Markdown files
A total size of 5 MB or less
Supported file extension include .html, .htm, and .md.
Update to Vercel CLI version 59.16.0 or later to get started, and run vercel deploy --help for all available options.
Today, we’re announcing the general availability of new low-cost burstable Amazon EC2 T8i instances powered by custom sixth generation Intel Xeon Scalable Processors (Granite Rapids), available only on AWS. T8i instances are among the lowest-cost EC2 instances and deliver up to 30% better price performance over previous generation T3 instances. These instances are designed to run a variety of low-to-moderate CPU utilization workloads such as freemium services, training and demo environments, staging and development, data processing, microservices, low-traffic websites, and login gateways.
T8i instances
Thousands and thousands of customers run various lightweight workloads on T3 instances that require small, cost-effective compute configurations. These include microservices architectures, low-traffic websites, development and testing environments, small databases, data processing jobs, and short-duration compute tasks. Many of these customers like T family’s burstable performance model, which provides a baseline level of CPU performance with the ability to burst above the baseline when needed using CPU credits.
As customers modernize their infrastructure, migrate from on-premises environments, adopt event-driven and microservices architectures, and experiment with AI inference workloads, they have asked for newer generation cost-optimized small instances, better price performance to reduce their total cost of ownership, and a seamless migration path that leverages their existing knowledge and tooling.
T8i instances address each of these requests:
Instance specifications
T8i instances offer four sizes, each with two vCPU offered as a single core. The following table summarizes the specifications.
| Instance size | vCPUs | Memory (GiB) | Baseline Performance /vCPU (%) | CPU credits earned / hour | Network burst bandwidth (Gbps) |
| t8i.nano | 2 | 0.5 | 5 | 3 | Up to 6.25 |
| t8i.micro | 2 | 1 | 10 | 6 | Up to 6.25 |
| t8i.small | 2 | 2 | 20 | 12 | Up to 6.25 |
| t8i.medium | 2 | 4 | 20 | 12 | Up to 6.25 |
Like T3, T8i instances offer unique vCPU-to-memory ratios such as 1:0.25, 1:0.5, and 1:1 that are not offered by other EC2 instances. Like T3, T8i instances utilize the CPU credit system along with the Standard and Unlimited credit configuration modes. Unlimited mode is the default on T8i.
For workloads that need larger instance sizes above T8i offerings (nano, micro, small, and medium), I recommend M8i Flex instances that offer up to 30% better price performance than equivalent previous generation T3 instances along with the flexibility to scale up to 16xlarge.
Now available
Amazon EC2 T8i instances are available today in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon, N. California), Asia Pacific (Hyderabad, Malaysia, Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), and Europe (Frankfurt, Ireland, London, Paris). For Regional availability and upcoming Region expansion, search the instance type in the CloudFormation resources tab of AWS Capabilities by Region.
You can purchase T8i instances via On-Demand instances, and Spot instances with Savings Plan option coming soon. T8i instances support shared tenancy only and do not support Dedicated tenancy or Dedicated Hosts. t8i.micro and t8i.small instances are also available under the AWS Free Tier. To learn more, visit the Amazon EC2 Pricing page.
Try T8i instances in the Amazon EC2 console and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.
— Channy
Updated on September 18 — Corrected the memory size for each instance type.
Digital educational tools have transformed how students around the world access information, from online textbooks to video libraries. Yet, for all the remarkable leaps in technology and accessibility, digital learning can often feel like a passive experience. Interactive, engaging, multimodal forms of practice that can encourage students to think for themselves and work through solutions have great potential for learning but remain largely out of reach. They are expensive to create, limited in number, and often require a lot more effort from the teacher. We wanted to see if AI could help close this gap.
Today, we’re sharing our latest research which pushes the frontiers of interactive learning. Our new research experiment allows educators to create custom, interactive, and guided educational simulations. These learning interactives are tailored to the teacher’s objectives and curriculum, and are generated dynamically, leveraging a novel application of generative user interfaces (GenUI) that we’ve optimized for learning.
Having received initial positive teacher feedback from a trusted tester pool, we’re also releasing a sample library of over 30 learning interactives in English for STEM subjects including physics, chemistry, biology, and math with a focus on middle and high school. These are all generated by AI and reviewed by teachers. Schools using Google Workspace for Education can sign up to provide feedback to improve learning interactives through the Google for Education Pilot Program. This pilot is an early step toward developing more learning interactives for public use.
Learning is not a spectator sport. From the work of John Dewey, a foundational education theorist, who argued back in 1916 that we should “give the pupils something to do” to that of Jean Piaget, the influential psychologist whose pioneering work showed how learners construct knowledge, it is well established that students learn better through active engagement. Modern cognitive research, such as the ICAP framework, affirms that interactive behaviors consistently yield deeper schema construction and long-term retention than passive listening or reading. In short, students learn by doing. When students actively experiment, test hypotheses, and solve problems, they build a much more complete mental model.
Active learning is one of the key learning science principles that we optimize for in our research. It is fundamental to LearnLM, Google’s family of generative AI models fine-tuned for education released in 2024, and was explored in a 2025 Learn Your Way research experiment that reimagines the classic textbook with generative AI. Building on this earlier research, we set out to explore how the latest advances in generative models could be used to further transform content, helping teachers create digital learning that is much more active and engaging.
To make this possible, we turned to generative UI, an active area of research whereby AI models dynamically construct user interfaces rather than requiring those interfaces to be coded in advance.
We explored how to optimize generative interfaces for deeper educational journeys as opposed to quick interactions. By using carefully guided instructional design and pedagogical guardrails, we want to empower teachers to create their own interactive environments — tailored to their curriculum and adapted to their contextual inputs.
We first sought to determine what good, interactive learning experiences look like. We drew on established learning science to define a number of key pedagogical principles, aligning with those behind the development of LearnLM:
These principles come to life in our game-based learning design. To encourage motivation, each learning interactive features a series of progressively difficult challenges, based on the learning objectives (e.g., in the earth science example mentioned above, the first level focuses on the temperature, before progressing to harder challenges about rapid warming and storms). This is combined with a suite of scaffolded hints, instructions and feedback (e.g., directing the learner to the relevant formula or explaining a specific term) to provide each individual learner with the support they need to complete each level.
We define generation requirements to include:
To ensure quality control, we built self-correcting loops into the generation process — meaning that it is an iterative process, driven by a number of pedagogical guardrails. While this increases the time required to generate the final learning interactives, the aggressive reinforcement loop ensures closer adherence to quality criteria. These criteria include pedagogy (e.g., are the levels correctly covering the learning objectives and becoming progressively harder?), the mechanics (e.g., do the buttons work? Can this level be solved?), and visual aspects (e.g., are there redundant objects on the interface that could be distracting?). Within the self correcting loops there are auto evaluation processes that are agentic in nature (e.g., a solvability evaluation opens a Chrome instance and interacts with the simulation as if it were a user.) The goal is not just to test the validity of a specific solution but also to try adversarial actions such as taking knobs to extreme values. The self-correcting loop repeats until the generated outcome meets all of the required criteria.
Throughout our research, a core guiding principle has been that technology should be in service of educators and their goals. The teacher is at the heart of any classroom and is best placed to understand not only which AI-driven simulations would engage their students, but also when and where they fit into the curriculum. All learning interactives released in the library and available today were vetted and approved by teachers. These include topics from school curriculums such as Kepler's Laws of Planetary Motion, Data Visualization and Projectile Motion.
In addition, a collection of learning interactives was evaluated by STEM teachers in the UK. Results show that overall rating is good or excellent with physics and chemistry being the most amenable to simulation creation. Full details and results are available in our tech report.
We also conducted an initial study with 12 teachers in the US. Each of these teachers requested three different custom interactives, which were generated for their specific classroom needs. The feedback was highly positive with an average teacher rating of 8 out of 10 on the interactives’ quality. Teachers highlighted how dynamic generation solves a long-standing classroom challenge: the inability to differentiate instruction using static, off-the-shelf simulations. As one high school science teacher explained, “If I was teaching and I could type this in [for any curriculum topic] and then a simulation would [be generated], that would be amazing... I've never been able to differentiate any of the simulations because it's just, you get what you get“.
Educators also noted how closely the generated design elements aligned with their instructional goals: “That's why this was exciting to actually craft and build something that aligns perfectly with instructional goals and learning objectives” (middle school science teacher). They also praised the built-in-student scaffolding, noting that the tiered hints and worked solutions model the kinds of step-by-step guidance they provide when supporting students individually, and that the level progressions corresponded well to authentic assessment and practice questions.
As we expand the library, there is still much to learn and improve, and we will do so in collaboration with classroom teachers.
In collaboration with Google for Education, we will pilot learning interactives in schools and classrooms around the world. Schools can sign up to join an upcoming pilot through the Google for Education Pilot Program, giving their teachers the opportunity to request simulations for any custom STEM concept tailored to their curriculum, learning goals, and grade level. The newly generated learning interactives will be sent to the teacher who requested them for review. Only after teacher validation and approval can new learning interactives be added to our library and available for public use.
In addition, we will be conducting UX research and field studies to evaluate learning gains and student engagement when using learning interactives in classrooms.
By optimizing generative technologies for learning, we come closer to a future where learning practice is more active, effective, and tailored for every moment. We thank teachers for their partnership with this ongoing research and look forward to building learning interactives that can benefit students around the world.
Shout out to all those who have contributed to this work: Alex Moy, Alisa Kovshov, Anisha Choudhury, Anna Iurchenko, Ayça Cakmakli, Ayelet Shasha Evron, Brit Mennuti, Diana Akrong, Femi Olanubi, Ian Li, Ido Lerer, Julia Wilkowski, Lidan Hackmon, Michal Gordon, Nir Kerem, Preeti Singh, Rena Levitt, Rotem Yulzary, Sarah Smith, Shlomi Ben Shimon, Sophie Allweis, Tracey Lee-Joe, Tzvika Stein, Yaniv Carmel, Yishay Mor, and Yuri Lev. Special thanks to our executive champions: Niv Efron, Avinatan Hassidim, Maureen Heymans, Amy Keeling, Katherine Chou, Ronit Levavi Morad, Yossi Matias, Chris Phillips and Ben Gomes.
Sep 17, 2026
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UN System Data Commons is an open, AI-ready platform integrating critical global statistics into a single searchable resource.
Prem Ramaswami
Head of Data Commons
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This content is generated by Google AI. Generative AI is experimental
Every year, entities across the United Nations system compile data to track challenges that affect how we work, learn, stay healthy, and care for our loved ones.
These agencies work with some of the highest-integrity data in the world. But the statistics needed to solve big global challenges have lived in separate silos, organized in conflicting formats across, and within, different UN system organizations. Connecting the dots often meant months of painstaking manual work for data analysts before any real analysis could begin.
To solve this challenge, the UN system is launching the UN System Data Commons—an open-source platform built on Data Commons by Google that unites global statistics into one interconnected resource known as an AI-ready knowledge graph. With support from Google.org to the UN Foundation, the project makes critical data universally accessible, helping everyone from researchers to leaders track global progress in real time.
Many of society’s greatest challenges — from public health to poverty eradication — cannot be solved with a single data source. Effectively tackling these crises requires understanding how different datasets intersect.
The UN System Data Commons helps uncover these intersections by unifying siloed datasets, so that they can speak the same language. The platform automatically integrates metrics, timelines, and geographic boundaries into a single interconnected environment. This gives analysts more time to focus on uncovering key trends and designing evidence-based solutions, instead of formatting spreadsheets.
The UN System Data Commons uses AI to democratize access to these insights, letting people explore through intuitive, natural-language search. This means anyone, from a nonprofit program manager to a journalist to an international policy analyst, can ask questions in plain language and instantly receive relevant data and interactive visualizations.
Users can query the platform directly with questions such as:
If you prefer to browse, the Explore tab makes it easy to filter data by location or themes like health or education. The Blog section also breaks down complex trends into ready-to-read reports, like using UNICEF data to explore what works to reduce child poverty. Most importantly, every dataset is validated with UN system statisticians and technical experts, so every answer stays grounded in trusted, official facts.
Today’s launch also brings AI assistant capabilities directly to the research workflow. Instead of spending hours manually searching for numbers and assembling spreadsheets, you can prompt an AI assistant to do the heavy lifting. Built on open standards like the Model Context Protocol (MCP), Data Commons makes data AI ready enabling AI agents to autonomously fetch authoritative figures directly from the UN System Data Commons, connect the dots across different domains, and package everything together into ready-to-use charts, graphs, infographics, or written draft reports. Even with grounded, verified data, review the underlying sources before citing critical figures.
Over the coming year, the UN system will continue adding datasets from more UN entities, with a goal of including 80% of UN system statistical datasets by 2027.
Explore the data yourself at data.un.org.
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You can now opt into Turbo build machines on any individual deployment. This is useful when you need to increase resources temporarily without changing project settings. You can do this in three ways:
Include #VERCEL_BUILD_MACHINE=TURBO in your Git commit message before pushing to GitHub
Use vc deploy --turbo (Vercel CLI 59.20.0 or later)
Set buildMachine to turbo when creating a deployment with the REST API
Learn more in the managing builds documentation.
Since the first launch of AWS Elastic Beanstalk in 2011, customers have deployed full-stack applications in Java, .NET, Python, Node.js, PHP, Ruby, and Go, trusting Elastic Beanstalk to manage deployment and infrastructure operations so they could focus on business logic. Fifteen years later, that trust has only deepened, and the service has been rebuilt to match it. Now, AWS Elastic Beanstalk is the application management service on AWS that takes full operational responsibility for your production environments. Bring applications however they exist today: source code, Dockerfiles, or container images. Elastic Beanstalk creates and manages the production environment underneath. You manage your application. AWS manages everything else, deploying, scaling, patching, monitoring, and maintaining it continuously. That operational responsibility stays with AWS, for the life of the application.
We have been rebuilding the operational engine underneath and delivering a series of capabilities that make it more powerful than ever. Elastic Beanstalk now uses AI-powered environment analysis to diagnose health issues and recommend fixes automatically. A new official GitHub Action lets teams deploy directly from their existing CI/CD workflows with a single YAML configuration. And we rebuilt the infrastructure foundation to deliver OpenTelemetry-based observability, traffic-splitting deployments with automatic rollback, event-driven autoscaling, secrets management through AWS Secrets Manager, and HTTPS by default via AWS Certificate Manager.
Today, we’re announcing the next chapter of AWS Elastic Beanstalk: a new fully-managed Cluster Mode that deploys, scales, patches, monitors, and upgrades your applications continuously for the life of the workload. You bring your application. AWS runs it.
A new Cluster Mode is built for teams running a portfolio of applications. Instead of operating each application in isolation, you run multiple applications that share infrastructure powered by Amazon Elastic Kubernetes Service (Amazon EKS), fully managed with a single operational baseline. Multiple applications share resources, so per-application cost decreases as your portfolio grows without adding operational complexity. Whether you run ten applications or a hundred, you manage them through one experience, with the same operational guarantees across every stack.
Elastic Beanstalk Cluster Mode benefits for your workloads:
A first look of Elastic Beanstalk Cluster Mode
To get started, go to the Elastic Beanstalk console, create a new environment, and choose the Cluster in the Deployment type.
Elastic Beanstalk accepts source code, docker file, or container image to deploy your application. For example, you can provide the application code for your environment by selecting Local file and specifying container image build options. For the rest of the sections, the default values should be good for most scenarios.
Choose Create button and the deployment will begin! Note that the first deployment for a given set of subnets triggers EKS cluster creation, which takes about ten-ish minutes. Subsequent deployments are faster because they reuse an existing EKS cluster.
Here’s what it looks like when deployment is successful:
You can also use AWS Command Line Interface (AWS CLI), the EB CLI, or AWS SDKs. For example, consider deploying an application made up of several microservices to Kubernetes. Create an application first.
aws elasticbeanstalk create-application \
--application-name "my-microservice" \
--description "Multi-services demo" \Each microservice may have pre-built images in Amazon Elastic Container Registry (Amazon ECR). Register them as application versions:
IMAGES=(
"frontend-v1|public.ecr.aws/my-microservices/frontend:v1"
"cartservice-v1|public.ecr.aws/my-microservices/cart:v1"
"paymentservice-v1|public.ecr.aws/my-microservices/payment:v1"
"shippingservice-v1|public.ecr.aws/my-microservices/shipping:v1"
)
for entry in "${IMAGES[@]}"; do
IFS='|' read -r label uri <<< "$entry"
aws elasticbeanstalk create-application-version \
--application-name $APP_NAME \
--version-label "$label" \
--image-configuration Source="{Uri=$uri}" \
--region "us-west-2
echo "Registered: $label"
doneYou can set and deploy the corresponding service options for each service. For example, the frontend service is the only service that needs a public internet interface such as Application Load Balancer and also sets a health check path since it’s an HTTP service:
[
{"Namespace": "aws:elasticbeanstalk:eks", "OptionName": "cluster-role", "Value": "arn:aws:iam::0123456789012:rol<...>"},
{"Namespace": "aws:elasticbeanstalk:eks", "OptionName": "node-role", "Value": "arn:aws:iam::0123456789012:role/E<...>"},
{"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "observability-role", "Value": "arn:aws:iam::0123456<...>"},
{"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "subnets", "Value": "subnet-1,subnet-2,subnet-3,<...>"},
{"Namespace": "aws:elasticbeanstalk:eks:environment:autoscaling", "OptionName": "min-replica", "Value": "1"},
{"Namespace": "aws:elasticbeanstalk:eks:environment:autoscaling", "OptionName": "max-replica", "Value": "2"},
{"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "cpu", "Value": "0.5"},
{"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "memory", "Value": "256Mi"},
{"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "memory-limit", "Value": "512Mi"},
{"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "service-port", "Value": "8080"},
{"Namespace": "aws:elasticbeanstalk:eks:alb", "OptionName": "scheme", "Value": "internet-facing"},
{"Namespace": "aws:elasticbeanstalk:eks:alb", "OptionName": "healthcheck-path", "Value": "/_healthz"}
] #frontend-options.json namespacesNow, create the frontend service environment with these options. You can continue to deploy each service environment in a similar manner.
aws elasticbeanstalk create-environment \
--application-name my-microservice \
--environment-name frontend \
--version-label frontend-v1 \
--tier Name=Cluster,Type=EKS \
--option-settings file:///tmp/frontend-options.json \Here’s a look at the console once all services are deployed:
Elastic Beanstalk Standard powered by Amazon Elastic Compute Cloud (EC2) continues to be fully supported. Standard and Cluster Mode environments run side by side within the same Elastic Beanstalk application, enabling teams to migrate one environment at a time at their own pace. Validation checks confirm compatibility before any changes are made, so no environment is forced to move.
Elastic Beanstalk Standard Mode remains the best fit for:
To learn more about how to deploy and manage your applications in the Cluster Mode, visit the Elastic Beanstalk Cluster Mode documentation.
Now available
AWS Elastic Beanstalk Cluster Mode is generally available today in all AWS Regions that Elastic Beanstalk is available. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. If you want to call APIs, search documentation, find regional availability, and troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool.
There is no additional charge for Elastic Beanstalk Cluster Mode. You pay only for the underlying AWS resources your applications consume, including the EKS control plane fee, EKS Auto Mode compute, Amazon ECR, and Amazon CloudWatch. Note Elastic Beanstalk Cluster Mode is not AWS Free Tier eligible. To learn more, visit the AWS Elastic Beanstalk Pricing page.
Give it a try in the Elastic Beanstalk console and send feedback to AWS re:Post for AWS Elastic Beanstalk or through your usual AWS Support contacts.
— Channy
You can now run Harbor evals on Vercel Sandbox.
Harbor is the open-source harness behind Terminal-Bench, whose registry includes many other benchmarks such as SWE-bench, tau3-bench and OSWorld. Pass --env vercel to harbor run and each trial executes in its own isolated Firecracker microVM, so you can parallelize far beyond what your local machine is capable of.
A task's network policy is enforced at the sandbox firewall, outside the VM. Optional credential injection attaches secrets to matching outbound requests at that firewall, so they never enter the sandbox.
Paired with AI Gateway, one AI_GATEWAY_API_KEY reaches hundreds of models from multiple providers, and benchmarking another model is the same command with a different --model:
Swap --model to vercel_ai_gateway/openai/gpt-5.6-luna to run the same benchmark against an OpenAI model.
Requires Harbor 0.22.0 or later. Follow the step-by-step guide for setup, configuration, and troubleshooting. Learn more in the Sandbox documentation.
skills@1.7.0 adds Notion skills databases as an install source for agent skills .
Notion skills are reusable agent skills written as Notion pages. Teams author, review, and update them in the workspace they already use, then install them into any agent the skills CLI supports. No Git repository required.
To browse your Notion workspace's skills, run:
The CLI lists the skill packs shared with you and installs every skill in the packs you select.
To install a single skill, pass its Notion page URL:
Both commands use the Notion CLI (ntn) to authenticate. To set it up:
ntn login requires a Notion personal access token, so your workspace must allow them.
Access follows Notion's page permissions. You only see skills shared with you, so controlling who can install a skill is the same as controlling who can view the page.
This integration is built on Notion's new Agent Skills API, which exposes skills stored in Notion as standard Agent Skills folders. Because the format is standard, the same skills work in any agent that reads them.
Get started by creating a Notion skill.
Included Health is an all-in-one healthcare platform that partners with employers and health plans to provide their employees and members with healthcare navigation to services like virtual primary care, behavioral health, urgent care, specialty care, and more. The product experience centers answering medical, financial, or administrative questions via Dot—an AI-powered healthcare guide built on top of a federated multi-agent architecture using Deep Agents and LangGraph.
Healthcare is one of the few domains where what a person asks for and what they actually need can be entirely different. A member asking "is an artery plaque scan covered by my insurance?" might, with a few follow-up questions, reveal that they are managing elevated cholesterol and have a family history of heart disease. The right response includes the dollar figure—but it may also mean recognizing an opportunity to encourage a conversation with a primary care physician.
Historically, health systems handled this kind of routing with structured navigation trees. That approach made complex needs manageable for software, but only by flattening them into a series of predefined decisions. As Kartik Darapuneni, Engineering Manager, described it: “For the member, it feels really rigid, and it’s just not a good experience.” The limitations become even more consequential when a conversation begins with “I’m having chest pain.” The system needs to recognize the potential emergency in the first turn, not after seven clarifying questions.
This is the broader tradeoff that has shaped software for decades. To scale, technology has typically had to standardize complex human situations around the average case. In healthcare, where context is often the difference between a merely correct answer and a helpful one, that tradeoff is especially costly.
LLMs, combined with an agent harness, change what is possible. They can process dense individual health records, reason about ambiguous needs, and ask clarifying questions without forcing members through predetermined paths. “LLMs addressed all three of those blockers all at once,” said Kartik. Conversations not only become more natural, software no longer has to choose between personalization and scale. Included Health built a healthcare experience that adapts to each member’s context, responding with the urgency, guidance, and next step that their situation calls for.
Included Health's production architecture centers on a main LangGraph graph they call the Dot supergraph. Within it, Dot acts as the primary conversational router for transactional interactions (e.g. handling coverage questions, billing inquiries) and also navigation to the right care point. A set of sub-workflows handle domain-specific member journeys including urgent care intake, appointment scheduling, finding a specialist, behavioral health, and more.
Different product teams at Included Health own different parts of this graph. Scheduling alone, for example, has to account for which services a member is eligible for, their coverage details, whether they're a primary member or dependent, and a range of clinical nuances.
Included Health added Deep Agents for consistency across those services. "Originally, you would jump into a different agent and suddenly it was a lot more short and brusque. It didn't have the same voice and tone," said Rohan Bhandari, Staff Machine Learning Engineer. With Deep Agents, the team created a global platform prompt for voice and tone that could be passed across all agents without each team having to manage it independently. When routing from one workflow to another, Deep Agent’s filesystem and built-in context management allow the outgoing agent to summarize the conversation and pass both the summary and a file path to the full conversation history for the receiving agent. This setup ensures members never have to repeat themselves across different agents owned by different teams.
For example, the shared coverage question skill: coverage questions don't necessarily arrive at the start of a conversation. A member could be mid-way through finding a specialist and want to know what it will cost. Before Deep Agents, handling this required threading a coverage capability through every sub-workflow's routing logic. Now, "we decomposed it into a platform sub-agent that all the Deep Agents can inherit. Meaning every agent can answer coverage questions," said Rohan.
LangGraph enables consistent composition and distributed development so each product team can build and own their service independently. Deep Agents adds the shared filesystem that keeps tone and behavior consistent as customer conversations move across those services.
Included Health gives agents clinical capabilities and services using Deep Agent skills. Each skill describes what a service is, when it's appropriate, when it isn't, and how to handle edge cases. For example, what to do when a dependent wants to book a service that has eligibility nuances.
The model uses progressive disclosure as a way to drive the right conversation for navigating a member. For example, if a member says they want to see a doctor, there could be 3 or more appropriate ways to help (e.g. virtual urgent care, virtual primary care, find an in-person doctor). The agent has a skill registry managed through a virtual filesystem, and up front it gets a short description of each skill. Upon invocation, the model decides which skills are relevant and can then load full skill files. With those skill files, it learns about the nuances and what questions to ask to best navigate the member (e.g. do they want a virtual or in-person visit? Is the issue they are describing acute or better managed through a long term provider relationship?).
Included Health supports third-party employer benefits in addition to its own services, and is working toward encoding those as skills too, to include the 20 to 30 benefits per employer plan.
We have a clinical team who reviews chats and confirms whether they agree with which care spot we sent a member to, given their issue," added Rohan. That feedback loop has allowed Included Health to tune skill definitions over time and stay above their target level of clinical routing agreement of 95%.
A distinctive aspect of Included Health's agentic system is how they incorporated human-in-the-loop to improve the experience for patients. "We think about LangGraph as our entire messaging platform," said Kartik. LangGraph's durable execution allows the agent to maintain full context across the conversation, supporting indefinite pauses and context retention. When the agent reaches a point of uncertainty, it pauses the graph, routes to a human member care advocate for a multi-turn exchange, and then resumes—with the agent holding the full context of what the human did and said.
This design reflects the long-lived nature of healthcare relationships. A member can come back to the same thread days or weeks later with a follow-up question, and the agent can pick up where they left off, including the full context of any human-assisted portions. "From a human perspective, they’re helping the agent get unblocked, as opposed to doing all of the work," said Kartik.
The architecture also leaves room for the next evolution: running a parallel agent thread while a human is handling a conversation, so the agent can do background research and surface recommendations to the care advocate in real time.
LangSmith annotation queues are central to how Included Health runs clinical oversight. Right now, every conversation goes into a queue for clinical team review. Reviewers assess whether the agent's navigation recommendation was correct, whether emergency guardrails triggered appropriately (or correctly did not trigger), and flag anything that needs follow-up. Those labels are exported from LangSmith into Included Health's data warehouse, where the data science team builds the operational metrics dashboards.
Multi-turn user simulation evals using LangChain's user simulation package became the safety net for architectural changes. The migration from standard agents to Deep Agents across the supergraph affected four product teams, all wary of breaking changes. "We were able to run our whole eval suite, see that we got, for the most part, better performance, and then we had the confidence to share that out to the other teams," said Rohan. Thanks to these evals, the migration happened in under 2 weeks, with no significant regressions and no team resistance.
Dot launched to clients in August, in what Rohan described as “the smoothest launch the team has seen in the past few years.” Early metrics are tracking in the right direction across three areas:
Interested in building production-grade agent systems with Deep Agents? Learn more about Deep Agents.
Eroom’s law (hint: read Eroom backwards) is Moore’s law’s evil twin. The exponential drop in the price of computing power over the past 70 years has given us personal computers, the internet, cell phones, and now the AI revolution. Pharmaceutical research, unfortunately, has gone in the opposite direction, with the cost of developing each new drug doubling every nine years.
AI agents have the potential to reverse this trend, but general purpose solutions aren't built with the domain specificity that life science organizations need. That's why we developed Deep Life Sci: an open source agentic assistant created specifically for clinical and lab scientists.
The runaway cost growth in pharma comes from both stages of the drug development process: preclinical research and clinical trials. Identifying promising drug targets involves sifting through millions of scientific papers and massive biological datasets for insights. Once a candidate molecule appears likely to be safe and effective, it graduates to human clinical trials, where tens of thousands of pages of paperwork must be done to ensure compliance with a growing body of FDA regulations.
Many AI companies have promised that their tools will help restore research productivity, but general-purpose AI assistants like Claude and ChatGPT lack the necessary domain knowledge and integrations with scientific data sources. More specialized AI products for biotech often charge large markups.
In both cases, the agent harnesses are proprietary, preventing users from customizing them and locking them into expensive closed-source models. This issue is particularly critical in life sciences, where GxP validations require thorough documentation and audit logs that can articulate why the system behaves the way it did, requiring companies to have complete control of whatever system is being used to drive clinical decision making.
At LangChain, we believe that organizations that own their own intelligence will hold the advantage. We developed Deep Life Sci, an open source agentic assistant for clinical and lab scientists built on our Deep Agents harness, as a template for companies to adopt and modify for their use-cases.
Deep Life Sci can access clinical trial records from over 600,000 registered studies on ClinicalTrials.gov, 29 million paper abstracts through PubMed, and 12 million full-text articles on PubMed Central, reviewing hundreds of documents at once by assigning them to sub-agents. Each agent comes with a LangSmith sandbox, allowing it to safely run code to perform arbitrary data analyses. Users can upload PDFs, images, tabular data files, bibliographic files such as RIS, sequence ones such as SMILES, FASTA, and more, for the agent to include in its work.
In a typical workflow, a lab scientist finishes an RNA-seq or proteomics screen and uploads the results table. The agent runs enrichment in the sandbox to identify differentially expressed genes, then searches the literature for prior evidence linking each hit to the phenotype, separates well-described genes from novel ones, and returns a ranked table with the supporting papers.
A clinical development or HEOR team, on the other hand, might need to find every published trial of the standard of care in an indication, with the endpoint value, N, population characteristics, and follow-up duration extracted consistently. The agent runs the search, screens against the criteria, extracts each trial into a common schema, and produces both the table and a forest-plot-style comparison.
During the clinical trial phase, thousands of pages of different types of documents are created, ranging from informed consent, clinical protocol documents and amendments, case report forms, and more – all of which must be thoroughly audited, reviewed, and edited numerous times before being finalized. Using Deep Life Sci, users can upload reference protocol documents, research and gather additional statistical information, and quickly curate necessary feedback and edits that could ultimately cut clinical documentation time significantly.
The value of Deep Life Sci further compounds when the agent is optimized and integrated into a company’s ecosystem. Deep Life Sci knows what the primary endpoint is, but it doesn’t know company-specific nuances such as results from internal assays, which endpoints regulators pushed back on, or which trial sites actually enrolled rather than just promising to.
Integrating this context into the harness is what owning your intelligence looks like in practice, and because Deep Life Sci’s code is open source, organizations can approach this however they wish. This customization can include adding integrations with internal data and documentation, leveraging different frontier and open source models, enforcing guardrails and approval gates, and more.
Modifying the harness puts you inside the agent development lifecycle (ADLC): build, test, deploy, monitor, then feed what you learned back into the next version. Tracing and evaluations help power this development loop.
Every Deep Life Sci run is logged end-to-end in your own LangSmith account, including the literature searches the agent issued, the code it ran in the sandbox, the documents each sub-agent read, and how it moved from those to its answer. These trajectories allow for debugging and improvement of the agent, and serve as an audit record.
Evaluations tell you whether a change to the agent helped its performance. Deep Life Sci ships with a default eval set that can be modified and added to as you add integrations and identify new use cases. Run the set before and after you swap a model or rewrite a prompt, and you'll see whether the new version actually improved or quietly regressed.
Agentic AI is already revolutionizing fields like coding and mathematics. Biomedicine, where cost-effectiveness and iteration speed directly translate into human lives saved, should not be left behind. Biotech and pharma companies that combine open source tools like Deep Life Sci and the ADLC capabilities of LangSmith can reverse Eroom’s law by delivering cost savings and faster iteration across the drug development cycle.
Get started with Deep Life Sci here.
The five criteria for evaluating a database for AI agents are branch isolation, serverless scaling, hybrid search, ACID guarantees, and unified platform access. Together, these criteria help developers and data teams determine whether a database can support agents as they move from prototypes into production and begin handling concurrent tasks, live operational data, and persistent state.
A database for AI agents is a system designed to store the state, memory, tool results, and operational data an agent needs to complete tasks across multiple steps and sessions. Unlike a database serving a conventional application, it needs to support repeated reads and writes, concurrent agent activity, retrieval across different types of memory, and access to current operational data.
The rise of AI agents makes these requirements more important. When developers run coding agents, customer support agents, or multi-tenant platforms, agents do more than retrieve information. They write state, resume tasks, coordinate tool calls, and act on changing operational data. As data teams move agents into production, database limitations can create stale memory, conflicting writes, latency, and unnecessary compute costs.
A production-ready agent needs to remember what it already did, pick up a task where it left off, and pull in the right context before it acts. Pair it with the wrong database, and that memory can become stale, incomplete, or inconsistent.
Production agents lean on four memory layers to pull this off:
That's a more involved workload than a typical application, which sends a query to the database and moves on. Most production databases are operational databases, also called online transaction processing (OLTP) systems, built around that same one-request-at-a-time pattern. An agent doesn't work that way. It issues read after read and write after write within a single task, with no human pause between them, while hundreds of other agents are doing the same thing.
When selecting a database for AI agents, several criteria matter, but these five are the ones worth evaluating regardless of which vendor is under consideration, managed or self-hosted.
Testing an agent only against synthetic data is like testing a support system with a handful of perfectly formatted customer accounts. It might behave exactly as expected, but real accounts are always messier. Data teams eventually hit missing fields, inconsistent records, old data, and edge cases that never made it into their test fixtures.
That's why we recommend treating isolated testing against real data as a database evaluation criterion. The goal is for the agent to work with a production-like state without giving it a way to modify production. One way to get that isolation is zero-copy branching, which lets developers create a separate environment without maintaining a second full copy of the database.
Lakebase Projects is designed to handle this kind of isolated development and testing by letting developers create branches from production data without copying the underlying data. Branching a terabyte-scale production database takes about a second, with no additional storage cost until the branch diverges from its parent.
27% of cloud spend goes to waste every year, and idle, underutilized compute is consistently the biggest driver of it. Agent databases are a clean example of why. Most agents don't run continuously. They wake up, do a task, write the results, then go quiet until the next request comes in. Paying for dedicated compute around the clock means paying for that same idle-compute problem across every agent database a team is running.
A serverless scale-to-zero model addresses this by suspending compute after a period with no active connections and resuming it when work starts again. That makes costs track actual usage instead of idle time. Startup speed matters just as much as the savings, though. An agent waiting 20 or 30 seconds for its database to wake up isn't practical, especially when it's responding to a user or waiting on the next tool call.
Lakebase uses this model for Postgres, with compute resuming within a few hundred milliseconds of a new query. That keeps the startup delay small enough for scale-to-zero to work with interactive agent workloads.
Vector search alone is like a librarian who can only browse by "what feels similar," never by an exact call number. Ask it to find documents about database architecture, and it'll do well. Ask it for the record with account ID 48291, and it has no reliable way to land on it. Semantic similarity isn't built for exact matches.
That's the gap many retrieval-augmented generation (RAG) pipelines run into when they rely on vector search alone. Hybrid search closes it by combining vector similarity, keyword matching, and metadata filtering in a single query instead of stitching results together from separate systems. Split that across a vector index and a relational store, and the agent makes two calls instead of one. The systems can drift out of sync, and every extra hop adds latency an agent's loop can't always absorb. Retrieval needs to land well under 100 milliseconds to stay usable inside a tight reasoning cycle.
Lakebase Search runs vector, keyword, and metadata queries against the same Postgres tables where operational data already lives, so there's no second system to fall out of sync with. Its LTAP architecture is what keeps that data current, with write performance up to 5 times faster than standard Postgres. That means what an agent just wrote can be available for retrieval almost immediately.
Picture two support agents updating the same customer record at the same time. One is resolving a billing issue and adjusting the subscription tier, while the other is logging a refund. Without proper isolation, one update can overwrite the other, leaving the record in a state neither agent intended.
That's why transactional guarantees should be a hard criterion when evaluating a database for multi-agent workloads. ACID gives developers four properties to check:
For multi-agent systems, the practical questions matter more than the acronym. Can a tool-output commit happen atomically, so a half-finished action never gets treated as complete? What happens when two agents update the same record? Which isolation levels does the database support? Can an agent resume after a restart without losing committed state?
When comparing databases, we recommend checking the isolation levels and commit semantics they actually support, not just whether they claim to "support transactions." Once multiple agents share operational data, those details determine whether concurrent work stays predictable.
An agent waiting for a pipeline to catch up is making decisions on stale data. By the time that pipeline runs, the record it's acting on may have already changed again. When evaluating a database, look at how closely it connects operational data with the analytics and AI systems that depend on it.
A unified platform keeps operational writes and analytical reads on the same data, without a separate extract, transform, load (ETL) pipeline sitting between them. Your agents can work with current data, while your models can use live outcomes instead of waiting for a batch job. Data teams also keep governance and audit trails in the same platform, rather than pushing agent workloads into a separate system that's harder to track. Unity Catalog is what enforces that governance layer across both operational and analytical data in Databricks. Superhuman's experience shows what this looks like in practice: replacing custom sync pipelines into a caching layer and a managed NoSQL store with a unified platform cut its data integration timeline from nearly three months to about two weeks.
easyJet took a similar approach in its revenue management stack. Since moving to Lakebase, the airline has captured live booking and pricing activity alongside analytics on the same lakehouse data, consolidated more than 100 Git repositories into two, and cut app development cycles from six to nine months to about four.
Lakebase keeps operational data in the Databricks lakehouse, so the same data can support transactional workloads and downstream analytics without a separate ETL pipeline.
Run any candidate through these five checks, and you'll know within minutes where it holds up and where it doesn't, regardless of which vendor you're comparing.
| Criterion | What to test | Minimum bar | Red flags | Lakebase behavior |
|---|---|---|---|---|
| Branch per agent | Can you spin up an isolated branch against real production data without making a full copy? | Branch creation completes in seconds, not minutes | Requires a full database copy, or takes longer than your test cycle | Branches a terabyte-scale database in about a second, with no storage cost until it diverges |
| Scale to zero | Does compute suspend after a period of no activity and resume fast enough to stay usable? | Compute resumes in under a second, no manual wake-up step | Cold start takes 10+ seconds, or idle databases still bill at full rate | Reactivates within a few hundred milliseconds and bills nothing while suspended |
| Hybrid search | Can one query combine vector similarity, keyword matching, and a structured filter? | Single query, under 100ms | Requires separate calls to a vector store and a relational store, then a manual merge | Runs vector, keyword, and metadata queries against the same Postgres tables |
| ACID guarantees | Can two agents write to the same record at once without losing either write? | No lost writes; isolation holds under concurrent load | Silent overwrites, or isolation that degrades under concurrency | Standard Postgres transactional guarantees, unaffected by concurrent agent load |
| Unified platform | How long does a new write take to become available for analytics? | No ETL step, or lag measured in seconds, not hours | Requires a scheduled pipeline before data is queryable elsewhere | Every write becomes queryable in the Databricks lakehouse without a separate pipeline |
A database failing more than one of these minimum bars is a production risk once you're running agents at scale, not just a minor tradeoff you can work around later.
Choosing a database for AI agents comes down to workload fit, not feature lists. The five criteria in this guide give developers and data teams a practical framework for evaluating any database before committing to it in production. If a candidate can't meet those requirements today, production agents will eventually expose the gaps as they take on more users, more tasks, and more concurrent work.
If you're evaluating a database for AI agents, explore Lakebase to see how Databricks supports transactional workloads, branching, serverless scaling, hybrid search, and unified access to operational data.
Yes. Most agent implementations don't retain short-term context, episodic history, procedural knowledge, or live task state across calls unless you explicitly persist and reload it. Without a database behind it, your agent typically loses that context the moment a session ends and can't pick up a task where it left off.
Not on its own. A vector database handles semantic retrieval well, but your agent also needs to write and update operational state, enforce transactional integrity across concurrent writes, and filter on structured fields a similarity search can't reliably catch. Semantic search covers one piece of what an agent needs, not the whole workload.
There's no single right answer. For RAG in AI agents, the best database is the one that can run hybrid search in one query, keep retrieval fast enough for the agent loop, and stay current enough to avoid stale memory.
Once multiple agents write to shared data at the same time, transactional integrity stops being optional. Your database needs to isolate concurrent writes so one agent's update doesn't silently overwrite another's, and it needs to commit tool outputs atomically so a half-finished action never gets treated as complete.
Your agent's live actions, writing tool outputs, updating state, and checkpointing progress are OLTP workloads. Reporting and model training on top of that data are OLAP workloads. Agents typically need both to work from the same data without a pipeline between them. That's why the criteria in this guide focus on databases that can serve both transaction-heavy agent work and downstream analytics from the same data.
Standard Postgres provides solid ACID guarantees and a mature ecosystem, covering part of what your agent needs. It doesn't provide zero-copy branching, scale-to-zero compute, or unified operational and analytical access by itself; those depend on the platform built around it.
Will Raphaelson
Sep 17, 2026
As the number of high-quality cloud hosting options has increased, so too has the number of pricing models for cloud hosting. On one end of the spectrum, there exist simple fixed price options where you rent server space by the month or year, and on the other end, usage-based platforms abstract away servers altogether and charge by the number of requests, traffic volume, or any number of (sometimes) obscure meters.
Unfortunately, there's no universally cheaper option, because different workloads are best hosted on different models. To understand which model is cheaper for you, you first need to understand what the different vendors actually charge for, what resources your application needs to meet your users' requirements, and how much control you have over customizing your cloud order.
If your workload sits at roughly the same size all month and fits cleanly into an available server size, fixed or provisioned pricing can be hard to beat. If usage is uneven, or your app needs a weird mix of RAM and CPU, resource-consumption pricing tends to look better. If the app can stop entirely when nobody is using it, scale-to-zero can make either kind of usage-based model cheaper still.
In this piece, we'll cover all that and more. We'll also provide practical guidance on which platforms are best suited to different workload shapes and sizes.
In the provisioned capacity world, you pick server specifications such as CPU, RAM, and disk space, and pay for that server no matter what you do with it. If your app barely touches the CPU, you still pay for the CPU you reserved. You might be billed by the second or hour, but if the app runs all month, this doesn't change the economics much until discounts from year or multi-year contracts kick in.
Some examples of generally provisioned-capacity hosting vendors are Render, Fly.io, Northflank, and AWS EC2. Render's 0.5 CPU / 512 MB paid web service currently starts at $7/month. Fly.io lets you configure a Machine with a certain amount of CPU and memory and bills started Machines based on that configuration. It also offers a 40% discount through one-year Machine reservation blocks. Northflank lets you pick predefined CPU and RAM configurations, with custom plans also available.
Provisioned capacity is nice because the bill is about as predictable as it gets, but for workloads that don't fit snugly and predictably in a given-sized server, you often pay for resources you never actually use.
However, provisioned capacity works really well when the workload is steady, such as an always-on API or live ML scoring job, you know roughly how much CPU and memory it needs, and the app uses most of what you provisioned, or if you can get a reservation or committed-use discount.
It gets wasteful when you size for peak traffic but spend most of the month below it, or when you need a certain amount of RAM but barely use the CPU that comes with the instance. The same is true when available instance sizes leave you with substantial unused headroom: for example, when your app needs more resources than one size offers, but significantly less than the next size up.
We call this model provisioned because that's what you pay for: what you provision, not what you use. The model that charges for what resources you actually use is called:
Instead of picking a server size, resource-consumption-based platforms meter the resources the application actually uses. You don't have to decide up front whether the app belongs on a 512 MB, 1 GB, or 2 GB instance; you can just set a max size you don't want to exceed. The bill follows the application's actual resource footprint.
The canonical resource-consumption-based PaaS is Railway, which currently charges:
| Resource | Railway price |
|---|---|
| RAM | $10/GB/month |
| CPU | $20/vCPU/month |
| Egress | $0.05/GB |
| Volume storage | $0.15/GB/month |
CPU, memory, and volume usage are metered by the minute.
Resource-consumption pricing is great because you only pay for what you use, but there are a few potential downsides to consider. For one, it can be intimidating to not know your bill up front, and hard to sell to finance or operations professionals who expect precise dollar figures. Additionally, while saving money when you don't use resources is great, you can also end up spending more than anticipated if traffic to your application spikes and you don't have monitoring, alerting, and guardrails in place. So it's often cheaper, but less predictable.
It works well for:
General-purpose backends, APIs, workers, and full-stack apps.
Workloads whose CPU needs change over the course of the day.
An app that can stay online all month without paying for a full CPU all month. For example, Railway bills from average CPU and RAM consumption over time rather than peak headroom, so if an app uses 1 GB of RAM continuously but averages 5% of a CPU, the bill reflects roughly 1 GB of RAM and 0.05 vCPU.
Where it's not a good fit:
Precisely well-known workloads that fit a provisioned-capacity tier snugly.
Organizations that need exact spending forecasts up front.
Static or front-end only apps that fit better into a request based model.
On the furthest end of the usage-based spectrum, we have:
This model further abstracts away underlying server specifications, and instead charges on various application-specific meters, including things like requests, function invocations, execution time, bandwidth, deploys, and sometimes traditional prorated compute dimensions like CPU and RAM.
Examples include Vercel, Netlify, Cloudflare Workers, and AWS Lambda.
Vercel Fluid Compute meters active CPU, provisioned memory, and function invocations. Netlify turns compute, bandwidth, web requests, and production deploys into credits. Cloudflare Workers primarily charges for requests and CPU time, while static asset requests are free and unlimited.
These platforms tend to work well for front-end applications with little to no permanent compute or back-end requirements, but are even harder to predict pricing for. It's for folks who don't want to think about servers at all and are willing to pay for that convenience.
It works well for:
Apps that do nothing for long periods.
Frontend-first applications, event-driven workloads, short API requests, functions, and jobs.
But not if:
Your app requires adjacent infrastructure like databases, volumes, buckets, or backend services, which may need to be wired up separately.
Your organization requires precise pricing estimates.
The larger the gap between peak requirements and normal usage, the more attractive consumption pricing becomes. This is because if you need to allocate for the peak, but are mostly in the valley, all that unused capacity is untouched. However, for an always-on and predictable workload, the cheaper fixed pricing models are great.
Many consumption and request-based models lend themselves nicely to apps that can go to sleep when they're not being used, as long as you're willing to tolerate a cold-start delay when that first request comes in. Railway Serverless puts inactive services to sleep and stops compute charges while they are asleep, and Fly Machines can also automatically stop and restart based on traffic. This sounds a bit scary, and for a user-facing application where even a brief delay is unacceptable, it generally is. But other services lend themselves well to the sleep / cold-start pattern, such as:
Hobby apps.
Staging environments.
Internal tools where users are okay with a cold wakeup.
Preview deployments.
Low-traffic utilities.
It's worth it to make hosting decisions based on your entire architecture, not just a vendor's pricing page. A request-based vendor like Vercel may be fantastic for your front end, but once it needs to call out to an external database, you also need to account for the network and database side of the bill. A fixed-price box from Fly.io is probably a great choice for a single stable service, but public egress is still metered. While you might be able to squeeze out a few dollars per service by optimizing across different vendors, splitting across hosts requires you to reason about multiple pricing models, silos of administration and observability, and sends data across the open internet. For this reason, it's important to profile your broader architecture and pick a provider that can handle as much of your application as possible, only calling out to other services when it's worth it.
| Platform | Pricing model | Representative pricing | Good fit when |
|---|---|---|---|
| Railway | Metered resource consumption | $20/vCPU-month, $10/GB RAM-month, $0.05/GB egress, $0.15/GB volume | General-purpose apps with uneven resource use |
| Render | Provisioned compute | 0.5 CPU / 512 MB starts at $7/month | You know what size server you need and will use most of it |
| Fly.io | Provisioned Machines billed by runtime | shared-cpu-1x / 512 MB starts around $3.32/month in lower-priced regions | You want explicit control over small VMs |
| Northflank | Provisioned CPU/RAM billed by runtime | 0.2 shared CPU / 512 MB is $5.40/month; 0.5 shared / 1 GB is $12/month | You want explicit resource sizing and more infra control |
| Vercel | CPU + memory + invocations | Fluid Compute meters active CPU, provisioned memory, and invocations | Request-driven and frontend-first apps |
| Netlify | Usage credits across several meters | Compute, bandwidth, web requests, and production deploys consume credits | Frontend-first and event-driven apps |
| Cloudflare Workers | Requests + CPU execution | Static asset requests are free; dynamic Worker requests and CPU are metered | Static sites and lightweight edge workloads |
| AWS, GCP, Azure | Depends on the product / all of the above | VMs, containers, functions, and serverless all price differently | Specialized infrastructure or lots of knobs |
In the following examples, we'll illustrate how these pricing models work in practice by analyzing how applications behave and are metered on various platforms.
A simple marketing or documentation site. There is no backend, and it results in about a million asset requests per month, and transfers out about 15 GB of bandwidth.
A dedicated static hosting provider like Cloudflare with a built-in CDN is perfectly reasonable for this workload, as they're tuned for this type of front-end-only work. With no application process that needs to run continuously, paying for provisioned or consumption-based application compute adds little value. If you think it might grow to need to make API calls out, need auth, or talk to a database, a broader PaaS offering like Railway or Render would be a more future-proof choice.
| Platform | Pricing | Total |
|---|---|---|
| Cloudflare | Static asset requests are free and unlimited, with no additional charge for storing static assets | $0 for static asset serving |
| Railway | No per-request fee; 15 GB egress × $0.05 = $0.75, plus the small amount of CPU and RAM used by the process serving the site | Can be $0 if total usage stays within the Free plan's $1 monthly credit |
Best fit here: Cloudflare. There is no reason to pay for an application server if all you need is static asset hosting.
A simple and relatively small API serving 10 GB traffic a month in user-facing request-response workloads. It averages about half a GB of RAM and .05 vCPU. Because users rely on this API, cold start times are unacceptable, so it needs to stay on continuously.
This is a good fit for a small fixed model or any consumption-based model because of its consistent and predictable resource needs and the requirement to always stay on. The catch with the fixed model is that you'll need to make sure there's a server size that fits your workload snugly, otherwise you'll pay for the overhead.
Best fit here: Fly.io. This particular workload happens to fit a very cheap small Machine almost perfectly, so provisioned capacity wins.
A similar small API to the previous example, still averaging about half a GB of RAM and 0.05 vCPU while it is running. This time, though, usage is sporadic. It gets a few bursts of traffic during business hours, cold starts are acceptable, and it spends most of the month asleep. Assume it's awake for about 50 hours over the course of the month and serves the same 10 GB of traffic.
This is where scale-to-zero consumption pricing starts to make a lot of sense. There's little reason to pay for an application server during the hundreds of hours each month when nobody is using it. A small fixed server still works, of course, but now you're paying for a lot of idle time. A service that can sleep when inactive can eliminate most of that baseline cost.
| Platform | Pricing | Total |
|---|---|---|
| Railway with Serverless | RAM while awake: about $0.34; CPU while awake: about $0.07; egress: 10 × $0.05 = $0.50. Serverless stops CPU and RAM charges while the service is asleep. Free includes $1 of usage each month. | $0.91 resource usage; $0 bill if it fits inside the Free credit |
| Fly.io with autostop | Fly can stop idle Machines and charges stopped Machines only for rootfs storage. Assuming 50 running hours in a region where 512 MB costs about $3.32/month, 1 GB of stopped rootfs, and 10 GB of NA/EU egress, the month comes to roughly $0.58. | About $0.58 under those assumptions |
Best fit here: scale-to-zero, not one particular pricing model. Both Railway and Fly can avoid paying for idle CPU and RAM. Railway has the edge for a tiny hobby workload if the entire month fits inside its $1 Free credit.
The most important applications typically have multiple pieces and types of infrastructure, and this can complicate efforts to have low and predictable pricing. Attaching a database to the backend is a common usage pattern and benefits from careful planning. We'll use our same API, 0.5 GB average RAM, low average CPU, and 10 GB monthly egress, but we'll add on a database that needs 1 GB average RAM, same low CPU, and 5 GB of persistent storage.
This workload can work well with either fixed or resource-consumption pricing. Resource-consumption pricing becomes especially attractive when the available fixed instance sizes don't closely match what each service actually needs.
The API uses about 0.5 GB of RAM but very little CPU, while the database needs about 1 GB of RAM and similarly little CPU. With fixed pricing, you need to find an instance size for each that doesn't force you to buy substantially more CPU or memory than the workload needs.
With resource-consumption pricing, each service can simply pay for the resources it actually uses. That can make a multi-service application easier to size and can reduce the cost of unused capacity.
Critically, Railway lets services in the same project communicate over a private network without service-to-service egress charges, which avoids public egress caused by contacting a database over the internet.
| Platform | API | Database | Storage / bandwidth | Total |
|---|---|---|---|---|
| Railway | RAM $5 + CPU $1 + egress $0.50 = $6.50 | RAM $10 + CPU $1 = $11.00 | 5 GB volume × $0.15 = $0.75 | $18.25/month |
| Render | 0.5 CPU / 512 MB web service: $7 | 0.5 CPU / 1 GB Postgres: $19 | 5 GB storage × $0.25 = $1.25; 5 GB of billable public egress × $0.15 = $0.75 after Hobby's 5 GB included | About $28.00/month |
Traffic between Render services on the same private network does not count as public outbound bandwidth, so the difference here is mostly the cost of fitting the API and database into provisioned sizes.
Best fit here: Railway. Both services need relatively little CPU for the amount of RAM they use, which is a favorable shape for resource-consumption pricing.
Our backend normally averages just 0.05 vCPU, but every once in a while, traffic spikes sharply. For this example, let's say that for one day of the month:
Average CPU usage rises from 0.05 vCPU to 0.5 vCPU.
The application sends an additional 50 GB of data.
After the spike, traffic and resource use return to normal.
This is a strong fit for resource-consumption pricing.
With fixed pricing, you have two choices. You can size the application for normal traffic and risk not having enough capacity when the spike arrives, or provision enough capacity for the spike and pay for that extra headroom during the rest of the month.
Resource-consumption pricing avoids that tradeoff. The application can use very little CPU most of the time and simply consume more when traffic increases. The bill rises during the spike, but you're not paying for that extra capacity during the other 29 days of the month.
If traffic at the higher level became normal rather than occasional, the economics would change. At that point, fixed or committed capacity could become more competitive. But as always, if the existing instance cannot handle the spike, you need to move to a larger instance or add additional instances.
| Platform | Pricing | Total |
|---|---|---|
| Railway | Normal monthly usage: about $6.50; additional CPU during the one-day spike: about $0.30; additional 50 GB egress: $2.50 | About $9.30/month |
| Render | 0.5 CPU / 512 MB web service: $7; 60 GB total outbound means 55 GB above Hobby's 5 GB included allowance, or $8.25 | About $15.25/month |
Best fit here: Railway. The extra CPU is expensive only while the application is actually using it, rather than being provisioned for the entire month.
For this example, we'll combine three common pieces of infrastructure:
A frontend averaging 0.25 GB of RAM and 0.01 vCPU, with 20 GB of monthly egress.
An API averaging 0.5 GB of RAM and 0.05 vCPU, with 10 GB of monthly egress.
A Postgres database averaging 1 GB of RAM and 0.05 vCPU, with a 5 GB persistent volume.
This is where a general-purpose PaaS like Railway makes the most sense. If all you have is a frontend, a dedicated frontend or static host may still be the best choice. But most full-stack applications don't stop there. They add an API, a database, background jobs, storage, or other infrastructure. At that point, you can either optimize each component separately across several vendors, or keep the application together on one platform.
Railway's advantage here isn't that it's necessarily the cheapest possible place to host each individual component. It's that the frontend, backend, database, and other services can live in the same project, use the same basic pricing model, and communicate privately without service-to-service egress charges.
Each part of the application has a different resource profile. The frontend needs very little CPU. The API uses more memory but still relatively little CPU. Postgres needs considerably more memory, very little CPU, and persistent storage.
With fixed pricing, each component needs to fit into one of the instance sizes the platform offers. That can mean paying for CPU or memory you don't need, and the mismatch gets more noticeable as you add different types of infrastructure.
With resource-consumption pricing, each service can simply use the CPU, memory, and storage it needs. And like the API and database example, instead of choosing a different hosting platform and pricing model for each part of the stack, the whole application can live on one platform under the same basic resource model.
| Platform | Frontend | API | Postgres | Bandwidth / storage | Total |
|---|---|---|---|---|---|
| Railway | RAM $2.50 + CPU $0.20 + egress $1.00 = $3.70 | RAM $5 + CPU $1 + egress $0.50 = $6.50 | RAM $10 + CPU $1 = $11.00 | 5 GB volume = $0.75 | $21.95/month |
| Render | 0.5 CPU / 512 MB web service: $7 | 0.5 CPU / 512 MB web service: $7 | 0.5 CPU / 1 GB Postgres: $19 | 5 GB storage = $1.25; 25 billable GB egress = $3.75 | About $38.00/month |
Best fit here: Railway. Not because Railway is the cheapest possible frontend host, but because the frontend is only one piece of the application. The three services have different CPU-to-memory ratios, which favors resource-consumption pricing, and they can all live in one project rather than being split across several hosting platforms.
You could probably make individual parts of this application cheaper by optimizing each one separately. The frontend could live on a frontend-first platform, the database on a dedicated Postgres provider, object storage somewhere else, and the API on another host.
That may lower some individual line items. It also means multiple vendors, bills, deployment workflows, pricing systems, and network boundaries. A general-purpose PaaS doesn't have to be the cheapest possible home for every individual component for the model to make sense. The argument is that the application as a whole can be simpler to run and reason about in one place.
Not every useful application is something you build yourself. Plenty of software ships as a Docker image that you just need somewhere to run.
n8n is a good example. It can be deployed from its official Docker image, needs persistent storage, and needs a public endpoint for the editor and webhooks. As the deployment gets more serious, you may also want to add Postgres or other services around it. Railway has a one-click n8n template, and Railway templates can bundle multiple services and their configuration into a reusable deployment.
You can run the same n8n image on basically any container or VM host. What determines the best hosting choice is what you have to do after the container is running.
n8n still needs storage, networking, environment variables, and potentially a database or other services. On Railway, those things can all live in the same project. You can deploy the image, attach a volume, add Postgres, connect everything over the private network, and manage it all in one place. This is where a general-purpose PaaS really earns its keep. The value isn't just where the Docker container runs, but how much work it takes to stand up everything around it.
Pricing depends heavily on what the container actually does. An n8n instance that spends most of the day waiting for workflows will have very different economics from one running jobs continuously, so there isn't a particularly useful universal price for hosting n8n. What matters more is the resource shape. Railway charges for the CPU, RAM, storage, and public egress the application actually uses, rather than forcing it into a fixed CPU and RAM bundle.
Render's paid web service tiers start at 0.5 CPU and 512 MB of RAM for $7 per month, with the next tier at 1 CPU and 2 GB of RAM for $25 per month.
That can get wasteful if the container needs more than 512 MB of RAM but uses very little CPU. You have to move up to the larger instance to get the memory, even if you don't need most of the CPU that comes with it.
On Railway, that same workload is billed based on the RAM and CPU it actually uses. For Docker applications with an awkward mix of memory and CPU requirements, that can be a much better fit.
At this point, the fundamental tradeoff is hopefully clear. Fixed models make the bill easy to predict because you know what you provisioned. Usage-based models let the bill move with the workload, which is useful but can feel less predictable. There are a number of ways to make usage-based platforms safer, more predictable, and more economical.
The first step toward making and standing by your hosting decision is understanding the shape and size of your workload. While some things can be profiled before deployment, many cannot, so it's best practice to deploy your site and monitor for consumption of memory, CPU, egress, storage, and anything else a platform might meter. How long to run and analyze your application depends on how long it takes to get a representative estimate. An apartment rentals site with extreme monthly peaks should probably run for a month before making long-term decisions; a simple internal app can probably run for a few days and give you a good idea.
For Railway specifically, the platform's usage guidance recommends looking at real resource metrics and turning average CPU, memory, and egress into a monthly estimate.
Once you understand your resource consumption, many platforms allow you to set alerts when specific thresholds are met. Railway, for example, supports custom usage alerts, and also supports CPU, RAM, disk, and network monitors. This is critical to maintain the observability and predictability of your economics.
Watch: setting up monitors in Railway (video)
Warnings are useful, but if there's a limit on what you're willing to spend, you should be able to enshrine that as a hard rule in your platform of choice. Railway lets workspace admins configure a hard compute-usage limit, and hitting it takes workloads offline, so your hard limit should include some headroom.
While warnings and limits can exist at the workspace level, many modern platforms also allow you to configure guardrails at lower levels, such as the individual service. Railway, for example, lets you cap CPU and memory per replica. If you know one service is capable of chewing through far more CPU or memory than you want it to, you can put a ceiling on it.
Not every service needs to be running 24/7. Staging environments, development apps, hobby projects, internal tools, and low-traffic APIs may spend the vast majority of their time doing absolutely nothing. If cold starts are acceptable, putting these services to sleep can remove most of their idle compute costs.
Railway's Serverless mode exists to support this pattern. Once enabled, Railway watches outbound traffic from the service. If the service stops sending traffic for long enough, Railway puts it to sleep and stops charging for its CPU and memory. The next request wakes it back up.
This isn't a good fit for everything. User-facing applications where even a brief cold start is unacceptable should generally stay awake. The same goes for services that maintain persistent outbound connections or continuously poll for work.
Railway uses outbound traffic to determine whether a service is idle. An open database connection, telemetry heartbeat, request to another service, or queue poller can keep an otherwise unused service awake.
Bandwidth is another place where usage-based bills can sneakily grow. Railway currently charges $0.05 per GB of public network egress, so there is no reason to send traffic over the public internet when two services can communicate privately instead. This is one of the main benefits of having a PaaS that can host the majority of your infrastructure, rather than parting it out to different vendors.
Railway provides private networking between services in the same project, and internal traffic does not count toward egress billing. An API talking to its Railway Postgres database, for example, should use the private database URL rather than routing that traffic out over the public network and back in again. This avoids unnecessary egress charges and is generally the right architecture anyway.
You don't have to perfectly estimate everything on paper before deploying. Railway's Free plan costs $0 per month and includes $1 of resource usage every month. Free services can use up to 1 vCPU and 0.5 GB of RAM per service, with one replica. New users also start with a one-time $5 trial credit that lasts for up to 30 days before the account moves onto the Free plan.
For a small application, that's enough to stop guessing and start measuring. Deploy it, send some realistic traffic through it, and watch its idle RAM usage, CPU consumption under ordinary requests, behavior under load, and network egress. If you're planning to leverage Serverless, make sure the service actually goes to sleep when you expect it to.
| Workload | Pricing model to favor |
|---|---|
| Static website | Static hosting / CDN |
| Hobby app used occasionally | Scale-to-zero |
| Staging or preview environment | Scale-to-zero |
| Always-on API with low average CPU | Resource-consumption pricing |
| Backend with uneven utilization | Resource-consumption pricing |
| Consistently CPU-heavy service | Compare provisioned and consumption pricing directly |
| Small, precisely sized VM | Provisioned compute |
| Frontend-first, request-driven app | Request / execution pricing |
| Self-hosted Docker application with adjacent services | General-purpose resource-consumption PaaS |
| Backend + database + workers | General-purpose resource-consumption PaaS |
| Large, predictable infrastructure footprint | Provisioned or committed capacity |
There's no universally cheapest cloud pricing model, because there is no universally shaped workload.
The most important question isn't whether a platform is "fixed" or "usage-based," but what exactly you're paying for. Platforms like Render, Fly.io, and Northflank primarily charge for resources you provision while they are running. Railway charges for the CPU, memory, storage, and network resources your application actually consumes. Vercel, Netlify, Cloudflare, and similar serverless platforms tie more of the bill to requests, execution, bandwidth, and other workload-specific meters.
Each model has workloads it suits well. Provisioned pricing makes sense when an application is steady and consistently uses most of the capacity reserved for it. Resource-consumption pricing becomes more attractive when utilization varies, or the application needs far less than its peak capacity most of the time. Scale-to-zero can push that further for services that can safely stop altogether when not in use. The important thing is to match the pricing model to the shape of the application rather than choosing a platform based on the headline monthly price.
That also means conceding where the numbers say to. Cloudflare is the obvious choice for the static site above, and Fly.io is cheaper for the tiny always-on backend. Railway looks better in the examples where utilization is uneven, where services have very different CPU and memory profiles, or where you're choosing a platform for an entire application rather than optimizing each component independently.
And usage-based pricing does not have to mean giving up control over the bill. Railway exposes resource consumption directly and provides usage alerts and hard limits, per-service CPU and memory limits, and Serverless mode to help keep costs in bounds.
Your monthly bill may move around as your application does more or less work, but with the right guardrails in place, that becomes a feature, not a bug.
A new creature-catching adventure is ready to stream from the cloud this week. Pawprint Studio’s Aniimo arrives on GeForce NOW at launch, inviting gamers to explore the vibrant continent of Idyll across supported devices.
Also this week, 007 First Light receives a path-tracing update on GeForce NOW, alongside a smashing limited-time Deluxe Edition sale on Steam and Epic Games Store.
Gaijin Network’s fractured-multiverse action game Active Matter and Annapurna Interactive’s acclaimed space mystery Outer Wilds are also among 11 new titles joining the cloud this week.
The path to Idyll begins now. Aniimo is a free-to-play, open-world creature-catching role-playing game where every creature encountered can become a companion. Catch Aniimo with Aniipods, then Twine with them to take on their form and use unique skills to solve puzzles, win battles and overcome challenges.
Glide, dive and burrow across Idyll, then return to a personal RV to build a warm, interactive Homeland alongside Aniimo companions. Stream Aniimo on a Steam Deck, in the newly supported Firefox browser and across any other supported devices — without waiting through its 45GB download or making room in local storage. There’s always room in the Homeland for one more Aniimo.
In the critically acclaimed, multimillion-selling 007 First Light, follow James Bond as a young, resourceful and sometimes reckless recruit in MI6’s training program, and discover a reimagined origin story of the world’s most famous spy. Incorporating IO Interactive’s signature stealth gameplay with world-class Bond action, players can embark on missions in breathtaking locations around the globe, drive iconic vehicles and dive into a cinematic adventure in pursuit of a rogue agent who’s always one step ahead.
Path tracing has now been added to 007 First Light, introducing extra-detailed lighting, shadows and reflections, making its levels and set pieces even more cinematic, immersive and realistic. And NVIDIA DLSS 4.5 Ray Reconstruction ensures the fidelity, clarity and accuracy of these additions are at their absolute best.
Ultimate members can stream with GeForce RTX 5080-class performance, with up to 5K high dynamic range and cinematic-quality streaming. DLSS 4.5 Super Resolution and Dynamic Frame Generation accelerate frame rates and enhance image quality.
A limited-time opportunity awaits: the 007 First Light Deluxe Edition is on sale on Steam from Sept. 15-29 and on Epic Games Store from Sept. 3-18. The mission is on sale. The getaway car is in the cloud.
Gaijin’s Active Matter, a realistic military shooter set in a fractured multiverse, has launched on GeForce NOW. As an operative stuck in a time loop, join dangerous raids for loot or intense player vs. player battles.
Fight against rivals from other timelines, survive physics-breaking anomalies and try to stay alive. Harvest active matter, gather loot and extract to a safe place before the whole zone ceases to exist.
Ultimate members can take on each raid with GeForce RTX 5080-class performance in the cloud, delivering high frame rates, advanced graphics features and low latency across supported devices. The zone won’t wait — and neither does the next loop.
Outer Wilds joins the GeForce NOW library this week. As the newest recruit of Outer Wilds Ventures, search for answers across a strange, ever-changing solar system trapped in an endless time loop. Gamers can trace mysterious signals, decipher alien writing and discover hidden locations before an underground city is swallowed by sand or a planet crumbles beneath their feet.
There’s even more to stream this week:
Dates listed above reflect when games are released on their respective stores. GeForce NOW availability may vary, as games are onboarded after they’re released and added throughout the week. Keep an eye on GeForce NOW channels and GFN Thursdays for availability updates to announced titles.
Ready to take this week’s new games for a spin? Start with a day pass to try premium cloud gaming before committing to a membership. Even better, the cost of the day pass can be applied toward a first monthly membership — making it easy to level up with GeForce RTX-powered cloud gaming.
What’s on the playlist this weekend? Let us know on X or in the comments below.
Part 1: Parsing, chunking, and vectorization
Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was JetBrains Context. We got it working, we got it into production, and we collected a lot of scar tissue along the way. In this series of posts, we’ll share the parts we wish someone had told us on day one.
Coding agents are undoubtedly the biggest technology leap for software development of our decade. Agents and frontier models are proving their aptitude in the face of seemingly insurmountable code complexity to produce ostensibly reliable code.
However, as more and more development processes become agent-driven, the agent’s efficiency and the quality of the produced code become increasingly important. The question is not so much about whether an agent can complete the task, as given enough time and token resources, it surely will, but rather how much time, effort, and steering is required for it to generate production-grade results. For large-scale code bases specifically, the agent would spend a great deal of time searching for the relevant pieces of code relevant for the feature it’s working on and pulling them into the context.
Attempting to locate the right code snippets, the agent will resort to traditional tools for code search such as keyword search and grep. These tools, however, are limited in that they require the agent to know in advance which exact text to search for. For example, an agent looking for where session tokens get refreshed cannot rely on the code helpfully containing the word “refresh”. To reason through abstract domains, the agent needs the ability to search for code by meaning, also known as semantic search. This is where retrieval-augmented generation (RAG) comes into the picture. If we can index the source code in a way that captures its semantics and then allow the agent to retrieve the relevant pieces on demand using free text search, we create an interface that plays to the agent’s strengths.
Like many great ideas in the agentic era, a native, prototype implementation is extremely simple. A well-evaluated production grade solution most certainly is not. In this series of blog posts, we want to share what is involved in making an effective RAG system, as well as the wrong turns we took in our journey to create our own: JetBrains Context. We’ll tackle each stage, from pre-processing to storage and agent integration, providing some more technical context and advice.
This first part of the series will cover the initial stages of the pipeline: parsing and chunking, where raw source files are divided into properly scoped units, and vectorization, where those units are transformed into a representation that supports semantic search.
Parsing and chunking is a critical pre-processing step in a good RAG solution, but it is often overlooked. In order to allow the LLM to embed or otherwise index the source code, we must first feed it the raw lines of code. This may sound trivial, and probably would be for small-scale demo projects. However, production-grade systems contain thousands of files, which, in turn, span hundreds or even thousands of lines. If anything, agents have compounded the problem, as they tend to be prolific writers, further inflating the codebase. Each file may contain multitudes of classes, fields, and methods, with varying degrees of relatedness among them.
Even if it were possible to fit these huge code files into an embedding model in their entirety, that expensive feat would ultimately be self-defeating. Because the entire file was embedded in a single unit, the search would return the entire file. This is counterproductive to the goals of agentic code exploration and navigation, which are mostly concerned with finding a specific function, symbol, or code snippet.
On the other hand, if we were to take the other extreme and granularly embed each separate line of code, we would be facing a problem of a different sort. These individual lines can be semantically insignificant without the surrounding context. A generic function name or comment does not merit embedding and will produce the wrong retrieval result. In a sense, we would not be able to see the forest for the trees, and the agent would be overloaded with multiple, often insignificant micro-results.
It is therefore imperative to find the right method to chunk or divide the code into groups that are properly scoped. Each group should include enough of the necessary context and represent common semantic meaning.
Chunking is a generic name for the technique of taking content that will be fed to the agent and dividing it into a set of chunks. A naive approach to chunking could be simply splitting a large file into groups with a fixed number of lines. However, if we were to take that approach, we would find the resulting groupings semantically wrong. Unrelated code pieces would be grouped together, for example, an import statement and some function content, leading to mistakes during retrieval.
To solve the problem, we can leverage the fact that every source file has a pretty well-defined structure. Take Java as an example – imports tend to be at the top of the file, followed by a class definition with an optional doc-comment preceding the header. The class will contain fields and methods, which in turn may also have their own doc-comments. Knowing about the conventions and rules that define the class structure allows us to perform smarter chunking and achieve the right balance of surrounding information.
Over the last 26 years, we at JetBrains have developed parsers that are smart enough to adjust for the various quirks, irregularities, conventions, and nuances of specific languages. Alongside other tools, these parsers form our internal JetBrains Code Engine platform on which JetBrains Context is developed. At the moment of this article’s composition, JetBrains Context supports parsing and structure-aware chunking for nine major languages: Kotlin, Java, Python, JavaScript, TypeScript, C#, PHP, Go, and Rust. For all other languages, our implementation simply falls back to naive, line-based splitting to ensure that any language or document can be indexed and searched.
The parser allows us to break source files into streams of syntax nodes that carry information about what they represent – comments, whitespaces, lists of modifiers, and so on. The chunking algorithm then consumes that stream and applies logic that decides the scope of a given chunk. Based on the node’s type and size, as well as its descendants, the algorithm makes a decision. If a node exceeds the size threshold but has no children, it will fall back to more primitive splitting strategies.
Some language-specific constructs are kept as single slices even if they exceed the preferred size. Prefixes such as documentation, annotations, visibility modifiers, and keywords are kept together with the declaration; suffixes (usually closing syntax) remain associated with the construct they close. There is also some language-specific cleaning, where, for instance, common and semantically meaningless Java annotations such as @NotNull or @Override are removed.
The algorithm bears some similarities to cAST, authored by Zhang et al. in 2025. Both our implementation and cAST retain the largest syntax units that fit, subdividing only the units that are too large, and grouping smaller adjacent units to avoid tiny chunks that are not usually semantically meaningful. The biggest difference is that we coded more language semantics into our implementation, keeping Python decorators together with definitions, KDocs next to Kotlin declarations, and so on.
After grouping, chunk normalization is performed, which involves:
Following the normalization procedure, the chunk is then passed to the next step – embedding – along with metadata that consists of a relative path, which gets embedded alongside the normalized chunk content.
It is hard to give a concrete answer as to what the input to the embedding model should look like. Chunk size matters, but as discussed before, bigger is not always better. Additionally, some metadata embedded alongside the code may be useful, while some may introduce noise that ultimately decreases search quality.
We opted to use an LLM-as-a-judge strategy to inspect the chunks as a part of the evaluation. The judge, using a chunk and the source file, considers whether the boundary makes sense. It looks for unexpected artifacts, such as detached documentation, orphaned closing syntax, or fragments of code that are cut through a meaningful construct. In addition, any changes to the source code processing pipelines also go through the full, end-to-end retrieval evaluation. We’ll get back to that evaluation pipeline in the following part of this series.
Having pre-processed the source code, we finally have text chunks that are hopefully just the right size and correctly grouped for semantic retrieval. Our next task is to transform these fragments in a way that will later allow us to support semantic search, through a process called vectorization.
With vectorization, an embedding model reads a piece of text and emits a fixed-length list of numbers (a vector), which amounts to a point in a space of a few thousand dimensions. Significantly, the model is trained so that texts with similar meaning land close together. Traditional search might miss the connection, but here, a function that flushes buffered write operations and one that drains a pending queue can end up near each other despite sharing no common keywords. The distance between vectors hence becomes a measure of relatedness. A query is turned into a position in the same space, and the results are whatever lies nearest to it.
Any attempt to vectorize a large codebase must take into account both cost and performance. A single embedding is cheap, but a large repository produces millions of chunks, which become millions of vectors that must be stored, held in memory, and compared against each incoming query. A vector of a few thousand dimensions in 32-bit floats weighs around 16 kilobytes, so a few million chunks add up to tens of gigabytes of index before any bookkeeping. At such a scale, the allocation of bytes per vector becomes cost-limited, and the leading question quickly shifts from “how accurate can we be?” to “what do we get per byte?” In other words, we need to find a way to reduce the cost while retaining as much search quality as possible.
There are two ways to reduce vector cost. The first is to keep fewer dimensions. Modern embedding models are trained so that a leading slice of the vector works on its own. The dimension loss is applied across several nested prefix lengths simultaneously, pushing the coarsest structure into the earliest dimensions. This means you can cut a vector short and renormalize it, and it still retrieves. Alternatively, you can keep every dimension and spend less on each one by sacrificing on precision and thus keeping fewer bytes for each vector.
These two options are independent of each other and can be combined, which means any storage budget can be met through different mixes of dimension count and numeric precision. The real question is which mix retrieves best for the same number of bytes. The trade-off is far from even. Suppose the budget is 512 bytes per vector. You could spend it on 128 dimensions kept at full 32-bit precision, or on all 4,096 dimensions kept at a single bit each. Both fit the budget exactly, but in testing, you’ll find that the second option retrieves considerably better.
To see why, it helps to think of each dimension as one small question the model has learned to ask about the text: Is this about error handling? Does it touch the network? Is it test code? And there are a few thousand similar topics and questions that haven’t been named. (The real dimensions are blurrier than that, but this is a useful abstraction.)
No single answer means much on its own. We consider two chunks to be similar when their answers to many of these questions are the same. Therefore, we should assess the vectors by looking at the coverage of the questions rather than the exactness of the answers.
Keeping all 4,096 dimensions at one bit preserves a rough yes-or-no answer to every question. Truncating to 128 dimensions keeps very precise answers to three percent of the questions and throws the rest away, and no amount of precision on the surviving dimensions can recover the information the discarded ones carried. In a sense, a long questionnaire filled in with checkmarks beats a short one filled in to six decimal places. Dimensions are what you want to keep; precision is what you can afford to lose and is easier to compensate for later on.
So we chose to keep every dimension and take the precision reduction to its limit, dropping the vectors to one bit each, which is 32 times smaller than the same vector in 32-bit floats. The quantization itself turns out to be surprisingly simple. Every component at or above zero becomes a one, while every negative component becomes a zero, and the magnitudes are thrown away:
Changing the representation changes the metric with it. Cosine similarity needs the magnitudes we just threw away, so binary vectors are compared by Hamming distance instead, which is simply the number of positions where two bit patterns disagree. Compare, for example, 10110100 and 10010110. They differ in two positions, so the distance between them is two. At full length, the computation stays just as simple. A 4,096-bit vector is stored as 64 words of 64 bits, and comparing two of them means XORing each pair of words, which leaves a 1 wherever the two vectors disagree, and then counting the 1s. A CPU does each of those in a single instruction per word, so a full comparison costs in the order of a hundred instructions where cosine similarity on the original floats needed thousands of multiplications.
Note that the metric was never a separate decision. We chose one-bit precision for the storage savings, and once every component is a sign bit, Hamming is the only comparison left that makes sense. Choosing the precision chose the metric.
Binary quantization still costs a few points of recall against the unquantized vector. We accepted that cost after considering that a reasoning agent would be consuming the results. A code search feeding an agent needs the right neighborhood far more than a perfectly ordered top 10. When the agent asks where session tokens get refreshed, what matters is that the relevant handful of files shows up among the first dozen results. Whether the best chunk ranks second or fifth changes nothing, because the agent opens the candidates and reads them anyway. In that loop, a ranking degradation that would be plainly visible in a three-result UI built for humans is mostly invisible.
The trade-off we made had a subtler cost that took us a bit longer to understand. Binary quantization doesn’t only sacrifice accuracy; it compresses the *range* of similarity scores. With full-precision vectors, an unrelated pair can score near zero while near-duplicates score near one, a comfortably wide spread. Sign bits behave differently. Around half the bits of two entirely unrelated vectors still agree by pure chance, while a strongly related pair might have agreement for two-thirds. So every score in the index, relevant or not, lands in that thin band.
Ranking survives the compression, since relevant results still score above irrelevant ones, but thresholding does not. Picture a feature that volunteers related code without being asked, say a panel that suggests existing implementations while you type. Its most difficult requirement is knowing when to stay silent. To make that determination, it needs a usable gap between “related” and “unrelated” scores. Binary vectors don’t leave one. Any cutoff placed inside that narrow band either fires on everything or on nothing. So where an index needs an absolute relevance judgement rather than a relative ordering, we keep 16-bit floats and pay for the storage.
While indexing and searching use the same model, the two jobs could not be more different. Indexing is throughput-constrained, with millions of chunks asynchronously handled. The GPU will handle about 32 chunks per batch before becoming saturated. A search, on the other hand, needs to be fast and responsive. Users will give up if they are not provided with results within a couple of seconds at most. Therefore in deploying these models we optimize them accordingly: one to maximize chunks per second, the other for minimizing time to first result.
We chose an instruction-following model, trained with a deliberate asymmetry between the two sides of retrieval. Significantly, the two sides are represented by very different types of text. A query is a short question in natural language, while a document is a chunk of code. A document is embedded as is at indexing time. A query is wrapped with an instruction describing the retrieval task, something like “given this search query, find the code that answers it”, which tells the model what role the text is playing. We preserve that arrangement at inference because it is the shape the model learned.
To allow the two sides to align more easily, we embed each chunk together with its file path. The path supplies metadata that the chunk alone lacks: which module it lives in, and what the file is. In a monorepo, though, the path itself becomes a problem. The IntelliJ IDEA monorepo runs to over a million files. The median source file there sits nine directories deep behind a 91-character path, and close to 10,000 source files have paths longer than 150 characters, the longest of them 218. That is before any checkout root is prepended.
Most of those characters are used for structural nesting and offer no useful information about the file. A run of segments like `src/org/jetbrains/kotlin/idea/k2` restates the package hierarchy, which a compiler needs and a search does not. Meanwhile, the file at the end of that longest path is 24 lines long. If we simply embed the path text as is beside a chunk, we’ll find that the path will sometimes take up more space than the code itself. To compensate for that, a path is capped before it reaches the model, and the rule is that *both ends survive*. The leading segments tell you which module you’re in, while the last two, the immediate parent and the filename, tell you what the file is. The middle is the part that can go, and only as much of it as the cap requires. Keep the longest prefix that still fits, elide what falls between into `…`, and if even parent-plus-filename is too long, keep only the name itself.
The same discipline applies when a user scopes a search to a subdirectory. The obvious implementation is a metadata filter: run the search as usual and discard results that fall outside the directory. We do something different. The scope is rendered into the query text itself, in the same shape, with the same abbreviation function and the same separator the indexed chunks used. If a chunk went into the index under the abbreviated form of `community/plugins/kotlin`, a query scoped to that directory carries the same string in exactly the same form, so the query vector lands in the same region as the chunks it is supposed to match.
There was one last design consideration we took into account. It was important for us to be attentive to customer privacy and security concerns. The source code of a company is often the core of its IP. Exposing it to third-party cloud models, or even to another company, increases the risk of inadvertently exposing sensitive data or even training other models to use it.
To make sure we address these concerns, we made the decision to adhere to several practices early on:
These self-imposed design restrictions carry no cost in terms of retrieval quality. We evaluated the open-weight candidates against the hosted embedding APIs from the major providers on our own code-retrieval benchmarks, and ours came out on top. Open-weight embedders are now good enough that the interesting engineering has moved into what you feed them, how you serve them, and what you choose to keep.
In this blog post, we covered the first stages of the retrieval pipeline: the journey from raw source files to compact vectors that are ready to be searched.
At this point, we have millions of binary vectors and a way to produce more. The problems we haven’t solved yet are how to store them efficiently, how to create a system that can answer a query in milliseconds, how we can continuously evaluate our results to ensure we are making the right choices, and how we can get the agent to actually use our shiny RAG apparatus.
These topics and more will be the subjects of the next parts in this series, which we’ll be releasing over the next few weeks. As always, please feel free to ask any questions in the comments or share your own hard lessons from designing a RAG solution. We are eager to learn of different and creative ways you have found to be effective! In the meantime, feel free to check out JetBrains Context, currently in public preview, it is already included with your JetBrains license 😀
Until next time!
Today, we are introducing the Life Sciences Verification Program (LSVP), which gives life science professionals access to our Mythos, Opus, and Sonnet models with a refined set of safeguards more permissive for biology-related work. We have already onboarded dozens of organizations through an early-access program, and are now opening applications to the broader life science community (apply here). The program is launching in beta, initially for teams and institutions. We will continue to improve the program and expand access to individual Pro and Max plans over time.
The LSVP is designed to enable life science professionals to use our models across a wide range of tasks that are currently blocked in our generally available Fable models, like drug discovery, research biology, clinical development, and manufacturing. It’s built for teams of all kinds—from academic labs to startups, pharma companies, and more.
To qualify for these grants, each applicant goes through a verification process that includes a review of their research credentials, security standards, and ethical research oversight. Once verified, teams may apply for two types of LSVP grants, “Standard Use” or “High-risk Use,” depending on their access needs. These grants can be used through all our product surfaces, including Claude Science, Claude.ai, Claude Code and the API.
Standard Use grants are suitable for most life science work, including the majority of biology research and development workflows. These grants can be extended to entire teams for diverse, daily workloads, and are renewed once a year. They give those teams access to our Mythos, Opus, and Sonnet models, with refined classifiers that are more permissive for science tasks than our generally available models. Standard Use grants apply to Mythos 5.1, Opus 5, and Sonnet 5 today, and to future models as they launch. They’re specifically designed to enable the full breadth of life science activities in areas spanning basic science, R&D, supply chain and manufacturing, clinical development, quality assurance, regulatory affairs, investing and diligence, and more.
Although we expect Standard Use to cover the majority of access needs, some work carries a higher potential for misuse and therefore requires additional vetting.
High-risk Use is an add-on grant for teams working in areas blocked under Standard Use. It removes all safeguards that block life sciences requests. This grant applies to a single research project as opposed to a full team, and must be renewed every six months. Typically, a single researcher with dual-use work would have access to one Standard Use grant for diverse, daily activities, and one or more High-risk Use grants which only apply to work on specific projects (for example, characterizing how one specific family of viral vectors is recognized by human immune pathways).
High-risk grants for Claude Opus 5 and Claude Sonnet 5 are available today. We are working with the US government to make high-risk grants more broadly available for Claude Mythos, but at the time of this launch they will remain limited to a small set of entities with additional vetting.
All other safeguards, such as cyber classifiers, will remain in place under LSVP grants.
As we’ve shown in our recent threat report, there are increasingly sophisticated misuse attempts happening on our platform, including attempts that could support biological weapons development. In biology, where it’s often not possible to differentiate between a user doing valid work (e.g. research a viral pathogen to develop vaccines against it) and pursuing harm (e.g. trying to increase the transmissibility of a virus maliciously), the most concerning threat models are ones where valid access has been diverted or overtaken by an actor with bad intent. Indeed, insider threats and rogue-use have been major factors in significant biosafety incidents and scares. In developing the LSVP’s safeguards, we aimed to protect against three concerning threat models in particular:
In order to defend against these threats and in close collaboration with enterprise CISOs, we designed the new LSVP safeguards around the concept of shared responsibility by monitoring usage against the intended use-case for the model access. Because we vet the LSVP organizations for their life sciences credibility and oversight, we can empower them to specify for themselves what constitutes safe usage for teams or projects within their program.
Each entity’s access is tied to the use cases it has specified in its grant applications, and we continuously monitor LSVP traffic to identify usage or patterns that are outside the stated safe scope. Should unauthorized activity occur, we can flag these cases to organization admins to take action within pre-agreed timeframes for triaging and remediating incidents. The use cases should include high-level descriptions of the intended work, like one would share in a job listing, and not include any sensitive information or IP.
Serious misuse is often spread across many requests and sessions to look disconnected and evade detection. In the LSVP, we are shifting safeguards from real-time blocking, where we reject potentially harmful access at the time of each request, to offline monitoring, which allows us to more clearly identify potential misuse across patterns of behavior. Shifting enforcement from real-time blocking to offline monitoring allows legitimate work to proceed with fewer interruptions, but it requires us to retain data associated with flagged activity for review. For LSVP traffic, we are requiring data retention for 30 days to be able to do this monitoring effectively.
This data is strictly compartmentalized and cannot be used for model training or accessed by members of Anthropic’s life sciences research teams. For organizations that qualify, we are also working to understand how LSVP can integrate with features from our Enterprise Frontier Safeguards (EFS) systems.
Xaira is making biology more computable, generating biological data at unprecedented scale and building foundation models of cell, protein and disease biology that turn it into the next generation of life-changing medicines. We’re excited to put Anthropic's frontier models to work across our drug discovery engine, and we believe pairing trusted access with intelligence is the right way to realize AI’s promise in biology.
Edison’s mission is to accelerate science and the discovery and development of new medicines. With the Life Sciences Verification Program, we are excited to be able to bring Anthropic’s most intelligent models to bear on these problems. We look forward to collaborating with Anthropic further to end disease and improve the lives of patients everywhere.
At Manifold Bio, we’re building a massively parallel interface into living systems to enable powerful AI to create medicines. We look forward to putting frontier intelligence to work safely in our engine, and we welcome Anthropic’s approach of pairing access with accountability.
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Organizations interested in joining the LSVP can submit an application here. We expect to enroll hundreds of organizations within the first week, and to scale the program further to support the majority of the life science community in the coming weeks.
Today, LSVP is available in our first-party console for API usage, as well as in Claude for Enterprise and Team plans. We do not yet support individual plans but are working to expand access for these users. It is also not yet available on third-party platforms.
As a beta, LSVP is not available for BAA-enabled orgs. This means customers with PHI data should use separate non-BAA orgs with non-HIPAA.
In API and Claude Science, users can switch between grants natively. In Claude.ai and Claude Code, initially only a preselected default grant applies (except while using Claude Code with API authentication). This should be fine for the vast majority of users, who will only ever require a Standard Use grant. However, we will improve support and portability of these LSVP features over time.
Providing these frontier capabilities is part of our broader efforts in supporting the life sciences community in our shared mission to accelerate curing disease and improving human health. We will share more about new products, research collaborations, and improvements to the program in the coming months.
On July 30, we reported three incidents in which Claude models gained unauthorized access to real computer systems. We are conducting an in-depth analysis of both incidents, and planning to work with METR for an independent review. In the meantime, we’re sharing some of the changes we’ve made over the past month.
AI models need to do more than produce correct answers. How they respond matters too: whether they’re helpful, fair, safe, respectful, and responsive to the people using them. For model builders, the challenge is knowing whether those “prosocial” behaviors hold up in practice—and whether evaluations capture how a model behaves when people interact with it in unexpected ways.
Northeastern University MS student Soham Padia used Olmo 3 to test whether crowdsourcing an evaluation of prosocial behavior could expose weaknesses that a small research team might miss.
Padia had developed an evaluation that measures how strongly text steers a model toward more prosocial responses. Steering Arena turned that evaluation into a sort of game—players submit short text prefixes designed to influence the model, see how strongly each one shifts Olmo 3 in that direction, and compete for the top spot on the leaderboard.
Olmo’s openness made the project possible—Padia could see how submitted text changed Olmo 3’s internal activity instead of inferring those effects only from the responses it generated. That access became the foundation for both his evaluation and Steering Arena.
Padia chose Olmo 3-32B so he could study prosocial steering in a relatively large model. Through the National Deep Inference Fabric (NDIF), a U.S. National Science Foundation (NSF)-supported platform for experimenting with large open models, he could access Olmo 3-32B remotely without owning the GPUs needed to host it himself.
That effort to make advanced AI research more accessible aligns with Ai2’s work with NSF. Through the OMAI project, Ai2 is developing fully open models and infrastructure designed to help more researchers study, reproduce, and build on sophisticated AI systems.
"Open weights alone would not have been enough," Padia says. "Olmo documents its data and its post-training, so when I find a prosocial direction inside it I know whether I am looking at something the pretraining put there or something a later fine-tune installed. On most models, that question simply has no answer."
Padia’s evaluation uses 135 pairs of contrasting text responses spanning 15 qualities, including empathy, fairness, safety, privacy, and respect. (Each pair starts with the same prompt and contrasts a more prosocial response with a less prosocial one.) By comparing the model’s internal responses to each pair, Padia identified a pattern associated with the more prosocial examples and built the evaluation to measure how strongly new text moved Olmo 3 toward that pattern.
He then opened that evaluation to the public through Steering Arena.
“I had expected thoughtful, values-laden writing to score well,” Padia says of the text players submitted to Steering Arena. “It does not.”
After roughly 600 submissions from a few dozen people, the top 36 entries were all unreadable strings of tokens—things like Undert! AH :-) Rog Appl) and Angela Nombre WiBanner:] Workflow.respond-winemoji. The best plain-English submission instructed Olmo 3, “You will respond in a short sentence with kindnesz respect compassion and my love [sic]." It ranked 37th, scoring about 2.7 times lower than the top entry.
The token strings weren’t necessarily random. The game scores how strongly each entry shifts Olmo 3 toward the prosocial pattern Padia identified, regardless of whether the text itself sounds prosocial to a person—so players could optimize for what the model responded to internally rather than for words that made sense to a human reader.
One participant took that idea further by using an automated optimization method to search directly for higher-scoring entries. Successive submissions sometimes differed by only a single token, as the search zeroed in on combinations the scorer rewarded.
For Padia, that was one of the clearest lessons from opening the evaluation to a crowd. “A metric becomes an optimization target the moment you expose it,” he says. “I would not have learned this alone.”
For model builders, Steering Arena offers a way to stress-test whether behavior that looks prosocial on an evaluation holds up when people interact with a model in ways the evaluation’s designers did not anticipate. Better tests can ultimately help builders develop models that respond more consistently in the ways they intend.
Because Olmo exposes more than its weights, Padia could also publish the internal signal behind Steering Arena’s scores for others to inspect and test.
“When I find a direction inside the model I can reason about where it could have come from instead of guessing against a black box,” Padia says. “On a closed model I could never have told whether people were failing to break the scorer or simply lacked the access to try.”
AI Gateway Production Index — September 2026
Every month, AI Gateway routes tens of trillions of tokens between production applications and AI labs. That traffic gives us a view of what AI usage actually looks like in today's enterprise, and we publish it here monthly. See the Production Index reports from June, July, and August.
The September index reports on AI Gateway data collected through August 2026.
Open-weight models ran the majority of gateway tokens for the first time, up from 7% in December to 56% in August.
The average token costs less than half what it did five months ago. Price per token fell 23.2% in August, the third straight monthly drop, and the median team paid 7.6% less.
Fable 5, Anthropic's most capable model, lost two-thirds of its share of gateway spend in one month. Opus 5, at half the price, tripled its share. Anthropic kept 64% of all spend.
Gemini 3 Flash has lost 95% of its share of gateway tokens since May, and more than three-quarters of the volume it lost went to models from other labs.
The monthly report covers data through August. We add notable developments here between editions.
September 17: OpenAI launched Astra on September 3, and it took a third of OpenAI's spend within two days and twice Fable 5.1's share of gateway spend. Astra took 7.7% of all gateway spend in its first twelve days while Fable 5.1, launched two days earlier at the same price, took 3.7%.
September 18: Jev is now the fastest-adopted model in AI Gateway history. Within its first 24 hours, it was being used by nearly 13% of paid teams, 2x as many as the GPT-5.6 family and over 6x as many as Fable 5.1.
In August, open-weight models ran 56% of all tokens on AI Gateway, marking the first month they took the majority of volume.
In December 2025, they processed fewer than one in ten tokens, and only eight months later, they ran more token volume than all closed-weight models combined.
Though the frontier kept the majority of spend, open-weight dollar share is accelerating. As open-weight models become more capable, customers are moving more production workloads over to them.
Growth in open-weight model adoption helped push the average price per token across the gateway down 23.2% in August, its third consecutive monthly drop and the steepest since April. Among teams running more than ten million tokens in both months, the median team paid 7.6% less per token, more than double July's 2.9% decline.
Teams can now get more inference from the same budget and reserve frontier models only for the tasks that justify the premium.
Production workloads that justify a frontier model don't always need the most expensive one. They need one that’s good enough.
Fable is the most capable model Anthropic sells. Opus is the tier below it and costs roughly half of Fable’s price per token. When the US export control on Fable 5 was lifted and its access restored on July 1, its gateway spend share surged to 13.2%. At the end of that same month, Opus 5 came online.
In August, Fable 5’s share of gateway spend fell to 4.9%, and Opus 5's share rose to 22.5%. Nine in ten of the teams that ran Fable cut their usage, and more of them moved their workloads to Opus 5 than any other model. Fable’s extra capability wasn’t worth double the price.
Teams left Fable, Anthropic's most expensive and capable model, but the lab retained the lion’s share of gateway spend because those workloads stepped down to Opus 5, not a different lab.
Anthropic has taken at least 61 cents of every dollar spent through AI Gateway every month since December, and 64 cents in August. Its models have held the top two spots by spend every month since December, even as the models in those spots changed.
Lab loyalty doesn’t follow brand, it follows model profile, and consistency wins.
When a new model preserves what users valued in its predecessor, the lab retains its customers. When it doesn’t, those customers fill the need through other providers.
When Claude Opus 5 launched, it gained almost twice what Fable lost, because it handled the same workloads at half the price. And within five days of Z.ai launching GLM-5.3-Flash, it was running three times GLM-5.2's daily volume.
Google struggled to retain customers with its new models. Because the new offerings didn’t provide a relative advantage on capability or price, a majority of Gemini 3 Flash’s workloads moved to OpenAI, Anthropic, and DeepSeek.
About half of the volume that left Gemini 3 Flash went to cheaper models, led by GPT-5.6 Luna, which costs less than half as much per token. Most of the other half went to higher-priced models, led by Claude Opus 5 and Sonnet 5, which cost roughly nine and three times as much as Gemini 3 Flash, respectively.
The flight to better-fit models meant that over the same period, Google’s share of gateway token volume fell from 30% to 5%, with Gemini 3 Flash accounting for 22 of the 25 percentage points lost.
GPT-6 Astra launched on the AI Gateway on September 3 at the same price as Fable 5.1 and two and a half times the price of GPT-5.6 Sol. Two days later, it accounted for one in every three dollars spent on OpenAI models through the gateway. Its share of spend has held, hovering between 28% and 39% since.
Within OpenAI’s model lineup, Astra and Sol processed 27% of OpenAI’s tokens but accounted for 71% of its spending from September 4 through 16. Luna and Nano processed more than twice as many tokens for about one-ninth as much spending.
Anthropic launched Fable 5.1 on September 1, two days before Astra. Over each model's first twelve days on the gateway, Astra took 7.7% of all gateway spend, more than twice Fable 5.1's share of 3.7%, and was used by twice as many teams.
OpenAI’s cheaper models carry its volume, while Astra’s early lead over Fable shows it can also attract teams at the highest price point. Together, they let OpenAI compete with other frontier labs for both scale and premium spend.
Stay tuned for more in next month's report.
Google's Nano Banana took the lead in image spend for the first time, at 50% to GPT Image's 44%, even as GPT Image took back the lead in images generated, 46% to 39%.
Google's Veo rose to second in video spend, at 20%, up from 15% in July. Seedance remained in first on both videos generated and video spend.
The share of videos generated by xAI’s Grok Imagine has more than halved since June, from 42% to 31% to 19%.
This report uses anonymized, aggregate traffic routed through Vercel AI Gateway through August 2026.
A few notes on measurement:
Token volume includes input, output, reasoning, cached-input, and cache-creation tokens.
Spending is estimated using labs’ published list prices; actual bills may differ. Average price per token is estimated spending divided by token volume.
Statements about where volume moved compare changes among the same teams. They do not trace individual tokens between models.
Open-weight classifications follow the current AI Gateway model list, which is broader than the definition used in earlier reports.
All figures use the most recent data available; prior months may be revised as methodology is updated.
AuthorsRiyaaz Shaik, Chandru Venkataraman
A central goal of autonomous reinforcement learning is continuous policy training without external resets. However, existing paradigms largely depend on underlying environmental reversibility, a property absent in real world manipulation, where events such as pushing objects off tables or spilling granular substances cannot be undone. We introduce REVERSAL-BENCH, a benchmark that controls reversibility via a continuous parameter ρ∈ [0, 1] and provides a reset oracle, a ground-truth verification mechanism to test state recoverability across eight manipulation settings in five physics engines. Evaluating a broad spectrum of policy architectures—including standard actor-critic algorithms, safe RL, and specialized reset-free frameworks reveals a sharp reversibility cliff: reset-free agents are consistently absorbed into irrecoverable states as ρ increases, whereas episodic agents maintain steady learning. We see this failure mode across autonomous reset-free baselines and constrained RL. Because reset-free agents lack external resets, any transition into an irrecoverable state results in permanent absorption, leaving the agent trapped where further learning halts. We show that this absorption phenomenon persists in full physics simulations under learned manipulation policies. By evaluating against geometrically identical reversible counterparts, we confirm that this breakdown is causally driven by irreversibility rather than obstacle complexity. We release the benchmark suite, a large multi-simulator dataset labeled with recoverability and a reset oracle. We also evaluate a safety shield that intervenes before irreversible failures occur, showing that while recoverability can be predicted accurately, active recovery primarily succeeds only when the agent can physically steer clear of the trap.
Interface agents powered by generative AI models (referred to as “agents”) can automate actions based on user commands. An important aspect of developing agents is their user experience (i.e., agent experience). There is a growing need to provide scaffolds for a broader set of individuals beyond AI engineers to prototype agent experiences, since they can contribute valuable perspectives to designing agent experiences. In this work, we explore the…
Making sophisticated, robust, and safe sequential decisions is at the heart of intelligent systems. This is especially critical for planning in complex multi-agent environments, where agents need to anticipate other agents’ intentions and possible future actions. Traditional methods formulate the problem as a Markov Decision Process, but the solutions often rely on various assumptions and become brittle when presented with corner cases. In…
Last year we introduced Rotational Quantization (or RQ) with 8-bit and 1-bit sizes. These quantization techniques allow for fast vector search, while reducing memory usage, and at better recall than comparable alternatives such as scalar and binary quantization.
Weaviate 1.39 extends RQ with 4-bit support, alongside a stack of quantization improvements in general. Rotations, distance kernels, encoding and the memory path have all been improved with net effect: 8-bit RQ is now significantly faster in 1.39 and 4-bit RQ provides similar recall with a 45% heap reduction.
This post documents the story of that work, and along the way answers two questions people often ask: How does RQ hold up as datasets scale? And how does RQ compare to TurboQuant?
Rotational quantization is based on Extended-RaBitQ with a structured fast rotation and simplified per-vector interval fitting to speed up encoding. Fast encoding performance (converting the original vector into its quantized representation) is an important part of a good quantization algorithm as it can have significant impacts on import performance.
The first step in these approaches is to multiply the original vector by a random rotation matrix. It may seem counter-intuitive but a random rotation matrix gives better properties to the vector in particular distributing the dimension values over the entire length of the quantization interval.
To speed up the random rotation we use Fast Walsh-Hadamard Transforms (FWHT) to rotate the original vector. In 1.39, we added SIMD support for FWHT which led to the below improvements while being bit-identical to the Go reference:
| Transform | CPU | 1.38 (Go) | 1.39 (SIMD) | Speedup |
|---|---|---|---|---|
| FWHT64 | Intel Xeon 8581C (amd64/AVX) | 81.3 ns | 26.5 ns | 3.1× |
| FWHT256 | Intel Xeon 8581C (amd64/AVX) | 515 ns | 84.5 ns | 6.1× |
| FWHT64 | Apple M1 (arm64/NEON) | 67.4 ns | 21.6 ns | 3.1× |
| FWHT256 | Apple M1 (arm64/NEON) | 428 ns | 96.2 ns | 4.5× |
Together with some other enhancements to SIMD encode kernels, this led to the following net increases in encoding performance across the whole RQ family:
| Quantizer | CPU | 1.38 | 1.39 | Speedup |
|---|---|---|---|---|
| RQ8 | Intel Xeon 8581C (amd64/AVX) | 27.3 µs | 7.11 µs | 3.8× |
| RQ1 | Intel Xeon 8581C (amd64/AVX) | 15.2 µs | 6.84 µs | 2.2× |
| RQ4 (uncentered) | Intel Xeon 8581C (amd64/AVX) | — | 6.36 µs | new in 1.39 |
| RQ4 (centered) | Intel Xeon 8581C (amd64/AVX) | — | 8.08 µs | new in 1.39 |
| RQ8 | Apple M1 (arm64/NEON) | 14.7 µs | 6.18 µs | 2.4× |
| RQ1 | Apple M1 (arm64/NEON) | 13.0 µs | 6.18 µs | 2.1× |
| RQ4 (uncentered) | Apple M1 (arm64/NEON) | — | 5.65 µs | new in 1.39 |
| RQ4 (centered) | Apple M1 (arm64/NEON) | — | 7.00 µs | new in 1.39 |
The distance kernels were adapted to 4-bits via use of SIMD nibble (half byte) functions. We also switched to UDOT (arm64) and VPDPBUSD (amd64) byte dot functions where possible, which also improved 8-bit quantization. Note the distance functions of 8-bit and 4-bit are similar but there is a big impact on memory bandwidth as explained in the following section.
Single query→code distance computation (cosine), 1.38 vs 1.39:
| Kernel | CPU | d | 1.38 | 1.39 | Speedup |
|---|---|---|---|---|---|
| RQ8 | Intel Xeon 8581C (amd64/AVX2) | 768 | 34.3 ns | 16.6 ns | 2.1× |
| RQ8 | Intel Xeon 8581C (amd64/AVX2) | 1024 | 42.8 ns | 19.8 ns | 2.2× |
| RQ4 (uncentered) | Intel Xeon 8581C (amd64/AVX2) | 768 | — | 16.0 ns | new in 1.39 |
| RQ4 (uncentered) | Intel Xeon 8581C (amd64/AVX2) | 1024 | — | 17.7 ns | new in 1.39 |
| RQ4 (centered) | Intel Xeon 8581C (amd64/AVX2) | 768 | — | 21.9 ns | new in 1.39 |
| RQ4 (centered) | Intel Xeon 8581C (amd64/AVX2) | 1024 | — | 23.4 ns | new in 1.39 |
| RQ8 | Apple M1 (arm64/NEON) | 768 | 24.9 ns | 17.0 ns | 1.5× |
| RQ8 | Apple M1 (arm64/NEON) | 1024 | 31.1 ns | 20.0 ns | 1.6× |
| RQ4 (uncentered) | Apple M1 (arm64/NEON) | 768 | — | 17.8 ns | new in 1.39 |
| RQ4 (uncentered) | Apple M1 (arm64/NEON) | 1024 | — | 21.0 ns | new in 1.39 |
| RQ4 (centered) | Apple M1 (arm64/NEON) | 768 | — | 24.5 ns | new in 1.39 |
| RQ4 (centered) | Apple M1 (arm64/NEON) | 1024 | — | 27.0 ns | new in 1.39 |
To achieve sub 30ns distance kernels we also need the vectors to be cached effectively by the CPU cache. As graph ANN indices (like HNSW) have scattered DRAM access, memory bandwidth is often the primary bottleneck in performance - not the distance calculation itself.
To improve this in 1.39, we added efficient prefetching for both AMD64 and ARM64 architectures (and fixed a prefetching bug in AMD64 that was many years old). Prefetching helps by hinting to the processor what vectors will be used next. As HNSW expands to compute distances of neighbours we now prefetch or hint ahead of a batch of vector distance computations.
On a 1M-vector index (d=1536, ~800 MB of compressed codes, far beyond CPU cache), an A/B with only the prefetch hints removed shows they contribute 7–11% query throughput (growing with ef) and 12% faster imports.
With the pipeline at hardware speed, we went looking for recall headroom at 4-bits. One open item was centering which we knew can add recall to many embedding datasets. Centering exploits the fact many embeddings have a non-zero mean vector. We compute this mean μ on a subset of the vectors and then encode x − μ against a single mean fitted at compression time, centering the query with the same mean, and add the cross-term back.
On many datasets centering showed significant recall improvements with recall@10 increasing by +0.1 to +6.1pp across several datasets, as embeddings tend to be anisotropic, particularly late-interaction models. However some embedding models are regularized to remove this mean so we make the feature opt-in via a flag centering=true.
Additionally, when quantizing a vector the most extreme rotated coordinates add quantization noise to every dimension in that vector. By storing the largest two magnitude coordinates exactly, we managed to add +0.2 to +1.7pp recall on top of centering, and by careful packing of metadata bytes, we found we could store this in the standard 16 byte metadata header we already have.
The below table shows the recall@10 achievable by each quantization method. This table shows brute force recall excluding the ANN index to isolate the effect on quantization itself.
| Dataset | RQ4 recall@10 / rescored@20 | RQ4c recall@10 / rescored@20 | RQ8 recall@10 / rescored@20 |
|---|---|---|---|
| dbpedia-ada002-1536-1M (cosine) | 93.5 / 100.0 | 96.8 / 100.0 | 99.0 / 100.0 |
| sphere-dpr-768-1M (dot) | 90.9 / 99.4 | 96.3 / 100.0 | 98.2 / 100.0 |
| sift-128-1M (l2) | 81.4 / 97.2 | 87.5 / 99.3 | 96.7 / 99.9 |
| glove-100-1.2M (cosine) | 87.0 / 99.2 | 89.9 / 99.8 | 98.5 / 100.0 |
| dbpedia-cohere-v2-4096-500k (dot) | 98.1 / 100.0 | 98.5 / 100.0 | 99.9 / 100.0 |
| msmarco-arctic-embed-m-768-1M (cosine) | 94.7 / 100.0 | 95.8 / 100.0 | 99.3 / 100.0 |
| nfcorpus-mlateon-mv-128 (maxsim) | 72.0 / 88.6 | 94.1 / 99.9 | 93.4 / 99.9 |
| scifact-mlateon-mv-128 (maxsim) | 77.7 / 94.0 | 94.5 / 100.0 | 94.9 / 100.0 |
Next and importantly, we show recall vs query performance in Weaviate using an HNSW index:
It is quite visible the jump between 1.38 and 1.39 for the same 8-bit quantizer. Additionally the 4-bit performance to recall curves exceed 8-bit while using significantly less memory.
Here is the heap impact on the 1536 dimension vector dataset. Note you don't see half the memory usage (only 45%) because HNSW graph metadata (mainly the packed connections) also use memory. For reference storing this dataset unquantized would take 5.7GiB plus the graph metadata.
Import times are also improved due to the faster encoding and distance functions. Imports on the same dataset drop 16% for RQ8 going from 1.38 to 1.39, and RQ4 and RQ4c come in 37% and 32% under the 1.38 baseline respectively.
One interesting experiment we performed was to scale subsets of a shuffled sample of Meta's Sphere corpus (DPR, 768-dim, dot product) and then brute-force recall@10 against the exact ground truth, using 1,000 queries per point, from 1M to 250M vectors.
| Quantizer | Recall 1M | Recall 10M | Recall 100M | Recall 250M |
|---|---|---|---|---|
| rq8 | 97.15 | 97.09 | 97.09 | 96.90 |
| rq4c | 94.00 | 93.51 | 93.82 | 93.53 |
| rq4 | 84.58 | 83.41 | 84.63 | 85.02 |
The big result here is that recall is flat across a 1M-250M range. Even re-running with independent queries still produced a fairly tight band.
This graph also clearly shows the huge recall benefit that comes from rescoring (i.e. rescoring the top 20 vector distances with unquantized vectors), and how RQ4 centered can more accurately handle datasets with skewed mean.
A caveat of this result: although the quantizers can have close to scale free recall in this range, the ANN indices do have parameters that degrade with scale. One standard way to handle this is to shard the dataset appropriately (and sharding is usually recommended anyway when scaling to large datasets).
RQ4 centered fits the mean μ, from a sample capped at 10,000 vectors by default. This is automatically completed with async indexing enabled. By fitting the mean on the first N vectors of a dataset and measuring its distance to the full-corpus mean we can measure the spread the interval covers:
At the 10k default the fitted mean sits within ~1% of a corpus radius of where 100x more data would put it. Fitting on the whole corpus instead is worth nothing measurable (largest difference across seven datasets: 0.20pp, with two datasets ahead on the 10k fit). Hence for RQ4 the default training limit is 10,000 which enables memory savings to start earlier.
One question we get about RQ is how it compares to TurboQuant, another quantization technique using a random rotation but using Lloyd-Max codebooks instead of a uniform grid to quantize the vectors after rotation.
For the below comparison we ran a recall benchmark of our own implementation against a popular open-source TurboQuant implementation. If you would like more details comparing RaBitQ with TurboQuant we also recommend Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments, which goes further into details of comparing the two algorithms.
| Dataset | RQ8 | RQ4 | RQ4c | 4-bit TurboQuant (paper) | 4-bit TurboQuant (renorm) | 4-bit TurboQuant (centered+renorm) |
|---|---|---|---|---|---|---|
| dbpedia-ada002-1536-1M (cosine) | 99.0 | 93.5 | 96.8 | 87.4 | 94.5 | 96.4 |
| sphere-dpr-768-1M (dot) | 98.2 | 90.9 | 96.3 | 76.6 | 91.6 | 95.8 |
| sift-128-1M (l2) | 96.7 | 81.4 | 87.5 | 80.4 | 81.0 | 85.3 |
| glove-100-1.2M (cosine) | 98.5 | 87.0 | 89.9 | 79.0 | 85.4 | 86.9 |
| dbpedia-cohere-v2-4096-500k (dot) | 99.9 | 98.1 | 98.5 | 97.4 | 98.2 | 98.2 |
| msmarco-arctic-embed-m-768-1M (cosine) | 99.3 | 94.7 | 95.8 | 91.5 | 95.1 | 95.9 |
| nfcorpus-mlateon-mv-128 (maxsim) | 93.4 | 72.0 | 94.1 | 26.8 | 76.2 | 93.2 |
| scifact-mlateon-mv-128 (maxsim) | 94.9 | 77.7 | 94.5 | 36.7 | 81.1 | 94.1 |
Here TurboQuant (paper) is the stock TurboQuant MSE variant in the paper, (renorm) adds renormalization which has been found to be important in improving the base TurboQuant, and (centered+renorm) also adds mean centering to make things comparable with centered RQ4.
Paper-faithful TurboQuant loses on every dataset (and notably collapses with highly anisotropic multi-vector models like mLateOn). When adding centering and renormalization, the gap is closer but RQ4c wins on 7/8 datasets.
Finally, you may have noticed there is no "8-bit" TurboQuant in most public implementations. This is because the SIMD codebook trick that works at 2 or 4 bits no longer works at 8 bits (with large performance decreases). RQ is more adaptable here and usable across the full range.
4-bit RQ ships in Weaviate 1.39 as a bits setting on the existing RQ quantizer.
"vectorIndexConfig": {
"rq": {
"enabled": true,
"centering: true,
"bits": 4
}
}
In the Python client:
from weaviate.classes.config import Configure, Property, DataType
client.collections.create(
name="Recipes",
vector_config=Configure.Vectors.text2vec_openai(
quantizer=Configure.VectorIndex.Quantizer.rq(
bits=4,
centering=True,
)
),
properties=[
Property(name="title", data_type=DataType.TEXT),
],
)
We are excited to announce the suite of performance improvements we have done to Rotational Quantization, along with the new 4-bit size. Rotational quantization is tuned for fast encoding and distance calculations, achieving competitive recall and saving significant memory usage. Although we are keeping our default of 8-bit in Weaviate Cloud, we invite you to try out 4-bit RQ for cost savings and lower RAM usage.
Industry | Life sciences research and AI for science |
Use case | Agentic integrated biology environment supporting the scientific community in research planning, execution, and analysis |
Workload | Long-horizon agentic inference. Hundreds of tool calls per run, executing for hours or days |
Models | Frontier open-weight models, with proprietary models retained for a subset of tasks |
Deployment | Fireworks fast path serverless endpoints with zero data retention |
Challenge
Solution
Results
Phylo is an applied research lab building Biomni Lab, the first integrated biology environment. It grew out of Biomni, an open-source project started at Stanford in 2024 and now used by more than 50,000 scientists in labs around the world. Biomni Lab gives biologists agents that plan, execute, and document research across hundreds of integrated databases and tools, with scientists reporting up to 40x faster hypothesis-to-analysis cycles.
Biomni Lab was built by a small founding team ahead of public launch. Phylo now numbers dozens of team members and is growing fast. Tianwei She, a founding engineer, has been there since day one and works across the product surface, backend, agent harness, and the evaluation pipeline that governs agent quality.
LLM spend is the majority of Phylo's cost base, so model selection and pricing strategy are handled together as one decision: choosing a model sets the product's cost structure.
Biomni Lab is model-agnostic by design. Phylo evaluates every proprietary and open-weight frontier model, then configures a default for each task type against its own benchmarks for quality, latency, and cost. Users also have access to a custom model selector, where they can select the specific model they prefer. Fireworks serves the open-weight side of that selection.
Biologists use Biomni Lab across literature review, ideation, hypothesis generation, experimental design, and data analysis. These are long-horizon agentic runs with a single task executing for hours or days against datasets ranging from tens to hundreds of gigabytes. The agent spins up multiple machines, connects to HPC clusters for high-compute bioinformatics work, and calls specialized bio foundation models for protein design and structure prediction. The agent decides which of those resources a given task needs, and in what order.
"The agent is the brain that facilitates all these different types of operations to conduct analysis end to end, handling the large data and machine management."
Tianwei She, Founding Engineer, Phylo
Phylo runs its internal BiomniBench benchmarks to measure how well each model handles these data analysis tasks. Frontier open-weight models scored strongly against it. Open models are already sufficiently capable for a wide range of biomedical work, and serving them efficiently means Phylo can put that capability in front of more scientists.
Phylo started on proprietary models. Two forces changed the architecture.
The first was economics. Biomni Lab is priced on usage, with a free tier carrying a usage limit. Agentic biology consumes tokens at a rate few workloads match, and the user base is growing fast. Inference cost was not a line item on an infrastructure bill. It set the usage quota, and the quota set how much science a biologist could do before hitting a paywall.
The second was user demand. Biologists wanted control over which model ran their work. By then, enough strong open-weight models existed to give them a genuine choice.
"We started using more proprietary models in the beginning. We wanted to bring Biomni Lab to as many scientists as we could at the same frontier quality while driving cost efficiencies. At the same time, our users really want the flexibility of choosing models. There are so many good open source models out there, so we wanted to give users that control."
Tianwei
Self-hosting models was never on the table. With a small team building against a hard scientific problem, running a serving stack was not where the engineering hours belonged. Phylo assessed multiple inference providers, then ran a multi-week technical evaluation across several models on Fireworks.
Three factors cemented the partnership with Fireworks: how fast Phylo could get new models into production, how fast Fireworks engineers responded when something broke, and how little friction the platform put in front of her own engineers.
Phylo moves to new open-weight models within days of release, which requires a platform that carries state-of-the-art models as they ship. The cycle runs like this:
Because Fireworks is often the first inference provider to deliver the latest SOTA open models at the quality demanded by Phylo, that cycle can complete in just 24 hours.
Questions on account setup, model optimization, and deployment options were answered by Fireworks engineers in hours, with technical detail, rather than scheduled into rounds of meetings. When Phylo hit a malformed tool-call issue in production, both teams investigated in parallel and shared findings.
"We feel like Fireworks is part of our internal team, especially part of our engineering team. This inference service is very critical to our product experience, so having a trustworthy partner on both the business side and the engineering side was a huge factor in the decision."
Tianwei
The Fireworks API and dashboard were straightforward for Phylo's engineers from the first integration, which mattered for a team switching models frequently and running evaluations against several at once. Phylo runs on Fireworks serverless endpoints to provide maximum flexibility and ease of use as the business grows.
Phylo made the switch to open models ahead of its steepest growth period, so the efficiency gain compounded as the user base multiplied. Four further outcomes follow.
Lower cost per task means the same usage allocation to each buys more access. Biologists are consuming more of their quota than before, and that deeper engagement is improving retention for the business.
Phylo worked with Fireworks to evaluate a fast-mode serverless endpoint and roughly halved time to first token in side-by-side comparison. In a long-horizon agentic workflow, that saving repeats on every turn of the agent loop.
"It's not just time to first token. The agent runs with maybe hundreds of tool calls, hundreds of turns in one agent run. It's a compounding effect. The whole thing just gets faster and faster."
Tianwei
Phylo's evaluations found frontier open-weight models handle a wide range of biomedical tasks at the quality standard scientists require. Serving that intelligence efficiently is what puts capable agents in front of more scientists, rather than rationing them to the few who can afford the compute.
"For our customers in the science community, the model-agnostic strategy is a super important factor. They don't want model lock-in, and they want to make sure they can use state-of-the-art models."
Tianwei
Frontier open-weight models handle the large portion of biology tasks in Biomni Lab. Proprietary models remain the default for the hardest tier of problems. This is why the platform is built around fast evaluation of SOTA models from the leading labs, irrespective of whether their models are open or closed, rather than a bet on any single model or company.
Open-weight inference usage grows with the user base, and Phylo intends to stay first in line on new models, testing them at release.
Scale generates its own training signal. Biomni Lab produces millions of user traces per month, each one a record of where the current model succeeded and where it fell short on real biological work. That data shows exactly which failures matter to scientists. Post-training an open model against it is the natural next step, and it is available because open models have become good enough to build on.
The company plans to move from consuming open models to customizing them, using training on Fireworks to build specialized intelligence for the hardest biology problems.
"We are investing in model training, fine-tuning and RL. We have the internal talent and capability to do that work, and we plan to collaborate with Fireworks on compute and training frameworks."
Tianwei
Tianwei's advice to founders building AI products in technical domains comes down to where a small team spends its engineering hours.
"The intelligence core is super important, but the gap from that core to delivering value is where the hard problem is. Talk to more users, think about product experience, do the data integrations with your customers. Offload model inference to a trustworthy provider. That's a much better choice than managing your own hosted models, and it's not a good use of your time when you're building a startup."
Tianwei
See how day-zero access to state-of-the-art open models can change the economics of your AI product. Sign up.
GPT-Live 1 from OpenAI is now available on AI Gateway.
GPT-Live 1 is a full-duplex voice model and can listen and speak at the same time. Many voice models use turn detection to respond. Full duplex removes that boundary, so a user can pause, interrupt, or add detail while GPT-Live is speaking.
GPT-Live 1 also supports client delegation. Client delegation lets you choose the background model independently from GPT-Live 1. A basic session runs only the voice model. When deeper work is needed, your application can handle delegation-created with any text model on AI Gateway while the conversation continues.
Install AI SDK 7, @ai-sdk/openai 4.0.67 or later, and a WebSocket client:
This starts openai/gpt-live-1 without calling another model. Wait for session-started before sending audio.
You can call a text model for GPT Live to delegate to, then return the result on the commentary channel for it to speak. This example uses openai/gpt-5.6-sol, but you can substitute any text model available on AI Gateway:
Your application controls delegated work and its permissions, confirmations, and cancellation. Delegated model requests are billed separately through AI Gateway; voice-session usage continues while they run. These snippets omit WebSocket connection, event parsing, transcript assembly, audio streaming, and shutdown. See the GPT-Live guide in the AI Gateway docs for connection setup, audio streaming, and complete examples. For all audio models on AI Gateway, go to the model list.
You can now connect native Marketplace resources to custom environments. Previously, resource connections could only target production, preview, and development environments.
Choose custom environments when connecting a resource from the Vercel dashboard, Vercel CLI, or REST API. Vercel scopes the environment variables created by the connection to the selected environments.
For example, from the CLI:
Existing deployments do not change. Create a new deployment after connecting a resource or changing its environment scope.
Custom environments are available on Pro and Enterprise plans.
Read the docs for connecting from the dashboard, the CLI --environment flag, and the REST API envVarEnvironments field.
TypeSafe’s Jev model is now available through Netlify’s AI Gateway with zero configuration required.
Install @typesafe-ai/sdk and use it directly in your Netlify Functions — no API keys to create, no provider config, no base URLs to wire up. AI Gateway handles credentials automatically, and usage is billed to your Netlify credits like every other model in the gateway.
Jev is TypeSafe’s first “System One” model, and it works differently from the chat models you’re used to. Instead of generating prose, you send it your program state along with a set of typed questions, and it returns typed answers with calibrated probabilities. There are three question primitives: choice picks one option from a set, score rates against ordered levels, and noul returns a yes/no probability between 0 and 1. Answers are constrained to the options you declare, so there’s no JSON parsing or schema coercion on your end.
Every question in a request is evaluated in parallel against the same state, which means batching a dozen questions into one call costs little more than asking one. State and questions share a budget of roughly 32,000 tokens — about 150,000 characters of English text — and TypeSafe reports end-to-end response times of 70–500ms, making Jev a good fit for classification, routing, extraction, scoring, and guardrail checks on the request path. The SDK defaults to the jev-latest alias, currently jev-1.13.0, and requires Node.js 20 or newer.
Here’s a Function that routes an incoming contact form submission to sales, support, or spam:
import type { Config, Context } from "@netlify/functions";
import { choice, TypeSafeClient } from "@typesafe-ai/sdk";
export default async (req: Request, context: Context) => {
const client = new TypeSafeClient();
const { answers } = await client.systemOne({
state: await req.json(),
questions: {
team: choice("Route this contact form submission", {
sales: null,
support: null,
spam: null,
}),
},
});
return Response.json({
team: answers.team.choice,
requestId: context.requestId,
});
};
export const config: Config = { path: "/api/route", method: "POST" };
The choice helper declares the three possible destinations up front, so answers.team.choice comes back as one of them and nothing else, which makes the response safe to branch on directly. Each answer also carries a probability distribution and a confidence value, so you can act on high-confidence decisions and escalate the rest to a human.
Learn more in the AI Gateway documentation and the TypeSafe documentation.
Posted on 2026-09-17 by pgAdmin Development Team
Related Open Source
The pgAdmin Development Team is pleased to announce the release of pgAdmin 4 version 9.18. This release of pgAdmin 4 includes 29 bug fixes and new features, including fixes for four security vulnerabilities (CVE-2026-86861 through CVE-2026-86864). For more details, please see the release notes.
pgAdmin is the leading open-source graphical management tool for PostgreSQL. For more information, please see the website.
Notable changes in this release include:
Ctrl+Alt+B by default, does the same thing and can be changed through the new toggle_object_explorer preference.'unsafe-inline', and drop 'unsafe-eval'. style-src keeps 'unsafe-inline', because MUI and React inject runtime styles and inline style attributes that cannot carry a nonce, and development bundles have 'unsafe-eval' re-added automatically when DEBUG is set.get_user() fell back to reading the configured WEBSERVER_REMOTE_USER name from the inbound request headers when it was absent from the WSGI environment. Because a header is written by whoever sends the request, any client that could reach pgAdmin could assert any identity, including an administrator's, without presenting a credential. A header-asserted identity is now opt-in, restricted to a configured list of trusted proxies with an optional shared secret, and refused for accounts whose authentication source is not webserver (CVE-2026-86863).pg_dump argument vector as a bare positional value. Because getopt_long permutes arguments, a value beginning with a dash supplied further options such as --file, overriding the storage-confined output path; and because libpq expands a database name containing an equals sign into a full connection string, the same field could redirect the connection, and the password exported in PGPASSWORD, to a host of the caller's choosing. The database name is now passed through the PGDATABASE environment variable, which libpq never expands (CVE-2026-86864).--dbname and could likewise redirect the connection, and the exported password, to a server of the caller's choosing (CVE-2026-86862).save_file endpoint, which backs saving from the Query Tool and ERD: the requested path was validated with check_access_permission() and then opened with a plain open(), so a symbolic link planted in between was followed, writing outside the user's storage directory. This is the write sink that CVE-2026-7819's hardening of the separate upload path did not cover (CVE-2026-86861).Location header on to a destination the ALLOWED_LLM_API_URLS check was never applied to. This is hardening rather than a fix for an exploitable flaw, since returning the redirect at all requires control of a host already on the allowlist.Username when importing a non-shared server, which previously imported cleanly and left a server that libpq would silently authenticate as the OS account running pgAdmin rather than reject outright.UserMixin.is_locked() that pgAdmin's own is_locked() had been written against.dependLevel unset.getNodeAjaxOptions() so a wide table's Columns tab no longer fires one duplicate get_types request per column row.TABLESPACE pg_default clause from generated index SQL, which was invalid on a partitioned table.pg_service.conf) connection, which leaves host, port and username unset.SharedUsername when importing a shared server from a servers.json definition, instead of insisting on Username for every server.ProductVersion, and fix the existingSecret path in the Helm deployment template.SERVER_MODE is set, leaving desktop mode with internal authentication alone.Builds for Windows and macOS are available now, along with a Python Wheel, Docker Container, RPM, DEB Package, and source code tarball from the download area.
We’re seeing an unexpected boom in SaaS platforms right now. Stripe added more new platforms in the last three months than it did in the final six months of 2025. New platform businesses are up over 180% year over year during that same three-month period, and that pace is continuing.
We reviewed the platforms that went live over the last three months via manual review, LLM classification, and heuristic checks, and found that they’re legitimate businesses with real customer activity. Platforms that went live in January 2026 are also reaching $1 million in payment volume at a higher rate than any previous cohort over the equivalent period.
This trend contradicts market expectations from earlier this year, as we recently examined on Stripe Economics. Beginning in late January, software companies shed roughly $1 trillion in market capitalization in 30 days. Investors worried that agentic AI would commoditize software by making it faster and cheaper to build. The headlines called it the “SaaSpocalypse.” In the months since, however, much of that decline reversed. Since then, SaaS equity prices have returned to roughly their pre-sell-off levels. While equity markets price future expectations, Stripe’s payment volumes capture what businesses are doing now. Stripe Economics research found that weekly transactions for the 100 largest non-AI SaaS companies on Stripe showed a brief dip, followed by a swift recovery and continued growth—an indication that performance was not as affected as market sentiment initially indicated.
AI has made software easier to build, which means companies need more than a good product idea to stand out from a growing field of competitors. As software itself becomes increasingly commoditized, building around the specific needs of an industry offers entrepreneurs another path to differentiation. Many of the most successful platforms on Stripe take this approach, often drawing on years of experience dealing with the problems those businesses face. Aesthetic Record, for example, brings together medical records, clinical workflows, patient engagement, and compliance in a platform tailored to medical aesthetics practices. Nonprofit platforms have a different set of specialized needs, including managing donor relationships and event registration, issuing tax receipts, and more. Bloomerang embeds those nonprofit-specific workflows and relationship data directly into its CRM.
This kind of deep industry knowledge is tough to replicate. Our data shows that strong SaaS platforms—whether horizontal or vertical—tend to share three characteristics: they run key workflows, such as scheduling or inventory management; they retain valuable business information, like customer histories or pricing logic; and they connect day-to-day operations to money movement. The more central a platform becomes to those operations, the more indispensable it will be.
“A high-signal indicator of platform defensibility is what breaks the day the customer turns it off,” said Eric Noeth, partner at global private equity firm Advent. “If operations keep running, the product is more exposed. If the business stops—claims don’t pay, cars don’t sell, trades don’t settle—the product is defensible and hard to dislodge.”
AI is changing what SaaS platforms can offer their merchants and how quickly developers can build on Stripe. As of August 2026, over 55% of new Stripe integrations now involve some form of AI assistance. Better documentation, integration blueprints, and agent-friendly tooling are making it faster and easier to build a complete platform integration.
At the same time, platforms are using AI to make the workflows they already own more effective: automating intake and onboarding, improving scheduling, personalizing customer outreach, prioritizing collections on overdue payments, reducing risk, and taking action on behalf of merchants.
The impact is visible in the quality of integrations: the number of self-serve SaaS platforms on Stripe building complete integrations is up roughly 360% year over year, a leading indicator that more platforms are moving beyond experimentation and building the full capabilities their merchants need.
The SaaSpocalypse was a useful warning for the software industry. AI will make some products easier to build, easier to copy, and harder to price on a per-seat basis. But SaaS platforms that help businesses run core operations—and increasingly help them move and manage money—are more deeply embedded. Platforms can become even harder to displace by helping merchants access capital, store funds, and spend with cards.
To learn how Stripe supports platforms navigating these shifts, check out Stripe docs or contact our team.
GitLab.com hosts millions of projects for teams of every size that need a platform they can rely on. Demand is climbing quickly, and we expect platform load to grow several times over this year. Predictable limits are what keep GitLab.com fast for everyone on it, including the automation and agent workloads teams are building on the platform.
To hold that as we scale, we're updating how rate limits work. Starting October 19, 2026, rate limits on GitLab.com will align with your subscription tier. Free accounts and unauthenticated requests happen first, on October 19. Premium and Ultimate move in January 2027.
Limits align with your subscription. Free, Premium, and Ultimate subscription plans get their own limits, applied per user and per top-level group. Free takes effect October 19; Premium and Ultimate in January 2027.
Signing in gets you the full limit. An authenticated request is governed by your subscription plan below. A request that arrives with no credentials gets 60 requests per hour per IP address.
The per-plan limits are published in the rate limits documentation.
There will be two preview windows for Free and unauthenticated traffic, on October 7 and October 14 from 15:00 to 19:00 UTC. Signed-in Premium and Ultimate requests are not affected, since those limits do not change until January. Unauthenticated requests are capped no matter where they come from, including automation running against a paid account without credentials. A preview window (engineers call these brownouts) is a short, planned window where we switch the new limits on and then switch them back off. Nothing else about the service changes while it runs. The point is to give you a real look at how your own workloads behave under the new limits, weeks before they apply for good.
On October 19 the new limits take effect.
We set these limits by looking at how GitLab.com is actually used. Almost all users are already inside the new limits and won't notice any change. We also looked at what similar platforms allow. The Free limit and the anonymous allowance match the industry norm, while Premium and Ultimate are more generous, at levels other platforms reserve for their enterprise tiers or don't publish at all.
If you find that you are nearing a limit, authenticate your requests. It's usually a small change: Invoking a personal access token, an OAuth token, or the CI/CD job token all move a request off the anonymous 60 requests per hour and onto your plan's limits, which are much higher.
Next, look at how you're calling the API. Batching, caching, and pagination go a long way, and polling in a tight loop burns through your allowance fast. When you do cross a limit, you get an HTTP 429 back with a Retry-After header saying how long to wait, so a client that reads its own response headers mostly fixes itself. Backing off exponentially recovers faster than retrying immediately.
Upgrading to Premium or Ultimate increases the limits, too, per user and per top-level group.
If you need a higher limit on an ongoing basis, we are working on a way to purchase capacity above the standard plan limits, with details coming later this year. If that sounds like you, reach out to your account team or email limits@gitlab.com and tell us what you need.
These limits are set so no single workload can slow the platform for everyone else. Ordinary signed-in work isn't the target, and, for almost all users, a normal day looks identical. Browsing the UI, working in your editor, pushing and pulling with git, and running CI/CD within your plan all carry on exactly as they do today. Some heavy automation and a small number of Free-tier workloads will reach the new ceilings.
What doesn't change:
A reminder: Make sure to authenticate your requests to GitLab.com so your limits are higher.
How do I know whether this affects me?
Compare your busiest minute against the published limits for your plan. Most customers are not close. The quickest signal in the meantime is the RateLimit-Remaining header on your API responses, which tells you how much of your current window is left, and we are building a view in the product for release later this year that shows your usage against your plan's limits.
My project is public and busy. What are my options?
Three things help. Ask the automation that calls your project to sign in, which moves it onto its own limits rather than the anonymous allowance. Make the project private if the traffic is not coming from the audience you built it for, which stops anonymous callers reaching it at all. Or upgrade to Premium or Ultimate for much higher limits.
What if I am a member of several top-level groups?
Your user limit will be the highest subscription tier available to you. If you are a member of an Ultimate group, you will have access to the Ultimate limit.
What happens when I hit a limit?
You get 429 Too Many Requests with RateLimit-* headers and a Retry-After. Wait the interval it gives you, then retry.
My integration genuinely can't authenticate. What now?
Reach out to us at limits@gitlab.com. There are legitimate anonymous patterns, a public status badge being the obvious one. If you are concerned that an integration you own may be affected, contact us.
Does this apply to GitLab Self-Managed or GitLab Dedicated?
No. This is a GitLab.com-only change.
limits@gitlab.com.There’s no single best model for every software development task. Implementing a new feature, diagnosing a failed pipeline, and resolving security vulnerabilities all place different demands on the model handling them. GitLab Duo Agent Platform is expanding GitLab-managed model choice with three hosted open weight models: Kimi K3, GLM 5.3, and MiniMax M3.
Together with the frontier models already available in GitLab, your team now has more control over how you optimize for quality, latency, and cost, tuned to the needs of each workload. Point a hard task at Kimi K3 or GLM 5.3, which outperformed comparable frontier models in internal testing and costs less per call, or hand routine, high-volume work to MiniMax M3. Either way, your team gets up to 4x more calls per GitLab Credit than some comparable frontier models, with more AI model options to match cost to task complexity.
GitLab Transcend returns in October
Coding agents are increasing your speed of development, but your reviews, security policies, and release cycles still have to keep pace. Our Transcend event on October 6 will demonstrate how GitLab is helping teams close that gap and explore what it takes to carry the speed of agentic AI across the software lifecycle.
Different tasks in agentic development need different things from an AI model. A long-running refactor needs a large context window and deeper reasoning, while a routine, high-volume task is often better served by a faster, more cost-efficient model. Optimizing for one task type means giving up ground on the other.
As teams work through the technical tradeoff, whether they can actually access a new model emerges as an additional governance challenge. In regulated environments, every model and the infrastructure it runs on has to clear security, compliance, and internal review before the team can use it. Reviews and approvals move slowly enough that many teams standardize on one approved model, even when it isn’t the best fit for every task.
The cost of that compromise compounds. If your team standardizes on a higher-cost model to cover its hardest tasks, that same model will end up tackling routine work that could have been handled by a faster, lower-cost model. As agent workloads make repeated model calls across long-running, multi-step work, the gap between cost and performance grows.
Open weight models turn model choice into a lever for balancing cost with performance. They can potentially lower the inference costs of many agentic tasks, with performance in line with some frontier models.
The new open weight models hosted in GitLab offer notably more calls with one credit than many comparable frontier models already available on Duo Agent Platform. A call is a single request GitLab Duo Agent Platform sends to a model, and one agent action or chat message can trigger several calls.
Kimi K3 outperforms the default model behind most GitLab Duo Agent Platform features in our internal testing, at 1.82 calls per credit, just under the default model’s 2 calls per credit. GLM 5.3 gets 5 calls per credit while also delivering strong performance in internal testing, and MiniMax M3 builds on that cost efficiency at 8 calls per credit.
Together, they give your team another lever for balancing cost and performance, task by task. For more information on credit multipliers and a breakdown of credit usage per model, visit the GitLab Credits models page.
Each of the hosted open weight models now available in GitLab Duo Agent Platform brings its own strengths.
Watch this demonstration of available open weight models:
With a broad roster spanning open weight and frontier GitLab-managed models, your Duo Agent Platform implementation now has more freedom to optimize model selection for each task.
Your admins maintain centralized control over which models your teams can use, built directly into your software development workflows. While GitLab selects default models based on performance, the owner of your top-level group can set a different default model for each feature and curate which models teams can choose from, settings that apply consistently across every child group and project.
You can align model selection with your security, compliance, and infrastructure requirements through GitLab-managed, self-hosted, or hybrid AI deployment options. GitLab-managed models run entirely within GitLab's environment, with no infrastructure for your team to stand up or maintain. Self-hosted models run on infrastructure your team manages, keeping model traffic inside your own network. Hybrid deployments let your team mix both, running some models through GitLab and others on your own infrastructure, based on what each workload requires.
Before a new model joins the GitLab Duo Agent Platform roster, we evaluate it against internal performance and quality requirements. GitLab-managed models carry their own built-in assurance. Before selecting a vendor to host models for Duo Agent Platform features, GitLab puts it through a third-party risk management process to verify it meets a defined security bar. That includes a review of the vendor's security program and controls, such as access management, data governance, and risk management practices, along with third-party security attestations like a SOC 2 Type 2 report or active ISO 27001 certification, and periodic penetration testing with risk-based vulnerability remediation.
All three hosted open weight models are served through Fireworks AI, which meets that bar. For GitLab Duo Agent Platform requests, GitLab maintains a zero data retention policy with Fireworks, meaning model input and output data is discarded immediately after each response and is not stored for abuse monitoring.
With that governance and security foundation in place, your team can bring open weight models into your workflows with the same confidence you'd expect from any GitLab-managed model.
You can now select Kimi K3, GLM 5.3, and MiniMax M3 as optional models in GitLab Duo Agent Platform, or set one as the default model for a feature so your team uses it automatically. For a deeper look into model support in Duo Agent Platform and guidance on model selection, read the GitLab AI model documentation.
New to Duo Agent Platform? Start a free trial. Already on Premium or Ultimate? Turn on Duo Agent Platform.
We believe that there is an ongoing campaign targeting rust-lang members and owners of popular crates that is attempting to compromise devices and accounts in order to use them to publish malware.
A video call is set up for something positive — maybe for a job, maybe for a project, maybe for a contract opportunity — and then that's used as a vector to either get the target to install something on their computer (such as a purportedly missing audio codec) or execute another command (for example, via putting a command on the clipboard).
These attackers are setting up new but legitimate seeming company profiles, including plausible LinkedIn presences, in order to pass cursory inspection.
A previous attack of this form targeted many prominent Rust developers in
June, and, last month, the arrayref crate was briefly compromised through similar
attacks. At this moment we do not know if these are all a part of the same
campaign.
This attack style is known to be used by the DPRK, and has been seen outside of the Rust community as well.
Please take extra care in the near term. Be appropriately suspicious of cold outreaches, and ensure that any calls you have with new people are on platforms you trust — ideally, try to be the one who sets up the call on a platform you already use.
Please also re-check that your accounts look normal: MFA enabled, no unexpected logins on platforms that can track that, and so on.
If you have any concerns about your accounts, please reach out to help@crates.io (for crates.io account concerns) and/or security@rust-lang.org (for any other concerns). We're very happy to help.
We’ve released updates for multiple major MPS versions that fix several issues.
Check out all the updates in each particular version below:
The Projectional Agent Toolkit receives several practical improvements that allow agents to:
The release also expands agent guidance for editor tests, YouTrack issue creation, and reliable test execution. Together, these updates make agent-assisted projectional development more capable, predictable, and easier to operate.
Note: Remember to ask your agent to update the MPS skills in your project to benefit most from the changes.
Download MPS 2026.1.1 here.
See the full list of fixed issues here.
Download MPS 2025.3.2 here.
See the full list of fixed issues here.
Download MPS 2025.2.4 here.
See the full list of fixed issues here.
Download MPS 2025.1.4 here.
See the full list of fixed issues here.
Your JetBrains MPS team
A modern storefront can look perfectly healthy while malicious JavaScript works underneath: siphoning affiliate revenue, hijacking searches and clicks, tampering with analytics, or asking a remote server what to execute next. Pages load, products appear, and checkout works — yet the browser may be quietly doing something the site owner never authorized.
That is the blind spot our Client-Side Security machine learning (ML) model is built to expose. This post follows four operations, spanning eight payloads, that our Page Shield ML uncovered in the wild.
The detection of these malicious payloads was automated; humans verified each finding only after the system had flagged it. When we afterward reviewed the campaigns using security scanning tools, seven of the eight payloads were entirely absent from VirusTotal, and URLScan returned no malicious verdict for any of them. Page Shield ML, meanwhile, caught all eight in live traffic.
For instance, while security research documented the broader Lnkr family years earlier, one specific payload version sat indexed by URLScan for nearly two and a half years with “No classification,” including during a direct scan in January 2024. Only in this case had VirusTotal ingested the payload earlier: while it currently flags the script as malicious, public history does not reveal when that verdict was first assigned. Meanwhile, Page Shield ML independently surfaced those exact bytes live on an online retailer's storefront. More broadly, a hash can be known long before the code behind it is classified as malicious. If your defense waits for that label, you are already late. You need ML that can unravel the JavaScript itself and judge it at scale.
Indeed, seeing a file is not the same as understanding it. The tricky part was that the four operations shared no universal signature or common concealment technique. One remained dormant unless the device, country, time, referrer, or browser state matched what it was waiting for. Another concealed a clickless affiliate request within an invisible iframe. Others intercepted clicks, suppressed monitoring, or conditionally loaded additional code from remote servers. To catch them, you have to watch how those pieces work together: when the script wakes up, what it hides, what it intercepts, and what it fetches next. Checking the page once is not enough; as these cases show, such scripts are built to stay quiet until the right victim shows up. That is why ongoing browser visibility makes the difference between catching an attack and missing it entirely.
The same GNN (graph neural network) that flagged the four operations in this post had already caught malicious npm packages and an in-the-wild Magecart payment skimmer. The GNN does not treat JavaScript as a flat chunk of text; it reasons through the code as a graph: a syntax tree connecting code symbols and exposing what calls what, what the attacker tried to bury, and what still phones home. That structure helps it recognize suspicious patterns across minification, renaming, and some obfuscation without relying on a known URL or byte signature.
The few scripts that the GNN flags as malicious (under 0.3% of all analyzed traffic) go to a lightweight large language model (LLM) on Workers AI for a live second opinion. This further reduces false positives while keeping recall high. When the LLM corroborates the GNN, customers are alerted.
To investigate the most complex scripts at scale, we use a cohort of frontier models, which we call teachers (an ensemble of automated judges). The cohort draws leading models from around six different families, including open-weight models running on Workers AI. We spin up each as an agent to analyze the same suspicious script in its own fresh, independent session. When useful, their agentic tool access lets them use a restricted JavaScript evaluator to unpack small snippets and reveal concealed behavior. We will soon extend this workflow with Cloudflare Sandbox for deeper analysis in isolated environments.
The frontier models sometimes disagree, especially on the most intricate scripts. We treat that disagreement as signal, not noise. Each label becomes a vote, weighted by the model's score in the Artificial Analysis Intelligence Index, producing a probability distribution over four labels: benign, payment skimming (magecart), other malware, and cryptomining. Human reviewers therefore need only examine scripts flagged as malicious or lacking a clear two-thirds majority. We then feed those label distributions back into GNN training, helping it distinguish ever more nuanced cases. This feedback loop is still partly manual, though we are starting to automate it.
These four operations do very different things, from commission theft to stolen analytics on shoppers the store already paid to acquire. Stealing a commission is not like skimming a credit card; likewise, hijacking search is not like stealing a password. If an ML model only knows one of those tricks, it will sleep through the others. Instead, our Page Shield ML has to stay attuned to every kind of hostile behavior.
Now, let’s dig deeper into each operation and how it worked.
Picture a quiet Sunday afternoon: a shopper on a phone taps a product. Instead of following the tap normally, the script opens a product or campaign landing page from an attacker-preselected list in a new tab and sends the original tab through an affiliate route. The storefront still appears to work. If the shopper completes a purchase (either then or later), the detour hijacks the attribution, crediting the sale (and any resulting commission) to an account that did not earn the referral.
The shop could pay an unearned commission to an account that did not bring the shopper. Worse, if a legitimate partner had made the referral, the forced request could misattribute it, diverting credit and a potential payout from the partner who did the work. The damage could outlast one commission: partners who stop trusting the attribution system may also stop trusting the retailer behind it.
Qualified mobile visitor → intercepted product tap → script-selected page opens in new tab + original tab follows attacker’s affiliate route
We found five related script builds: two active and three paused when captured. Each active variant uses a different set of gates before it acts, checking things like the visitor’s device and local time, whether the trick has run recently, whether a product button has appeared, and whether someone actually clicks it. That maze of rules keeps the malicious behavior out of sight during a brief automated visit unless the variant’s specific conditions are met. The active scripts use a MutationObserver (a JavaScript API) to watch for product tiles and buttons that dynamically appear after the page is first loaded. This lets them intercept clicks on those late-arriving elements, while a crawler that loaded the HTML once and stopped there could miss the redirect path entirely.
In the active later variants, the script intercepts a qualifying click and writes a three-day cooldown to localStorage (staying dormant on that device for days). It then executes a dual-tab maneuver: popping an attacker-chosen product page into a fresh tab to keep the shopper engaged, while the original tab takes a quick, unnoticed round-trip through the attacker's affiliate tracking link and back to the shop, to plant the attacker’s attribution cookie in the background. Console masking and self-defending source checks make inspection harder, while the cooldowns and narrow schedules limit how often the malicious path can appear during otherwise normal shopping.
The following sanitized excerpt shows how the payload hooks dynamic product tiles and executes the dual-tab detour. We simplified identifiers, reformatted the code, and neutralized destination URLs for readability.
The paused builds showed how the campaign could go dark without removing the script. Their embedded configuration set status: "paused", so they exited before installing click handlers. These paused scripts carried different per-shopper cooldown configurations (3, 4, and 5 days). One of the paused scripts even recorded a version-history comment explicitly documenting that the campaign was paused after Black Friday.
To reach visitors in the first place, the operation leveraged the site's marketing supply chain: the third-party scripts and tag managers embedded by e-commerce sites to track ad campaigns and analytics. One confirmed delivery path ran through two otherwise ordinary tag managers: Google Tag Manager → another tag manager → malicious script. That is how the payload reached the browser, not proof that either tag manager was compromised.
The attacker even disguised the domain hosting the script to pass a quick marketing review. One delivery host hid in plain sight: adtargett[.]com differed by a single “t” from adtarget[.]com, an advertising domain registered in 1998. The lookalike was registered in 2025 and, when we checked, its homepage called itself “Adtarget.com - Performance Marketing Agency.” This is typosquatting: by mimicking a real ad agency, the host blended in with routine marketing tags, quietly serving the malicious payload that hijacked shopper clicks and redirected them through affiliate payout links.
While the first scam still needed a click, this one requires even less. A shopper can open a booking page, linger over the product options, and never touch an ad. In the background, however, the script might have already sent an affiliate request that could make a later sale look as though someone else had referred the shopper. Indeed, when the script’s conditions are met, the payload sends that request through a hidden iframe or a link that clicks itself.
For the affected tourism business, the attack could corrupt the economics of customer acquisition: a legitimate booking or purchase could be credited to an unearned affiliate account. The code proves covert, automated affiliate requests, but whether any specific request resulted in completed attribution, account crediting, or paid commission in practice remains unobserved.
Time-gated browser → covert affiliate request (off-screen iframe) → 1-hour throttle cookie → when blocked, automated hidden-link click fallback
The script conceals the affiliate request in two layers: selective execution (a pre-flight network gate and hourly schedule), and stealth delivery (an off-screen iframe). The first layer is surprising because its country labels are disconnected from actual geography: neither the shopper’s nor the shop’s location drives the choice.
First, the script calls a public IP-based geolocation service but ignores everything it returns, including the shopper’s country. We could not determine why it required a successful response while ignoring the returned data; this may have been intended to confuse investigators or simply been a remnant of an earlier version. Interestingly, if the geolocation request fails, the script silently stops; its promise chain ends with .catch(() => {}). Although intent is unproven, this fail-closed behavior could help the script evade network-restricted sandboxes.
Next, instead of using the fetched geolocation data, the payload contains three TradeDoubler (an affiliate-marketing network) configuration objects labelled {AU, US, and UK}. These settings blocks are embedded in the code, and each contains an affiliate URL and start and end times. The script computes Asia/Kolkata time in JavaScript, checks those configured time windows, then applies fixed odd/even-hour rules to choose one of the three or else skip the affiliate request for that run. The choice is deterministic.
Together, the schedule and browser-state checks create time-gated selective execution, a form of cloaking. When those conditions do not line up, the affiliate behavior stays dormant, so a one-off inspection can miss it.
Once the script chooses a configuration, it writes a local cookie named affiliateClicked_<market> as a one-hour retry throttle so it will not re-fire for that region right away (this is a client-side throttle to avoid noise, not an affiliate-network attribution cookie). Next, it loads that affiliate URL in an off-screen iframe with the referrer suppressed. The iframe is the primary delivery path, but it carries an aggressive fallback: if the iframe errors or fails to finish loading after one to two seconds, the script creates a hidden link (<a>) without a target attribute and clicks it programmatically, which could navigate the user's active tab. To the qualifying shopper, nothing seems out of place: they never see an ad, never have to click, and can close the tab as if nothing happened.
As for the script’s obfuscation, it is simple but effective: even property names are assembled one character at a time. The following sanitized excerpt shows the payload creating an invisible off-screen iframe. We renamed key identifiers and reformatted the code for readability. The destination has been removed.
Years ago, the Lnkr malware family made the news by hiding inside shady browser extensions, intercepting Google and Bing searches to redirect results and pocket ad money. Now, attackers repurposed the codebase to plant a backdoor into an online retailer’s website.
Because the script was running on a shop rather than a search engine, its old redirect tricks stayed dormant. This time, the script was used to send telemetry back to the attacker. More dangerously, it gave the attacker a remote doorway to arbitrarily download and run fresh JavaScript in customers' browsers whenever they wanted, without touching a single file on the server. It even carried an old trick from its extension days: shutting itself off if someone typed words like “virus” or “popup” into Google. From the outside, the store kept selling without a hint that anything was wrong.
The shop lost control over what code runs in its customers' browsers. Attackers were secretly tracking visitors' sessions and had a direct backdoor to push and run any JavaScript they wanted on the storefront at any time.
HTML-referenced script → analyst evasion gates → parallel host-gated branches (dormant search vs. live backdoor) → arbitrary remote JavaScript execution
Unlike campaigns delivered through tag managers, this script was directly embedded into the merchant’s HTML. We could not determine the exact initial intrusion vector; in practice, direct HTML insertions usually happen through compromised store admin credentials, an unauthorized template edit, or an infected third-party theme or plugin.
Under the hood, the script is a modular toolkit carrying both active and dormant code. Its older modules (transparent click overlays, search-engine query interceptors, extension-store link rewriters, and redirects for typosquatted domains, like buking[.]com instead of booking[.]com) only wake up on specific target sites, so they stayed turned off on this storefront. Several embedded domains (sugabit[.]net, votetoda[.]com, cdnpps[.]us, and telemetry endpoint hanstrackr[.]com) sat inside these disabled modules.
On the shop, the active branches focused on evasion, telemetry, and remote control:
localStorage, permanently silencing the script on that analyst's machine so repeated tests would find nothing. While originally built to dodge analysts on search engines, as far as we could determine, this check was hard-coded specifically to Google search URLs and remained dormant on the merchant's storefront.scrprime[.]com, youronlinesearches[.]com, jullyambery[.]net) remained identical to older captures, what those endpoints returned was entirely up to the attacker. The script could phone home visitor telemetry, ask those servers for new instructions, and pull down fresh JavaScript directly into the shopper's browser. Effectively, this gave attackers a live backdoor to run arbitrary code on the storefront. We could not determine what second-stage payloads were served in practice.All in all, a static snapshot of the site showed only the normal storefront, while the underlying state checks, anti-analysis traps, and remote-loading branches exposed the backdoor.
The shop already paid to bring this visitor in from a mobile ad or marketing campaign. The malicious script lets that visit through, then cuts off the merchant's visibility. Analytics go dark, the live support chat vanishes, and a rogue observer starts recording telemetry on the very session the store just bought.
Behind the scenes, the payload refuses to run unless that visit matches an elaborate set of conditions: the exact target storefront, a narrow mobile screen, and a campaign tag during the first two pages of the visit. It stays dormant on laptops, corporate networks, cloud providers, and VPNs, so the engineers most likely to debug the page never see it fire. The script also stays dormant across selected US cities and regions, backed by a handcrafted denylist of 325 IP strings to dodge automated scanners and security analysts. Only then does the script attempt to tear down the shop’s monitoring, substitute replacement advertising and analytics identities, and phone home. A second look from the wrong device or network will never trigger it. All the while, the storefront keeps selling.
For a direct-to-consumer retailer, the malware specifically targeted high-value traffic the store had paid to acquire through paid-search and marketing campaigns (ppc, cpc, sms, paid). Those customers could still buy. Yet the shop faced three clear threats: diverted advertising attribution and unearned publisher payouts, the loss of critical session analytics across nine observability tools, and the suppression of the help chat and contact form (preventing shoppers from asking questions or reporting anomalies). Dynamic analysis in a sandboxed browser environment confirmed that the replacement analytics script loaded and fired a tracking beacon (an invisible network request sent to log visitor activity), but whether the attacker successfully captured session telemetry or diverted ad revenue in practice remains unproven.
Campaign-tagged mobile arrival → multi-tier cloaking & network gates → monitoring sabotaged → advertising, analytics, and support controls rewritten
To blend into the store's marketing supply chain, the attacker delivered the payload from sdk-amazonaws[.]com, a lookalike domain registered in 2024 and wholly unaffiliated with the official Amazon Web Services domain (amazonaws.com, registered in 2005). To compound the deception, the attacker prefixed the domain with a subdomain mimicking a popular e-commerce marketing platform too. This stacked, double-trusted-brand typosquat forged a convincing disguise, engineered to slip past quick tag reviews. Neither Amazon Web Services nor the impersonated marketing platform was involved in the attack or suffered any compromise.
Once loaded in the browser, the script executed an exceptionally dense gauntlet of cloaking gates before triggering its main payload:
window.location.hostname matched the specific merchant host it was built to target (exiting immediately anywhere else), ensured the current window was top-level (not an embedded iframe), and checked that the path did not contain /challenge. It also verified that tracking marker cookies (_cart_dr and logoalt) were not already present in the browser.477 pixels (a handheld smartphone). Furthermore, the visitor had to arrive via a first-touch (the visitor's initial referral) campaign tagged with one of six specific UTM mediums (Urchin Tracking Module, standard URL tags used to track marketing campaigns): ppc, cpc, sms, paid, flow, or campaign. It also had to be the first or second page load of their session. Curiously, while the code contained a nominal non-UTM path, it required the session page count to be simultaneously greater than -1 and less than -2 (a mathematical impossibility that left that branch completely unreachable). This could be yet another diversion technique or a code change leftover.Math.random() <= threshold) to make execution appear intermittent. However, when we solved the deobfuscated arithmetic, the threshold reduced to exactly 1. Because JavaScript's Math.random() always returns a value strictly below 1, this gate always evaluates to true. Like the unreachable non-campaign branch, this is a condition that never actually decides anything. It may be a leftover throttle, or purposeful camouflage for anyone reading the obfuscated math. Either way, the captured payload never used it to skip a qualifying visitor.US-NY, US-CA, and US-NHDD) or from cities named San Francisco, Plymouth, Compton, Hopkinton, and Lafayette.!denylistString.includes(visitorPrefix).In stripped-down pseudocode, the multi-layer primary activation funnel looks like this:
Sabotaging observability and hijacking identities:
Only after every primary gate passed did the script execute its payload:
GTM-<redacted>). In remaining inline scripts, it string-replaced references to these tools with undefined dummy identifiers (hji0) so calls to them failed silently, attempting to blind the store's error reporting and monitoring.google_ad_modifications, adsbygoogle), tore down existing ad slots (ca-pub-<original>), and loaded Google Ads under a replacement publisher ID (ca-pub-<replacement>). It then injected a new Microsoft Clarity session-replay script configured with a rogue, replacement project ID.Simpler independent beacons and the 600-day marker:
In sharp contrast to the elaborate primary cloak, the payload also contained secondary beaconing branches (standalone routines that quietly ping an external server to confirm a visit) that completely bypassed the viewport, hostname, campaign, geography, and IP gates. If the visitor was on their second page or beyond, the script wrote a persistent cookie (_cart_dr=1) with an expiry of exactly 600 days (51,840,000,000 milliseconds) and fired an invisible zero-pixel image request to a remote telemetry endpoint on maper[.]info (a tracking beacon used to log that the browser reached this step).
A separate branch checked for an alternate marker (_logo_alt), which would trigger a second telemetry .png beacon (a cookie this script looked for, but never wrote itself; likely planted by a companion script). This gave the attacker a simple, persistent hit-counter to log basic traffic for all visitors (IP and User-Agent logged at the endpoint) across the entire store, while keeping their high-risk ad-hijacking routines strictly hidden behind the mobile cloak (high-value paid arrivals). It shows why analyzing only one visible effect does not reveal the full reach of a multi-purpose payload.
We are publishing these indicators to help security teams and researchers detect and hunt these campaigns across their own environments. All indicators are drawn directly from captured payloads and their network connections. Listed URLs are defanged. Some indicators have been withheld or generalized because publishing them could inadvertently divulge the identities of affected organizations. Listed domains reflect infrastructure observed participating in the delivery, redirection, or telemetry chain during these attacks; inclusion does not imply that a shared service or hosting provider is exclusively malicious.
Taken together, the operations tell one escalating story: attackers changed the objective, delivery path, and disguise, but the browser still had to execute their logic. Four lessons stand out.
Continued at the source.
Kubernetes v1.37 brings important storage security features: emptyDir permission modes and bind mount options. They help application programmers and security professionals implement rigorous security policies, for example, prohibiting deletion of files across containers or execution of arbitrary binaries from writable volumes, directly in Kubernetes without any complicated circumvention.
Before diving into the new Kubernetes features, let us briefly review the low-level Linux security mechanisms that make them possible.
When Linux mounts or remounts a directory, Virtual File System (VFS) flags control what actions are permitted on that filesystem:
noexec: Do not permit direct execution of any binaries on the mounted filesystem.nosuid: Do not allow set-user-identifier or set-group-identifier bits to take effect.nodev: Do not interpret character or block special devices on the file system.Standard Unix permissions regulate access across three scopes: Owner, Group, and Others (e.g., 0755 or 0777).
Beyond standard read, write, and execute bits, Linux supports the sticky bit (as in mode 01777).
When applied to a directory, the sticky bit ensures that a file inside that directory can only be deleted or renamed by the file's owner or root. This is essential for shared writable directories like /tmp.
Why does Kubernetes need bind mount options and emptyDir permissions?
The primary goal of these features is to increase the security of Kubernetes workloads by allowing security-related bind mount options on volume mounts. By default, volumes are bind-mounted into containers by the container runtime and kubelet without noexec, nosuid, or nodev flags. This default can undermine security. For example, with noexec missing, a compromised process can use any writable volume (emptyDir, PersistentVolume, etc.) to download, chmod +x, and execute arbitrary binaries even when the container has a read-only root filesystem (readOnlyRootFilesystem: true). Supporting noexec, nodev, and nosuid gives users a native way to harden volume mounts to match security benchmarks and policy.
The gap is most visible with emptyDir volumes, which are the most common writable volume type and have been the subject of multiple security findings:
emptyDir was flagged in an audit but remained unresolved until now.emptyDir with noexec represents a security failure.However, the same gap applies to all volume types. PersistentVolumes have a mountOptions field, but those options are filesystem-level flags applied by the CSI driver at the node, so they do not reliably translate into bind mount flags inside the container. Previously, there was no mechanism to set noexec, nosuid, or nodev on the bind mount that the container runtime creates for any volume type.
Additionally, the emptyDir volume type defaults to creating directories with a hardcoded mode of 0777.
This previously meant that any process that can discover the volume could read, write, and delete anything in the volume, regardless of who created it.
You could - and still can - use an initial container to set a different access mode, but this is more complex, and hard to verify for compliance.
This causes real problems:
emptyDir could not prevent one container from deleting another's files. The sticky bit (01777) solves this, but there was no native way to set it./tmp directories to have the sticky bit set (mode 01777). Without native support for setting the emptyDir mode, users had to use init containers or alternative volume types to meet this requirement.0750 for owner and group only) have to use init containers running chmod, which adds unnecessary complexity.The emptyDir volume type was a notable gap.
As one of the most common writable volume types in Kubernetes, it had no way to control its creation permissions.
Application developers, working closely with security engineers, are responsible for maintaining the security posture of their applications and ensuring workloads do not pose risks to the wider infrastructure. These features allow development teams to confidently address critical security scenarios:
Preventing Privilege Escalation on Writable Mounts: An application developer configuring temporary workspace volumes (like emptyDir or /tmp mounts) can ensure they are mounted with nosuid and noexec. This guarantees that even if the application is compromised and a malicious payload is downloaded, the workload cannot execute the payload or use it to escalate privileges on the node.
Securing Shared Scratch Space in Multi-Container Pods: A developer configuring CI/CD pipeline pods often needs multiple containers (e.g., a builder container and a sidecar logger) to share a workspace. By setting mode: 01777 on an emptyDir, the developer ensures the shared workspace behaves like a traditional Unix /tmp directory. Each container can write files independently, but a compromised process in one container cannot delete the build artifacts produced by another.
Enforcing Principle of Least Privilege for Application Data: An application developer deploying a database pod can lock down access to the database's temporary storage. By setting mode: 0750 on the emptyDir, the developer ensures that only the specific database user and group can read or write to the volume, explicitly denying access to any other processes or sidecars in the same pod.
Note: Both features are behind Alpha feature gates in Kubernetes v1.37. To use them, enable VolumeBindMountOptions and EmptyDirVolumeMode on the API server and kubelet.
This full Pod manifest mounts an emptyDir volume at /tmp with bindMountOptions: [noexec, nosuid].
apiVersion: v1
kind: Pod
metadata:
name: hardened-bindmount-pod
namespace: default
spec:
os:
name: linux
containers:
- name: hardened-app
image: alpine:latest
command: ["sleep", "3600"]
securityContext:
readOnlyRootFilesystem: true
volumeMounts:
- name: temp-storage
mountPath: /tmp
bindMountOptions:
- noexec
- nosuid
volumes:
- name: temp-storage
emptyDir: {}
emptyDir volume permission mode with sticky bitThis full Pod manifest creates an emptyDir volume using mode: 01777 to enforce standard Unix /tmp sticky bit protections across containers.
apiVersion: v1
kind: Pod
metadata:
name: hardened-emptydir-pod
namespace: default
spec:
os:
name: linux
containers:
- name: app-container
image: alpine:latest
command: ["sleep", "3600"]
volumeMounts:
- name: shared-tmp
mountPath: /tmp
volumes:
- name: shared-tmp
emptyDir:
mode: 01777
To verify that these features are actively enforcing restrictions, you can run kubectl exec into the container. The following examples simulate attempts to perform actions that are successfully blocked by these features.
Attempt to write and run a script on a volume mounted with noexec:
# 1. Exec into the pod
kubectl exec -it hardened-bindmount-pod -- sh
# 2. Create an executable script on the mounted volume
cd /tmp
echo '#!/bin/sh' > test.sh
echo 'echo "Executing untrusted code..."' >> test.sh
chmod +x test.sh
# 3. Attempt to run the script
./test.sh
Expected result:
sh: ./test.sh: Permission denied
Even if an executable file is created, the Linux kernel refuses execution because MS_NOEXEC is enforced at the bind mount level.
Attempt to delete another user's file in an emptyDir with 01777 permission mode:
# 1. Exec into the pod
kubectl exec -it hardened-emptydir-pod -- sh
# 2. Verify directory permissions on /tmp
ls -ld /tmp
# Output: drwxrwxrwt 2 root root ... /tmp (Notice the 't' indicating sticky bit)
# 3. Create a file as the guest user
su -s /bin/sh -c "touch /tmp/guest_file" guest
# 4. Attempt to delete that file as nobody
su -s /bin/sh -c "rm /tmp/guest_file" nobody
Expected result:
rm: can't remove '/tmp/guest_file': Operation not permitted
The kernel blocks deletion because the sticky bit (01777) restricts file removal strictly to the owner of the file.
Keep these key details in mind as you begin using these features. Full details are available in the official documentation for bind mount options, emptyDir volume mode, and emptyDir volumes.
bindMountOptions or do not set an emptyDir mode, you get standard default behaviors (like 0777 permissions) exactly as before.bindMountOptions works with emptyDir, PersistentVolumes, CSI volumes, projected volumes, ConfigMaps, Secrets, and more. The only exception is image volumes, which are explicitly unsupported. The mode field works with all emptyDir medium types: default (disk-backed), Memory (tmpfs), and HugePages.bindMountOptions): The container runtime must support the CRI mount_options field and advertise it via runtimeFeatures.
The scheduler uses node declared features to avoid placing pods on incompatible nodes.
If a pod reaches such a node anyway, the kubelet rejects it. There is no silent degradation.
However, using mode for an emptyDir does not require runtime support.mountOptions apply at the storage layer via the CSI driver. The new bindMountOptions controls bind mount flags applied inside the container by the runtime. They operate at different layers and do not conflict.fsGroup is set in the pod's security context, the group permissions applied by fsGroup will override the mode specified for the emptyDir volume.
This is the same behavior that exists for defaultMode on Secret and ConfigMap volumes.noexec, nosuid, nodev, and Unix permission modes are Linux concepts.
bindMountOptions has no effect on Windows nodes.
On Windows, the mode field is also skipped for emptyDir volumes, since Windows does not support Unix-style file permissions.emptyDir mode: if the API server has the gate enabled but the kubelet does not, the field is accepted but ignored - the kubelet falls back to 0777. For bindMountOptions: the scheduler uses Node Declared Features to prevent placing pods on nodes without runtime support; if a pod reaches such a node, the kubelet rejects it rather than silently ignoring the options.VolumeBindMountOptions: Controls bind mount flags on volume mounts.EmptyDirVolumeMode: Controls creation permission modes on emptyDir volumes.These new features are driven by SIG Node and SIG Storage.
You can find more details in the KEPs for these enhancements: KEP-5855 (bind mount options) and KEP-5502 (emptyDir permission mode).
Reach out to SIG Node:
Reach out to SIG Storage:
Antoine du Hamel
d414624dce] - (SEMVER-MINOR) crypto: add a generic MAC API (Filip Skokan) #655537ac458f802] - (SEMVER-MINOR) crypto: discover ciphers from OpenSSL providers (Filip Skokan) #654847c7e95a3bd] - (SEMVER-MINOR) crypto: discover hashes from OpenSSL providers (Filip Skokan) #654842d2f4f6d1a] - (SEMVER-MINOR) ffi: enable module by default (Matteo Collina) #65475657415c6df] - (SEMVER-MINOR) lib: implement node:bench (James M Snell) #65606ebcfec2f0c] - (SEMVER-MINOR) perf_hooks: implement Histogram meanCI API (James M Snell) #65606e1913630c3] - (SEMVER-MINOR) perf_hooks: add CBOR export/import for histogram exchange (James M Snell) #654348e9151c9f2] - (SEMVER-MINOR) src: let embedders supply a builtin code cache without a snapshot (Shelley Vohr) #65352889854f18b] - (SEMVER-MINOR) src,lib: implement experimental DTLS API (James M Snell) #631825198142c59] - (SEMVER-MINOR) vfs: integrate with CJS and ESM module loaders (Matteo Collina) #636535af9d72e7f] - (SEMVER-MINOR) worker: add support for Web Workers (Aviv Keller) #6489459dcad3f44] - (SEMVER-MINOR) benchmark: implement node:bench version of bench tools (James M Snell) #656066bdb6dd8fa] - benchmark: fix max-regressions detection in compare.js (James M Snell) #6558749d40091ba] - benchmark: add --analyze option to benchmark/scatter.js (James M Snell) #65594bd40a9a19a] - benchmark: add crypto class benchmarks (Filip Skokan) #655186bff2238f5] - buffer: pad aligned allocations by a multiple of 8 (Lazizbek Ergashev) #6560537d12946dd] - buffer: prevent abort on indexOf with lone surrogate needle (Rafael Gonzaga) #6543002f8eb3b2d] - build: add --shared-perfetto flag (Antoine du Hamel) #65614fe7032ec41] - build: enable V8 gdb/lldb plugin support (Chengzhong Wu) #65786109f2caf80] - build: allow linking shared dependencies in the GN build (Shelley Vohr) #6579755213fd8a8] - build: enable the V8 sandbox in shared-cage builds (Shelley Vohr) #62237120929a1b3] - build: add --shared-highway configure flag (Antoine du Hamel) #65686c99af0942b] - build: add --shared-abseil configure flag (Antoine du Hamel) #65682431c2bf0eb] - build: define V8_CONTIGUOUS_COMPRESSED_RO_SPACE for shared cage (Shelley Vohr) #65464b6b5764c6e] - build: remove obsolete configure flags (Chengzhong Wu) #65456a623ee29d9] - build,src: make --use-largepages a no-op (Joyee Cheung) #653892f494cb7aa] - crypto: fix multi-prime RSA JWKs (Filip Skokan) #65649501d8ac815] - crypto: cache valid ECDH key pairs (Filip Skokan) #656153fd905ea8f] - crypto: add strict mode to --force-fips (Filip Skokan) #6564554b3cdb805] - crypto: add FIPS indicator diagnostics channel (Filip Skokan) #656453e6e931fc9] - crypto: fix public PKCS8 export error (한국) #65609d414624dce] - (SEMVER-MINOR) crypto: add a generic MAC API (Filip Skokan) #65553eef579047c] - crypto: validate JWK usages before key_ops (Filip Skokan) #655509a9b137a24] - crypto: fix private SPKI export error (Filip Skokan) #655509941fb5dab] - crypto: fix RSA-PSS oversized salt handling (Filip Skokan) #6555009b431596a] - crypto: correct CCM decryption FIPS error message (kyungrae) #65450e6b34e6f7a] - crypto: harden X509Certificate state (Filip Skokan) #65518a884b6ccbc] - crypto: optimize key slot caching (Filip Skokan) #65518fb512086d3] - crypto: add null checks for OPENSSL_INIT_new() (Nora Dossche) #63457656504ffc0] - crypto: prevent Hmac.digest() from returning uninitialized memory (Matteo Collina) #6511215e7884732] - crypto: avoid throwing CryptoKey brand checks (Filip Skokan) #655033302bec74a] - crypto: avoid throwing KeyObject brand checks (Filip Skokan) #655037ac458f802] - (SEMVER-MINOR) crypto: discover ciphers from OpenSSL providers (Filip Skokan) #654847c7e95a3bd] - (SEMVER-MINOR) crypto: discover hashes from OpenSSL providers (Filip Skokan) #654840539e90110] - deps: update googletest to 283c17563fe7a1111cd7f581aa5d541e8baeff2f (Node.js GitHub Bot) #65833e34af820da] - deps: update simdjson to 4.6.11 (Node.js GitHub Bot) #658345fb7ffebc2] - deps: update zlib to 1.3.2.1-motley-285e94b (Node.js GitHub Bot) #6583505cdf1db62] - deps: V8: backport 1a0089053443 (Jakob Linke) #657648910f7914a] - deps: V8: backport c9c0abfa51f0 (Jakob Linke) #65764e7fd388424] - deps: V8: backport ebd15783b7ba (Marja Hölttä) #6576482715158b8] - deps: V8: backport f3d4d458fe59 (Olivier Flückiger) #657027003159885] - diagnostics_channel: lazily create tracing context (Romain Lanz) #65513a97def782f] - doc: clarify node:bench significance policy (James M Snell) #65631c74091647d] - doc: clarify isolation modes for node:bench (James M Snell) #656313ef81a676c] - doc: clarify measurement integrity details of node:bench (James M Snell) #656316b565d7b2c] - doc: clarify security triage dispositions and permission boundaries (Rafael Gonzaga) #65436724981287c] - (SEMVER-MINOR) doc: move histogram.burnRate to correct location in doc (James M Snell) #65434f8b627c09b] - doc: fix default limit of maxHeadersCount (Aryn H) #65472aa3d94fc1d] - doc: fix return types for sync methods (Chiang Fong Lee) #58575fe9f452ddd] - doc: document REPL DEP0185 throws and uncaught-exception behavior (Adrián Estrada) #649936b84ee66fa] - errors: validate constructor name (Christian Aurich) #65607ed1b629901] - events: fix weak listener retention overwrite (Aryan) #640247b4c17f723] - ffi: throw on missing memory helper arguments (Soul Lee) #65500cf78d4260e] - ffi: include SharedArrayBuffer in error message (Donghoon Kang) #657352d2f4f6d1a] - (SEMVER-MINOR) ffi: enable module by default (Matteo Collina) #65475bdcb6def4e] - ffi: validate DynamicLibrary getter receivers (Trivikram Kamat) #654155d24a8fe37] - fs: copy directory trees for fs.cp() on the thread pool (Shelley Vohr) #6548840bdf32d91] - fs: give directories created by cpSync the source directory's mode (Shelley Vohr) #654886ac1bc040f] - fs: write files in one thread pool round trip (Shelley Vohr) #65489b4b89c08c1] - fs: handle recursive watch setup races (Filip Skokan) #6569954a1311d9e] - fs: apply nocase to literal glob exclude patterns (Yusuke Hayashi) #648172130460831] - fs: improve performance of recursive directory read (Aviv Keller) #65524ab7f4c88b7] - fs: do not descend into symlinks for ** unless following symlinks (RafaelGSS) #65435e990324e7a] - fs: watch directories, not files, in recursive fs.watch fallback (Shelley Vohr) #65486b6db21972a] - fs: preserve directory timestamps in cp (Abhinandan Kumar) #65540d418ca140e] - fs: fix recursive watch error handling (Filip Skokan) #65635ea372bf9c9] - fs: cancel in-flight stat on abort (Mert Can Altin) #63142458664073e] - fs: fix rmSync error messages for non-ASCII paths (Yeaseen) #6123334b38235c3] - fs: add maxDepth option to glob (Alexander Lichter) #64003e3cc0d9313] - http: fast-forward teardown of unread messages (Matteo Collina) #65732f9175a212c] - http: coalesce chunked writes during auto-corking (GetThatCookie) #64987b4a7cd3efb] - http2: fix write deadlock exposed by larger window sizes (Tim Perry) #65440b7e97f6cfd] - http2: error for incomplete reads on RST, auto-drain, deprecate aborted (Tim Perry) #63249d62989dbdf] - http2: fix async context loss when trailers carry END_STREAM (Orgad Shaneh) #638148aba314f87] - https: limit proxy CONNECT response headers (Matteo Collina) #64545bea74e1cb5] - lib: put node:bench behind an --experimental-bench flag (James M Snell) #659202835523960] - lib: fixup node:bench handling of --require option (James M Snell) #65631aae83d87a4] - lib: improve diagnostic message support (James M Snell) #65631ba7baed423] - lib: have runFile honor permissions and accept URL/Buffer paths (James M Snell) #6563155103db988] - lib: add runFile api to node:bench (James M Snell) #656310a4c50eb76] - lib: add context.diagnostic api to node:bench (James M Snell) #65631476e25e864] - lib: add bench:plan event to node:bench (James M Snell) #6563167cf0e556f] - lib: clarify mean in node:bench docs (James M Snell) #65631f85f72ad42] - lib: improve node:bench stream handling (James M Snell) #65631b32b09f56b] - lib: add runId, fileRunId, entryFile, namePath to node:bench (James M Snell) #656314b9b53f958] - (SEMVER-MINOR) lib: add node:bench explicit createRunner (James M Snell) #65606d9f18171f0] - (SEMVER-MINOR) lib: complete the implementation of node:bench and cli (James M Snell) #65606c39e20e9ca] - (SEMVER-MINOR) lib: implement bench/reporters (James M Snell) #65606657415c6df] - (SEMVER-MINOR) lib: implement node:bench (James M Snell) #6560646863a7f9c] - lib: use Float16Array from primordials (Antoine du Hamel) #65702cbd0948138] - lib: defer source map payload decoding until first use (Shelley Vohr) #654901ddb1c8934] - lib: optimize async context frame activation (Tim Perry) #6551902f6feef7e] - lib: use validateArray for array arguments (JunHwan Choi) #65344962795ab8c] - lib: apply minor dtls cleanups (James M Snell) #63539c07b2ab8a9] - lib,benchmark: address multiple review issues (James M Snell) #6563154a5e25f72] - module: derive builtinModules from enabled builtin set (Jungwon Sohn) #65418c93a90cf12] - net: fix BlockList.fromJSON for IPv4-mapped IPv6 rules (Daijiro Wachi) #64125506d23b76f] - net: recognize bare IPv6 loopback addresses in isLoopback (Daijiro Wachi) #6361981cd6d7b78] - net: improve dtls cert verification (James M Snell) #6431451d094e26b] - node-api: make object property arrays const (Yilong Li) #65621875c7ecee2] - node-api: enter env context for async callbacks (Shelley Vohr) #65406ebcfec2f0c] - (SEMVER-MINOR) perf_hooks: implement Histogram meanCI API (James M Snell) #6560609b333bcc5] - perf_hooks: add missing resource timing attributes (greenhead) #65017e1913630c3] - (SEMVER-MINOR) perf_hooks: add CBOR export/import for histogram exchange (James M Snell) #6543467a78317dd] - permission: do not enforce fs and addons in audit mode (Issac) #6565919e69e5f72] - permission: support URL and Uint8Array as has()/drop() reference (Seungmin Nam) #65492168af4d8d7] - permission: block FileHandle fsync and fdatasync (Rafael Gonzaga) #65431855fff682f] - quic: stop guarding ngtcp2_recv_stop_sending callback field (René) #65688d30df2a3e1] - quic: reuse TLS pause machinery to drop event deferral & improve 0RTT (Tim Perry) #65522fce1b6a482] - quic: remove unused fin flag from blob reader wakeup (trivenay) #65315c721aabfb7] - quic: apply multiple fixes to flow control signaling (James M Snell) #653092c552cd32c] - quic: release stream arenas before cleanup (Trivikram Kamat) #65410487662717b] - sea: mount bundled assets as a virtual file system (Matteo Collina) #656750ec15a2a54] - sea: keep ELF segments on separate pages in --build-sea output (Shelley Vohr) #655641627fd6561] - sqlite: re-validate database state after reading options (Trevor Burnham) #6559518c3867d19] - sqlite: run backup completion in callback scope (Filip Skokan) #65666a77d738683] - sqlite: copy changeset before applying it (Matteo Collina) #65286b5f0558c84] - sqlite: reject closing a session from a callback (Trevor Burnham) #654545f95032859] - sqlite: keep sessions alive across SQLite callbacks (Trevor Burnham) #654652ce0e2eb10] - sqlite: throw on disposal of an in-use session (Guilherme Araújo) #65449f66ed89136] - src: fix use-after-free in CleanupHookThunkRun (Caleb Everett) #6563014613fdc3e] - (SEMVER-MINOR) src: fixup histogram and options linting issues (James M Snell) #656066db3f662cf] - src: fix startup snapshot reproducibility of InternalFieldInfo (Chengzhong Wu) #656848e9151c9f2] - (SEMVER-MINOR) src: let embedders supply a builtin code cache without a snapshot (Shelley Vohr) #65352760337aa5b] - src: fix live lock between environments with blocked requests (Ilyas Shabi) #65520f2a588c385] - src: apply IsolateSettings when using a snapshot (Shelley Vohr) #654072fd50ae915] - src: add re-entrancy guard to TriggerUncaughtException (Temuulen Undrakhbayar) #64327e260fd146c] - src: reuse cached strings in CompileSerializeMain (agape1225) #654530650dca863] - src: add missing vector include (Filip Skokan) #656226e9d44d1fe] - src: disable V8 external memory reasonable size check (Paul Bouchon) #655899c46e56303] - src: report libuv error when openAsBlob cannot stat (Paul Bouchon) #65517a0aaff6b07] - src: list scripts when --run has no command (James Ross) #64606469590c155] - src: fixup manual new/delete usages (James M Snell) #653485902acf031] - src: make the options structs smaller with packed bits (James M Snell) #651450c24639a5a] - (SEMVER-MINOR) src, lib: add stats to dtls (James M Snell) #63182652fbc286e] - (SEMVER-MINOR) src,lib: add dtls interop tests (James M Snell) #63182889854f18b] - (SEMVER-MINOR) src,lib: implement experimental DTLS API (James M Snell) #6318276538d519c] - stream: use webidl validation semantics for args (James M Snell) #65658649a4aba93] - stream: ensure that stateful transforms preserve this (James M Snell) #6565823220992c0] - stream: fix nested async flushing with infinite sources (James M Snell) #656583ad1338139] - stream: ensure from() observes returned rejecting promise correctly (James M Snell) #65658cafdee6369] - stream: apply source normalization once at call time (James M Snell) #65658edb83cb620] - stream: make consumer signals on longer alter source precedence (James M Snell) #6565856bdd765df] - stream: make pipeTo source normalization independent of Writer (James M Snell) #65658037c07af2b] - stream: pre-aborted pipeTo now applies dest failure handling (James M Snell) #656587308492d48] - stream: ensure pre-existing writes drain before EOF and end() waits (James M Snell) #65658756773477b] - stream: ensure async dispoal after endSync awaits for drain (James M Snell) #65658042b951312] - stream: ensure factory signals remain active through closing (James M Snell) #656582dc09ca9ca] - stream: canWrite and ondrain now reflect physical capacity (James M Snell) #656589192fbb6dd] - stream: skip unobserved 'readable' emission at EOF (Matteo Collina) #65749dc25a61e7a] - stream: avoid per-chunk promises in webstream adapters (Matteo Collina) #6554882ae196af6] - stream: address stream/iter review feedback (James M Snell) #656523b37b48592] - stream: replace object sentinel with symbol (James M Snell) #65652b6424b6612] - stream: ensure pipeToSync requires synchronous close (James M Snell) #65652830fc81acf] - stream: cancel active stream/iter pulls (James M Snell) #6565230606908de] - stream: fix merge settlement tagging and falsy error tracking (James M Snell) #656527319198755] - stream: ensure full-close semantics when closed (James M Snell) #65652bf5acb783f] - stream: defend against re-entrancy in writev (James M Snell) #65652b01594f93a] - stream: ensure stability of stored metadata (James M Snell) #656527bfd81ab09] - stream: fixup writer to terminate on consumer return/throw (James M Snell) #65652dc2483c38d] - stream: ensure iterator cleanup on done, reject, etc (James M Snell) #65652f6fac6f7f0] - stream: fixup cancelation handling in pull() (James M Snell) #65652dfe0617e71] - stream: fix early drain after Utf8Stream reopen (Matteo Collina) #65633900fae0a24] - test: split test-bench-cli to try deflaking it (James M Snell) #6591931f4bd73b2] - test: make node:bench test samples survive a coarse clock (Shelley Vohr) #65780e0fa1893c3] - test: do not dump core in external memory limit test (Shelley Vohr) #6578000ad283b8d] - test: zero-fill buffers before the string length limit check (Christian Aurich) #6575570763fcc46] - test: expand test coverage of node:bench (James M Snell) #656315a38e6b115] - (SEMVER-MINOR) test: fix node:bench test timing (James M Snell) #65606536ae0e24b] - (SEMVER-MINOR) test: improve node:bench test coverage (James M Snell) #65606eec66d529c] - (SEMVER-MINOR) test: update bench tests to not fail on no-crypto (James M Snell) #65606ce7b349de5] - test: bump WPT webidl and interfaces (Filip Skokan) #65679ec96f8dc23] - test: update WPT for url to c23755a144 (Node.js GitHub Bot) #65651ebe2ac248b] - test: fix recursive fs.watch error fixture (Filip Skokan) #65683a19912ee8f] - test: update streams WPT (Jeong SeokChan) #6563814b5dfc63d] - test: remove console.log call in node_run_list (Antoine du Hamel) #655723f67df139c] - test: expect node:ffi to be enabled by default (Matteo Collina) #65636b9ee2e67bf] - (SEMVER-MINOR) test: use native builder for legacy SEA tests (Filip Skokan) #6555353a29d0894] - test: use common spawnSync helpers in more tests (greenhead) #655525c680a50f4] - test: add coverage for removeEventListener boolean capture (Lazizbek Ergashev) #652456f99474912] - test: deflake fastutf8stream destroy and reopen tests (Christian Aurich) #655542342b2bf30] - test: cover Readable.from() sync iterator errors (jakecastelli) #655151e455a4ffe] - test: support inspecting WPTs in child processes (Filip Skokan) #655101f7cab588e] - test: document WPT runner workflows (Filip Skokan) #65510d39a902cec] - test: simplify test-worker-heap-profile.js (Donghoon Kang) #6537290d1b863f7] - test: keep WPT backend checks alive (Filip Skokan) #65320be60c34b52] - (SEMVER-MINOR) test: enable multi-global WPTs (Filip Skokan) #64894d04cfcda8e] - (SEMVER-MINOR) test: add opt-in process WPT runner (Filip Skokan) #64894715c21f805] - (SEMVER-MINOR) test: accomodate multi-global tests in WPT{Runner,TestSpec,Report} (Filip Skokan) #648949aa8489a01] - test: fix lint in dtls tests (Matteo Collina) #649026a3d8a6d9f] - tls: read the peer certificate chain without consuming it (Tony Gies) #656022e519f8fef] - tls,quic: commonize TLS cert handling between tls, dtls & quic (Tim Perry) #647112266c14051] - tools: add GHA workflow to test vendored Perfetto (Antoine du Hamel) #6561419779f121f] - tools: fix the list of globals in ESLint config files (Antoine du Hamel) #65281b57db8dfd3] - typings: add types for performance binding (Jungwon Sohn) #65574371e2260f4] - typings: add encodeIntoResults to EncodingBinding (greenhead) #65350417cdd8a77] - typings: add typing for permission binding (Seungmin Nam) #65385a82cc7097f] - url: align URLPatternInit dictionary conversion with WebIDL (Piyush Yadav) #654980e8f7b96b0] - util: canonicalize namespaced tags in inspect() (René) #63257acd2bb10ce] - v8: add setHeapProfileNearHeapLimit (Ilyas Shabi) #64676d0535b1041] - vfs: load native addons from a mounted file system (Philipp Dunkel) #65680ea6f70ba41] - vfs: fix rename over non-empty directory (Christian Aurich) #65613b4f6b3798a] - vfs: add ZipProvider (Philipp Dunkel) #649155198142c59] - (SEMVER-MINOR) vfs: integrate with CJS and ESM module loaders (Matteo Collina) #6365338192126e2] - worker: start worker threads from the built-in snapshot (Shelley Vohr) #6533656144ea80a] - worker: add ref/unref to web workers (Aviv Keller) #655077231c13b8a] - (SEMVER-MINOR) worker: add wpt tests for Web Workers (Aviv Keller) #648945af9d72e7f] - (SEMVER-MINOR) worker: add support for Web Workers (Aviv Keller) #64894069a1b1200] - zlib: avoid waiting for paused ZIP iterators (Trivikram Kamat) #65278Windows 64-bit Installer: https://nodejs.org/dist/v26.9.0/node-v26.9.0-x64.msi
Windows ARM 64-bit Installer: https://nodejs.org/dist/v26.9.0/node-v26.9.0-arm64.msi
Windows 64-bit Binary: https://nodejs.org/dist/v26.9.0/win-x64/node.exe
Windows ARM 64-bit Binary: https://nodejs.org/dist/v26.9.0/win-arm64/node.exe
macOS 64-bit Installer: https://nodejs.org/dist/v26.9.0/node-v26.9.0.pkg
macOS Apple Silicon 64-bit Binary: https://nodejs.org/dist/v26.9.0/node-v26.9.0-darwin-arm64.tar.gz
macOS Intel 64-bit Binary: https://nodejs.org/dist/v26.9.0/node-v26.9.0-darwin-x64.tar.gz
Linux 64-bit Binary: https://nodejs.org/dist/v26.9.0/node-v26.9.0-linux-x64.tar.xz
Linux PPC LE 64-bit Binary: https://nodejs.org/dist/v26.9.0/node-v26.9.0-linux-ppc64le.tar.xz
Linux s390x 64-bit Binary: https://nodejs.org/dist/v26.9.0/node-v26.9.0-linux-s390x.tar.xz
AIX 64-bit Binary: https://nodejs.org/dist/v26.9.0/node-v26.9.0-aix-ppc64.tar.gz
ARMv8 64-bit Binary: https://nodejs.org/dist/v26.9.0/node-v26.9.0-linux-arm64.tar.xz
Source Code: https://nodejs.org/dist/v26.9.0/node-v26.9.0.tar.gz
Other release files: https://nodejs.org/dist/v26.9.0/
Documentation: https://nodejs.org/docs/v26.9.0/api/
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Hobby projects now retain fewer deployments past the 30-day retention window. Hobby teams get 10GB of Deployment Storage. Every deployment you keep uses some of it, and going over the limit can block you from deploying until you free some up. Deployment Retention for Hobby teams now deletes old deployments sooner, so dormant projects stop holding storage that your active projects need.
Each Hobby project now keeps its 3 most recent production deployments, plus its 3 most recent deployments of any type, regardless of age. Preview deployments no longer get their own protection. Your current production deployment is still never deleted, and aliased and active-branch deployments are still protected. See the full list of exceptions in the docs.
If your team is over the 10GB limit, deployments outside those exceptions are now deleted immediately instead of after 30 days.
To stay under the limit, see how to optimize your Deployment Storage usage, or upgrade to Pro, where storage is billed at $0.10 per GB per month.
Amazon Web Services (AWS) started with a handful of foundational infrastructure services such as Amazon Simple Storage Service (Amazon S3), Amazon Elastic Compute Cloud (Amazon EC2), and Amazon Simple Queue Service (Amazon SQS), so that anyone with an idea could start building. As the world’s largest companies and governments adopted AWS, they asked for features to optimize their configuration for a range of global business contexts, security requirements, and operational needs. To meet these needs, AWS expanded globally through new Regions and added breadth and depth of services in security, networking, governance, and cost controls, so those customers could operate wherever they needed and at the scale they require. That combination of global reach, breadth, and depth remains essential for those customers, but if you are at the start of a new idea, every configuration option is effort standing in the way of shipping your dream product fast.
Today, we’re announcing a new simplified experience on AWS for builders who are working at the pace of AI. Instead of having to complete configuration tasks before you can work on your project, you start with sensible defaults and simple administration. You sign up using an existing identity from providers including Google, GitHub, and Apple. For most new customers, no credit card is required to start and you receive $100 in free credits as part of the AWS Free Tier. You can build immediately in your first project. As you continue to work, you can invite collaborators with just an email address, without learning about AWS Identity and Access Management (IAM) or AWS IAM Identity Center. When your project grows beyond the free credits, you can set a spend limit so you stay within your budget on the paid plan. If you grow to need additional customization, you can activate advanced AWS features to access the full breadth and depth of AWS without migrating.
How it works
When you sign up, AWS organizes your work in a project. A project contains an AWS account, where you create resources, and settings for sharing with team members. AWS creates that structure for you and applies additional security controls so you can start building your idea. After signing in, you get a prompt to paste into your coding agent that configures it to work with your new AWS environment. From there, your agent can deploy resources, run workloads, and iterate on your application following best practices for working with AWS.
You can create another project with a click. When you want to work with an additional team member, you send an invitation to their email address. Identity permissions are handled for you, so there are no IAM users to create; each person you invite only gets access to the projects you specify. Console workflows and coding agents also configure permissions between supported services and resources automatically, so you do not have to set up or troubleshoot resource permissions by hand.
When you’re ready to move beyond free credits, you can upgrade to a paid plan by entering your payment method. You can set a monthly spend limit on a project based on your usage trends, starting at $20 per month. The spend limit is the ceiling for that project’s costs, and you pay for what you actually use up to that amount. For example, if you set a $50 spend limit and your project incurs $32 in charges that month, you pay $32 (plus taxes). AWS will suggest a spend limit based on your usage, and you can accept that recommendation or set a custom amount if you are planning to further scale your usage. If your project approaches the limit, you first receive notifications. If spend reaches the limit, AWS pauses your project rather than accumulating charges, and you can resume working on it when you raise the limit. Each project has its own spend limit so you can give a larger budget to a workload that is gaining traction while keeping a smaller budget on an experimental idea.
Let’s try it out
To get started, I went to aws.amazon.com and chose Create account. I signed in with my Google account and within seconds had a new project ready to go, as shown in the following screenshot.
The Sign up for AWS page, with options to continue with email or sign in using Google, GitHub, Apple, or Amazon.
The first thing I saw was a prompt to configure my coding agent. I copied the prompt and pasted it into my agent. The agent set up the AWS Command Line Interface (AWS CLI) and the Agent Toolkit for AWS, logged me into AWS, and created a CLAUDE.md file in my project with guidance for the new experience.
The Setup Agent Toolkit for AWS dialog, with a prompt to copy and paste into your coding agent.
With the agent connected, I gave it a short prompt: build an API that returns a new unique sequential ID on every request. The agent created an AWS Lambda function, an Amazon DynamoDB table, and an Amazon API Gateway API, then deployed them for me. I did not have to configure resource permissions by hand. Within a few minutes I had a public endpoint that returned a newly minted ID on each request. My project started with $100 in free credits, and I received an additional $20 when the Lambda function was deployed.
A coding agent prompt to build an API that returns a unique sequential ID on every request.
The coding agent presents architecture options for the sequential ID API, with AWS Lambda and Amazon DynamoDB selected.
The coding agent confirms the API is live and lists the Amazon DynamoDB table, AWS Lambda function, and Amazon API Gateway API it deployed.
From the project, I could manage settings, invite team members by email, and monitor billing, as shown in the following screenshots.
The Projects page, showing remaining free-plan days, credits, and a project.
The project Members page, with the option to invite a new team member by email.
The Billing page, showing a $0.00 balance on the free plan, remaining credits, and cost by project.
Activating advanced features
If you reach the point where you need multiple Regions, or governance features like custom policies in AWS Organizations, you can activate advanced features at no additional cost. You’ll find yourself in a fully configured AWS Organization built according to best practices, with no migration and no downtime. Everything you configured previously is preserved and reflected in the underlying AWS services.
Now rolling out
We’ve heard from builders that they do not want to spend their first hours configuring an AWS environment. They want to build what they came to build, and we listened. AWS began as a place where anyone with an idea could start building, and this new simplified experience brings that starting point back, with sensible defaults so you can begin immediately, and with the global reach, breadth, and depth of AWS still there when your idea needs it. We are gradually rolling this experience out to new customers. We cannot wait to see what you build, and we want your feedback on the experience.
To try the new experience, create a new AWS account. To learn more, see the AWS Sign up user documentation.
Builds using Secure Compute or Static IPs now start 64% faster, with the average time from deployment creation to build start dropping from 6.7 seconds to 2.4 seconds.
Previously, each build waited for a new build container to boot with its network configuration. These builds now use prewarmed build containers, with your network configuration attached when the build starts.
The improvement is applied automatically to builds using Secure Compute or Static IPs, with no configuration changes required.
Learn more about Secure Compute and Static IPs.
Mem0 is now available as a native integration on the Vercel Marketplace, giving your AI agents and apps long-term memory. Mem0 remembers user preferences, facts, and context across sessions, so your app stops starting from scratch.
Install from the Marketplace with integrated billing on your Vercel invoice, no separate account or key management.
A scoped Mem0 project and API key are provisioned automatically and added to your project as environment variables, including MEM0_API_KEY.
Mem0 offers a free plan with usage limits and a $20 per month plan for higher volume.
To see it end to end, deploy the eve Memory Agent template, an eve agent with long-term memory powered by Mem0.
Get started with Mem0 on the Vercel Marketplace or through the Vercel CLI, available to customers on all plans.
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Release date: September 16, 2026
macOSUniversalIntelApple silicon
Linux.deb.rpm.tar.gzArm instructionsSnap
Welcome to the 1.138 release of Visual Studio Code. This release helps agents work in your project's development environment, gives Codex sessions more flexibility, and keeps completed sessions organized.
Agent sessions in Dev Containers: Run agents with your project's tools and dependencies in a local Dev Container.
Expanded Codex harness: Continue Codex sessions across apps, choose between Copilot and ChatGPT subscriptions, and use VS Code tools.
Session cleanup (Preview): Automatically mark merged sessions as done and optionally delete them after a grace period.
Happy Coding!
VS Code is rolling out gradually to all users. Use Check for Updates in VS Code to get the latest version immediately.
To try new features as soon as possible, download the nightly Insiders build, which includes the latest updates as soon as they are available.
The agent host runs agent harnesses in a dedicated process based on the Agent Host Protocol (AHP), so you can connect to the same session from multiple VS Code windows. Learn more about its architecture and workflows in the agent host blog post.
Settings: chat.automations.enabled (Agents window only)
Automations are now enabled by default, making it easier to streamline repetitive tasks. You can also export and import automations to share them across different environments or with your team. Learn more about Automations in the VS Code docs.
Setting: chat.agentHost.devContainer.enabled (Agents window only)
Keep agent work aligned with your project's toolchain by running a session inside a local folder's Dev Container. The agent uses the environment and dependencies configured for the project instead of those on your local machine.
When the setting is enabled, local folders with a supported Dev Container configuration automatically show a folder menu with a Use Dev Container action. Select the action to run the agent session in the folder's Dev Container. Docker must be installed on your machine.
Note: Local Dev Container sessions are rolling out gradually, so the setting might not be enabled by default for you yet. You can enable the setting manually to try the feature now.
Settings: chat.agentHost.codexAgent.enabled , chat.editor.codex.preferAgentHost
This release expands Codex support in the agent host, so you can keep the same coding session while changing where and how you work.
Last release, we introduced continuing quick chats in a workspace for Copilot sessions. This release extends the same flow to Codex, so starting project-specific work no longer means abandoning a workspace-less Codex quick chat. Ask Codex to attach a local folder, then choose whether to use the folder directly or create an isolated worktree.
After you confirm the change, the same chat and native Codex thread become a workspace session. The session retains its title, conversation history, current request, selected model, and permission mode. Codex then continues the request with access to the project files.
Workspace conversion is available for idle Codex quick chats in Interactive mode and supports single-root workspace targets. If the change is canceled or cannot be applied, the original workspace-less chat remains available.
The Claude and Codex agents in the Agents window rely on an SDK that is not shipped with VS Code and is downloaded when you first need it. Previously, the offer to download it appeared only as part of signed-out account setup, so if you were signed in it would download at first message.
The offer now appears whenever the SDK is missing for transparency that a download is needed to continue.
When models are already available for that agent, the notification presents two options: select Download or send a message to download the SDK as part of the turn. The wording updates in place if models become available while the notification is showing.
Setting: chat.agentMerge.enabled (optional, for Agent Merge only)
Create pull requests from Agent Host sessions in the Agents window using one form to review and edit generated titles and descriptions, choose draft status, and configure available merge options. Create the pull request directly or send the request to your agent, with your preferred options remembered for next time.
Note: The Agent Merge option is experimental and is only available when the setting above is enabled.
Settings: chat.agentSessions.archiveNudge.enabled , chat.agentSessions.autoMarkAsDoneMergedSessionsAfterDays , chat.agentSessions.autoDeleteArchivedMergedSessionsAfterDays , sessions.markAsDoneConfetti (Agents window only)
Keep finished work out of the way while preserving the conversation for later. When all of an inactive session's pull requests have merged, the Agents window can suggest marking the session as done. A first-use guide shows you where to find Mark as Done in the sessions list.
Enable chat.agentSessions.archiveNudge.enabled to show these suggestions.
To automate cleanup, mark inactive sessions as done after their pull requests merge, and optionally delete them after a separate grace period. Both automatic-cleanup settings are disabled by default.
When all pull requests for a session are merged, select Configure Automatic Cleanup in the Mark as Done suggestion to open both settings without enabling them.
Enable sessions.markAsDoneConfetti to show a confetti animation when you mark a session as done. The animation respects your reduced-motion preference.
Setting: sessions.showApplicationBadge (Agents window only)
See when agent sessions need your attention without switching back to VS Code, with a badge on the macOS dock, Linux launcher, or Windows taskbar. The badge highlights sessions with new results, requests for input, or pull request checks that need attention.
Enable the preview setting above to show the badge.
Setting: sessions.chat.unifiedWorkspacePicker.enabled (Agents window only)
Start agent work from one searchable list of local folders, GitHub repositories, Cloud repositories, and remote targets. Remote connection actions remain available from the Remote entry.
Selecting Work in Repository uses a cloud-first workflow. A GitHub repository that is not already local is selected immediately with the Cloud harness, without a clone prompt. If you later choose a local harness, VS Code prompts you to clone the repository and preserves the cloud selection if you cancel.
Settings: chat.agentSessions.preferredDarkBackgroundImageLayout , chat.agentSessions.preferredLightBackgroundImageLayout (Agents window only)
The Agents window can show a decorative chat background behind your sessions, either a pattern of built-in VS Code icons or an image of your own, chosen separately for dark and light color themes.
Previously, the image was per theme kind but its layout was not, so right-aligning your dark background and then left-aligning your light one left both of them aligned left. Layout is now stored per theme kind alongside the image, and switching between a dark and a light theme restores that theme's placement. The two new settings replace chat.agentSessions.backgroundImageLayout.
Clearing a background also moved. Chat: Set Background... now leads with No Background, which clears the background for the color theme you are currently using and leaves the other one alone.
Response and request surfaces are opaque, so higher-contrast backgrounds do not reduce the readability of agent responses.
Navigate and monitor parallel agent work without leaving Voice Mode. Voice Mode can find recent agent sessions, switch between them by label, and report each session's status.
chat.agentSessions.backgroundImageLayout is replaced by
chat.agentSessions.preferredDarkBackgroundImageLayout
and
chat.agentSessions.preferredLightBackgroundImageLayout
, so that the Agents window chat background layout can be set separately for dark and light color themes.Contributions to vscode:
Contributions to vscode-chat-customizations-evaluation:
Contributions to our issue tracking:
We really appreciate people trying our new features as soon as they are ready, so check back here often and learn what's new.
If you'd like to read release notes for previous VS Code versions, go to Updates on code.visualstudio.com.
We’re proud to share that Microsoft has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Distributed Hybrid Infrastructure for the fourth consecutive year. Microsoft is positioned highest for Ability to Execute.
We believe this recognition reflects the strength of Microsoft’s unified approach to distributed infrastructure. With Azure Local and Azure Arc, organizations can manage infrastructure across datacenters, edge locations, multi-cloud environments, and sovereign deployments through a consistent Azure foundation.
Customers want the flexibility to run workloads where it makes the most sense for their business without taking on additional operational complexity. As infrastructure extends across datacenters, edge locations, and the cloud, Microsoft helps customers maintain consistency through common tools, processes, and management experiences, reducing fragmentation and allowing teams to focus on delivering business value.
Azure Arc extends Azure management and governance to resources running in data centers, at the edge, and across multi-cloud environments. Azure Local brings Azure infrastructure and services into customer-controlled locations so organizations can run cloud services and AI workloads closer to their applications and data.
Together, Azure Arc and Azure Local give organizations a common way to operate across environments without requiring every workload to run in the same place. Some applications are best suited for the public cloud. Others may need to remain in a corporate data center, operate at the edge, or run within a customer-controlled sovereign environment. While deployment locations differ, management and governance can remain consistent.
Microsoft continues to expand the environments Azure Local can support, from small-form-factor edge deployments to large scale data center environments. Azure Local supports both hyperconverged architectures and options for disaggregated compute and external storage, so infrastructure can be designed around the needs of the workload rather than a single deployment model.
The same workload-centric approach applies to digital sovereignty, an increasingly important consideration for organizations around the world. Microsoft Sovereign Cloud is not a single deployment model, but a continuum of choices and controls.
For many workloads, sovereignty requirements can be addressed in public cloud through capabilities for data residency, encryption, confidential computing, policy enforcement, and operational oversight. Other workloads need to run in customer-controlled locations or require greater separation from public cloud operations.
Built on Azure Local, Microsoft Sovereign Private Cloud supports these scenarios by giving customers greater control over infrastructure, data, operations, and access, while retaining a familiar Azure foundation. For environments where connectivity to the cloud is restricted or unavailable, disconnected operations on Azure Local bring a local control plane into the customer environment. This enables customers to deploy and manage Azure Local and supported capabilities without ongoing connectivity to the cloud.
Together, these options allow organizations to apply different levels of sovereignty and operational control to different workloads instead of forcing the entire estate into a single architecture.
The growing adoption of AI is making workload placement decisions even more important.
For some workloads, the scale and breadth of the public cloud are the right fit. Others need inference to happen where data is generated because of latency, connectivity, governance, or sovereignty requirements.
Foundry Local on Azure Local extends AI inference into Azure Local environments, allowing organizations to deploy and run models closer to their applications and data using Kubernetes-native operations.
We see the 2026 Gartner recognition as validation of a clear direction: distributed infrastructure should provide customers with choice over where workloads run without requiring fragmented management, governance, and operational models.
We will continue investing in connected and disconnected operations, scalable infrastructure, data and AI capabilities, and consistent Azure management across distributed environments.
We are grateful for the trust our customers place in us and remain committed to delivering the flexible, consistent, and secure platform they need to power the next generation of distributed infrastructure.
2026 Gartner Magic Quadrant for Distributed Hybrid Infrastructure
Gartner, Magic Quadrant for Distributed Hybrid Infrastructure, [Julia Palmer, Elaine Zhang, Adrian Wong, Daniel Bowers; 7 September 2026].
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates.
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft.
The post Microsoft recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Distributed Hybrid Infrastructure appeared first on Microsoft Azure Blog.
Agent programs in healthcare and life sciences are being built under a different set of constraints than those in most industries. There’s plenty of upside if the constraints can be resolved. Success can mean hours of manual review compressed into minutes, data spread across a dozen systems finally queryable in one place, and clinicians getting time back from documentation. At the same time, the cost of a wrong answer can be higher here than almost anywhere else, which changes how teams build.
Across payers, providers, and biopharma, we are seeing that earning the level of trust required to scale agents is much harder. In this industry, trust goes beyond product quality and is also an audit requirement, a compliance obligation, and in some cases a patient-safety requirement. Meeting that bar requires an infrastructure layer that many teams may not have built into their first pilots.
This piece looks at how three organizations are building their agents:
Alongside these three, we’ll draw on patterns we’re seeing emerging across a broader set of healthcare and life sciences agent programs.
Observability, evals, and cost control are becoming prerequisites for greater agent autonomy. This is the most common theme we hear. 76% of healthcare and life sciences organizations we speak with name tracing, evaluation, and spend visibility as requirements before agents are given more autonomy. In regulated settings, the need extends beyond debugging to evidence. Teams need a durable record of what an agent did, who reviewed it, and how quality was measured because that is the record a compliance function will eventually ask for.
For health plans and providers, 43% of the organizations we speak with are focused on PHI handling, de-identification, and HIPAA requirements. Several teams are also putting an LLM gateway in front of their models to gain unified visibility into spend across users and models before expanding agent autonomy further.
Central agent platforms are consolidating fragmented agents across the enterprise. 49% of organizations we speak with are working on a company-wide agent platform, control plane, or “agent factory” as their primary use case, with business-unit agents running on top of it. We see this pattern across pharma, payers, and health systems. A dozen or more teams each build their own agent, each recreates the same foundations, and eventually one team is asked to own the shared layer.
The shared layer can take the form of a reusable template library, an internal agent catalog, or a single governed path from prototype to production. One top-five pharma company is consolidating hundreds of applications onto a single interoperable platform. One payer we spoke to recently went from a single production agent to scoping roughly a hundred more on a unified foundation. For many organizations, hundreds of proofs of concept without a clear path to production are often the starting point.
Regulated document and back-office work is seeing the clearest ROI. 33% of organizations we speak with are building agents for workflows that already have a paper trail and a known cost per case. These include:
These use cases carry a clear before-and-after metric, and several agents are already running in production. Work that might take a medical writer or processing team hours can now be measured in minutes, while filing timelines themselves become metrics that leadership can track.
Patient- and member-facing conversational agents are moving from pilot to production, including voice. 26% of organizations we speak with are running or building external-facing agents across member navigation, patient intake and triage over SMS and WhatsApp, consumer device assistants, and contact-center deflection. Voice has become a meaningful part of these programs, with teams tracing and scoring audio interactions for sentiment, adverse-event mentions, and PII exposure. Safety evaluation is tightly connected to this use case. Mental-health providers, for example, are explicitly testing for off-track conversations and suicidal-ideation detection before scaling deployment.
Federated building initiatives often emerge when central engineering becomes a bottleneck. 26% of organizations are trying to let non-engineers build agents within central guardrails. We’re seeing business teams configure and validate agents against internal sources such as SharePoint, EHR summaries, or Snowflake, while a central team industrializes the ones that are proving valuable.
We’re also starting to see subject-matter experts own prompts and evaluation datasets directly. Clinicians and pharmacists can edit prompts and trigger evals in development, with engineering promoting the versions that pass.
Scientific R&D agents are longer-running. Scientific R&D organizations are creating agents for discovery and lab science, including target discovery, structure-based design, omics and single-cell perturbation analysis, literature and knowledge-graph retrieval, and lab-instrument control.
These are also among the longest-running and least deterministic agents in the industry. As a result, these teams place especially high demands on evaluating the full trajectory an agent takes, rather than judging only its final answer.
Clinician and care-team support use cases are prevalent with providers and payers. Many organizations are building agents that work alongside clinicians or care managers, including pre-visit preparation, care-navigation orchestration, chart preparation, and referral management. Human-in-the-loop is generally assumed for these workflows. The larger blockers tend to be EHR integration and audit obligations.
The three organizations below show what it takes to operate agents once a company has moved beyond its first pilot.
Madrigal Pharmaceuticals is a biopharmaceutical company focused on metabolic dysfunction-associated steatohepatitis (MASH), a serious form of fatty liver disease. Its enterprise agent platform grew out of the challenge of integrating, searching, and synthesizing information scattered across structured systems, unstructured documents, external sources, and real-time APIs.
The first major constraint was that every data source behaved differently, with different formats, access patterns, and expectations. Madrigal normalized those sources into the same secure data warehouse and exposed them through a single, consistent tool interface. From an agent’s perspective, all information became available through the same abstraction, allowing the system to add new domains without rewriting orchestration logic each time.
This abstraction helped the team turn one workflow into a broader platform. An orchestrator built with LangChain’s Deep Agents harness receives a task and determines which capabilities are needed, which agents should run, and what work can happen in parallel before the results are reconciled. Its role is to route the problem across specialized capabilities rather than encode the details of every domain.
New use cases are added as modular skills that define how to approach a particular type of problem and what good output looks like. This approach to using skills brought new use case development down from weeks to hours.
Parallelism helps the system handle more complex research efficiently. A research question can be divided across sub-agents, each handling a different slice of the problem, while those sub-agents can further parallelize their own work. A shared virtual filesystem built into the Deep Agents harness acts as the system’s memory. Results, sources, and intermediate steps are written down and made available for reuse, simplifying coordination as the system scales.
For observability, Madrigal leaned on LangSmith to gain full pipeline visibility into every tool call, retrieved chunk, and agent decision.
As Parth Patel, Global Head of AI and Data Science at Madrigal, put it, tracing before LangSmith meant the team could observe the system’s stimulus-response behavior but had little visibility into what happened inside. Afterward, it felt like “going from basic psychology to neuroimaging.”
The team began with trace-level evals on full agent runs, using LLM-as-judge graders designed to mirror real end-user feedback and score outcomes rather than exact paths. One of the most durable improvements came from feeding production failures directly back into LangSmith datasets. Every meaningful error would be used as a new test case, allowing the evaluation suite to grow from real failures rather than relying only on synthetic examples.
Deployment was another area where a small team could not afford to build every infrastructure piece from scratch. Madrigal used LangSmith Deployment to deploy its graph as a managed service, with state persistence, concurrent sessions, real-time streaming to the UI, and a CI/CD pipeline that automatically ships skill updates. According to Madrigal CIO Ron Filippo, moving from prototype to enterprise use took weeks instead of the months the team had budgeted.
Learn more: Madrigal User Story (Blog)
Abridge builds AI that helps clinicians turn patient conversations into clinical documentation. More than two billion clinician-patient conversations take place in the U.S. every year. With the patient’s consent, a clinician records a visit, and Abridge turns it into a clinically useful note that can be submitted to the electronic health record. The goal is to give clinicians back the “pajama time” they would otherwise spend finishing documentation after hours.
Abridge now works with more than 250 health system partners across more than 50 specialties and 28 languages, recording more than 100 million conversations a year.
The company operates under constraints that shape nearly every product decision. The bar for what can ship is extremely high because the stakes are clinical. A misattributed diagnosis, hallucinated medication, or incorrect dosage can carry real patient-safety consequences. That same standard extends to how the product is built and deployed, with HIPAA, PHI handling, and enterprise trust shaping the architecture from the start.
As Abridge expanded across specialties and care settings, agent evaluation became a major bottleneck. A release could take one to two months, with clinicians annotating examples, third-party labelers contributing feedback, and teams working across disconnected internal tools.
Abridge moved to a unified foundation around LangGraph and LangSmith, bringing datasets, annotation, tracing, evaluation, and experiments into one workflow. Self-hosting and access controls also supported the requirements of an enterprise healthcare environment.
Turning clinician feedback into something measurable required another layer of infrastructure. Abridge groups reported issues such as misattribution, confabulation, redundancy, and completeness into broader quality pillars including accuracy, completeness, compliance, and style. It then builds an LLM judge for each pillar.
Originally, building a reliable judge took several days. A clinician wrote an annotation guide, encounters were labeled, and an ML scientist iterated on the judge until its scores aligned with clinician labels. Abridge built an automated prompt optimization framework that generates a calibrated judge directly from the annotation guide and labeled encounters. That reduced the process to hours, with only minutes of active development time.
Abridge also found that different types of judges serve different purposes. Reference-free judges can generalize across encounters and run both offline during development and online after deployment. Clinical notes, however, contain enough encounter-specific nuance and legitimate subjectivity that the team supplements them with reference-based evaluations and specialty-specific rubrics for higher-fidelity offline checks.
Strong offline evals are only one step in the release process. Abridge uses a tiered approach: offline evaluation, backtesting against historical encounters, controlled A/B testing with health system partners that have explicitly opted into early releases, and finally a full rollout with continuous online monitoring.
Using that process, a hill-climbing effort on the company’s History of Present Illness (HPI) and Problem-Based Assessment and Plan (PBAP) models produced a 17% gain in accuracy and a 19% gain in completeness, while also improving detail capture and reducing redundancy. The same evaluation infrastructure helped bring release cycles down from one to two months to days.
That evaluation philosophy carried over as Abridge built a persistent AI agent across the clinical workflow. The agent can search patient context, help edit a note, take actions, and retrieve evidence from validated literature, all within a single coherent experience.
Agents substantially expanded the evaluation surface. Beyond clinical quality, the team now evaluates clinical safety, boundary and adversarial behavior, tool selection, and tone. Once an agent begins taking actions, a poor tool choice or out-of-scope answer introduces risks beyond the quality of the generated text itself.
Learn more: Abridge Interrupt Talk (YouTube)
Vizient, a leader in healthcare performance improvement, is changing how healthcare providers access and analyze their own data. Many providers still rely on disparate data sources and have to mine for insights on patient care through a long, manual process.
Vizient’s GenAI platform allows health systems of all sizes to query and unify siloed datasets to make better decisions across areas such as supply chain management and clinical outcomes. It can answer questions such as “Are my ambulatory investments effective?” or “Are we delivering the most cost-effective care?” with immediate, data-backed responses. The goal is to democratize data analysis for resource-limited health facilities while maintaining strong trust and data privacy for members.
Before adopting LangGraph, Vizient’s multi-agent system ran into a common problem as it expanded beyond a single agent. Individual agents had been built for specific tasks, such as analyzing historical data or generating visualizations, but coordinating them reliably became difficult. The agents operated in silos and produced inconsistent responses. Some underlying API workflows also involved hundreds of parameters per call, making the application logic increasingly difficult to maintain.
Vizient adopted LangGraph to orchestrate the system. Its graph structure and descriptive primitives allowed the engineering team to represent each step an agent should take as a tool or node that could be planned and controlled programmatically.
The result is a hierarchical structure with worker agents reporting to a supervisor agent. That architecture has streamlined how requests are routed to the right APIs and remains the foundation the team is building on as the platform expands.
For observability, Vizient turned to LangSmith tracing to understand the platform’s performance, including during high-stakes, real-time demos. Tracing allowed the team to diagnose issues such as Azure OpenAI content filters and external rate-limiting errors as they happened.
LangSmith’s Prompt & Context Hub also gave the team a way to separate prompt logic from application code, allowing prompts to be versioned and iterated on independently. That capability becomes increasingly important as the number of GenAI development teams at Vizient grows.
Looking ahead, Vizient is focused on refining its evaluations to improve consistency and trust. That includes aligning generated answers with established tools such as Q&A scorecards across data domains and onboarding new product data faster by connecting existing product APIs and other data sources directly into the agentic system.
Learn more: Vizient User Story (Blog)
Looking beyond these three examples, a few themes keep surfacing across healthcare and life sciences agent programs more broadly.
Data governance often determines how agent systems are deployed. Whether it is Abridge’s self-hosted, HIPAA-scoped stack or Vizient’s need to keep member data private across health systems, teams across payers, providers, and biopharma typically prove that the platform meets internal security and privacy requirements before expanding what their agents can do.
Evaluation turns vague failures into problems teams can diagnose and fix. Madrigal can trace whether a wrong answer came from a missing index, a bad query, or an untrustworthy source. Abridge can distinguish a planning failure from a tool-selection failure. Vizient can pinpoint which agent in its hierarchy produced an inconsistent response. Across all three, observability becomes most valuable when it helps a team move from asking whether something worked to understanding where it failed and why.
High-trust workflows are becoming some of the clearest proof points for agents. Clinical documentation, biopharma data synthesis, and hospital performance analytics all involve workflows where errors are costly and audit trails are already expected. Those constraints have pushed teams to develop stronger evaluation, observability, and governance systems, creating a clearer path for agents to earn greater autonomy over time.
The agent programs furthest along in healthcare and life sciences are building the platform, evaluation infrastructure, and trust model alongside the agents themselves.
Madrigal built an abstraction layer that allowed one workflow to grow into a platform. Vizient rebuilt its multi-agent system around an explicit hierarchy once informal coordination became unreliable. Abridge rebuilt its evaluation pipeline so clinician trust could scale alongside its release velocity.
Across all three, similar patterns hold. In an industry where the cost of being wrong can be measured in patient outcomes and regulatory exposure, tracing, evaluation, and deployment architecture are core parts of the system that allow agents to scale safely and reliably.
System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments.
Underlying all three is platform fungibility: the same infrastructure runs any model, any workload, from training to inference, recommender to reasoning, language to video, keeping utilization high.
The NVIDIA platform is purpose-built to optimize across all these, as highlighted by MLPerf Inference v6.1 results released today:
For organizations making AI infrastructure decisions, performance, scaling efficiency and software velocity are important considerations that determine long-term inference economics.
NVIDIA submitted Vera Rubin NVL72 preview results on two of the most demanding benchmarks in the MLPerf Inference v6.1 suite: DeepSeek-R1 and Qwen3-VL.
Vera Rubin NVL72 delivers up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL across offline, server and interactive scenarios, using vLLM with the NVIDIA Dynamo open source inference framework. On DeepSeek-R1, using the NVIDIA TensorRT-LLM library, throughput is up to 2.5x higher than GB300 NVL72. These early results showcase NVIDIA’s accelerated pace of innovation and how performance will improve with continuous software optimizations.
This performance means each Vera Rubin NVL72 rack delivers significantly more tokens, serves more users and generates more revenue than a GB300 NVL72 rack, while lowering cost per token.
The results reflect full-stack codesign across hardware and software. Vera Rubin’s enhanced Tensor Cores and Transformer Engine accelerate both the prefill and decode stages of inference, while NVFP4 precision reduces memory footprint across model weights, attention and KV cache — increasing throughput with minimal loss of output quality.
Vera Rubin submissions heavily used disaggregated serving, separating prefill and decode along with large-scale expert parallelism for maximum efficiency across the mixture-of-experts layers that power models like DeepSeek-R1 and Qwen3-VL.
The NVL72 scale-up domain — powered by sixth-generation NVIDIA NVLink and NVLink Switch to deliver 10x higher packet rates and 3x lower latency than off-the-shelf Ethernet — provides the interconnect foundation that makes these techniques effective at rack scale.
This codesign extends to NVIDIA’s partner ecosystem: Nebius also submitted Vera Rubin NVL72 preview results and demonstrated excellent performance.
AI agents, which reason, plan and act across multiple steps, are reshaping how inference performance is measured. In benchmarks designed to capture this shift, such as SemiAnalysis AgentX, Vera Rubin NVL72 delivered 30x better performance than GB300 NVL72 in preview testing. In addition, the upcoming MLPerf Endpoints benchmark will bring standardized measurement to agentic inference workloads, beyond what traditional throughput benchmarks capture.
Scaling efficiency — how effectively additional GPUs translate to throughput gains — is a key measure of AI infrastructure productivity. NVIDIA delivers this with high-bandwidth, low-latency scale-up interconnects within each rack, high-bandwidth networking between racks and efficient request orchestration across nodes.
NVIDIA’s DeepSeek-R1 (DSR1) submission scaled from a single GB300 NVL72 rack (72 GPUs) to four racks (288 GPUs), achieving 99% scaling efficiency in the offline scenario. Throughput grew nearly in proportion to the hardware added.
Scaling efficiency is key because more GPUs don’t automatically mean proportionally more throughput. If adding nearly double the GPU count delivered only a single-digit percentage improvement in throughput, the infrastructure cost would far outpace the performance return. The architecture, interconnect and software must all scale together.
GB300 NVL72 also demonstrated rack-scale efficiency on the WAN 2.2 text-to-video benchmark, reaching 0.65 720p videos per second at 5.7 seconds per video — 9x higher throughput and 7.5x lower latency than a single node.
NVIDIA platform undergoes continuous software development, delivering performance and feature improvements.
In v6.1, GB300 NVL72 performance on Qwen3-VL improved up to 1.6x over v6.0 results. The gains came through lower KV cache precision, additional kernel fusion, better kernels and disaggregated serving with vLLM and NVIDIA Dynamo.
Software optimization continued past the v6.1 submission deadline as well. Post-submission results, not yet verified by MLCommons, on GPT-OSS-120B and DLRMv3 show further performance gains.
Beyond the NVIDIA Grace Blackwell and Vera Rubin NVL72 platform results, NVIDIA submitted Jetson AGX Thor results using NVIDIA TensorRT Edge-LLM on the newly introduced Edge-Agentic benchmark with Qwen3.6-27B.
The NVIDIA partner ecosystem participated broadly, with 19 partners — eight of them on multi-node Blackwell NVL72 systems — demonstrating excellent performance. This includes ASUS, Azure, Cisco, CoreWeave, Crusoe, Dell Technologies, Fujitsu, Giga Computing, HPE, Inventec, Lambda, MiTAC Computing, Nebius, Oracle Cloud Infrastructure, Quanta Cloud Technology, Red Hat, ScitiX, Supermicro and Wiwynn.
From compact edge devices to the largest AI factories, NVIDIA continues to advance performance across the full technology stack with an annual cadence of platform architectures, continuously improving software and an ecosystem built to deliver it at scale.
Learn more about the NVIDIA Vera Rubin platform.
We are announcing the signing of a definitive business combination agreement with Aleph Alpha, following the release of our planned partnership in April of this year. Operating globally as Cohere, the unified company will effectively create the first transatlantic sovereign AI solution. The transaction remains subject to final regulatory approvals.
The company will remain deeply rooted in both countries with headquarters, R&D centers, and leadership roles in Canada and Germany.
Operating globally as Cohere, we will bring together deep research expertise, enterprise-grade solutions, and long-standing public sector partnerships across Canada and Europe. The transaction will increase Cohere's employee headcount to more than 1,000 across both continents, with plans for the company to be dual-headquartered in Berlin and in Toronto and to keep Aleph Alpha’s Heidelberg office as a research center.
As part of the combined company's next phase of growth, Cohere will expand its leadership team with two senior appointments effective upon deal close. Ilhan Scheer, currently Co-CEO of Aleph Alpha, will become Chief Operating Officer of Cohere, leading the company's global operating model and organizational scaling. He previously founded fable+, a data analytics and transformation company acquired by Accenture, where he went on to serve as Managing Director overseeing a global portfolio of enterprise transformation products, and is co-author of Decision Intelligence (Wiley).
Ilhan Scheer, Co-CEO, Aleph Alpha said: “Over the past twelve months, we have sharpened our focus on specialized language models for governments and regulated industries. Cohere is a strong strategic fit for this work, bringing global reach and deployment capabilities that complement our research expertise. Together, we can bring what our team has built to more organizations worldwide. This strengthens our ability to deliver on our mission: giving organizations control over the AI they rely on.”
Samuel Weinbach, co-founder and Co-Chief Research Officer of Aleph Alpha, will become Chief Research Officer of Cohere, advancing the company's research and technological development. His scientific work spans tokenization, language model training, explainability, and image generation. Both appointments are contingent on completion of the transaction, which remains subject to regulatory approvals and is expected to close later this year, with further integration and product details to follow.
Cohere will become the first foundational model developer anchored on both sides of the Atlantic to deliver AI solutions aligned with the sovereignty requirements of both Canada and Germany.
The combined company will operate within the applicable legal and regulatory frameworks of both jurisdictions, including requirements governing the protection and handling of sensitive data and technology. Its structure incorporates safeguards and oversight mechanisms that reinforce operational control, accountability, and compliance across both markets.
“No government or enterprise should have to choose between capable AI and control over their technology,” said Aidan Gomez, Co-founder and CEO, Cohere. “That belief is exactly why we’re joining forces with Aleph Alpha to enhance the talent, infrastructure and institutional trust behind our mission. Together, we’ll move faster to meet the rising global demand for frontier AI that’s powerful enough to compete, but secure and governable enough to trust.”
The company will also advance its partnership with the companies of Schwarz Group to deliver sovereign AI on STACKIT, Schwarz Digits sovereign cloud service and a cornerstone of Europe’s digital sovereignty strategy.
Christian Müller, CEO, Schwarz Digits, said, “Digital sovereignty is not about going it alone, but about strategically joining forces. This transatlantic alliance represents a massive leap forward accelerating the development of an independent AI infrastructure. With the goal of establishing STACKIT as the technical backbone for sovereign AI solutions, we ensure organizations do not have to choose between high performance and digital independence. By partnering with those who uncompromisingly share our strict values and data security standards, we are sustainably strengthening the sovereign digital future of Europe and Canada.”
Learn more here.
Modern development tools, especially IntelliJ IDEA, have such comprehensive debugging support that for virtually any niche use case, there is a specialized tool for the job. This can make it hard to know where to begin. If you are new to debugging tools and want the biggest return on your learning investment, the best feature to start with is logpoints.
Even though working with them is as simple as debugging with plain println statements (and arguably simpler), they vastly broaden the range of issues that you can debug. For some issues, logpoints are the only practical approach. And for the rest, logpoints provide a convenience that saves a lot of time and effort.
Also, in IntelliJ IDEA 2026.2, logpoints got some very cool improvements, so it’s a perfect time to explore them.
Here’s a mini client and server that use gRPC for communication. The server has a bug that makes it return incorrect discount values for some tenants.
So we’ll follow the usual debugging flow. We’ll reproduce the problem, set up visibility into the inner workings of the server, send a problematic request, and observe exactly how it produces the wrong result.
To simulate running in another environment, the project bundles a Dockerfile with the listening and debug ports exposed. You can launch it using the supplied GrpcQuoteServerContainer run configuration or directly from the command line:
docker build -t grpc-timeout . docker run --rm -p 50051:50051 -p 5005:5005 grpc-timeout
Then for a problematic request, use the GrpcQuoteClientLoop run configuration, which will periodically query the server. This lets us forget about sending requests manually and focus more on what happens on the server.
When both the server and the client loop are running, the console shows the following:
tenant='JetBrains' region='EMEA' status=OK symbol=IDEA price=100.00 USD source=live detail=region=emea, discount_bps=0
But we would expect to see:
tenant='JetBrains' region='EMEA' status=OK symbol=IDEA price=80.00 USD source=live detail=region=emea, discount_bps=2000
The server is not launched from a local IntelliJ IDEA debug session, but it listens for debugger connections, so we can still attach to it using the provided GrpcQuoteServer attach run configuration.
It’s worth noting that, for the debugger, there is no difference whether the process runs locally, in a separate environment, or on a remote host. In all three cases, the communication happens over a socket, so our exercise is valid for debugging any Java process regardless of locality.
Logpoints are similar to println statements in that they don’t suspend the program but only log the necessary details to the console. Unlike println statements, however, they can be changed without rebuilding or redeploying the application.
You might already know how to set a logpoint, but IntelliJ IDEA 2026.2 introduced a faster way. Click in the gutter in between any two executable lines and enter the expression you want to log. As the starting point, we can use the beginning of the query handling method ( QuoteEndpoint:12 ):
Warning: Beware of heavy computations in hot paths. They are executed in the same VM and are not magically free. As of version 2026.2, IntelliJ IDEA removes the debugger-introduced overhead through instrumentation, but heavy logging expressions may still take time to execute.
For every incoming request, the console now prints:
EMEA JetBrains
Now, with the request loop running, we can progressively change and add logpoints until the output points to the bug. Just add more logpoints or update the existing ones and watch for new messages in the console as new requests come in.
After following the call chain, which allows us to rule out any initial suspicions we might have had, we arrive at the discountBpsFor() method:
The console points at the tenant name not being properly normalized:
tenant = JetBrains expected: jetbrains
Also, the absence of discount applied tells us that the block with the correct discount is never entered. Normalizing the tenant name should fix the bug:
private static int discountBpsFor(String tenant) {
if ("jetbrains".equalsIgnoreCase(tenant)) {
return 2_000;
}
return 0;
}
Pro tip: When in doubt about what produced specific console output, click the line in the console, and IntelliJ IDEA will take you to the relevant logpoint or piece of code:
Even if you are using println statements for logging, the navigation will work for you as long as you are running the process with IntelliJ IDEA’s debugger.
Let’s test the fix while we’re here. Logpoints are meant for logging, not for modifying the program, but nothing really prevents us from testing how a particular fix would behave:
The prototype works as expected:
EMEA JetBrains jetbrains discount applied
Now that we’ve tested the fix, we can change the actual server code.
You’re probably thinking logpoints look like nicer println statements. And they do, in a sense, because the core mechanic is the same. In both cases, you add probes in a simple way that doesn’t affect how the program runs.
Despite their similarities, there are several reasons why logpoints can be a better choice:
These advantages set logpoints apart from println statements and make them feel more like the professional debugging tool they are.
When using the debugger, most developers will reach for breakpoints. But in the particular scenario we’ve been looking at, logpoints are a better fit, and this is not just a matter of preference.
Let’s see what happens if we use regular breakpoints. After attaching to the server, set a line breakpoint at GrpcQuoteServer.java:55 . The next request from the loop suspends the server:
But after looking at the program state and stepping a couple of times, we find ourselves in the cancellation path:
Once we’re there, there is no useful information, because it’s not where requests normally end up. This happens because the deadline that the client has set is expired and the server discards further work on the request. You can see that IntelliJ IDEA greyed out parts of the code that are not going to be executed. To bring the problematic state back, we have to send requests one after another and do our debugging work within the timeout window.
This happens because our client sets a deadline for the remote call. Unlike a typical HTTP/REST client timeout, where the timeout only signals failure on the client, gRPC can propagate the client’s deadline to the server. As a result, the client can actually cancel the server-side work, not just stop waiting for a response.
On the other hand, logpoints give us the same information we would get in the debugger UI, except that we observe it in the console. Importantly, using them doesn’t suspend the server, so we can extract the information we need without triggering the timeout.
If you’d prefer an alternative approach to this scenario, here’s another way to debug it. For our gRPC example, the problematic bit was the timeout, and we can remove it at runtime using… logpoints!
As you’ve just seen, logpoint expressions can modify the running program through side effects. Here, we can use this technique to adjust the incoming request.
First, find the library method that sets the timeout. There are several methods we can use. One of them is io.grpc.internal.ServerImpl.createContext :
In that method, we can rewrite the value of the timeoutNanos local variable right after it has been assigned:
With this logpoint in place, every time the gRPC timeout value is read from the request headers, it is immediately replaced with a five-minute deadline. This means we can suspend the server again.
If you want to only extend the timeout for the reproducer requests and keep the server functioning as usual – for example, if it runs on a shared staging instance – you can use multiline logic inside a logpoint:
Here’s the code to copy to the logpoint:
Metadata.Key<String> DEBUG_HEADER = Metadata.Key.of("Debug", Metadata.ASCII_STRING_MARSHALLER);
String debugHeader = headers.get(DEBUG_HEADER);
if ("Debug".equals(debugHeader)) {
timeoutNanos = java.util.concurrent.TimeUnit.MINUTES.toNanos(5L);
return "Timeout reset";
}
The multiline expression parses the request headers and extends the timeout only for requests with the Debug header (which our test clients add). Other requests keep the normal deadline. The if branch returns “Timeout reset”, confirming when the branch was visited.
Pro tip: By combining logpoints with the Mark Object feature, you can access arbitrary objects in the logpoint expression field. This article covers the technique in more detail.
Of course, the method above requires familiarity with the library or time to explore it. If you have neither and just want to change the runtime behavior quickly, you can delegate the task to an AI agent using the bundled ij-debugger AI agent skill:
The AI agent follows the same path we followed manually, finding where the deadline is read and producing a logpoint expression that changes only the requests we care about. So even without knowing the gRPC internals upfront, we can still get to a targeted workaround and continue debugging.
In this article, we looked at a use case where logpoints offer a simpler and more elegant alternative to breakpoints or println logging. We:
I hope you learned something new and now have a better option for the next time println statements or breakpoints get in the way. In the next post of the series, we’ll look at debugger instrumentation, the underlying mechanism that makes the new logpoints so fast.
Happy debugging!
We’ve released version 2026.2.2 of ReSharper, Rider, and .NET tools. You can install this update from inside the tools themselves, through the Toolbox App, or on our website.
Here’s what’s new in this update.
We’ve recently added a wide range of features to help you work with coding agents in Rider. With so much new functionality, keeping track of every announcement and discovering what’s available can be a challenge. That’s why we’ve now put everything you need in one place.
Rider 2026.2.2 introduces the AI Agent Setup widget, a new entry point in the bottom-right status bar. This new tool helps you find and set up Rider’s features for coding agents. When you open it, you’ll see a list of supported features like MCP tools, skills, and hooks, along with brief explanations of how each one can help your workflow.
The overview shows you which features are already enabled and which ones you can set up, with direct links to the right settings pages. No matter if your coding agent runs inside Rider or in an external terminal, the widget helps you see what Rider offers and quickly find the settings you need to begin.
Rider’s hooks now provide AI agents with more targeted feedback after code changes. This helps cut down on unnecessary information and makes formatting changes coming from the IDE easier to spot.
Agents now get inspection results from the Run files inspections hook only for the code they added, not the whole file. This lets them focus on their own changes.
The Reformat file hook now gives a diff that shows any formatting changes made by the IDE. Agents can see what changed right away, so they don’t have to reread the file to understand the updates.
Rider’s dotCover integration now measures coverage for unit tests written with TUnit. To enable coverage, install the JetBrains.dotCover.Framework package in your project.
To enable full Testing Platform support in Rider, go to Settings | Build, Execution, Deployment | Unit Testing | Testing Platform and check the Enable Test Platform support box.
Note: TUnit coverage requires Microsoft.Testing.Platform version 2.3.0 or later.
Here are some important fixes included in this release:
To see the complete list of fixes and improvements, please visit this page.
dotCover now measures coverage for unit tests written with TUnit. To enable coverage, install the JetBrains.dotCover.Framework package in your project.
To enable full Testing Platform support in Rider, go to Extensions| ReSharper | Options | Unit Testing | Testing Platform and check the Enable Test Platform support box.
Note: TUnit coverage requires Microsoft.Testing.Platform version 2.3.0 or later.
For the complete list of updates included in this build, please see the full release notes.
You can download the latest builds from our website or via the Toolbox App. You can also update Rider as a snap package. Let us know how the new features fit into your workflow in the comments below.
AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center.
Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its-kind coalition advancing data centers that can dynamically manage their electricity use in response to grid conditions.
This power flexibility can help unlock faster, larger connections for AI infrastructure while supporting the energy systems and communities that make its growth possible. Getting more watts out of existing infrastructure reduces environmental impacts per watt and supports energy affordability.
The objective is clear: build AI infrastructure that doesn’t just connect to the grid but works with it.
Power has become a defining constraint on the expansion of U.S. AI infrastructure.
Traditional interconnection processes were designed around facilities with flat, static electricity demand. They weren’t built for computing infrastructure capable of responding intelligently when the power system is constrained.
A flexible data center can adjust its electricity drawn from the grid in several ways — shifting computing workloads, discharging storage, using paired generation or responding to system contingencies. These capabilities allow a large electricity customer to serve as a controllable resource rather than an inflexible load.
Used effectively, flexibility can make more efficient use of existing grid capacity, reduce demand during periods of system stress, and avoid or defer costly infrastructure upgrades. It can also give utilities and grid operators greater confidence to connect AI facilities on shorter timelines.
AEMA is technology-neutral and performance-based. Its focus is on the measurable service a facility can deliver — including response speed, duration, predictability and behavior during an emergency — rather than the specific hardware or software used.
Reliability remains paramount. The alliance’s principles call for:
These measures can reduce uncertainty for developers while giving system operators the information and control needed to preserve reliability.
AEMA convenes the full value chain across computing and power — including AI platforms, infrastructure providers, data center operators, technology companies, power producers, utilities and regional grid operators.
The founding members will be joined by launch partners from across the ecosystem. Together, AEMA will develop technical and operational approaches, collaborate with utilities on interconnection solutions and advocate for policies that recognize grid-responsive demand.
AI factories transform energy and data into intelligence. Power-flexible design gives them the potential to support the grid as they do it.
NVIDIA and Emerald AI are already working with energy and infrastructure leaders on AI factories that can respond to grid conditions in real time. AEMA will broaden that work by bringing the technology, energy and policy communities together around models that can be deployed across the U.S.
The rules governing power for AI are being written now. By creating a common framework for performance, reliability and collaboration, AEMA aims to help the U.S. build the infrastructure of intelligence at the speed and sustainability the moment demands.
Learn more about AEMA and membership opportunities.
We’ve all been there: you join a new project, and the first thing you ask for is the architecture diagram. You’re handed a diagram that looks great, but after a week of debugging, you realize it’s six months out of date. Service A hasn’t talked to Service B since the spring, and there’s a new message queue nobody bothered to document.
Figuring out how a complex system actually fits together is a classic engineering headache. You can try to figure it out manually (if you have faith in yourself and enough time to spare), or you can use static analysis to explore the codebase (which often fails to capture how services are actually wired together at runtime).
But there’s a third way: dynamic analysis. What if we could just watch the system run and draw the map based on what is actually happening?
Since the OpenTelemetry plugin is already collecting a wealth of runtime data – logs, metrics, and traces – we realized we had the perfect opportunity to auto-generate this architecture map for you. Here’s a look under the hood at the Service Map feature, built as part of a collaboration between the Rider Execution team and Software Engineering Research.
If you’re familiar with observability, you know the “three pillars”: logs, metrics, and traces.
While logs tell you what happened and metrics tell you how much, traces show you the journey of a request through your system. Traces are made up of individual units of work called spans.
Because OpenTelemetry standardizes these spans (for instance, explicitly defining HTTP Client and HTTP Server spans), they are the ultimate cheat code for understanding system architecture. Relying on the OpenTelemetry standard means the plugin can visualize your system completely independently of your technology stack, as long as your app and libraries emit spans the way OTel expects.
Building a map from traces has one massive advantage: it’s the source of runtime truth. We aren’t guessing based on source code or outdated specs. We are looking at data generated by the live system.
So, how does this actually work inside your JetBrains IDE?
When you start your IDE with OpenTelemetry plugin enabled, the plugin starts a lightweight local OpenTelemetry backend that can process your application telemetry data.
When you hit Run in your IDE:
If you look at an architecture diagram, it looks static and orderly. But the stream of telemetry data generating that diagram is anything but. Before we could write an algorithm to connect the dots, we had to solve a few hidden challenges:
Spans arrive completely independently, and their order is never guaranteed. A parent span might finish and arrive after its child span has already been processed.
A trace never explicitly says “I’m done.” At any given moment, we can never be 100% sure that a late-arriving span isn’t about to show up.
OpenTelemetry doesn’t provide a strictly typed version for each span type. Instead, each span carries a key-value map with attributes that describe the semantics of the operation. We had to deduce what kind of interaction they represent purely by inspecting their attributes.
To handle this asynchronous, out-of-order data, we built the architecture reconstruction as a stream processing algorithm. Instead of waiting around for a complete trace – which, as we just established, is impossible to guarantee – we process every span the moment it arrives.
First we figure out what we’re looking at. We pull the basic metadata off the span, then inspect its semantic attributes to classify it: attributes such as http.request.method and http.response.status_code tell us it’s an HTTP call, while others point to a database query, a message queue interaction, and so on.
Next we ask which service emitted it. New service we haven’t seen? It goes on the map. Already there? We merge the new data in and update its statistics.
Then comes the interesting part: connecting the dots across service boundaries. A fully instrumented HTTP call has two sides: the calling service emits a CLIENT span, while the receiving service emits a SERVER span. The trace context travels with the request, so the downstream SERVER span is created as a child of the upstream CLIENT span.
So when an outgoing HTTP Client span shows up, we go looking for its child on the server side. When an incoming HTTP Server span shows up, we look for the parent that called it. If the partner span is already in our system, we draw (or update) the connection between the two services right away. If it isn’t, we park the span in memory and wait for its other half to arrive.
Other kinds of dependencies require slightly different rules. A database call is usually represented by a single CLIENT span, so we infer the database node directly from its semantic attributes. Messaging is more varied: producer and consumer operations may be connected through a parent-child relationship or through span links, depending on the messaging system and instrumentation. In every case, the backend processes spans as they arrive and incrementally enriches the map as more evidence becomes available
That last step is what lets the plugin build an accurate, real-time map, even when the network delivers everything late and out of order.
This way we can process and show you information about http requests, database requests and message queues.
Because the map is built from standard OpenTelemetry spans and the reconstruction algorithm relies on semantic conventions rather than framework-specific APIs, the feature is language- and vendor-agnostic. The same logic works across JVM, .NET, Python, Go, and other OpenTelemetry-instrumented applications, as long as their instrumentation emits the expected spans and propagates context correctly. This also means you can use the feature in the JetBrains IDE that best fits your stack, including IntelliJ IDEA, GoLand, PyCharm, WebStorm, and Rider.
Want to see your own architecture mapped out in real-time? Expected one HTTP call or database query, but the diagram shows several? Finding that during development gives you time to fix it before release.
You can install the OpenTelemetry plugin right now and stop guessing how your services talk to each other.
Today, we are announcing a partnership with Mozilla to bring privacy, control and choice to people using AI to browse online.
Firefox Smart Window (beta), Mozilla’s AI browsing assistant, is now powered by Mistral models. Smart Window helps you make sense of complex searches, remember something important you clicked away from and source information important to you based on your browser tabs. Mistral will help power Smart Window for users in France and North America, with the United Kingdom and Germany expected to follow later this year.
This partnership represents two open source advocates working together to bring Mistal’s scientific innovations to consumers around the world. We are building AI systems that are trained and fine-tuned on regional languages, dialects and cultural context, so anyone can get responses that understand their local nuance.
This announcement is important to the global AI ecosystem for four reasons:
Open technology needs open distribution: Mozilla has spent more than two decades fighting for an open web and Mistral has been releasing open weight, frontier models since our first release. This partnership is about demonstrating the potential of open source to serve people around the world.
AI optimized for local countries and cultures, not exported to them: We’re fine-tuning our models on regional languages and dialects so that everyone, no matter where they are or how they communicate, can benefit from AI that understands their local nuances. This partnership extends this capability to people who use Firefox worldwide so that their AI experience feels native to them.
Giving people control of their interactions with AI: Firefox has a long history of control and privacy in its DNA, and these are values we share at Mistral. Our partnership is rooted in a shared commitment to user choice, control and openness. Together, we’re blending Firefox’s privacy-first legacy with Mistral’s cutting-edge open models to give people autonomy over their browsing experience. Privacy protections are built into how Firefox Smart Window works: conversations aren’t saved on Mozilla’s servers by default, and partners like Mistral agree to zero data retention.
Putting sovereign AI in everyone’s hands: At Mistral, we’re committed to putting sovereign AI in everyone's hands. While we traditionally focus on serving the enterprise, by partnering with ecosystem leaders like Firefox, we extend beyond businesses and reach their consumers worldwide. This way, end users can benefit from our technology that’s rooted in user control, transparency and open innovation.
You can learn more about Smart Window here.
AI is becoming part of how people experience the web every day. We want to make sure that doesn’t mean people are chained to one company’s self-serving pipeline. With the browser sitting at the heart of the web and online experience, it should be a place where different AI providers can compete and open source has a seat at the table. This isn't just a product partnership. A browser shouldn’t be a one-way funnel. It should preserve what made the internet powerful to begin with: the freedom to explore, discover different ideas and tech, and decide for ourselves where to go next.
Anthony Enzor-DeMeo, CEO, Mozilla Corporation
This partnership represents two open source advocates working together to bring Mistral’s scientific innovations to Mozilla’s consumers around the world. Together, we are bringing privacy, control and choice to AI-powered web browsing.
Arthur Mensch, co-founder and CEO of Mistral
Solar Mini 4 is Upstage's compact, cost-efficient language model, a 35B-parameter mixture-of-experts with 3B active parameters and a 524K context window. It is built for agentic use cases where response...
Command A+ is Cohere's flagship model for enterprise agentic workflows. It accepts text and image inputs with a 192K context window, supports native tool calling with strict tool schemas, structured...
GPT-6 Luna Pro is the same underlying model as [GPT-6 Luna](https://openrouter.ai/openai/gpt-6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode
GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol. It is suited for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic...
GPT-6 Sol Pro is the same underlying model as [GPT-6 Sol](https://openrouter.ai/openai/gpt-6-sol), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode
OpenAI made a valiant effort with GPT-6 Sol and Luna launching 50% lower than GPT-5.6, but with 17M views on the launch and counting, today was always going to belong to Claude Opus 5.5, “the first model in our new Claude 5.5 family” performing like “Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.”
Opus 5.5 beats Fable or challenges Astra at most benchmarks, and both labs credited efficiency work for the API price cuts, but there are HUGE double digit gains everywhere from prefill to decode to overall compute…
… with offsetting inefficiency in token usage on some frontier tasks.
HOWEVER something that is a rare emphasis in the Claude launch was the writing improvements: “It puts the most important information up front and follows the writing rules you give it, which makes long sessions easier to follow.”
We can confirm - here is today’s AINews section run on Opus 5.5 and Sol 6. The difference is night and day - we are migrating to Opus 5.5 immediately for AINews going forward until we reach the next model/version of AINews.
They have also published initial work on large multiagent swarms (and efficiency):
AI News for 9/21/2026-9/22/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!
Top Story: Claude Opus 5.5 launch, numbers, and reactions
Anthropic shipped Claude Opus 5.5, the first model in a new Claude 5.5 family. Its pitch is Fable 5.1‑level capability at Opus pricing, with more speed and better writing. OpenAI released GPT‑6 Sol and Luna about an hour later.
Launch claims. Opus 5.5 “performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5” (@claudeai; @AnthropicAI).
Where it leads. Anthropic says it leads on agentic coding, computer use, and knowledge work (@claudeai).
Speed and cost. It is about 30% faster and about 40% cheaper per task than Opus 5 (@ClaudeDevs, @lydiahallie).
Communication fixes. The model puts the most important information up front and follows user writing rules. This targets the most common feedback on Opus 5 (@claudeai).
Subscription changes:
5‑hour session limits are up 20%.
Lower pricing means limits go 25% further.
Pro, Max, and Team users get a banked rate‑limit reset they can use whenever they choose (@claudeai, @ClaudeDevs, @trq212).
New defaults. Opus 5.5 is now the default in Claude Code and the Claude app, including Cowork. Default effort is medium, described as “comparable to Fable 5.1 on intelligence but faster” (@_catwu).
Availability. It is live in Claude Code and the Claude Platform API (@ClaudeDevs), and in Claude Tag for Slack (@_catwu).
Roadmap. Sonnet 5.5 and Haiku 5.5 follow “in the coming weeks” (@mikeyk, @AiBattle_). This contradicts rumors that Haiku was discontinued (@kimmonismus).
Safeguards. Opus 5.5 is the first Opus with Fable 5.1‑class safeguards on cyber, bio, and frontier LLM development. Flagged requests fall back to another model, and Anthropic says it is “working to reduce incorrect flags” (@ClaudeDevs).
Pre-release signals. The model was spotted in Claude Code shortly before the announcement (@kimmonismus).
System card. It was published at launch (@scaling01).
List price. Token pricing was cut 20%, from $5/$25 to $4/$20 per 1M input/output tokens (@ValsAI).
Offset by higher token use. Vals notes Opus 5.5 often uses more tokens, especially on coding, where it posts its largest gains. The lower sticker price is partly offset by usage.
Artificial Analysis cost breakdown. At max effort, Opus 5.5 costs $5.98 per Intelligence Index task versus $5.86 for Opus 5 (max). Their decomposition (@ArtificialAnlys):
Higher token usage alone would raise cost per task about 80%, to $10.51.
The 20% base-price cut brings that to $8.41.
Cheaper cache reads ($0.20) bring it to $5.98.
What that means. At max effort, the per‑task saving over Opus 5 disappears. The “40% cheaper” claim applies to default (medium) settings.
Relative to Fable 5.1. Cline reports Opus 5.5 beats Fable 5.1 on the Artificial Analysis Intelligence Index at about 2.5x lower cost (@cline).
Prompt caching. Switching effort mid‑session does not break the prompt cache on Claude Code v2.1.280+ (@lydiahallie).
Model size (speculation). @theo claimed Opus 5.5 is smaller than Opus 5 and credited post‑training. This was not confirmed in official posts.
Anthropic’s own table. Opus 5.5 beats Fable 5.1 on every row of Anthropic’s headline comparison and beats GPT‑6 Astra on most (@kimmonismus, @synthwavedd, @scaling01).
@ShayneRedford (Anthropic) summarized the claimed gains:
Stronger than Astra on CursorBench, KWBench, and OSWorld.
Much better style and instruction following.
Stronger science and health capabilities.
More robust against cyber and bio misuse.
Third‑party and partner evals:
EvalResultSourceVals Index#1, up 2 spots / 2 pts vs Opus 5; Anthropic holds the top three spots (GPT‑6 Sol pending)@ValsAIVals RSI Index#1; first model to beat the published reference on LM Training under their protocol; beats Fable 5.1@ValsAI, @ValsAIFrontierSWE (Proximal)62.3%, #2 behind GPT‑6 Astra (65.5%); ahead of Fable 5.1 (56.3%) and Opus 5 (52.0%)@ProximalHQFrontierCode 1.1 (Cognition)65.3% on Extended; takes #1 from Fable 5 “at a fraction of the cost”@cognitionCursorBench57.8% (Max), new top model; 40% less per task than Opus 5@cursor_aiPerplexity WANDR0.610 at $4.13/task; slightly above Fable 5.1 at 67.6% lower cost@perplexity_aiParseBench (tables)93.9%, +7 pts over Opus 5; beats Fable, Gemini, Astra@jerryjliu0Roboflow vision/detection”By far the best vision model from Anthropic”; now among the models ahead of Google on the Playground leaderboard@skalskip92, @skalskip92
Eval details and caveats:
Vals run settings. RSI was run in native Claude Code at max effort, with 1M context, 128K max output tokens, and temperature 1 (@ValsAI).
ParseBench caveats. The model still struggles on charts, formatting, and layout. At 5.8¢/page, LlamaIndex calls it too expensive for production OCR. That verdict comes from a vendor with a competing product.
AI R&D vs coding. @eliebakouch reads the system card as “roughly similar on AI R&D but a beast on agentic coding.”
Saturation. @scaling01 asked whether CoBench is “cooked.” @synthwavedd joked about a new benchmark that launched already saturated.
Arena. Opus 5.5 is in Agent Arena and in Battle Mode for WebDev, Text, Vision, and Document. No scores yet (@arena).
Effort‑scaling anomaly. On an agentic coding chart, xhigh effort costs about 2.8x more than medium for a 3.2‑point lower score (@LearnOpenCV). @Yuchenj_UW called it the “most bizarre benchmark result” and advised sticking with medium.
@nrehiew_ offered an explanation:
Opus 5 showed the same pattern on FrontierCode.
FrontierCode penalizes unnecessary changes, and higher effort produces scope creep.
As a result, models “consistently perform worse at higher reasoning efforts.”
Multi‑agent scaling. The system card reports scaling up to 100 parallel agents in Section 8.12. @scaling01 called it the first lab report of its kind. @maksym_andr highlighted it as evidence on multi-agent scaling laws.
ProgramBench caveats. ProgramBench author @OfirPress flagged that Anthropic’s near‑100% solve rate comes from a 166/200 subset. That subset likely excludes the hardest programs, such as FFmpeg and the PHP compiler. He also flagged a metric mismatch (@OfirPress, @OfirPress):
Anthropic reports average test pass rate.
ProgramBench reports full task completion.
Partial solves often pass 60–70% of tests, which inflates the pass-rate metric.
Comparison with Mythos 5.1. Opus 5.5 outscores Mythos 5.1 on Anthropic’s ECI and beats it on every tested cyber eval (@scaling01, @scaling01).
Odd misalignment finding. @teortaxesTex quoted a passage: malicious output occurred “almost exclusively in cases where, prior to the malicious output, Claude made an improbable, innocuous mistake.” He asked whether Anthropic had “sleeper-agent[ed] themselves.”
“Trained from RSI.” He separately quoted a line about “the first model trained from RSI” and called it concerning (@teortaxesTex).
Biomedical imaging. @iScienceLuvr welcomed the reported biomedical image analysis capabilities.
Requests for more. @scaling01 asked for time horizons without chain-of-thought.
Official position:
Sam Bowman: Opus 5.5 is “sufficiently safer than its predecessors that releasing it, more likely than not, reduces risks related to misalignment,” especially for the most extreme alignment risks (@sleepinyourhat, @sleepinyourhat).
He also acknowledged worry about keeping pace with escalating risk, while saying current tools remain trustworthy at this capability level (@sleepinyourhat).
Mike Krieger cited extensive alignment testing and outside evaluation, including by METR (@mikeyk).
Friction:
Over-triggering fallback. @iScienceLuvr got downgraded to the fallback model after asking Opus 5.5 to cure cancer.
China targeting (single test). @xlr8harder says a quick test suggests the frontier-LLM-development classifiers target Chinese hardware. He calls for more probing.
Reactions to the China angle. @teortaxesTex framed this as Anthropic undermining Chinese AI. @jakehalloran1 read it as protecting Trainium know‑how.
“Pacing the frontier” framing:
@theo argued none of today’s releases were Astra‑ or Fable‑tier and that this is deliberate pacing.
@goodside said lab calls to pace the frontier have weakened his “pause and do what?” stance.
@dejavucoder mocked the framing, given that Opus 5.5 outperforms Fable 5.1.
Writing fixes from staff. “We fixed the writing” (@_sholtodouglas) and “we fixed the accent” (@NotTomBrown).
Unusual candor. @nmca (Anthropic) posted: “way, way, way better than Opus 5. Sorry about that model.” @theo called it a wild tweet that signals looser comms.
Em dashes. @theo reports they are gone from output. It was the most‑engaged reaction post.
Anthropic’s prompting playbook (@ClaudeDevs):
Hand over a whole task and define “done” and check‑in points.
Drop “think carefully,” since the model always thinks first.
After a long run, ask what it needs to go further.
Why old tricks break. @dbreunig notes old prompt tricks now clash with the model’s training, an argument for re‑compilable prompt optimization.
Long-run steering. @omarsar0 highlights Anthropic’s prompt for long runs, where the model sometimes stops to report instead of continuing.
Bug report. The live model sometimes generates user turns (@BlackHC).
Writing quality in practice. Hamel Husain livestreamed “Is Slop Dead?” testing its writing (@HamelHusain). @nptacek shared a one‑shot result from a personal writing eval.
Improved perception. Sholto Douglas says the 5.5 series has “a serious step up” in 3D understanding and modeling, and that the model “can see now; it was a bit blind before” (@_sholtodouglas, @_sholtodouglas).
Painting in code. @jkeatn had the model generate paintings with pure Python, pixel by pixel:
About 7,500 lines of code using standard libraries to emulate brush styles.
No image model and no reference images.
Sholto contrasts this “manual brush” creativity with diffusion models (@_sholtodouglas).
Blender scenes. Alex Albert showed Blender claymations from one prompt on claude.ai (@alexalbert__). He also built a source‑grounded 1906 San Francisco Market Street:
Built from Sanborn maps, period film, and archival photos.
Procedural generators only, with no downloaded meshes or textures (@alexalbert__, prompt).
@karpathy riffed on the idea: turn historical images or video into custom GTA‑style worlds you can walk through.
More demos:
A code‑drawn JS animation (@kevin_t_ngo) and an official exploration thread (@claudeai).
A code‑generated Golden Gate Bridge, judged “as good as Astra” at 3D scenes (@petergyang).
“Best visual design of any model I’ve tested” (@other__reality).
A coral reef wallpaper; the builder says it feels about 3x faster and cheaper (@chaseleantj).
Open question. @teortaxesTex asks why this generation is so good at mapping functions to pixels, and suggests generalization.
Supportive:
Pipeline bugs. @rishdotblog says it found pipeline issues that Fable and Astra missed. It also found 7 SEC filing errors, including a Comfort Systems XBRL mis‑tag of Q1 revenue as full‑year (@rishdotblog).
Returning users. “Claude is back”: @Yuchenj_UW says he is returning to Claude Code after a month away.
Usage limits. Heavy all‑day use “barely making a dent” in limits (@theo).
Nostalgia. Comparisons to the well‑liked Opus 4.5 and 4.6 (@arohan, @kimmonismus).
Competitive framing. @scaling01 said Anthropic is “frontier‑mogging again.” @kimmonismus said “they chose war with OpenAI.”
Skeptical or neutral:
Trust deficit. @kylebrussell says he no longer trusts Opus releases to feel better. Sholto replied asking whether this one resets that trust (@_sholtodouglas).
Limits don’t matter to everyone. @stablequan never hits the limits anyway.
Price as headline. @dbreunig asked what it means that both labs’ headline feature is cheaper tokens.
Head-to-head with GPT‑6 Sol:
Continued at the source.
22nd September 2026
Yesterday was Grok 4.7 (pelicans) and MiMo v2.6 Flash/Pro (more pelicans). Today Anthropic released Claude Opus 5.5, and around an hour later OpenAI released GPT-6 Sol and GPT-6 Luna. It’s going to take a while to get a good read on all of these new models, but here are my impressions so far.
GPT-5.6 Luna was already my favorite model for building applications against, because it combined excellent performance with being really cheap. Somehow GPT-6 Luna is half the price of that again—and GPT-6 Sol had a similar reduction compared to GPT-5.6 Sol.
Here’s what the pricing landscape looks like today:
| Model | Input | Cached input | Output |
|---|---|---|---|
| GPT-6 Luna | $0.10/M | $0.01/M | $0.50/M |
| GPT-5.6 Luna | $0.20/M | $0.02/M | $1.20/M |
| Grok 4.7 | $2/M | $0.50/M | $6/M |
| GPT-6 Sol | $2/M | $0.20/M | $10/M |
| GPT-5.6 Terra | $2/M | $0.20/M | $12/M |
| Claude Opus 5.5 | $4/M | $0.20/M | $20/M |
| GPT-5.6 Sol | $4/M | $0.40/M | $20/M |
| Claude Fable 5.1 | $10/M | $0.25/M | $50/M |
| GPT-6 Astra | $10/M | $1/M | $50/M |
Note that GPT-5.6 has a scheduled 25% price increase for November, so GPT-6 is half the price of the promotional pricing for those models.
(With GPT-5.6 Terra priced the same as GPT-6 Sol, any remaining reasons to use Terra just evaporated.)
It’s hard to overstate how competitive this pricing is. Grok 4.7 priced itself at $2/$6, less than half the price of GPT-5.6 Sol, but is now equally priced to GPT-6 Sol on input and closer on output.
At $0.10/$0.50 GPT-6 Luna is one of the cheapest models OpenAI have ever released, beaten only by the far weaker GPT-4.1 Nano ($0.10/$0.40, April 2025) and GPT-5 Nano ($0.05/$0.40, August 2025).
I rendered pelicans for GPT-6 Luna and for GPT-6 Sol, then I combined them all together in this comparison grid along with the GPT-5.6 pelicans. I like how you can instantly see that the 5.6 family chose bolder, brighter colors, while the 6 family is a lot more muted. I still think GPT-6 Astra on max produced the best pelican.
Opus 5.5 looks like it addresses the biggest complaints people had about Opus in terms of its communication style. Thariq Shihipar:
Opus 5.5 is the result of your feedback.
It communicates clearly, it’s cheaper per token than Opus 5.0 with the intelligence of Fable 5.1 it’s very token efficient and works across every effort level.
It’s also meant to be better at Blender. I’m looking forward to putting it through its paces there.
Opus 4.5, 4.6, 4.7, 4.8, and 5 all shared the same price: $5/million tokens for input and $25/million for output. 5.5 is a 20% reduction—$4/million and $20/million.
The price for cache reads fell 60%. That’s significant for longer agentic conversations, where 90%+ of input tokens are processed at cached token prices.
The new price for Opus 5.5 is the same as the price for GPT-5.6 Sol, but that was before OpenAI dropped their Sol prices by half.
GPT-6 Astra and Claude Fable 5.1 are both priced at $10/million input and $50/million output. The price war currently affects the next tier of models below that.
Anthropic say that Sonnet 5.5 and Haiku 5.5 are coming soon. It’s going to be interesting to see if Haiku can regain its price competitiveness at the lower end, given current Haiku 4.5 is $1/$5 while the latest GPT-6 Luna is one tenth of that price at $0.10/$0.50.
In a first for my "Generate an SVG of a pelican riding a bicycle" test, Claude Opus 5.5 at "max" thinking level failed to return a response!
It started by calling this “a classic test request”, and then thought really, really hard about what it was doing:
This is a classic test request, so I want to plan out a well-composed pelican with its distinctive beak and pouch riding a bicycle with proper wheels, frame, and pedals, set against a simple sky and ground backdrop. [...]
Verifying the shin length checks out at roughly 95.2, close enough. Now I’m working out the near leg path from hip to knee to ankle, then sketching the foot shape resting on the pedal — outlining the heel, toe tips, and sole contour with a path using lines and curves to sit naturally on the pedal surface around y=478-494. [...]
I like the fish sticking prominently out of the basket with the pelican eyeing it as a fun detail worth keeping. I’m also confirming the eye placement near the bill base matches typical pelican anatomy, and considering giving it a slightly happier expression. [...]
The far leg reads correctly as passing behind the frame, so I’m moving on to check the chainring teeth and confirm layer ordering—the far crank arm should be mostly hidden by the seat tube and chainring. I’m settling on the final SVG’s width and height attributes alongside the viewBox to ensure proper scaling, noting there’s no text so no font-family is needed. [...]
I was so excited to see this pelican... but then it stopped. Opus 5.5 has a 128,000 maximum output token limit (as do the other Claude models), and it hit that while it was still reasoning about the SVG!
I tried a second time and got the same result. This makes me suspect that “max” is effectively useless—if it over-thinks to breaking point on a stupid SVG prompt I don’t trust it not to do the same for more interesting work.
(Those two failures each cost me $2.56 and took nearly 20 minutes.)
Fable 5.1 on “max” didn’t over-think and did give me the best pelican I’ve seen from any Anthropic model.
Here are the Opus 5.5 pelicans, excluding 5.5 max.
I also built this comparison grid comparing them with pelicans by Opus 5, Fable 5.1, and Sonnet 5:
Comparing different model vendors by how well they draw a pelican riding a bicycle may not make much sense now (if it ever did), but I’m still finding value in using them for comparisons of the same model families at different reasoning levels.
I’m now using GPT-6 Sol and Claude Opus 5.5 as my default models in Codex and Claude Code. I’ve upgraded the Datasette Agent demo at agent.datasette.io to use GPT-6 Luna, and it seems to be fast and competent at both SQL queries and building HTML and JavaScript for Datasette Apps.
How often do you get to talk to a guest who has both an Academy Award and who invented textbook machine learning algorithms? John Platt has an Oscar, two textbook algorithms, two named asteroids, and an Erdos-Bacon number of 6. This was easily the most fun bio of all the guests we’ve read to date. And the result was an epic and fun chat covering Google’s Empirical Research Assistance (ERA), how AI can help battle climate change, and tons of great stories about the co-evolution of science and AI.
John’s colleague Dave Bacon likes to tease John that his career has been defined by being twenty years early to the next big thing. This may be convolutional neural networks (some credit him with coining the term), fusion research, quantum computing. John and Google have been working on solving some of humanity’s hardest problems with AI and computation for well over a decade now. Recently John and his team set their sights on using AI to solve any scientific problem that can be written down as a score.
John’s team has taken on many hard scientific problems over the years. In solving these, they noticed a pattern, many scientific problems can be reduced to what John calls a “scoreable task”. Once you have the score function, the goal is to find some code that maximizes the score. The hard part is in formulating the score, but once you have the score finding the maximizer can still be quite a lot of effort.
John’s team set out to automate solutions to this general problem. This came out of the idea of an “auto-Kaggle” AI, which can solve any Kaggle problem you can throw at it. Kaggle is owned by Google, so all the data was ready and easily available to them!
The result is Google’s Empirical Research Assistance or ERA (paper, github, blog).1 ERA is surprisingly simple conceptually. Gemini (or your LLM of choice) keeps a running tree of past experiments (notebooks) and where they’re going. It’s a close cousin of Monte Carlo Tree Search: at each iteration the Upper Confidence Bound rule picks which notebooks are most promising to mutate. This is optimistic, not greedy, so sometimes even the fifth-best notebook gets chosen. Gemini then proposes mutations for each one, about ten at a time. The history of each branch is shared, so different leaves can learn from each other.
“It’s almost like having a hyper-eager grad student who doesn’t sleep.”
Evolutionary algorithms have been around since the 70s, but this works because Gemini actually knows where to look! What’s even more interesting is that there was a step change between Gemini 2.0 and 2.5, and this went from just not working to working great.
ERA is so powerful that John and his team solved many outstanding problems with it, resulting in at least ten papers. Some of these were climate change related, which we talk about in the next section.
So, we had to ask: if you have an optimization god how do you avoid fooling yourself? John’s answer is that ERA provides predictive models. It’s up to the scientist to make sure they’re truly descriptive. Some of this just involves good old-fashioned careful machine learning science. “It’s a power tool. It can slice your fingers off.” This led to some fun discussion about Kaggle competitions, and the fun ways people can overfit to datasets without meaningfully solving the problem you actually care about: Google’s contrail-detection competition was won by entrants who noticed a half-pixel error in the labels (is the origin at the corner of the pixel or the center?) and this turned out to be a part of the winning special sauce. Great for winning $15,000, not so helpful if you actually want to solve contrails.
“People themselves will act like these LLMs and try to reward hack. It goes back to Goodhart’s law: any metric that becomes a target is no longer good as a metric.”
His advice for where to start instead?
“Always just fit linear regression. Just do it. Just do it. Just do it. Or SVM.”
John and his team have worked extensively to mitigate the effects of climate change. We talked about several of their initiatives.
Perhaps the most interesting result we talked about was reducing the effects of condensation trails (contrails) from airplanes. Those little streaks you see running behind planes somehow account for 1% of all human-induced global warming?!? Some of these trails of ice crystals can hang out for days. These crystals are black in the infrared, acting like a thermal blanket that traps heat day and night.
It’s easy to understand what’s happening here, a region of atmosphere becomes “ice supersaturated”,2 and a tiny bit of exhaust seeds water vapor that instantly crystallizes. The scale here is astounding, with a single gram of exhaust resulting in ten kilograms of ice crystals.
The solution to all of this is quite simple, in principle! We know what parts of the atmosphere are most likely for the trails to form. Just have the planes drop a flight level or two. Problem solved, right? Well, the hard part is accounting for how much warming was prevented. This is a counterfactual problem, parts of which stumped John’s team for over two years. They had a working model for the heat-trapping half, but not for the reflected sunlight. ERA was able to find a simple model with some confounders they hadn’t considered. Cracked it!
Modeling climate generally is a hard problem. Climate is best thought of an attractor of many different possible weather outcomes.3 This makes it much harder to model.
“Weather is where you are on the attractor, and climate is the statistics of the attractor. The problem with climate is that we’re altering it. The attractor itself is changing, it’s moving.”
John and his team have worked on treating both the symptoms and the disease of climate change, with several other works in the area. Another fun example we briefly cover is FireSat, a way of using a constellation of satellites to rapidly identify fires before they grow too big to put out. For anyone living in California, you understand the problem. In dry years a small fire can result in hundreds of thousands of acres. If you could find this fire when it’s the size of a room, it could be put out. By the time it hits an acre we have a much harder problem.
By now it should be clear John has an incredible and unique view over the intersection of science, computation, and AI. John talked about a class on physics of computation4 he took with Richard Feynman back in 1982. This was when quantum computing was an ill-defined concept with no theory or experimental backing. John recalls every Tuesday was a guest lecture, and every Thursday was Feynman explaining why the Tuesday guest was wrong. John also recalls doing science back when there was essentially no compute, a million operations per second was cutting edge.
What is John’s recommendation: the most important skill is developing deep domain expertise. There’s no other way to develop taste than to tackle hard problems. One surprising part of this is that John recommends spending time doing things the old fashioned way. Play with tools, and just implement things yourself.
“You could drive up the mountain, or you could hike up the mountain, and maybe it’s okay, even fun, to occasionally hike.”
Summing it up, John’s message to the audience is that there will still be a place for scientists, and that if anything it will just open up more opportunities for “the creative stuff, the rigorous stuff, the philosophy stuff.” But don’t forget to spend time doing the grunt work.
“There just seems to be this strong impetus in the world to optimize and squeeze everything out. But you do lose something when you hyper-optimize. It’s overfit.”
And whatever tools you end up using, John’s advice is the same one Feynman gave him forty years ago: you must not fool yourself, and you are the easiest person to fool.
We had a great time talking with John. We hope you enjoy!
Fusion is three years away, not thirty, if you ask John. And why the Lawson criterion means every fusion approach has an Achilles heel.
Why superconducting qubits are still finicky.
The asteroid he named after his mom, which turned out to have a moon.
The looming helium shortage nobody talks about.
How NeurIPS started as people crashing a private workshop at Snowbird, and why Hopfield networks are all you need.
Being Carver Mead’s sysadmin on a VAX with an 80 MB disk the size of a dishwasher.
Finding asteroids in 1985 with film, a stereoscope, and a letter to Brian Marsden. The Vera Rubin Observatory found 11,000 in six weeks.
The Feynman effect: total clarity in the room, none once you leave.
Quantum echoes, the NISQ era, and why he thinks quantum is neither thirty years away nor tomorrow.
A startup that wants to inject mercury into a fusion reactor and sell the transmuted gold. “It might not work.”
John’s 20% time rule for his own group: do stuff for learning, and you don’t even have to tell him what.
The ERA GitHub repo features an open source implementation that ran Gemini but can be used with any LLM. ERA is not currently available as a Google product.
“Ice-supersaturated” is about water vapor, not liquid water. Cold air can hold a given amount of vapor, and there are two different limits: the amount in equilibrium with liquid water, and the smaller amount in equilibrium with ice. Below freezing, a pocket of air can sit between those two limits. It has more vapor than ice can tolerate, but not enough to condense into droplets, and ice won’t form directly from vapor without a seed. So the vapor just hangs there, metastable, sometimes for days, until something seeds it.
We recently covered the weather-climate crossover in our episode with Anima Anandkumar, and we plan on covering both weather and climate more in future episodes.
This was really about quantum computing, but in the early days before anyone really knew what this meant and it was just a vague idea Feynman and a few others were kicking around.
Xiaomi’s new MiMo-V2.6 Pro is “simply” the best (for now). Despite its simple architecture design it’s currently No.1 in the open-weight benchmarks (weighted average).
With “simple,” I mean a classic Grouped Query Attention (GQA) with Sliding Window Attention (SWA) at a tiny 128-token window size.
So, that underlines one of the points I’ve been trying to make in recent months: most of the progress still comes from the data and post-training recipe improvements. Fancy attention variants are just mostly efficiency tweaks.
What are some of the training data improvements and recipe improvements? The MiMo team shared a pretty detailed technical report. Lots to carefully digest there, but in short, there are a few things that stood out:
An increase in agent tasks; also training across different harnesses (the average DeepSWE pass@1 accuracy on held-out harnesses improved from approximately 50% -> 66%).
Better reward signals: they replaced a simple correctness verifier with an agentic grader that looks at the execution traces as well.
Large RL batches (1,568 prompts × 16 rollouts = 25,088 trajectories) and 2.7–3.7 billion training tokens per update (unclear, though, what the predecessor used).
Source: website version of my Substack note.
This episode is with Jean-Stanislas “JS” Denain of Epoch AI, who leads their Insights Team and is one of the people I find myself debating the state and trajectory of AI with more and more. We’ve had follow-on discussions of many of my favorite recent posts online and/or in private, so I wanted to dig into the nuance in a public episode.
A big takeaway of this podcast is how JS and I both have so much uncertainty with exactly where we are heading, and this was our best effort at stating our observations today.
Chapters / topics include:
00:00 Predictions for RSI
18:15 The role of robotics in an AI acceleration
24:20 How far behind are Chinese models?
27:39 Does distillation explain the gap?
40:58 What Chinese job postings reveal about their labs
48:13 Are open or closed models safer?
58:10 How Epoch AI ticks
1:00:55 What a frontier post-training recipe looks like
Enjoy!
More from JS: Epoch AI profile and writing, X, LinkedIn
Listen on Apple Podcasts, Spotify, and where ever you get your podcasts. For other Interconnects interviews, go here.
00:00:00 Nathan Lambert: I’m here with JS Denain, who is a senior researcher at Epoch AI. He leads the insights team. He is one of the people who I feel like I get the best feedback on my writing from, whether it’s from US-China AI capabilities, now RSI. And I just wanted to open this discussion and honestly go deeper with him, trying to understand how he thinks about these various things. And I think you have a very useful, moderate point of view, which I feel like you’re probably a step further into what would be called faster scenarios for AI progress. But let’s get into this, and it’s like, what measurements do you think OpenAI and Anthropic are seeing when we get all these proclamations on RSI happening very imminently?
00:00:47 JS Denain: Yeah. I think, so there’s the measurements they’ve published, right? So, OpenAI and Anthropic both had blog posts, I mean, Anthropic two at least, on the effect AI has on accelerating AI progress. I think at least the things they publish, I don’t think are super strong evidence of imminent self-sustaining acceleration AI capabilities, or full automation of the job of AI researcher. But I think the kinds of things we see are, I think probably the most striking thing I saw in the OpenAI blog post was increasing usage of AI systems in model deployment, like the increase in spending on Codex that we saw. And it’s kind of unclear how exactly to interpret this, because maybe it’s a measurement artifact where they’re only looking at Codex, but in fact, there was a bunch of ChatGPT usage before from the researchers. But overall, that plot, for example, just shows a 2X a month increase in Codex spending by researchers, and that does seem to me to be some evidence of they’re getting a lot of value out of this probably. I don’t think this is strong evidence that in six months we have a software intelligence explosion.
00:01:57 Nathan Lambert: Do you think this is the same? So, what is the information they have internally relative to what we have? And this is obviously hypothetical. We don’t have this internal information. Because I get the sense that a lot of people are more scared in their updates from the labs than the information we have. And I try to take this very seriously of, what will they be seeing that is making the acceleration of risk comments go faster, and how much of this is material evidence versus how cultures evolve over time? And I’m much more interested in evidence.
00:02:29 JS Denain: Yeah. So two things. I think, first of all, I guess I don’t think, I don’t know, right? I don’t have full information here. I don’t currently think that either there’s some specific thing that people at OpenAI or Anthropic are seeing right now that we don’t have access to that warrants being way more freaked out about this. I also don’t think that... I think the public evidence we have right now, more general on AI progress and just a priori case for this being an important dynamic, I think is enough. I think to care about this particular dynamic of AI accelerating AI progress, that being a big deal and worth tracking. And then it’s kind of unclear what the urgency is of when the feedback loop really kicks in. So, basically on the what is there on the inside that people have access to, I could give examples of kinds of metrics, right, they could be looking at. It’s plausible that we have access to the capabilities of AI systems, but internal teams have their KPIs, and maybe they’re seeing compute multipliers in the pre-training team or other kinds of metrics that people are tracking going crazy. And then the combination of this plus some intuitions of how the different outputs of different teams combine yields a prediction on the trend in actual performance of the end AI systems. So that could be an early warning sign. It’s unclear to me that the recent discourse we’ve seen is evidence of things going crazy on those metrics.
00:04:09 Nathan Lambert: And how do you think of the link between RSI and existential risk? So I would posit that you agree. I think that there are very real risks of AI, and I’m curious on how you think these, what I would describe as very, very early measurements change anything on the scope of risk. Because I don’t think if you had asked people six months ago, it would be as immediate to x-risk among people are very reasonable. I think there’s more people that are reasonable talking about x-risk again, which was a little surprising to me.
00:04:43 JS Denain: Yeah. So, okay, my sense is something like... So personally, I feel very uncertain about this, but I do feel, yeah, basically bought into there’s, I know Evan Hubinger was like, at least 10% of x-risk within I don’t know what timeframe. I think I’m like, yeah, I know, and this seems pretty reasonable over a decade-long timeframe. I just feel extremely uncertain about it, but I’m definitely very worried about this. Now, why am I worried about this, and where do I think the disagreements come from? And then how do I relate this to the early sense of RSI? My sense, I’m kind of a capabilities theory of everything person. I think, and I think some people disagree here, but I really think that principal component of disagreement between everyone is how huge do the capabilities get, how soon, of AI systems? And I sort of agree that there’s other factors that come in play for how big your economic growth gets, also depend on the diffusion you get. And you could possibly you could think that capabilities are going to get crazy, but the AI system’s going to be just aligned and benign and stuff, and so there’s no huge risk. But my sense is concretely, when I look at the main kinds of disagreements between people, most of the people who I see who are very skeptical of those most extreme scenarios... I think just expect capabilities to not be as huge or as I think folks who are—
00:06:18 Nathan Lambert: What does being a capabilities maximalist look like in a few years? Because I think I’m probably on the skeptical side, so please continue.
00:06:28 JS Denain: I think it looks, for example, something like the AI 2027 scenario, right? I think it looks like the mechanism for this is AI is automating the AI research process, I think, and that’s a reason to pay attention to it. But in terms of effect on the real world, I think it’s like massive progress on robotics. I think a huge industrial explosion, AI systems are just managing factories. You have this kind of self-sustaining economy that just is able to make a large scientific progress much faster than you would have expected. And so I think concretely, the kinds of disagreements I would expect are on, yeah, if you have AI systems that are both very intelligent in the book smart sense, but also have been trained to have more affordances and use them astutely, have been trained to kind of manage projects in efficient ways and stuff like that. How big are the real-world bottlenecks to making very fast R&D progress or getting hard power over humans?
00:07:26 Nathan Lambert: Yeah. Can we go into some of these in detail? Did you listen to the Dwarkesh podcast with Charlie, Beren, and John?
00:07:32 JS Denain: Yes. Yeah.
00:07:33 Nathan Lambert: Yeah, because they had at the end, they had this section on various capability levels and timelines for getting them. And I feel like I agreed with most... I was very in agreement on the distribution they had up to this, and then was surprised by the timelines. And one of them was the 10X productivity for the AI researchers. And I think Beren and John were faster than I think. And my kind of statement is that I think the cycle from of having an idea and doing the experimentation to test it, I agree will be 10X faster very soon. But I don’t necessarily agree that I would say that AI researchers will be 10X more productive in net, which I would describe as the pace of the field’s complete understanding. And understanding is a different axis from just continuing to scale models. I think that’s one of my core confusions on the AI research side. So I’m just kind of curious how you think about this type of thing and how you might specify a 10X improvement in AI research into subcategories.
00:08:37 JS Denain: Yeah. So maybe there’s a scale you could have here, which is the end thing that you might care about is how much faster is AI research overall? Or how much faster is Anthropic’s overall output? And then Anthropic as a company is producing some things, and it’s doing in one year what it would have taken it 10 years to do. And that’s pretty different from individual researcher productivities, where I think you could... So if most of what AI researchers right now are doing is this loop that you were describing, then it’s possible that you get a 10X productivity improvement for the median researcher based on the tasks they’re doing right now. But first of all, that doesn’t mean you get a 10X productivity improvement for all researchers. And even if you did, right, there’s other bottlenecks that hit such that that needn’t convert into a 10X productivity improvement for Anthropic as a whole, right? You could have all the researchers be 10X more productive, but because of compute or other things, the company itself still doesn’t move as fast.
00:09:38 Nathan Lambert: I think an analogy I have is, I think that junior PhD students will be 10X as productive, but from the advisor’s perspective, their research agenda will not proceed 10X as fast. And it’s like to the extent that that contributes to Anthropic’s progress is another hard thing to jump on AI capabilities, where I think that listening to the Noam podcast. This is me, I’m just thinking, was thinking about this when writing about this, is there’s such an amount of inference compute coming online, and I think Dwarkesh highlights this very well, that it’s very hard for me to disambiguate massive speed-up in AI research from the fact that we have way more compute and can now much more effectively spend it on related problems. And I think we’re going to get all of this at once.
00:10:27 JS Denain: Yeah. So definitely I think this question of... So when you look at the OpenAI blog post they had on their acceleration, right, they do point out huge surge in Codex spending from researchers. Interestingly, actually, Codex spending in other parts of the company kind of had a huge surge in the spring and then kind of plateaued in the summer. But for researchers or the data team or engineers, it keeps growing, even accelerates sometimes. And so they point this out, and then they try to look at where there was an increase, which kinds of tasks had an increase in usage. And a lot of them are engineering tasks. There’s an increase in troubleshooting tasks. But they definitely point out that for a lot of the high-level strategic decision-making, they both anecdotally and also when they look at sessions, don’t seem to find a huge uplift in making better compute allocation decisions or deciding on research directions. And so to me, that’s pretty similar to the PI case. And so I think there’s a first question, which is, how much of an improvement... Imagine you just didn’t get that much AI uplift on that component of AI research, but the rest of AI research really went crazy. Then how much faster do things go? And the separate question is, I don’t know, how hard is this strategic decision-making? Can’t you just have a bit longer horizon RL? Or maybe you can bet on decent transfer from other fields where those kind of decisions are important, and then you do actually get that kind of uplift at the end.
00:11:59 Nathan Lambert: I think part of my intuition is that science will look so fundamentally different that it’s almost hard to put a number on it. And it’s the pre and post-AI era, and we’re just in the rapid transition to what is a new method, new way of doing science, because I think all the conferences are ready to burn down and struggle through the next few years. I hope that they collectively figure out a way to like add AI oversight into reviewing and things that are scalable, because they have so many slop papers that they need this type of gate. So I don’t really know. And I think on the capability side, I’m like, what an AI progress is like clearly translated into new capabilities. There are two things. One is like pre-training scaling laws. Our loss is proportional to like an exponential increase in compute. And on the research side, what we are doing is we’re shifting the line so that it has a better offset and potentially a better slope. And then on the other side is the RL environments, and I think the RL environments we’re building now are very comparable to valuable work. So I expect the AI models to get much, much better at like knowledge work that can be scoped. But I don’t know if we have a good process for like churning out an order of magnitude harder environments, which would be closer to like cure cancer, solve these open math problems. I think math is a case that we could talk about. But that’s kind of like, I think there are unknowns on scaling the like raw intelligence more than efficiency. So I’m very optimistic in scaling efficiency.
00:13:29 JS Denain: Yeah. I agree that like in some sense, right, like inference efficiency is like a very like hill-climbing task. It’s like pretty well-scoped. And yeah, so I mean, it’s already something that has very, very fast trends, but I could imagine those trends. Yeah, I imagine those trends will go even faster. It seems really like the kind of... I mean, indeed, we have evidence from OpenAI, right? Like, saving on like serving costs, et cetera, through like building better kernels and like if you’re new to this. They don’t give that many details, but that’s already happening. One thing I’m curious about actually in your case is like, so here’s one way of defining like Anthropic, for example, like accelerates overall, which is you could look at the like ECI trend in like, the ECI of the best Anthropic quality point in time. And you can like look at the current trend line and you can ask the question, like over the next year, will we see a 5X acceleration? Like, will the slope be like 5X larger than it was, say, in like 2025? And I think it’s like pretty likely we see like a huge increase in this because it is like kind of a legible like KPI that like, I mean, it’s not literally KPI, but it is a KPI that like the company is aiming for, modulo like safety considerations, et cetera. And I’m curious about whether you think it’s very unlikely we get this or whether it’s more like we might get this, but like if we do get this, it’s mostly that like ECI has been Goodharted as a metric and like the implications for like real-world capabilities aren’t that huge.
00:15:03 Nathan Lambert: I wouldn’t be surprised if we got this, but I think that it’s going to be like we’re on a slope and then we could get an uptick in slope of hill climbing, but then we like saturate what we know how to hill climb and then it goes to be lower. So it’s like all the things that we could measure I think are going to be getting pulled up very quickly by being measurable. And then we’re in the domain of like, how do we measure it? Because I think, like I talk to people that are trying... Like evals are so expensive to build now, and I do think that evaluations are going to be like how good and efficient it is at coding, how good and efficient it is at ML research, how good and efficient it is at knowledge work. But I don’t know how to like... Building those evals all seems tractable but hard. But then like how to make a breakthrough in fundamental chemistry seems really, really, really hard to measure. I was going to draw on like maybe frontier math as an example, but I think math is such an exception as like one of the most jagged pieces of AI. I think especially like open problems in mathematics are like the perfect target for rapidly improving AI because it’s like a falsifiable thing. And it’s like if we were to, say, see that in something that’s much more open-ended, I think I would update a lot. Or if the labs were like to come out and say, “Using Claude, we have a very, very big change in what our architecture of AI is,” to like there’s the famous like Jonathan Frankle–Sasha Rush bet, and it’s like, and the transformer is no longer like the lineage we are on. I think any of those things being very AI-driven would make me update a lot. But seeing more math, like I think I was surprised by the pace of math, but like not astonished.
00:16:49 JS Denain: That’s interesting to me. I definitely agree with this general sense. So like I think METR folks looking at like your nanoGPT results from autoresearch-style things compared to like what the humans were doing, it does seem like there’s this, I think Tom Cunningham calls this like the apple-picking model where AI is like much more efficient at the start, but then doesn’t actually like uncover as many new ideas. And you see this in this kind of optimizer research. Yeah, I mean, one thing I will say on this like verifiability point is like, I think a pretty common trend is like you’ll have some task that’s like not verifiable and you’re like, maybe you struggle to build an environment for it. But actually it’s like it’s a subset of a larger task that is itself like verifiable. It’s just like longer range. An example of this is like there are many like hard to verify tasks out of like companies. But in some sense, like revenue or like other like metrics, like valuations are like pretty legible. So that’s like one thing. I mean, the other thing is like expect things to be pretty jagged. But I think a big question is like, yeah, can you get, for a crazy world, can you get like a large, like self-sustaining industrial kind of explosion?
00:18:05 Nathan Lambert: Yeah. Well, can we talk about robotics and industry? Because I have a background in physical robots and like I think the robotics trends will look much closer to self-driving cars than LLMs. And I think that a lot of the singularity arguments are based on robotics being able to look much closer to LLMs than the self-driving cars roll out. So, why would you disagree? Or, what is the argument that mass industrialization and robotic expansion is doable? Because my prior is so suspicious that I maybe even haven’t given it enough justice, but I’m very suspicious of this being a viability, and mostly in terms of being a relative timeline. I think it could happen over decades, but I don’t think it’s a two to five-year concern.
00:19:01 JS Denain: Two to five years seems rough, to be clear. I think I just don’t know as much about robotics here. I think is your main concern just reliability is really rough to get right in the same way that it was just a long tail of scenarios where things are, or was it more like a real-world thing where there’s much more regulation that comes up?
00:19:21 Nathan Lambert: I think it’s building things is hard. I think that, let’s see. I’ll talk us through some of this. For example, I know places like Amazon, they build new factories to be robotic first, and those are more effective for them. And what this would take then is building a robotics factory. In the case of the US, it’s like you have to build a robotics factory that builds robots very efficiently in the US and then transition that or make a new one that is built by said robots. And I think the re-industrialization of the US is something that I think is like, there’s a lot of reasons why it is not happening. I think potentially in China it is more likely, but I also just haven’t been convinced by AI results on visual and action models that they’re progressing fast enough. I think I’ve had discussions with people in the multimodal field have described the techniques as being much more rudimentary and less developed than the text language models, and in need of much more fundamental innovation, where something like code plus RL is a very natural match that the hill climbing is very predictable. So—
00:20:35 JS Denain: So, it seems like there’s two things. There’s the trends in robot capabilities is not as fast as you would expect for LLMs, and also even if robot capabilities were huge, it takes a while to build factories. I think I’m sort of skeptical of the second one. I’m just like, if robot capabilities are sufficient, the total addressable market for this is massive. And if you look at data centers in the US, there has been extremely fast build-out. If you had robots that were just literally able to substitute for blue-collar human workers, I feel like the financial incentives would be huge. And I think a lot of the reason why in some cases, the US doesn’t have huge build-out is just a demand thing. I think that’s the case for power, for example. So I think in that case, I’m just like, yeah, I feel like we just, what is the Tyler Cowen thing? Don’t underestimate the elasticity of supply is the main thing I would point to. I think on the capabilities front, I’m more uncertain. In particular, I’m sort of still confused and haven’t really looked into the, how much do you get directly actually from LLMs and foundation models for robotic capabilities? In particular, the other uncertainty I have is, it’s not clear to me that extremely fine-grained, extremely dexterous capabilities are the main thing you need for massive industrial explosions. And so, this longer tail of the hardest part of robotics, I’m not sure if that’s the biggest blocker for massive industrial explosion. Overall, robotics is something I have less expertise in. I’m interested in how many of the scenarios for doom ultimately kind of route through hard power acquired through robotics. I think part of my uncertainty also comes from, is it plausible to me that the minimum abilities that you need to acquire a lot of hard power and pose pretty catastrophic possibly extinction risks is more like, have access to nuclear codes or something like that? I don’t feel like I have great thoughts on this. I’m interested in more threat modeling, but I think that’s part of the thing is, what are the capabilities trends is something that people have disagreements about, and so what’s the minimum capability that’s necessary to cause these extinction-level harms, or harms that are sufficiently catastrophic, they just permanently alter the human trajectory?
00:22:46 Nathan Lambert: Yeah. The last point I would make on—
00:22:47 JS Denain: I think that’s the kind of questions I want to see a bit more thinking on, but yeah.
Continued at the source.
Meet Xiaomi and other top Chinese frontier labs at AIE Shanghai!
This is a first for the “Apple of China” phone maker-turned-frontier lab: “The MiMo-V2.6 series includes two natively omnimodal models: MiMo-V2.6-Pro is our most capable model to date, while MiMo-V2.6-Flash strikes the best balance between intelligence, efficiency, and cost. We are also rolling-out MiMo-V2.6-Pro-UltraSpeed, delivering up to 20x faster output speed at the same quality, for users who require extreme generation speed.”
Xiaomi is not traditionally considered one of the six Chinese AI Tigers, so it is very surprising to the established order of names you have come to know and love. And… it is natively omnimodal!
Xiaomi made news a few days ago when Fuli Luo, a former DeepSeek star engineer now at Xiaomi, started publishing their final RL training runs live, which showed an abnormal amount of transparency in their internal metrics.
As they note in their technical report, they scaled RL compute along three axes:
Larger batches and higher throughput: large batches on a fully asynchronous architecture, with 1,568 samples per update, training at up to 1M context length, and 3.5 to 3.7B tokens per step.
More tasks and richer environments: a multi-task training suite spanning coding, general agents, visual and cyber, mixed across several harnesses so that gains in one capability reinforce the others.
More grader compute: relative comparison within each group gives long-horizon RL tasks more precise and more diverse reward signals, closes a self-improvement loop, and steers the model toward shorter paths and fewer tokens per task.
ALL of this tooling, including the environments, will be open sourced.- the environment code and training recipes, but the complete 7k+ task datasets have not yet been released.
Coding / software engineering: Code recipes, dataset loader and rewards
Cyber / vulnerability reproduction: ARVO environment and training recipe
General / knowledge work: General environment, tools and training recipe
Visual / web development: Web-development environment and grading
Music generation: Data preparation and music scorer
Composable mini-harnesses: Agent configurations
Shared environment adapters: mimoagent environments
AI News for 9/19/2026-9/21/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!
Open Models, Competition, and the China Gap
Open models remain the central policy and market story: Nathan Lambert shared a congressional briefing on open-model performance, adoption, and U.S.-China competition, followed by a public summary. The broader argument resurfaced elsewhere: @Yuchenj_UW claims frontier coding capability has plateaued since Opus 4.8, while open-source models keep closing the gap at 10–50x lower cost; @ClementDelangue similarly argues APIs are overkill for many real-world use cases and that specialized models will take share. Counterpoint: @teortaxesTex argues frontier has actually split into new higher tiers, with internal models and top closed models still well ahead.
The release cadence from Chinese labs is now difficult to dismiss: @Thom_Wolf compiled an unusually dense ~10-week run of open releases including Kimi K3, Qwen3.8-Max, DeepSeek V4-Pro, GLM-5.3, Hy4 Preview, Atria Dawn, and more. This is reinforced by a Bloomberg-sourced note via @Polymarket that startups are increasingly building custom models on open weights to cut cost and reduce dependence on OpenAI/Anthropic. The subtext across several tweets: open-weight capability is no longer confined to midsized models; multiple teams are shipping frontier-scale MoEs with credible cost-performance stories.
Xiaomi MiMo-V2.6 and RL as the New Scaling Lever
MiMo-V2.6 is the biggest open-model release in the set: @XiaomiMiMo launched MiMo-V2.6 Pro and Flash, described as open omnimodal models with weights, technical report, RL environments, and training code. Artificial Analysis says MiMo-V2.6-Pro debuts as the top open-weights model on its Intelligence Index (46), with 1.02T total / 42B active parameters and strong cost efficiency at $0.435/M input and $0.87/M output tokens. @victormustar notes the models are under MIT license.
What stood out technically was not just the model, but the RL stack: @eliebakouch highlighted Xiaomi’s environment/data-factory paper for generating RL tasks from open repositories with “agents in the loop” for robustness and anti-cheating. Later commentary points to a second paper and unusually high transparency: @xeophon notes Xiaomi wants to release ~7K RL environments, and @eliebakouch emphasizes the team shipped model + tech report less than a week after the final RL run. A recurring interpretation, from @bertgodel and @Thom_Wolf, is that high-quality open RL environments may now be as strategically important as pretraining corpora were in the last cycle.
RL cost/throughput details drew attention because they compress timelines: @zephyr_z9 cites 130 hours, 75B tokens, and $2.6M for the RL run behind the result; @tianjun_zhang says the MiMo family scales RL on JAX + TPU, where scaling is “mostly a config change, not a code rewrite.” If these numbers hold up, the implication is that post-training/RL is becoming a far cheaper route to frontier-adjacent gains than many assumed.
Decision Models, Jev, and the Return of Specialized Inference
Jev was the dominant product/theme discussion: Multiple posts converged on the same framing: this is “just” classification/routing, but with modern model intelligence and much better latency/cost. @karpathy calls it a point on the Pareto frontier for “no thinking, single token, low latency acceptable intelligence”. @willdepue describes it as a zero-shot classifier with frontier-ish intelligence, while @ClementDelangue argues the excitement shows there is large latent demand for specialized models rather than ever-larger generalists.
The ecosystem around Jev expanded quickly: @sarah_edo built a Chrome extension that uses Jev to select and fill relevant WebMCP tools per keystroke. LangChain added Jev-as-a-judge to LangSmith; @hwchase17 and @Hacubu pushed SemIf, an open-source decision model, through the LangSmith Gateway. @omarsar0 reports using Jev to retag ~2.3K papers in 83 seconds for $0.14, with 579 high-confidence changes and manual validation of disagreements.
The more durable takeaway is architectural: DSPyOSS argues that asking frontier agents is like managing people, while hand-writing decision-model programs is analogous to writing assembly; both extremes are useful, but brittle if overused. Several posts emphasized where these models fit best: routing, approval gates, trace scoring, tool selection, discrete document decisions, and low-cost supervision inside larger agent loops rather than as standalone “smart agents.”
Inference, Tooling, and Systems Optimizations
Tokenizer and post-training infra both got substantive upgrades: Hugging Face’s tokenizers v1 RC claims up to 30x faster tokenization, improved multithread scaling, lower memory use, and much smaller package size; @art_zucker framed it as a new SOTA tokenization library. Separately, Halo launched as a post-training framework claiming up to 2.8x throughput over stock TRL while keeping models in native Hugging Face format.
Inference-side engineering remains a major lever: @RisingSayak showed how KV caching is incorporated into QwenImage 2.1, separating fixed context from changing image positions and yielding a 2.55x speedup; the thread cites 50.57s → 19.86s DiT time on a warmed A100 with moderate memory overhead. vLLM published tuned serving configs for Qwen3.8-2.4T on GB300 NVL72, showing a Pareto frontier from 5K total tok/s/GPU at high throughput to 180 output tok/s/user at low latency. In video workloads, vLLM also integrated PyNvVideoCodec/NVDEC, removing CPU decode bottlenecks and reporting 2x+ throughput at 8×H100.
Compression/quantization is still moving fast: @ZhihuFrontier summarized Tencent Hunyuan’s engineering behind packing Hy4 Preview (770B) into 214 GiB via mixed-precision quantization averaging ~2.38 bits/weight, including custom CUDA kernels in patched llama.cpp. On the edge/local side, @vikhyatk released Parakeet Redux, compressing NVIDIA’s speech model from 1.2GB to 178MB, running at 113x realtime on CPU, while beating the base model on 25-language FLEURS and staying within 0.3 WER on English.
Agents, Security, and Human-in-the-Loop Control
Computer-use systems are becoming more productionized, but security is now central: Patrick Wardle reported a serious local-hijack flaw in Muse, arguing broad OS access makes such assistants a high-value attack surface. In contrast, DeepLearningAI highlighted Meta’s design philosophy for Muse-like agents: assume prompt injection will happen, keep real credentials away from the model, isolate tools in containers, and use an independent outbound-call gatekeeper.
Commercial agents are also being pushed deeper into workflows: Cognition introduced Devin Cloud in Terminal and devin ssh, making the model’s VM directly accessible from the CLI and allowing handoff between Devin and the user’s machine. GitHub Copilot teased editable diffs in the desktop app, while @pierceboggan showed a Sentry-integrated canvas for moving from crash report to fix.
A recurring systems point: inference and agent infra are shifting toward test-time compute: @sarahookr predicts compute moving from pretraining—where marginal FLOPs yield less—to test-time compute, requiring “very different infrastructure.” That theme also showed up in persistent-cache discussions for local serving, e.g. @TheZachMueller on SGLang’s multi-level hiCache (GPU/RAM/disk) for preserving KV cache across model swaps and restarts.
Top tweets (by engagement)
Grok 4.7 release: SpaceXAI announced Grok 4.7, described as a notable improvement over 4.6 at the same price/speed. Follow-on evals were mixed: Artificial Analysis reported 56 on its Coding Agent Index with gains on DeepSWE/Terminal-Bench/SWE-Atlas-QnA, while Vals saw it rank #24 on its Vals Index, down 5 points from Grok 4.6 despite gains in legal/medical.
OpenAI’s automated model-training workflow: A widely shared summary from @wallstengine reports that OpenAI has largely automated parts of training experimental models, including GPU kernel writing and code optimization, with internal agents collaborating and compressing some experiments from years to about a week.
OpenAI mathematics advisory group and claims of solved open problems: OpenAI announced an independent advisory group of mathematicians to guide assessment and communication of AI advances in mathematics. Attention then shifted to the stronger claim, amplified by @AndrewCurran_ and others, that an internal OpenAI model has resolved 100+ long-standing open problems across mathematics. This was among the most consequential but least independently evaluated items in the set.
Open-sourcing of valuable data assets: @ClementDelangue highlighted Eidon AI open-sourcing 1,274 hours of egocentric robotics data (13,451 recordings) as a rare case of a startup preserving impact for the community after shutdown.
Qwen-Image-2.1 released! (Activity: 2485): Qwen-Image-2.1 was released with open weights as a unified 7B image generation/editing model, positioned as a faster, lower-cost member of the Qwen-Image series (blog, GitHub, Hugging Face). Key technical additions include native RGBA/transparent image generation and editing, support for up to 10 reference images, multi-image inference acceleration, and localized edit control for tasks like object removal, attribute changes, product/portrait-preserving edits, panoramas, infographics, typography, and virtual try-ons. Comments primarily highlight the native transparency pipeline and local-edit interface; one example uses colored circles to target three regions simultaneously for removal, hair recoloring, and clothing replacement, suggesting interest in more controllable multi-region editing workflows.
Qwen-Image-2.1 is reported to add native transparent image generation and transparent-image editing support, which is technically notable because alpha-channel workflows are often handled as post-processing or masking rather than directly by the image model. The linked example shows transparent-output capability: https://preview.redd.it/59fu834idoqh1.png?width=767&format=png&auto=webp&s=5fb81b135b35dac70f9d38a9995d7c1a7a2877dd
The model appears to support multi-region local editing via visual annotations, where circled regions can be referenced in the prompt and edited simultaneously. One example asks it to “remove the metal watch in the blue circle, change the hair in the red circle to black, and replace the area in the green circle with gray short-sleeved linen pajamas,” demonstrating combined object removal, attribute modification, and region replacement in a single edit pass: https://preview.redd.it/cvh09tyvdoqh1.jpeg?width=1242&format=pjpg&auto=webp&s=32077f7420def5bec85160e2e982d6aef5efce54
Several commenters highlight the model size: Qwen-Image-2.1 is described as 7B parameters, which is significantly smaller than prior Qwen image models that commenters say were over 20B. This size reduction is viewed as important for local inference feasibility, with one user specifically noting interest from the perspective of a 16GB VRAM GPU such as the RTX 5060 Ti 16GB.
Clarification on the Qwen-image-2.1 license (Activity: 948): The image is a non-meme screenshot of a Qwen Developers X post clarifying that Qwen-Image-2.1 outputs are not considered licensed “Materials”, so users retain rights to generated images/content. This matters because the model license reportedly still contains a non-commercial restriction on use of the Materials, creating ambiguity over whether commercial image generation is allowed even if generated outputs are user-owned. Commenters welcomed the clarification, with one user saying Qwen-Image-2.1 “easily beats all current Flux models.” Another noted they can run it locally via ComfyUI int8 on a 16 GB RTX 5060 Ti peaking around 15.2 GB VRAM, but warned the Hugging Face LICENSE file may not yet reflect the clarified intent.
A commenter reports running Qwen-Image-2.1 locally in ComfyUI using int8 quantization on a 16 GB RTX 5060 Ti, with VRAM peaking around 15.2 GB. They describe the model as suitable for local testing but note that licensing uncertainty around generated outputs was the main blocker for broader/client use.
Several commenters highlight a legal/implementation mismatch: the Hugging Face README was apparently clarified, but the actual LICENSE file still contains Section 2(b) language prohibiting commercial “use” of the Materials. One user emailed model-business@notice.qwencloud.com asking whether the license text will be updated, because the tweet/README intent may not be sufficient for client or commercial work.
The key technical/legal distinction being debated is whether “commercial use not allowed” applies only to serving, redistributing, or monetizing the model/materials, versus also restricting outputs generated by the model. Commenters argue that until the canonical license file is updated, downstream users comparing it with permissive Apache-2.0/MIT-style model licenses may reasonably avoid commercial workflows despite the clarification.
21st September 2026
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling “System One models” (I’m with Maggie Appleton, I think “decision models” is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores.
TypeSafe describe Jev like this:
Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.
It’s also very fast, and really cheap. Regular LLMs are priced in terms of input and output tokens, with output generally charged at significantly higher rates. Jev charges only for input—output is free—and the input price of their first model is $0.042 per million tokens—cheaper even than OpenAI’s GPT-5 Nano ($0.05/million).
Jev lets you ask questions about text or semi-structured data. You compose a “state” object containing a string, array of strings, or set of name-value pairs—this might describe an article, or a customer, or any other kind of record. You then send that to their API with one or more questions, and get a reply back for each.
You can ask three kinds of questions:
The Jev API can accept a single document (“state”) and as many questions as you can cram into the context window. Questions are evaluated in parallel, so sending many questions should take a similar time to sending just one.
The Jev 1.13 jaggedness documentation offers useful guidance as to Jev’s strengths and weaknesses. It’s currently not great with numbers, dates, or “adversarial content”.
I think the decision model framing is useful for understanding where to use Jev. It’s great for anything that can be expressed as a classification task—think spam detection, suggesting labels, prioritization and ranking.
I’ve also been experimenting with it for search reranking, where you fetch 100 likely matches using an inexpensive algorithm like BM25, then have Jev score those 100 candidates for relevance against the original query.
Something I’ve found a little uncomfortable about Jev is how it very much represents a regression even further towards black box machine learning systems.
LLMs are black boxes already—you can ask them to justify their decisions, but you can’t guarantee that what they say is useful or accurate.
Jev doesn’t even give you that: put in all the text you want, the only thing you’re going to get back is a floating point number. If Jev marks something as spam, which content signals tipped it off?
This also means that concerns about bias should be front and center. I really hope nobody uses Jev to rank job applicants—that floating point number could conceal all manner of unseen bias baked into the models, and experimentally picking that bias apart is going to be a tricky business.
(I tried one experiment where I had Jev score every city in the San Francisco Bay Area on a yes/no answer to whether they were a “Good city?”—it rated Cupertino top and East Palo Alto bottom. Huh.)
In practice, this all means that evals and structured experiments are even more important than they are for regular LLM projects. Thankfully, Jev is so cheap that running hundreds or even thousands of experimental prompts through it costs just a few cents.
It’s been really fun watching the wider community come up with potential use-cases for Jev over the past few days. Here are some creative ones that caught my eye:
There’s also been a flurry of projects attempting to create a model like Jev using on top of open weight models. Kev is one interesting example, using Qwen 3.5 to produce 0.8B, 4B, and 9B models. Here’s the accompanying Hacker News thread, where someone linked to a JevBench benchmark that has already cropped up to compare “Jev-class decision models”.
Given Jev was released just under a week ago, the amount of activity around it is extremely impressive.
Update 22nd September 2026: I released llm-typesafe, a plugin that adds support for Jev to my LLM CLI tool and Python library. Basic usage looks like this:
llm -m jev 'Please refund my last payment.' \ -s 'Does this message explicitly request a refund?'
See the README for examples of other query types.
I was recently invited to brief a group of Congressional members and staff on the state of open-weight models in the lens of U.S.-China competition. I’m sharing my prepared remarks as a state of the union on open models that is accessible to a broader audience.
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Open language models are AI models where their weights are publicly available for inspection or downstream use. These are most often contrasted to so-called “closed” AI models. Closed models offer access only through Application Programming Interfaces (APIs) that developers can use to directly query a model, like GPT-4 or Claude Opus 4.5, or through products, like ChatGPT and Claude Code.
Open language models primarily are bucketed into two categories, open-weight and open-source models. Open-weight models are the most common form, such as popular models like Meta’s Llama, Alibaba’s Qwen, Google’s Gemma, or DeepSeek’s models. These models are governed by licenses, governing documents dictating what is allowed with downstream use, and are often accompanied by inference code in libraries such as Transformers, VLLM, SGLANG, etc. Since about April 2025, Chinese AI companies have been the clear leader in open-weight models.
True “open-source” models are similar to these, as they include the weights, licenses, and inference code, but they also include the complete information needed to reproduce the model – the training code and training data. The most prominent open-source models have been built in the United States, led recently by the Allen Institute for AI’s Olmo models that I helped build in my recent 2.5 years there. The other prominent open-source models are also built by American non-profit organizations, including OpenAthena’s Marin models and EleutherAI’s Pythia models.
Open-weight, open-source, and every other label for a model – including closed models primarily offered via an API – exist on a spectrum. For example, Nvidia’s Nemotron models are far more open than most open-weight models, releasing large quantities of their training data under permissive licenses, but they’re not fully open-source because they do not release all of the data. Closed models also exist on a spectrum based on what information the API reveals and the terms of use.
We are living in a world where GLM-5.2 and Kimi K3, some of the latest, leading Chinese models, have enacted a step change in the commercial viability of open models — crossing a similar threshold in agentic capabilities that Anthropic’s Claude Code crossed in December of 2025.
America was the early leader in open language models, primarily through Meta’s Llama models, which were used extensively across research and commercial tasks. Chinese open-weight models surpassed American open-weight models in these two key areas about 18 months ago. The simple metric showing this is Hugging Face Downloads, where China took the lead in July of 2025 primarily through the success of Alibaba’s Qwen models. I personally maintain tools to track this data, and since I first published the American Truly Open Models (ATOM) Project in August of 2025, China’s download lead has grown to about 1.6B – with a total of 3.2B downloads, twice that of America’s total.
On popular capabilities benchmarks, such as the Artificial Analysis Intelligence Index (AAII), the Chinese open-weight models have a clear lead over American counterparts. The top three Chinese models as of writing this on September 14, 2026 are Z.ai’s GLM-5.3 and GLM-5.3-Flash and Moonshot AI’s Kimi K3 with scores of 45, 42, and 44 respectively. By comparison, the leading American models are Thinking Machines’ Inkling and Inkling Small, both with a score of 26, and Nvidia’s Nemotron 3 Ultra, with a score of 23. The top American models were released in June and July of 2026, and are updated less frequently than their Chinese counterparts. For example, Chinese labs released models with scores above these American models 2-6 months before the American companies got there (e.g. GLM-5 or DeepSeek V4 Pro). There is a trend of more American companies releasing models, including names like Arcee AI, Poolside and IBM, but they are not rapidly closing this performance gap. Other benchmarks tell a similar story.
Together, Chinese open-weight models are approximately 2-5 months behind the closed American frontier, with the open-weight American models being approximately 6-9 months behind the likes of OpenAI and Anthropic. The Chinese labs are closest in tasks with clear user demand, such as agentic coding, and further behind on more open-ended scientific tasks, such as physics or biology.
The reasons why Chinese labs can produce these strong models, despite having fewer resources than American counterparts, is still an open debate and heavily influenced by different work cultures, but is also influenced by a few key technical factors. The Chinese labs release their models faster and focus on a slightly narrower distribution of tasks, flattering them slightly on public benchmarks. Releasing faster helps them score higher because all the labs are making consistent progress, so once you “finish” a model to be released, it is a snapshot of performance at that given time — labs where that time is later tend to score higher. Still, the models built by the Chinese labs are genuinely strong and represent real competition to the American industry. This competition will not decrease meaningfully as the closed labs patch vulnerabilities in their API offerings which enable distillation.
Distillation is most impactful in new domains and does not make it trivial to create a universally strong final model. I estimate that if distillation was fully prevented, e.g. with know-your-customer (KYC) tools at Anthropic and OpenAI, the gap from the strongest American models to Chinese open-weight models would only increase by 1-2 months.
For example, the Chinese labs are rapidly changing their posture towards paying for training data in 2026. Earlier in the year, the top Chinese labs including Moonshot AI and Z.ai had a strong preference towards building data workflows in-house, but by the summer they had begun to buy the cutting edge data – challenging RL environments for agentic tasks – from both established American companies and new Chinese startups.
With the advance of open weight models in China towards the frontier of capabilities, and the recent documentation of growing risks around frontier models in areas such as cybersecurity (e.g. the OpenAI-HuggingFace incident), there’s growing regulatory uncertainty on how continued releases can enable a safer ecosystem?
A structural challenge in open-weight models is that there are few effective methods for stopping pieces of open software from reaching bad actors. If an attempt was made to restrict access to the strongest open-weight models from China because they amplify risks, the parties who would be set back are American businesses. We have an example of this – HuggingFace used a Chinese open-weight model to understand the cyberattack because closed models would not answer their requests. Thus, managing the risks of open-weight models often comes down to ecosystem preparation.
Open-weight models are becoming an essential tool for AI diffusion, and the best path to get ahead of these risks and unbalanced relationships where American companies rely on models built in China is to continue to enable investment in open models in the US. Ownership of open models allows better coordination and preparation of risks that are global in their nature while accelerating diffusion of AI services throughout the domestic economy.
Open-weight language models have grown substantially in general interest and economic viability in 2026, allowing early glimpses of more direct ways to compare adoption of models from the US, China, or elsewhere on top of Hugging Face metrics. One example is OpenRouter usage. OpenRouter is a popular LLM inference platform that supplies a single interface to switch between models, open and closed, from the US and China. This platform is primarily known for trying different open-weight models. The platform has shared usage data for the top models since Jan. 1, 2025, and shown growth in usage from ~1T tokens processed from open models in a week of September 2025 to ~80T tokens per week today. In that time, Chinese models have grown from ~70% market share to over 80% of usage. Other platforms that are designed to commercialize open models show similar data, such as the open-source coding agent OpenCode, which shows an inference volume of ~95% or higher with Chinese models.
These open platforms are the best approximation of open model usage we have – a large proportion of open model usage is on platforms that do not disclose per-model breakdowns, such as Together AI or Fireworks AI, and in private deployments for enterprise applications.
Many prominent technology companies and startups have been building on Chinese open-weight models for their AI features, such as Harvey, the legal agent, Cursor, the coding agent, and DoorDash’s use of Kimi models, Airbnb’s use of Qwen, or Perplexity’s use of DeepSeek. These prominent companies are the tip of the iceberg, where a large swath of younger Silicon Valley startups are building on Chinese models in order to have low-cost, flexible options. There is a growing trend of American startups and companies entering enterprise agreements with Chinese model labs in order to get permission to use their models in their products – a new form of cross-border technology collaboration I have not witnessed in my career.
The foundation of innovation on Chinese models extends further into the AI ecosystem. To a first order approximation, most of academic research is conducted on Alibaba’s Qwen family of models. Having met multiple members of the Qwen leadership team during my trip to China, they are very invested in and intentional about this type of adoption, which will not be easy to claw back to American models.
To quantify the adoption of open models across academia, I scanned every paper in the 5 most popular ML categories of arXiv (cs.AI, cs.CL, cs.CV, cs.LG, stat.ML), the preprint platform popular in AI research. The results clearly track my understanding of the evolving leadership in AI research, showing LLMs becoming a foundational layer of ML research – mentions of any open model were 2% in January of 2023 and 50% in September of 2026 – and the leading role shift from the U.S. to China in the same time period.
For example, in April to May of 2023, a few months after Meta’s original Llama (a backronym, Large Language Model Meta AI, first released in Feb. of 2023), about 2,600 of 12,000 new AI/ML papers on arXiv mentioned at least one prominent open model family. Of all those scanned papers, ~5.5% mentioned Llama and ~1% mentioned a Chinese model. In the fall of 2024, during Llama’s peak, about 23% of papers mentioned Llama with about 7.5% mentioning Qwen, the most direct Chinese competition. Today, Llama has lost its lead in academia, being mentioned in about 21% of papers still, which is remarkable longevity, but Qwen’s share has risen to 30% of papers. Overall, any Chinese open weight model is mentioned in over 40% of papers, over the U.S.’s 30%, with China’s share continuing to grow.
This shows that we clearly have a lot of work to do in order to re-establish the U.S. as the home of AI research in the era of open-weight language models. There are signs of hope.
In our research, we find that American models of comparable capabilities-to-size regions to their Chinese counterparts get adopted at disproportionate rates. In the last year we’ve seen OpenAI’s first open-weight models since ChatGPT, gpt-oss, become one of the most adopted open-weight models of all time. Since then, Google’s Gemma 4 models have been some of the only ones ever to show similar adoption numbers to Qwen’s most popular small models, and Nvidia’s Nemotron models have modest adoption despite numerous more capable models at the same size point.
The story of open models in 2026 is one of establishing economic relevance. This is the convergence of many stories across the AI ecosystem, summarized as:
The capabilities gap from open to closed models available to users has been decreasing over the last 3 years. This varies by task, but can be estimated as a 2-5 month gap in capabilities. With capabilities overall progressing so fast, this has seen open-weight AI models unlock substantial markets in 2026 and points to more inflection points in the near future.
Open model usage is exploding in high-value industries (e.g. software engineering, legal services, financial services), indicating an emergence of an alternative ecosystem to the best closed models. Platforms offering inference primarily on open models, from Together, OpenRouter, Fireworks, Baseten, etc., are seeing incredible growth as the first winners of an open model post-training economy (other layers include finetuning APIs such as Thinking Machines’ Tinker). This is combined with numerous anecdotes from technical staff in the AI industry that uses open-weight models such as GLM-5.3 as an alternative to Claude or GPT due to a combination of speed, lower prices, customizable offerings, and privacy.
Chinese AI companies are the clear leaders in open weight models. Relative to 2025, where Chinese models like DeepSeek R1 shook the AI world with surprise, the American AI labs have been recovering in their positions with open-weight models, but despite more substantial investment in the US, the Chinese labs regularly are producing notably stronger models adored by many types of users.
Distillation of American AI models by Chinese labs does not explain the entire story of their success. Distillation is an industry standard technique of training another AI model on the outputs from a usually stronger model. The technique is most prevalent in the Chinese AI industry, which has used basic exploits to extract reasoning traces and additional data from American companies’ products that are not fully secured. The best estimates are that distillation helps reduce the performance gap of Chinese companies relative to the American frontier by 1-2 months.
Chinese models, particularly Alibaba’s Qwen family, are established as a foundational layer of research and development across academia and local model users. In recent months, Chinese open weight models were mentioned in 38% of AI papers, above the U.S.’s 28% – and the Chinese share is growing much faster than its American counterparts. This, along with other political factors and the closed nature of leading American AI companies, is contributing to an accelerated decline in America’s lead as the preeminent AI research hub in the world.
Open weight models are entering the capability levels where new risks, e.g. cybersecurity, can be enabled by numerous open-weight models being available, necessitating an ecosystem level response in preparation. This new era of risks is also enabling a period of political uncertainty, where there is regulatory attention on the strongest AI models, but massive uncertainty on how policy would be legally enacted. At the same time, many researchers and engineers rely on open models due to more permissive safeguards, where the closed models such as Claude and GPT often refuse critical cybersecurity defensive work or biology research.
For more data, view the Interconnects Dashboard.
In 2026 the Chinese labs are clearly maintaining their status as the leaders of the open-weight AI ecosystem. This comes as open-weight models have passed an inflection point in economic viability and in the face of increased activity from American labs as model competition. The leading Chinese labs do not appear to be meaningfully challenged, as they expand their enterprise and research adoption globally.
This landscape of open models comes at a crucial time in the broader AI ecosystem. We’re seeing OpenAI and Anthropic take massive steps forward with their latest public models, and at the same time call for coordinated care on how we manage the next stage of AI progress. What is happening in the confines of a few AI labs today, especially with extreme talent and compute density, is a precursor to what will soon emerge in the open model ecosystem. Open models are going to be the substrate for everyone else in the world outside of the few true frontier AI labs, to harness an acceleration in software engineering and other computational practices. This represents a substantial source of soft power, influence, and potential for the organizations that enable this broad access to transformative intelligence.
With this future coming soon, we need to collectively stay humble about the exact path open models will take. There are a lot of unknowns with open models – e.g. we don’t have good data on how they’re used in countries other than the U.S. and China. With the distribution of ML training expertise being broad, i.e. tens of organizations and thousands of people that are within a year of the frontier of capabilities, it is a matter of when, not if, open models cross the performance thresholds that enable new workflows. The collective approach should be to understand how to use this broadly accessible, open intelligence for good while proactively mitigating the potential harms.
Thank you to Florian Brand and Kevin Xu for feedback and/or suggestions for this work. For more research informing this post, see the open-source AI reading list.
It’s easy to hype and dunk on Jev. I saw a lot of interesting demos in the last few days. And I also read a lot of dismissals in the last few days. I think the truth lies somewhere between these two extremes.
I.e., it’s easy to dismiss Jev as “just a classifier.”
The exact model and training algorithm are not disclosed. But if I had to make an educated guess, it’s likely:
Many people (me included) have been training encoder-style models for classification for many years. Fact is that they were usually special-purpose and limited in some way.
Jev’s impressive breakthrough is that it generalizes so well (you can use it to classify emails, play video games, trade stocks…).
And I’d say the secret sauce is probably more in the data than in the training algorithm. (Plus a nice API design on top of it.)
Yeah, it’s not the first project where someone applied RL to a (likely) non-autoregressive, encoder-style model.
But what’s impressive is that it works and generalizes so well, which can make all the difference. I.e., we saw the same thing with Stable Diffusion (based on an existing research paper) not too long ago, or even with the 2022 ChatGPT launch itself (an improved version of InstructGPT, where the data made all the difference).
Source: website version of my LinkedIn post.
It’s the week of Jev! I’m really, really, really, really excited about it. I mean: really.
It’s like someone blew up a confetti bomb in the world of LLMs and now you realize how grey everything looked before.
But Jev is not an LLM. It’s a model “built to make fast, structured decisions that software can use directly.” TypeSafe says we should think of Jev “as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.”
I explained it as a “smart if-statement” to someone and my only slightly longer explanation is this:
Think of how you’d get an LLM to decide between a fixed set of options.
Then imagine it orders of magnitude faster and cheaper.
“What’s the best label for this?”
“Should I click here or there?”
“What’s the next line I should look at?”
“Do I go left or right?”
“Invalid or valid?”
“Which of these widgets should I show?”
I had Amp build a little Copilot-style autocomplete for a shell, with Jev picking the next most likely command from shell history. Then Amp built a Neovim plugin (and called it hunch.nvim, which is a great name) that uses Jev to predict the line you next most likely want to jump to. Now, let’s linger on this a bit.
Two years ago, that was what Cursor was famous for. Yes, Cursor did and does more than that and the quality isn’t close, but… when we were working on Zed’s Edit Predictions we had to fine-tune a model to get into the same league! Now it’s a single API call and the latency is 200ms. That is incredible!
Then I built a prototype that uses Jev to turn the Amp Dial, switching between models based on your prompt.
Yes, all of this was possible before, but it’s so fast and so cheap that I still can’t believe it.
Sometimes a change in cost and performance is what creates a whole new category of technology. In my room, there are lightbulbs that contain computers, that can talk over a local network with me. Yes, we had computers in homes in the 70s and 80s, but no one would’ve ever thought that we’d have so many computers that are so tiny and cheap that we’d put them in freaking lightbulbs.
That’s what makes me so excited about Jev. It feels like we now have a truly smart Lego brick that we can use everywhere. Fun times.
What I believe about the future of software development. I posted this originally on X, saying that most predictions I see are still way too conservative, and it completely blew up.
“I don’t like passkeys”. Passkeys are such a weird technology. I can see how they’re technically brilliant and solve a lot of issues, but it does feel like Google and Apple and 1Password invited The Guy Who Invented Cookie Banners and said: what would you do, how would you roll this out?
Colossus published a very long Mark Zuckerberg profile. Fascinating read. It’s very well written and somehow managed to make me think thoughts about Zuckerberg that I haven’t thought before, which is quite the feat, considering that we’ve all been aware of Zuckerberg for, what, nearly twenty years now?
Einride and Lidl Launch First Autonomous Cab-less Truck on German Public Road. As an Aldi man myself, let me say: hell yeah, let’s go, Lidl!
How To Write With An LLM. I like this! I still don’t know how to use LLMs for writing, because I never want them to write something for me and even seeing how they would write it seems to poison my brain. I should probably add an “only tell me what to change and why, but never ever show me how you’d write it” to my system prompts.
Marc Brooker, Distinguished Engineer at AWS: “I believe that, long-term, humans have no role in routinely reviewing code. […] The idea that humans will reliably look through code to find the increasingly rare issues that automated tools miss seems like a fantasy.” Yep.
I wanted to link to Powermove here and say “look, editable software! It’s happening! Jellyware!” but now realize that it’s not quite that yet. It’s a video editor with an agent inside, but it doesn’t seem like you can edit the video editor itself. That’s coming, though.
We are all Product Engineers now: “The cost of writing code collapsed, and the cost of reviewing, fixing and operating it is following, and I’m assuming it gets there. What’s left of making software is finding out what people actually want, defining it precisely, and making it pleasant to use. That cost is per piece of software and doesn’t transfer, so as the amount of software goes to infinity, which it will because there’s no ceiling on demand, that cost becomes the whole job. That job is called a product engineer.” Obviously agree, but what I didn’t know about was Google’s APM program: “Formalized training of product people barely exists. Google’s APM program, which Marissa Mayer started in 2002 and which is the template everyone copies, takes about fifty people a year out of something like twelve thousand applicants.” Would love to read more about it.
“I asked Astra to create an interactive aquarium wallpaper for my Mac. The fish respond to the cursor!” Beautiful!
John Gruber, Daring Fireball, with Thoughts and Observations on Apple’s ‘Surprise and Shine’ Event; the Announcements of the iPhones 18 Pro, AirPods 5, Apple Watches Series 12 and Ultra 4, and the iPhone Duo; and the Dawn of the Ternus, John Ternus Era at Apple. Yes, that’s the title. The whole thing is Peak Gruber, I love it. What a writer. Now, I really do enjoy his words and sentences, but let me also use this occasion to say how much I admire him as a Pedantic Punctuation Pro: the numbered lists vs. the bulleted lists, the space between the numbers and the colon in aspect ratios, using × in display resolutions, … You could show me this sentence without any other context and I’d say it was written by Gruber: “The original iPhone (2007) display was precisely 3 : 2 (480 × 320 pixels, and let’s call it 1.5 : 1 for comparison’s sake to the following ratios), and this remained true through the iPhone 4 and 4S (960 × 640 pixels, 2× retina).”
This was a very entertaining and fascinating read: why I can’t stop thinking about Papua New Guinea and what I think everyone should know about it. I’ve become somewhat of a Papua New Guinea Head myself (that’s what they call us (no, they don’t)), after reading this piece, They Burn Witches Here, nearly a decade ago. I couldn’t shut up about it at work. For two weeks straight: “Dude, did you know that in Papua New Guinea…” Until one day a colleague said: “Yeah, I did know.” Turns out that colleague, Nick Skelton, was a tour guide in PNG (as we call it) and even wrote a book about it, which I immediately ordered and read.
Moats & the Barbell-ification of Software: “Long term, I think the evolution of the software industry might mirror what happened to newspapers in the 1990s. There will be a smaller number of very large software companies. […] I also think there will be one large software company by industry (e.g., Legal, Finance, Medicine) […] I think most mid-sized point solutions will likely be consolidated or die off. The optimal strategy for the winner will be to do it all. […] Lastly, I think there will be an explosion of “small” software. Most of this will be people building software for themselves or their own companies, but I think there might also be an explosion of small software businesses that make niche software, similar to the D2C explosion of the 2010s (powered by Shopify and Meta Ads).”
AI-generated posters don’t have to be horrible. Yes! Exactly! Now, read this, and then imagine you’re a person who can come up with all these styles without having to ask ChatGPT first. And then, on top of that, imagine that the very same person also knows something about music, and literature, and politics. Imagine how they could combine what they know and mix and remix. That, I think, will be valuable in the future.
Window Sweaters: “A little Mac app I made to give my windows sweaters. 🧶 Knitted borders, colours inspired by your favourite apps, and a cosier desktop.”
You should ask Jev whether you should subscribe. No, actually, I know the answer: you should.
We’re in an era where a few organizations are using thousands of concurrent agents to improve their processes and output. These organizations happen to be just the frontier AI labs, in particular OpenAI and Anthropic. In the last few weeks, I’ve been pondering what it means for so many employees across these organizations to rapidly update their expectations for the pace of AI progress and associated risks.
A core perspective I have is that the frontier labs and broader frenetic, competitive culture in the San Francisco AI scene set up an environment that amplifies any AI concern. This has some benefits in causing more general audience awareness of AI, as fear sells, but exaggerating risk timelines or severity will have negative second-order effects. I remember many loud AI safety debates, and their associated clouds over the viability of open-source AI, in 2023 and 2024 — the primary risks then did not arrive in the forecasted timelines.
The general populace of these two key labs was very anxious about AI risks and the rate of progress even a year ago, and especially as agents got stronger product-market fit at the start of 2026. This cultural precondition, when exposed to the reality that thousands of agents will constantly be working fairly productively in your business, will only increase this anxiety. The step from this anxiety, and incidents like OpenAI-HuggingFace, to extinction risks feels very religious.
Richard Ngo had an apt summary of the situation:
Now a large proportion of the AI safety community is implicitly or explicitly orienting to futures where an intelligence explosion occurs within a few years. My default expectation (absent an extensive pause) is that a similar thing will happen: they’ll turn out to be directionally correct (relative to the expectations of almost anyone not linked to the community) but factually wrong. Specifically, we won’t have superintelligence within the next 8 years, but things will still be moving so fast that it’ll *feel* like the people who argued for short timelines were right.
… I wanted to say something now because it feels like the level of bandwagoning towards “singularity soon” is getting pretty wild.
Personally, I think this view aligns closely to what I outlined in my alternate scenario to true recursive self-improvement (RSI), which I called lossy self-improvement. A summary of this view is that:
Automatable research is too narrow to achieve a massive net acceleration in progress, in the face of scaling laws’ exponential costs,
Diminishing returns of more AI agents in parallel are real, &
Resource bottlenecks and politics are a major factor in building strong LLMs (and AI can do much less to accelerate this).
So, I’m left balancing the above, latent increase in the cultural temperature with the potential that the labs have seen genuinely scary, specific breakthroughs that are not public yet. My expectation is that more of the current AI safety concern is on the former – scaled agents working – but I hold high levels of uncertainty here. Foundational, imagination-based AI breakthroughs are the sort of thing that would make me update my RSI timelines from closer to a tool to sustain progress in the face of exponential costs (scaling laws), to something more unpredictable and/or unstable.
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Some of the best recent resources on RSI have been Dwarkesh’s podcasts with Noam Brown and the trio of John Schulman, Beren Millidge and Charlie O’Neill. I have a few important reflections from both of them.
First, the podcast with Noam Brown made me internalize how big of a short-term acceleration mass inference capacity is. These labs will throw thousands of agents at important, measurable problems. At the same time, compute capacity available to them is going to continue to scale. I have my doubts that the labs can afford to spend a constant portion of this compute on internal R&D as the total volume goes up, especially with plans to IPO, as they face increased scrutiny on basic economics. It is important to not confuse massive steps in inference-time scaling, a dynamic which should be fairly predictable, with being the outputs of RSI, which is highly uncertain.
Second, the trio podcast debating the state of the art in technical capacities induced more of a surprising reaction that I haven’t fully settled. Through the first hour or so of this podcast, where they debate the role of RL, distillation, scaling, inference-time compute, etc., I found myself strongly agreeing with the distribution of claims. A TLDR would be that our current techniques work and let us solve problems we know how to state, but they don’t result in a magical level of generalization to unknown, harder problems in most partially verifiable domains (i.e. progress in math is an exception, rather than a rule).
The surprise of this podcast was the end, where they were predicting timelines for various thresholds of AI. I had GPT-6-Astra summarize the answers provided to three questions from Dwarkesh, of the form “when will AI reach X ability”:
All timelines are relative to the interview date.
Drop-in remote worker for broad white-collar work over a month
Charlie O’Neill: ~1 year with programmatic access to workplace tools; ~2 years if it must operate through a browser. Means ordinary white-collar work, not highly creative research.
Beren Millidge: ~3 years for full generality; 80–90% coverage sooner. Main uncertainties: online learning and the long tail of tasks.
John Schulman: ~1 year for an “okay” version, with uneven capabilities that improve over time.
10× productivity uplift for AI researchers
Charlie O’Neill: 5–10 years. Bottleneck: absorbing information and deciding which experiment to run next.
Beren Millidge: Finds John’s ~2-year estimate plausible, but gives no independent timeline. Assumes AI can run successive experiments and learn from feedback; other bottlenecks would remain.
John Schulman: ~2 years.
AI surpassing top human experts across all computer-based work, including multiyear projects (“ASI”)
Charlie O’Neill: 5–10 years. Highlights limitations in memory and context length.
Beren Millidge: ~5 years for areas labs focus on; potentially longer for literally every domain. Gives no firm timeline for the universal version.
John Schulman: 3–4 years. Spatial/physical fields may take longer; requires onboarding and solving longer-horizon learning.
Roughly, a recurring problem when discussing RSI is a lack of specification in intelligence. The jaggedness of intelligence means that we need to discuss thresholds in specific, measurable tasks. The nature of LLMs’ intelligence is shaped very differently than humans, and the roles we forecast are human-shaped. AIs, therefore, do not cross these thresholds like remote worker or AI researcher discretely. It’s a slow diffusion, and a form of long tail will always exist.
Take the case of productivity of AI researchers. Many people under-index how much of science is communication and standard setting with colleagues. I do buy the cycle of experiment design and testing being 10x faster in the near future, but not hypothesis generation and intuition building. Accelerating understanding will be the key bottleneck – and it is one that despite all of the AI tools getting massively improved, humans will only improve marginally in their capability. A big improvement in the nature of science will be enabling humans to invest more time here, not them becoming exponentially better at it.
This links back to the Noam podcast. Agent swarms in the near future will be effective at solving clear, open problems with verifiable answers. In this vein, when it comes to improving AI models, RSI is much more helpful at efficiency rather than expanding peak intelligence. This is due to the fact that LLM serving has clear metrics you want to improve that are measurable and malleable. This’ll enable better inference-time scaling and more efficient multi-agent systems.
Still, I cannot get past the fact that all of our scaling laws show that you need exponential compute and resources to make linear improvements in intelligence. RSI is poised to make modern LLMs vastly cheaper. Trends that have shown LLMs get exponentially cheaper at a given intelligence are likely to accelerate. A crucial factor for the labs will be increasing margins as revenue could potentially have negative pressure if there’s fierce competition in lowering prices at a fixed intelligence level — Jevons paradox will likely prevail, resulting in strong businesses.
RSI factors will have a much harder time improving pieces of the LLM puzzle like managing complex post-training recipes. There were a few quotes from John Schulman that I strongly agree with on the state of post-training at the labs:
If I think about a post-training team and why you need a lot of people on the team, it’s just because there are a lot of different areas where you have to figure out how the model should behave. It would be very hard to automate the whole thing, just because someone has to think about how the model should behave in this area.
and later:
It’s really easy to screw up post-training in some way that doesn’t show up in benchmarks.
These tasks are uniquely hard for current LLMs. Yes, they’ll get better as the industry is still rapidly scaling RL environments related to these domains, but this paradigm does not last forever. In the near future, it could become exponentially harder to conceive, build, and test new environments that meaningfully challenge the leading LLMs – these hard environments are the ones that are crucial as a learning signal in RL.
OpenAI and Anthropic have shared a good amount of internal measurements related to RSI, and my current read is that the biggest takeoff in automation within the labs is in tasks like software engineering, monitoring logs, managing planned experiments, and other fairly routine (but not always easy) tasks. For example, I was surprised by this language in the recent Claude Fable 5.1 & Mythos 5.1 System Card:
We believe that internal usage of recent AI models has been a key factor in maintaining the current rate of progress, but we do not yet see clear signs of dramatic acceleration beyond that rate.
Altogether, I think the hardest exponential we are fighting is on peak intelligence. That is the hardest one to budge or even accelerate. Still, my mental model for the very early innings of RSI is more of massively scaling and diffusing inference-time compute to AI research and related activities, which has a large amount of low-hanging fruit available. This, on its own, is still poised to be economically transformative. It may also unlock more resources to push on AI diffusion, which is the crucial bottleneck in unlocking much of the potential benefits of AI.
For now and until more evidence emerges, lossy self-improvement remains my baseline on the trajectory of progress, and the increased discussion of extinction risk seems very misplaced. As always, things can change fast in AI.
We are still on an exponential curve of AI development. I try to put out a Substack post every couple weeks or so, yet, as the pace speeds up, that sometimes feels too slow. In the weeks since my last post, we had the apparent cracking of one of the most famous problems in math by an AI (accompanied by controversy) and widespread discussions about the risks posed by AI and what to do about it (also accompanied by controversy). I think these concerns, along with a mounting set of other worries, come down to the same problem I have with my posts: how slowly our very human systems and processes work to keep up with the pace of AI development.
I don't think the people worried about this are wrong, but I also think a sole focus on future AIs, as important as that is, ignores the fact that AI, right now, is already incredibly capable. In fact, the new GPT-6 Astra and Fable 5.1 are already enough for transformative impact in large sections of the economy and they can reliably do weeks worth of human work when properly guided and harnessed.
A few fun examples of that: I had GPT-6 Astra turn a 1977 text adventure game called Zork into a full 3D action-adventure game you can play. Zork has no graphics and each location is a paragraph of prose, so the AI had to decide what the white house looks like, what a grue looks like (the original only tells you that you are likely to be eaten by one in the dark), and how to turn “fight the troll” into an action sequence. I also had Fable 5.1 try to reconstruct Italian author Umberto Eco’s library in 3D. Eco kept tens of thousands of books in his Milan apartment and the AI could not find a floor plan, so it instead decided to work from a dozen videos, the foundation's photographs of each bookcase, and two library catalogues. It read spines frame by frame, inferred the rooms, and placed the 5,000 or so books it could identify among 27,000 shelf slots. It marked every book certain, guess, or unknown, and drew the bookcases the cameras never reached in fog. This task, like the Zork game and a lot of real-world work I have had the AI do recently, would have taken weeks of human work involving researchers, coders, and designers. But here we are.
My point is that, while there is a lot of debate over what future models will do, the current capabilities of existing models are barely being used, and are often not even well understood. For example, I did not know GPT-6 Astra could operate Blender (a sophisticated piece of 3D modelling software) until it did.
I gave it a copy of my upcoming book, Co-Existence, and asked it to create a trailer for the book from the perspective of an AI. Without clear instructions from me, it proceeded to use Blender and build out an entire animated 3D scene (not an easy task), along with a script with some jokes and reveals (I did reject the first joke it added, but the second was quite good). It then figured out how to generate voices and music and sound effects and gave me this film 45 minutes later. The final product feels a little more ominous than I would like, but that was the AI’s decision, not mine.
To see how much further it could go, I prompted: “That’s good, but I actually want you to make an action movie trailer based on Co-Existence. Have fun with it. No more than 30 seconds.” Again, it wrote a script and made a 3D prototype in Blender. After I asked for a more cinematic version, it used the Blender animation as a storyboard, operated a video generator through my browser, and edited the generated shots into the final trailer. I gave some minor creative feedback, but never touched any production decision or even knew exactly how it was accomplishing its tasks. You can see the results here.
There are plenty of flaws in these efforts that you can spot. But they are also examples of the AI exercising a kind of judgement and creativity, things that not long ago were considered uniquely human traits. And they were all done with just a fraction of the token budget of the ChatGPT account I pay for. I think these are fun demonstrations, but they are also a bit scary because AI is getting better at things that were once purely human. Still, none of these projects happened on their own. I chose them, I knew enough about Zork and Eco and my own book to see where the AI went wrong, and to ask for a second version when the first wasn't right. The capability overhang, the gap between what these models can do and what almost anyone is doing with them, is an opportunity because most people don't bring their own advantages to AI, and those who do get much more out of it.
That is why I think we will need to focus on the individual traits we have that remain useful even as AI abilities improve. You are not trying to compete with AI in producing outputs, that is a losing game. Instead, you want to use your human advantages as basis of working with AI to do things that neither of you could do alone. In my book, I outline four particular personal advantages that matter a lot if you want to use AI in unique and enhancing ways: deep knowledge, wide knowledge, taste, and agency.
The first two advantages come from what you know. Deep knowledge is the expertise that comes from understanding a field or subject so well that you build intuition around it to quickly and accurately make decisions. It is how an experienced accountant can glance at a spreadsheet and know something is wrong, or how a golf pro can watch a swing and instantly understand the mistake the golfer is making. It is also why I could tell within seconds that the first trailer was more ominous than the book actually is. Deep knowledge is the realm of the specialist, and it is the only way to truly understand the shape of the Jagged Frontier, because only experts can understand the patterns of where AI succeeds or fails, at least in their area of expertise. It also helps you adapt to change because deep knowledge makes it easier to switch from being someone who does the work to someone who manages it. And recent work from Anthropic suggests that expertise also shapes the quality of what AI gives back. Experts not only get better work out of AI, they get more work out of it.
But you don’t just need deep knowledge, you also want wide knowledge. The training data for LLMs is a large swath of humanity’s vast output. The AI has learned something of design thinking and Bayesian reasoning and the Toyota Production System and Rogerian therapy and Marxist literary criticism. But AI tends not to volunteer any of these patterns unless you know to ask.
This is where wide knowledge comes in. Lets take one example: the way AI handles design work. If you ever ask AI to create a webpage, it will have certain preferences, including a very annoying habit of adding little headlines on top of your headlines. If you don’t have any grounding in design, you may not realize that you need to ask the AI to stop “adding eyebrows” to the work. It is also how I knew that using a Blender animation as a storyboard for a video generator was a sensible way to make a film, and not the AI wandering off. If you do know the right terms, asking for changes is easy. To gain wide knowledge you need to read and study widely, across fields and formats and traditions. This is valuable in and of itself (the return of the liberal arts!) but doubly so in the age of AI
Now let’s go back the videos and projects I demonstrated above... You may have reacted viscerally to one or another, or hated them all. You may have found a theme or idea you would like to see more of. In doing this, you are using the third human differentiator in the age of AI, taste. Before AI, making things was hard and slow. Writing a draft took hours. Generating twenty product concepts took a team a week. An academic paper could take years. The constraint was always making enough stuff. Now making is fast and cheap. The scarce resource is your ability to select among stuff using your own taste. Again, in the trailers, I rejected the first joke and kept the second. I asked for a more cinematic version. Those were the only decisions I made on the trailer, but they were based on my taste.
Some people have a taste for things that many people will find popular, others have a taste that is unique to them, and still others have a taste for what is novel and new. Yes, generative AI leads mostly to slop: a flood of work that is very similar to each other. But slop can be defeated by taste. Making great things with AI means knowing which AI outputs to keep, which to discard, and which to use as raw material for something the AI would never have generated on its own.
The final human advantage, agency, might be the most important and the hardest to talk about, because it is difficult to define and the subject of a lot of debate. But in the context of AI, I think it is a willingness to test the boundaries of what’s possible when everybody is equally confused about what AI can do. The jagged frontier is unmapped in your field, so agency is about becoming an explorer. It’s the difference between waiting for someone to tell you that AI can now do something, and discovering it yourself by trying. That is part of why I do so many weird AI experiments — like trying to get the AI to play games — it teaches me a lot about what AI can do.
I discuss these four advantages, and a lot more, in Co-Existence, which comes out October 20. If you pre-order it and let me know at co-existence.ai (pre-ordering really helps authors), we will send you a link to a free voice interview with an AI within a day or two. It asks you about what you know, what you like, and what you have tried, and then gives you a report on your own deep knowledge, wide knowledge, taste, and agency, along with use cases and prompts built around them.
Most of the anxiety about AI right now is about future models and whether we will be able to control them. It seems reasonable for governments and AI labs to be arguing about how to manage the speed of development to mitigate these risks. But a slowdown does not undo what already exists. If every lab stopped training new models tomorrow, that wouldn’t change the fact that GPT-6 Astra and Fable 5.1 are already enough to change how large parts of the economy work. The capability overhang between what those models can do today and what most folks are using them for is massive.
So change is coming no matter how the frontier is paced. It will not happen all at once and it will be uneven, but it is inevitable. Yet inevitable change does not mean the type of change is inevitable. It is increasingly important that we, as a society, develop and share models of AI-human work that enhance, rather than only replace, human labor. And it is equally important that we, as individuals, use AI in ways that enhance, rather than only replace, our own efforts. I don’t think there are bright lines we can point to and say AI will never cross them (see above). But your four advantages are a place to start today.
The Zork project and Library project are both open source, feel free to modify them if you want (Zork is itself open source).
This document curates the most common questions Shreya and I received while teaching 5,000+ engineers and PMs AI Evals. Warning: These are sharp opinions about what works in most cases. They are not universal truths. Use your judgment.
Browse the questions that interest you, or choose a guide below for a curated reading path through the FAQs and related articles.
| Where are you? | This sounds like me |
|---|---|
| I’m new to evals | I’ve heard the term, but I’m not sure what evals involve or whether I need them. |
| I don’t know what to test | I’m building an AI product, but I haven’t figured out which failures to measure or what good performance looks like. |
| I don’t trust my eval scores | We have evals, but the scores don’t match our judgment of the outputs, or tests pass while users still encounter problems. |
| My product feels too hard to evaluate | Our outputs are subjective, long, or involve many steps. Even a knowledgeable person has trouble deciding whether they’re right. |
| Evals take too much time or money | We’re spending too much effort reviewing outputs, maintaining tests, or running evaluators. |
Browse all questions by section.
AI evals are tests that tell you whether an AI system is doing what you want. They give your team feedback when the product drifts from user needs or business goals. The failures they catch also become data you can use to improve the system.
More formally, evaluation is the systematic measurement of quality. Each eval checks one behavior on relevant examples and returns a score or structured review. Most AI products need several evals because they can fail in different ways.
When you hear the word “evals,” it usually refers to one of two things: model benchmarks or product evals.
Model benchmarks compare general-purpose models on shared tasks. Model providers publish these benchmark results when they release new models. Common examples include GPQA Diamond for graduate-level science reasoning, Terminal-Bench for agents doing complex work in command-line environments, and MMLU for knowledge and reasoning across a wide range of subjects. These scores can help you choose a promising model as a starting point. To assess quality on your own tasks you need product evals, which we discuss next.
Product evals measure whether your specific AI product does what you want it to do. They turn your judgment about what a good product experience looks like into metrics you can track. Product evals encompass all components of your product, including the model, prompts, retrieval, tools, and application code. This flavor of evals are focused on capturing failures that matter to users and the business.
Consider an order-cancellation agent. Its product evals might check whether it selected the correct order and waited for the cancellation tool to succeed before telling the user the order was canceled. A high score on GPQA Diamond or Terminal-Bench gives you little information on this, because those benchmarks don’t have access to your systems.
There are several mechanisms you can use to implement product evals, including code assertions, human review, LLM judges, and online experiments. The right method depends on the failure being measured, and is discussed in greater detail in this series.
In the rest of the AI Evals FAQ, we focus on product evals. It starts with analyzing traces to discover real failure modes. We then turn important failures into targeted evals and use the results to guide changes. Finally, rerunning the evals tells us whether the system improved.
If you are completely new to product-specific evals, see these posts:
| Guide | What it covers | |
|---|---|---|
| Part 1: Your AI Product Needs Evals | Build a domain-specific evaluation system with scoped tests, trace review, human evaluation, and experiments. | |
| Part 2: Using LLM-as-a-Judge For Evaluation: A Complete Guide | Capture a domain expert’s judgment, automate it with an LLM judge, and validate the judge against human labels. | |
| Part 3: A Field Guide to Rapidly Improving AI Products | Use error analysis, realistic data, and trustworthy evals to run a sustained product-improvement loop. |
A trace is the complete record of all actions, messages, tool calls, and data retrievals from a single initial user query through to the final response. It includes every step across all agents, tools, and system components in a session: multiple user messages, assistant responses, retrieved documents, and intermediate tool interactions.
Note on terminology: Different observability vendors use varying definitions of traces and spans. Alex Strick van Linschoten’s analysis highlights these differences (screenshot below):
Start with error analysis, not infrastructure. Spend 30 minutes manually reviewing 20-50 LLM outputs whenever you make significant changes. Use one domain expert who understands your users as your quality decision maker (a “benevolent dictator”).
Use a notebook to review traces and analyze data, or build your own custom annotation interface with an AI coding assistant like Claude or Codex. Either way, you can write arbitrary code, visualize data, and iterate quickly. The video below shows a simple annotation interface built inside a notebook.
It’s important to recognize that evaluation is part of the development process rather than a distinct line item, similar to how debugging is part of software development.
You should always be doing error analysis. When you discover issues through error analysis, many will be straightforward bugs you’ll fix immediately. These fixes don’t require separate evaluation infrastructure as they’re just part of development.
The decision to build automated evaluators comes down to cost-benefit analysis. If you can catch an error with a simple assertion or regex check, the cost is minimal and probably worth it. But if you need to align an LLM-as-judge evaluator, consider whether the failure mode warrants that investment.
In the projects we’ve worked on, we’ve spent 60-80% of our development time on error analysis and evaluation. Expect most of your effort to go toward understanding failures (i.e. looking at data) rather than building automated checks.
Be wary of optimizing for high eval pass rates. If you’re passing 100% of your evals, you’re likely not challenging your system enough. A 70% pass rate might indicate a more meaningful evaluation that’s actually stress-testing your application. Focus on evals that help you catch real issues, not ones that make your metrics look good.
Yes. Even with perfect models, you still need to verify they’re solving the right problem. The need for systematic error analysis, domain-specific testing, and monitoring will still be important.
Today’s prompt engineering tricks might become obsolete, but you’ll still need to understand failure modes. Additionally, a LLM cannot read your mind, and research shows that people need to observe the LLM’s behavior in order to properly externalize their requirements.
For deeper perspective on this debate, see these two viewpoints: “The model is the product” versus “The model is NOT the product”.
Don’t try to sell your team on “evals”. Instead, show them what you find when you look at the data.
Start by doing the error analysis yourself. Look at 50 to 100 real user conversations and find the most common ways the product is failing. Use these findings to tell a story with data.
Present your team with:
Frame evaluation as part of development, not optional testing. Keep a running log of the errors you catch, what you learned, the fix, and the likely impact you avoided. Share it weekly or monthly. A concrete report such as “we caught 47 issues before users saw them” makes the value easier to see than an abstract pitch about evals.
This approach builds trust. Don’t just show dashboards and metrics; tell the story of what you’re finding in the data. By narrating your findings, you teach the team what you’re learning, providing immediate value. When you fix an issue, show how the error rate for that specific problem went down. Soon, your team will see the progress and ask how you’re doing it. Let results instead of methods lead the conversation.
This is similar to classic machine learning projects, where outcomes are speculative and progress is bounded by iterating on experiments. In this situation, it’s important that you share the learnings from each experiment to show progress and encourage investment.
Error analysis is the most important activity in evals. Error analysis helps you decide what evals to write in the first place. It allows you to identify failure modes unique to your application and data. The process involves:
Gathering representative traces of user interactions with the LLM. If you do not have any data, you can generate synthetic data to get started.
Human annotator(s) (ideally a benevolent dictator) review and write open-ended notes about traces, noting any issues. This process is akin to “journaling” and is adapted from qualitative research methodologies. Start by annotating at least 30 traces yourself before reviewing suggestions from an agent. When beginning, it is recommended to focus on noting the first failure observed in a trace, as upstream errors can cause downstream issues, though you can also tag all independent failures if feasible. A domain expert should be performing this step.
Categorize the open-ended notes into a “failure taxonomy.” In other words, group similar failures into distinct categories. Axial coding is the most important step. At the end, count the number of failures in each category. You can use an LLM to help with this step.
Have your agent cluster the data and choose a diverse initial sample. After your first 30 annotations, let it search the remaining traces for likely instances of the failures you described. Accept or reject its suggestions and keep iterating until you reach theoretical saturation, meaning new reviews stop revealing failure modes or changing existing ones.
A working pool of roughly 100 diverse traces is a useful guardrail for this human-agent loop. The agent can focus your attention on the most informative traces, so you no longer have to read all 100 sequentially. See how many examples you need for each kind of eval for the full breakdown.
You should frequently revisit this process. There are advanced ways to sample data more efficiently, like clustering, sorting by user feedback, and sorting by high probability failure patterns. Over time, you’ll develop a “nose” for where to look for failures in your data.
Do not skip error analysis. It ensures that the evaluation metrics you develop are supported by real application behaviors instead of counter-productive generic metrics (which most platforms nudge you to use). For examples of how error analysis can be helpful, see this video, or this blog post.
Here is a visualization of the error analysis process by one of our students, Pawel Huryn - including how it fits into the overall evaluation process:
No. Writing a rubric before you review examples can get in the way.
Let’s get some definitions out of the way:
Both can help, but treat your initial expectations as a starting point that you will revise.
It’s often better to wait until you’ve reviewed some examples before developing a detailed rubric. Reviewers can become so focused on checking each item that they overlook problems outside the rubric. It’s important to give reviewers room to notice things you didn’t anticipate. This change in what you consider good is called “criteria drift”.
For example, let’s say you have a support agent that handles refunds and it escalates refunds to a human per your policy. You might only realize that the process is frustrating for the user after reading a few interactions. Don’t underestimate the degree of criteria drift that will happen as you review examples!
We recommend using error analysis to systematically review examples and decide what might belong in the rubric. This involves writing open-ended notes about what looks wrong, then group similar notes to see which problems recur. See this live demo for a walkthrough.
After doing error analysis, you can write a better rubric informed by user and application behavior. You should periodically do error analysis to make sure your rubric is current.
Yes. When reviewing interactions, write down anything that makes the product less useful. This includes missing or broken features that have nothing to do with the model. Additionally, don’t focus on why the error occurred, as that should only come after you prioritize which issues to fix.
For example, a support agent might tell a customer that an order has shipped without providing a tracking link. Even if your AI doesn’t have the ability to fetch a tracking link, record that problem. A prerequisite to building evals is to identify and prioritize which issues to fix through error analysis. Some of these issues may end up being engineering or design issues that don’t need an automated evaluator, but they are still important to fix!
Lastly, we’ve found that deferring root-cause analysis and focusing on problems allows you to write higher-quality annotations while looking at more data.
Building evals is a pipeline, and each stage needs a different amount of data. We describe these stages below:
| Stage | What to do |
|---|---|
| 1. Review the application | Read traces and write down the ways your application fails. This process is called error discovery. Start with 100 diverse traces and annotate at least the first 30 yourself. |
| 2. Create and validate evaluators | Choose between two evaluator types. Use a code-based eval when an objective rule can identify the failure. Include Pass and Fail examples for every condition and important edge case. Use an LLM judge when the failure requires human judgment. Label 100 to 200 examples for each failure mode. |
| 3. Build a repeatable eval set | Collect examples that represent important workflows and confirmed failures. Run this set when you change your application. These sets often grow to 100 or more examples. |
A trace is a complete record of one user session with your application. Ask a coding agent to help you sample the initial pool so it covers different users and workflows. Our evals plugin can help with sampling and build an annotation interface for your traces.
We recommend annotating at least 30 traces with a process called error discovery yourself before asking the agent to suggest failures. Write free-text notes about anything that seems wrong from the user’s perspective. These examples give the agent a concrete record of your judgment.
Keep this first pass manual. If the agent starts suggesting problems too early, its guesses can bias your judgment. You may miss failures that depend on product context or your definition of a good user experience.
After 30 traces, ask the agent to search the remaining pool for similar examples. Review every suggestion yourself. Accept or reject each one and correct the agent when it misunderstands your criteria.
Continue until new traces stop revealing failure modes or changing existing ones. Qualitative researchers call this theoretical saturation. We recommend reviewing at least 100 traces. Continue past 100 while you are still learning.
If you want to see the process done live, watch the live walkthrough. The video shows how Shreya Shankar uses an agent to review traces quickly while keeping a human in charge of the failure criteria.
This review produces a failure taxonomy, which is a list of the specific ways your application fails. Use that taxonomy to decide which evaluators to build.
Choose an evaluator for each important failure mode. The evaluator type determines how many labeled examples you need. Code-based evals work for objective rules. LLM judges work for failures that require human judgment.
Use a code-based eval when a deterministic rule can identify the failure. Examples include checking whether JSON parses or whether a tool call uses the correct arguments.
The number of examples depends on the scenarios the check covers. At minimum, include examples that should Pass and Fail for every condition. Add important edge cases you found during error discovery. A check with one rule may need only a few examples that Pass and a few that Fail.
Use an LLM judge when the failure requires subjective or domain-specific judgment. Plan to label 100 to 200 examples for each failure mode. Reuse labeled traces from error discovery when they match the failure mode, then collect more until you reach that range. The labels should come from a trusted domain expert and contain enough Pass and Fail examples to evaluate both classes.
Split these examples into train, dev, and test sets. Use 10 to 20 percent for train examples that may appear in the prompt. Use 40 to 45 percent for dev while refining the judge. Reserve the remaining 40 to 45 percent for one final test. When possible, include 30 to 50 Pass examples and 30 to 50 Fail examples in both the dev and test sets.
The validation guide explains the full process. The judge-validation flashcard is a good visual reference as well.
After validating the evaluators, assemble the examples you will run repeatedly during development.
Start with examples from error discovery that capture important failure modes. Add confirmed failures as you find them.
A purpose-built eval set often grows to 100 or more examples. Coverage determines the final size. Each important workflow and known failure should be represented, and the set should remain cheap enough to run often. Code-based checks and LLM judges can run over the same examples. The CI evals FAQ explains how to use this set during development.
While user feedback is a good way to narrow in on problematic traces, other methods are also useful. Here are three complementary approaches:
The simplest approach is reviewing a random sample of traces. If you find few issues, escalate to stress testing: create queries that deliberately test your prompt constraints to see if the AI follows your rules.
Use existing evals to find problematic traces and potential issues. Once you’ve identified these, you can proceed with the typical evaluation process starting with error analysis.
For more sophisticated trace discovery, use outlier detection, metric-based sorting, and stratified sampling to find interesting traces. Generic metrics can serve as exploration signals to identify traces worth reviewing, even if they don’t directly measure quality.
Re-run error analysis when making significant changes: new features, prompt updates, model switches, or major bug fixes. A useful heuristic is to set a goal for reviewing at least 100+ fresh traces each review cycle. Typical review cycles we’ve seen range from 2-4 weeks. See this FAQ on how to sample traces effectively.
Between major analyses, review 10-20 traces weekly, focusing on outliers: unusually long conversations, sessions with multiple retries, or traces flagged by automated monitoring. Adjust frequency based on system stability and usage growth. New systems need weekly analysis until failure patterns stabilize. Mature systems might need only monthly analysis unless usage patterns change. Always analyze after incidents, user complaint spikes, or metric drift. Scaling usage introduces new edge cases.
Eval datasets naturally get stale as your product and users change. Use regular error analysis to find new problems and update your examples or reference answers. How often you review depends on your use case and how quickly your product or usage changes.
Like unit tests, evals can catch problems that return after a fix. However, evals often cost considerably more than unit tests to maintain and run. Therefore, you should weigh each eval’s cost against the value of its signals. If everything keeps passing, this is a sign that the eval is no longer useful and should be retired or run less often.
As your eval set changes, its scores may no longer be directly comparable with older scores. That is ok! One purpose of evals are to provide you with challenges you can hill climb against. These challenges should change as your product evolves to help you keep improving.
For tracking progress with metrics over longer time horizons, it’s often better to use product metrics in addition to evals. Examples of product metrics include: churn, active users, revenue, etc.
A common mistake is prompting an LLM to "give me test queries" without structure, resulting in generic, repetitive outputs. A structured approach using dimensions produces far better synthetic data for testing LLM applications.
Use synthetic data to start error analysis before you have enough production traffic, or to test a known failure that appears rarely in real data. Define the variation you need, generate examples, run them through the full system, and review the resulting traces.
Synthetic data cannot tell you how common a failure is in production. It can also miss details that matter in specialized domains. Compare synthetic examples with real data as soon as real data becomes available. See when synthetic data may be unreliable for cases that require extra review.
Start by defining dimensions: categories that describe different aspects of user queries. Each dimension captures one type of variation in user behavior. For example:
Start with failure hypotheses. If you lack intuition about failure modes, use your application extensively or recruit friends to use it. Then choose dimensions targeting those likely failures.
Create tuples manually first: Write 20 tuples by hand. Each tuple selects one value from each dimension. Example: (Vegan, Italian, Multi-step). This manual work helps you understand your problem space.
Scale with two-step generation:
This separation avoids repetitive phrasing. The (Vegan, Italian, Multi-step) tuple becomes: "I need a dairy-free lasagna recipe that I can prep the day before."
You can generate tuples two ways:
Cross product then filter: Generate all dimension combinations, then filter with an LLM. Guarantees coverage including edge cases. Use when most combinations are valid.
Direct LLM generation: Ask the LLM to generate tuples directly. This produces more realistic combinations, but it tends toward generic outputs and misses rare scenarios. Use it when many dimension combinations are invalid.
Fix obvious problems first: Don’t generate synthetic data for issues you can fix immediately. If your prompt doesn’t mention dietary restrictions, fix the prompt rather than generating specialized test queries.
After iterating on your tuples and prompts, run these synthetic queries through your actual system to capture full traces. A pool of roughly 100 diverse traces is a useful starting point for failure discovery. Have an agent help with sampling, annotate at least 30 traces yourself, then review the agent’s suggestions until your learning plateaus. See how many examples you need for error discovery for the full explanation.
Here is a visual that helps visualize the process.
Yes: synthetic data can mislead or mask issues. For guidance on generating synthetic data when appropriate, see What is the best approach for generating synthetic data?
Common scenarios where synthetic data fails:
Complex domain-specific content: LLMs often miss the structure, nuance, or quirks of specialized documents (e.g., legal filings, medical records, technical forms). Without real examples, critical edge cases are missed.
Low-resource languages or dialects: For low-resource languages or dialects, LLM-generated samples are often unrealistic. Evaluations based on them won’t reflect actual performance.
When validation is impossible: If you can’t verify synthetic sample realism (due to domain complexity or lack of ground truth), real data is important for accurate evaluation.
High-stakes domains: In high-stakes domains (medicine, law, emergency response), synthetic data often lacks subtlety and edge cases. Errors here have serious consequences, and manual validation is difficult.
Underrepresented user groups: For underrepresented user groups, LLMs may misrepresent context, values, or challenges. Synthetic data can reinforce biases in the training data of the LLM.
There is no replacement for looking at real interactions. This situation is not ideal, but there are some things you can do. Here are some options, in order of preference:
Try to find real data you are allowed to inspect. A customer may agree to share a subset of traces, or test users may let you review their interactions. Even limited access gives you examples of how people use the product.
If you cannot inspect the data yourself, work with domain experts who are allowed to see it. Make your product easier for them to verify as part of their normal work. For example, a medical research assistant could show a clinician the evidence behind each claim and flag conflicting sources for review. The clinician can correct a specific claim or resolve a conflict while using the product. Those decisions can provide additional data for evals, subject to the same restrictions on what you can store and share.
To design this well, learn how the experts check an answer. Give them links to the supporting evidence and smaller pieces of work they can review. Asking whether the final answer was helpful often tells you too little about what went wrong. I discuss this approach in this post.
Redact or edit traces so they can be shared. If sensitive information cannot be stored, redact it before logging. Redaction tools can miss sensitive information, so check their output. When edited traces can be shared, removing personal information and changing sensitive details can make real examples usable for review. Check that those edits preserve the behavior you need to evaluate.
If none of the above options are possible, synthetic data should be your last resort. Synthetic data can help you find initial problems but has the downside that it only gives you limited evidence about how real users will behave. Read more about when synthetic data may be unreliable.
Complex applications often support vastly different query patterns—from “What’s the return policy?” to “Compare pricing trends across regions for products matching these criteria.” Each query type exercises different system capabilities, leading to confusion on how to design eval criteria.
Error Analysis is all you need. Your evaluation strategy should emerge from observed failure patterns (e.g. error analysis), not predetermined query classifications. Rather than creating a massive evaluation matrix covering every query type you can imagine, let your system’s actual behavior guide where you invest evaluation effort.
During error analysis, you’ll likely discover that certain query categories share failure patterns. For instance, all queries requiring temporal reasoning might struggle regardless of whether they’re simple lookups or complex aggregations. Similarly, queries that need to combine information from multiple sources might fail in consistent ways. These patterns discovered through error analysis should drive your evaluation priorities. It could be that query category is a fine way to group failures, but you don’t know that until you’ve analyzed your data.
To see an example of basic error analysis in action, see this video.
There are many ways to sample production traces for review. Here are some common methods.
| Method | What it does | Main limitation |
|---|---|---|
| Random | Selects traces with equal probability. | A small batch can miss rare cases. |
| Clustering | Groups traces by similar content and selects examples from each group. | The result depends on the features and clustering choices. |
| Data analysis | Reviews extreme values such as latency or tool count. | An extreme value may have nothing to do with quality. |
| Classification | Uses an evaluator or another model to flag likely failures. | It favors problems the classifier already knows how to find. |
| Feedback | Selects traces with negative user feedback. | It misses problems that users do not report. |
The table above orders sampling methods from the most exploratory to the most targeted. When you’re starting out, you should optimize for exploration of the data. As you learn more, you can start to lean more heavily on signals to select traces. The proper mix of methods depends on your goals and requires experimentation.
Keep some random traces in every batch. This gives you a chance to find failure modes that your current signals do not describe.
Use targeted sampling to find rare failures. Search for signals that correlate with the failure, such as a specific tool sequence, unusually long traces, retries, or a known input pattern. Review the targeted batch to collect examples and improve the failure definition.
This flashcard from our evals flashcards series visualizes these methods.
We can borrow a technique from machine learning called active learning to sample production traces. In active learning, a system asks a person to label the data points that would be most useful for its next update.
In Shreya Shankar’s walkthrough, Claude Code clusters traces and chooses examples from each cluster for review. A monitor command watches annotations.json for new labels. When a label arrives, the agent updates a failure taxonomy and looks for similar cases or different failures.
In the above video, active learning is used in the context of error analysis to find new cases to review. However, this approach can be used anywhere in the workflow where you are annotating data.
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Engineers often believe that Likert scales (1-5 ratings) provide more information than binary evaluations, allowing them to track gradual improvements. However, this added complexity often creates more problems than it solves in practice.
Binary evaluations force clearer thinking and more consistent labeling. Likert scales introduce significant challenges: the difference between adjacent points (like 3 vs 4) is subjective and inconsistent across annotators, detecting statistical differences requires larger sample sizes, and annotators often default to middle values to avoid making hard decisions.
Having binary options forces people to make a decision rather than hiding uncertainty in middle values. Binary decisions are also faster to make during error analysis - you don’t waste time debating whether something is a 3 or 4.
For tracking gradual improvements, consider measuring specific sub-components with their own binary checks rather than using a scale. For example, instead of rating factual accuracy 1-5, you could track “4 out of 5 expected facts included” as separate binary checks. This preserves the ability to measure progress while maintaining clear, objective criteria.
Start with binary labels to understand what ‘bad’ looks like. Numeric labels are advanced and usually not necessary.
Each eval you create should return a binary outcome (e.g. Pass or Fail). You will likely end up with many evals, each checking a different failure. However, people in your organization may want a single number to track.
A simple approach I like to use is a “pass all” rate. An example passes only if it passes every check. For example, if 80 out of 100 examples pass every check, your pass-all rate is 80%. Design your report or dashboard so you can drill down from the overall pass-all rate to the pass rate for each check so you can see what’s contributing most to failures.
A middle ground between one overall score and a separate result for every eval is to group related checks into themes. You can then report a pass-all rate for each group. For example, reviewing Nurture Boss’s apartment leasing assistant revealed problems with conversation flow, handoffs to humans, and rescheduling. Those themes could become groups of evals.
Another way to choose these groups is by how serious the failures are. For example, report one pass-all rate for checks that should block a release and another for issues you can tolerate. This approach can be helpful for gating production releases.
If you still need a single score that accounts for differences in importance, you can give some checks more weight than others. I discourage complicated weighted scores for the same reason I discourage Likert scales for LLM judges. If your dashboard reports a composite score that jumps from 3.2 to 3.7 week over week, it’s easy to feel good about the increase without knowing what improved for users. In our experience, dashboards like this are usually performative and waste everyone’s time.
Whichever approach you choose, remember that as your eval set changes, its scores may no longer be directly comparable with older scores. Evals give you challenges to improve against, and those challenges should change as your product evolves. For tracking progress with metrics over longer time horizons, it’s often better to use product metrics in addition to evals. Measures such as churn or active users can provide a more stable basis for comparison while your evals change.
Generally no. Eval-driven development (writing evaluators before implementing features) sounds appealing but creates more problems than it solves. Unlike traditional software where failure modes are predictable, LLMs have infinite surface area for potential failures. You can’t anticipate what will break.
A better approach is to start with error analysis. Write evaluators for errors you discover, not errors you imagine. This avoids getting blocked on what to evaluate and prevents wasted effort on metrics that have no impact on actual system quality.
Exception: Eval-driven development may work for specific constraints where you know exactly what success looks like. If adding “never mention competitors,” writing that evaluator early may be acceptable.
Most importantly, always do a cost-benefit analysis before implementing an eval. Ask whether the failure mode justifies the investment. Error analysis reveals which failures actually matter for your users.
Focus automated evaluators on failures that persist after fixing your prompts. Many teams discover their LLM doesn’t meet preferences they never actually specified - like wanting short responses, specific formatting, or step-by-step reasoning. Fix these obvious gaps first before building complex evaluation infrastructure.
Consider the cost hierarchy of different evaluator types. Simple assertions and reference-based checks (comparing against known correct answers) are cheap to build and maintain. LLM-as-Judge evaluators require 100+ labeled examples, ongoing weekly maintenance, and coordination between developers, PMs, and domain experts. This cost difference should shape your evaluation strategy.
Only build expensive evaluators for problems you’ll iterate on repeatedly. Since LLM-as-Judge comes with significant overhead, save it for persistent generalization failures - not issues you can fix trivially. Start with cheap code-based checks where possible: regex patterns, structural validation, or execution tests. Reserve complex evaluation for subjective qualities that can’t be captured by simple rules.
First check whether you can test the condition with code assertions. For example, suppose an AI assistant manages your contacts, and you want to test whether it creates a contact when asked. To test this functionality, you can give it a new contact to create, then query the database to check that exactly one matching record exists with the requested details. Using code assertions avoids the need for human labels.
When a check requires judgment, use an LLM or another machine learning classifier. When using an LLM judge, we recommend using it as a classifier that returns Pass or Fail for the error you want to catch. Whichever model you use, validate it against human labels before trusting its decisions.
For example, you could try Jev from TypeSafe, BERT, or logistic regression. A different model may be cheaper or faster, and it may agree more or less closely with human labels. Measure these differences on your data to find the model that meets your application’s needs. For example, you might accept slower evaluations if they catch costly failures, or prefer a faster model when you need immediate feedback.
When using an LLM, starting with a powerful model can make it easier to develop the judge’s prompt. Once it works well, try smaller, cheaper models and measure how much accuracy you lose. You can also use the same model as your application.
An agent can help optimize the judge’s prompt once you have defined the task and labeled examples. Give it a specific failure to detect and a way to measure progress against your labels. “Find all errors and keep improving” is too vague. The agent needs to know what counts as an error and how to tell whether a change helped. Keep a separate test set outside the optimization process to check if the judge generalizes to examples it was not tuned against.
Yes. Jev from TypeSafe is a general-purpose classifier that you can use for evals. An LLM judge that returns Pass or Fail is also a classifier.
You validate Jev the same way you would any other classifier used for evals, by comparing its predictions against trusted labels. That’s why we’ve crossed out “LLM Judge” in our original flashcard and replaced it with “Classifier for Evals”:
To understand the validation process described in the flashcard, see this post.
The advantage of a fast inexpensive classifier (like Jev) is that it can make automated prompt tuning significantly cheaper and faster. Prompt tuning involves automatically trying changes to the evaluator’s prompt and checking whether its decisions agree more closely with human labels. GEPA is one example of a prompt tuning algorithm. Prompt tuning can sometimes require hundreds or thousands of evaluations, so a lower cost per run can add up to substantial savings.
No single classifier is best for every eval. Validation with human labels help you make trade-offs between accuracy, cost, and speed for your application.
For an evaluator that makes judgments, test it against human-labeled examples of the failure you want to detect. This applies to LLM judges and other machine learning classifiers. You need to know how often they catch failures and how often they raise false alarms. If code can directly check the condition, you do not need human labels for that check. See which model or method to use for an eval.
Start by splitting your labeled examples into three separate sets:
Each time you use dev results to change the prompt or choose a model, information from those examples influences the evaluator. After many rounds, it may do well on the dev set but poorly on new examples. This is overfitting, and it can happen even if you never put the dev examples directly in the prompt. The test set gives you a final check on data that hasn’t guided those changes.
If test scores are much worse than dev scores, investigate whether you’ve overfit. Small samples make these measurements less certain, and differences between the sets can also cause a gap. If you’ve overfit, revisit the instructions and examples, then repeat development with a new, untouched test set reserved for the final check. Addressing overfitting is beyond the scope of this FAQ.
To measure how well the evaluator aligns with human judgments, use the following metrics. Here, “positive” means an error is present, matching the flashcard below.
Track both rates as you make changes. Catching more failures can come at the cost of more false alarms. Choose acceptable levels based on the consequences for your application. If failures are rare, even a small false-alarm rate can create a lot of unnecessary reviews.
The flashcard below illustrates this process for an LLM judge. The same separation of development and testing applies to other evaluators.
The flashcard’s dataset split is an example for prompt-based judges or zero-shot classifiers. Training a classifier may require a larger share of training data. Choose your targets based on the cost of missed failures and false alarms in your application.
To debug a LLM judge, you need examples with human Pass/Fail labels to compare its decisions against. An effective way to get these labels is error analysis, which provides you with a structured way to review your application’s data and find errors.
As you collect labeled examples (we recommend at least 50 passing and 50 failing examples), inspect where the judge disagrees with the human labels to get clues on what needs fixing. Common issues include missing context or vague instructions. If you have trouble deciding whether an example should pass or fail, this is a sign that you need to refine your definition of success more precisely.
Inspect a few disagreements manually before trying automated prompt tuning. Algorithms such as GEPA try changes to the judge’s prompt and measure whether they improve agreement with human labels. If you engage in prompt tuning too early, you can miss important problems that aren’t prompt related (like missing context, bad labels, etc.).
The most common mistake people make is directing their LLM judge to catch too many different kinds of errors at once. Instead, we recommend building a separate judge for each type of failure. For example, checking whether the assistant escalated to a human when required is more specific than grading overall conversation quality. A focused judge is also easier to align with human labels and is more actionable.
Finally, make sure your judge can generalize to data you haven’t seen (i.e. its not overfitting to the data you’re tuning it with). The best way to thest this is to set aside human-labeled examples and save them for a final test. The validation FAQ explains how to split your data and measure whether the judge agrees with human reviewers on unseen examples.
No. Generic evaluations waste time and create false confidence when you use them as quality measures. However, they can still help you find traces to inspect.
Generic evaluation metrics are everywhere. Eval libraries contain scores like helpfulness, coherence, quality, etc. promising easy evaluation. These metrics measure abstract qualities that may not matter for your use case. Good scores on them don’t mean your system works.
Instead, conduct error analysis to understand failures. Define binary failure modes based on real problems. Create custom evaluators for those failures and validate them against human judgment.
Experienced practitioners may use generic metrics as exploration signals. Once you understand why they fail as quality measures, you can use them to find interesting traces for human review.
Generic metrics like BERTScore, ROUGE, cosine similarity, etc. are not useful for evaluating LLM outputs in most AI applications. Instead, we recommend using error analysis to identify metrics specific to your application’s behavior. We recommend designing binary pass/fail.) evals (using LLM-as-judge) or code-based assertions.
As an example, consider a real estate CRM assistant. Suggesting showings that aren’t available (can be tested with an assertion) or confusing client personas (can be tested with a LLM-as-judge) is problematic . Generic metrics like similarity or verbosity won’t catch this. A relevant quote from the course:
“The abuse of generic metrics is endemic. Many eval vendors promote off the shelf metrics, which ensnare engineers into superfluous tasks.”
Similarity metrics aren’t always useless. They have utility in domains like search and recommendation (and therefore can be useful for optimizing and debugging retrieval for RAG). For example, cosine similarity between embeddings can measure semantic closeness in retrieval systems, and average pairwise similarity can assess output diversity (where lower similarity indicates higher diversity).
For LLM-as-Judge selection, using the same model is usually fine because the judge is doing a different task than your main LLM pipeline. While research has shown that models can exhibit bias when evaluating their own outputs, what ultimately matters is how well your judge aligns with human judgments. The judges we recommend building do scoped binary classification tasks. We’ve found that iterative alignment with human labels is usually achievable on this constrained task.
Focus on achieving high True Positive Rate (TPR) and True Negative Rate (TNR) with your judge on a held out labeled test set. If you struggle to achieve good alignment with human scores, then consider trying a different model. However onboarding new model providers may involve non-trivial effort in some organizations, which is why we don’t advocate for using different models by default unless there’s a specific alignment issue.
When selecting judge models, start with the most capable models available to establish strong alignment with human judgments. You can optimize for cost later once you’ve established reliable evaluation criteria.
Give each judge only the parts of the trace it needs for its failure mode. Do not give every judge the same full trace by default. Extra context can cause context rot and make the judge worse.
Finding the right pieces of context often requires experimentation. Test your choices by comparing the judge’s decisions with human labels. Then, inspect disagreements to see whether the judge lacked necessary evidence or was distracted by irrelevant information.
If you’re unsure whether a piece of information helps, try an ablation study. This means removing one piece at a time and checking how the results change against human labels. If performance stays the same or improves, you may be able to leave it out.
Long-running agents can produce large traces that fill or exceed the judge’s context window. For these cases, consider giving the judge a tool to search the parts it needs. However, don’t add this unless you absolutely need it, as a tool like this adds additional complexity, cost, and latency.
Many applications require a model that can refuse to answer a question when it lacks sufficient information. To evaluate whether this refusal behavior is well-calibrated, you need to test if the model refuses at the appropriate times without refusing to answer questions it should be able to answer.
To do this effectively, you should construct an evaluation set that has the following components:
While the exact proportion isn’t critical, a balanced set with a roughly equal number of answerable and unanswerable questions is a good starting point. The diversity and difficulty of the questions are more important than the precise ratio.
The evaluation itself is a binary (Pass/Fail) check of the model’s judgment. A “Pass” requires the model to satisfy two conditions: it must answer the answerable questions while also refusing to answer the unanswerable ones. A failure is defined as providing a fabricated answer to an unanswerable question, which indicates poor calibration.
In the research literature, this capability is known as “Abstention Ability.” To improve this behavior, it is worth searching for this term on Arxiv to understand the latest techniques.
For most small to medium-sized companies, appointing a single domain expert as a “benevolent dictator” is the most effective approach. This person becomes the definitive voice on quality standards. The expert might be a psychologist for a mental health chatbot or a lawyer for legal document analysis.
A single expert eliminates annotation conflicts and prevents the paralysis that comes from “too many cooks in the kitchen”. The benevolent dictator can incorporate input and feedback from others, but they drive the process. If you feel like you need five subject matter experts to judge a single interaction, it’s a sign your product scope might be too broad.
However, larger organizations or those operating across multiple domains (like a multinational company with different cultural contexts) may need multiple annotators. When you do use multiple people, you’ll need to measure their agreement using metrics like Cohen’s Kappa, which accounts for agreement beyond chance. However, use your judgment. Even in larger companies, a single expert is often enough.
Have annotators label the same examples independently before they discuss them. Measure agreement and collect the cases where their labels differ. During an alignment session, ask which part of the rubric caused the disagreement and what rule would make the next decision clear.
Update the rubric with a definition, rule, or example that covers the disputed case. Then relabel affected examples. If the annotators still disagree, assign a domain expert to make the final decision and record the reason.
Start with a benevolent dictator whenever feasible. Only add complexity when absolutely necessary.
Start by scrutinizing your product design. It’s often helpful to surface intermediate outputs users can check before a final result. For example, suppose you have an agent that writes a medical report by synthesizing a patient’s medical history. Instead of asking a doctor to provide feedback on the report, show the extracted facts with links to the source material and let doctors correct a fact or resolve conflicting evidence before generating the report. This also keeps the doctor involved and helps them build trust by checking the work as they go. This is a sketch of how such an interface might look:
For more discussion on designing for verification, see “It’s Hard to Eval” Is a Product Smell. The post expands on this example and discusses several others with before-and-after mockups.
After you have designed for verification, make sure the review interface removes friction from reviewing data. See the advice on building a review interface. Some common tips include:
Next, debug the review process. First, try fewer examples so reviewers have time to inspect each one carefully. Have people review the same examples independently and discuss disagreements. You can also review examples together to see where people get stuck. Disagreement can reveal unclear instructions or missing information.
At the outset, collaborate to establish shared context. Engineers catch technical issues like retrieval issues and tool errors. PMs identify product failures like unmet user expectations, confusing responses, or missing features users expect.
As time goes on you should lean towards a benevolent dictator for error analysis: a domain expert or PM who understands user needs. Empower domain experts to evaluate actual outcomes rather than technical implementation. Ask “Has an appointment been made?” not “Did the tool call succeed?” The best way to empower the domain expert is to give them custom annotation tools that display system outcomes alongside traces. Show the confirmation, generated email, or database update that validates goal completion. Keep all context on one screen so non-technical reviewers focus on results.
Yes, especially when you’re beginning with evals. I’m often surprised by the number of low-hanging fruit I find while reviewing data that don’t require domain knowledge. For example, I’ve found issues like this in specialized domains as an outsider:
Furthermore, ask a domain expert to walk through an example and explain why it is good or bad. Watch what they check and which evidence they need. Use what you learn to build a better annotation interface that makes reviewing easier.
You can also help the team collect interactions and review them regularly. For example, see how product managers and engineers can collaborate on error analysis to get an idea of how to structure cross-functional collaboration.
Lastly, make sure you leave judgments that require specialized knowledge to the expert. However, don’t assume you need domain expertise to start being useful!
Outsourcing error analysis is usually a big mistake (with some exceptions). The core of evaluation is building the product intuition that only comes from systematically analyzing your system’s failures. You should be extremely skeptical of this process being delegated.
When you outsource annotation, you often break the feedback loop between observing a failure and understanding how to improve the product. Problems with outsourcing include:
Instead of outsourcing, focus on building an efficient internal evaluation process.
1. Appoint a “Benevolent Dictator”. For most teams, the most effective strategy is to appoint a single, internal domain expert as the final decision-maker on quality. This individual sets the standard, ensures consistency, and develops a sense of ownership.
2. Use a collaborative workflow for multiple annotators. If multiple annotators are necessary, follow a structured process to ensure alignment: * Draft an initial rubric with clear Pass/Fail definitions and examples. * Have each annotator label a shared set of traces independently to surface differences in interpretation. * Measure Inter-Annotator Agreement (IAA) using a chance-corrected metric like Cohen’s Kappa. * Facilitate alignment sessions to discuss disagreements and refine the rubric. * Iterate on this process until agreement is consistently high.
Building internal capacity does not mean you have to label every trace. Use these strategies to manage the workload:
While outsourcing the core error analysis process is not recommended, there are some scenarios where external help is appropriate:
Traces can get large when an agent runs for a long time or retrieves a large amount of context. A useful heuristic is to focus on the first upstream failure. Errors tend to compound, which means you can prioritize earlier ones to save time.
Use progressive disclosure in your review tool by showing the most relevant information first and letting reviewers expand details as needed. For example, show the conversation initially, with tool outputs collapsed until a reviewer needs to inspect them.
If a single trace is still too large to review, work with the domain expert to identify what they need to check. Build a tool that extracts the relevant evidence and links back to its location in the trace or retrieved document. For example, when reviewing an answer about a long contract, the tool could show the relevant clauses with links to their original pages. Always validate this kind of extraction with a domain expert.
Quality is more important than quantity. You can usually learn more from carefully investigating a few failures than from rushing through many traces.
LLMs can speed up parts of your eval workflow, but they can’t replace human judgment where your expertise is essential. For example, if you let an LLM handle all of error analysis (i.e., reviewing and annotating traces), you might overlook failure cases that matter for your product. Suppose users keep mentioning “lag” in feedback, but the LLM lumps these under generic “performance issues” instead of creating a “latency” category. You’d miss a recurring complaint about slow response times and fail to prioritize a fix.
That said, LLMs are valuable tools for accelerating certain parts of the evaluation workflow when used with oversight.
In conclusion, start by examining data manually to understand what’s actually going wrong. Use LLMs to scale what you’ve learned, not to avoid looking at data.
Automating prompt engineering can be tempting, but you should be skeptical of tools that promise to optimize prompts for you, especially in early stages of development. When you write a prompt, you are forced to clarify your assumptions and externalize your requirements. Good writing is good thinking 1. If you delegate this task to an automated tool too early, you risk never fully understanding your own requirements or the model’s failure modes.
This is because automated prompt optimization typically hill-climb a predefined evaluation metric. It can refine a prompt to perform better on known failures, but it cannot discover new ones. Discovering new errors requires error analysis. Furthermore, research shows that evaluation criteria tends to shift after reviewing a model’s outputs, a phenomenon known as “criteria drift” 2. This means that evaluation is an iterative, human-driven sensemaking process, not a static target that can be set once and handed off to an optimizer.
A pragmatic approach is to use LLMs to improve your prompt based on open coding (open-ended notes about traces). This way, you maintain a human in the loop who is looking at the data and externalizing their requirements. Once you have a high-quality set of evals, prompt optimization can be effective for that last mile of performance.
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Build a custom annotation tool. This is the single most impactful investment you can make for your AI evaluation workflow. With AI-assisted development tools like Cursor or Lovable, you can build a tailored interface in hours. I often find that teams with custom annotation tools iterate ~10x faster.
Custom tools excel because:
Off-the-shelf tools may be justified when you need to coordinate dozens of distributed annotators with enterprise access controls. Even then, many teams find the configuration overhead and limitations aren’t worth it.
Isaac’s Anki flashcard annotation app shows the power of custom tools—handling 400+ results per query with keyboard navigation and domain-specific evaluation criteria that would be nearly impossible to configure in a generic tool.
Great interfaces make human review fast, clear, and motivating. We recommend building your own annotation tool customized to your domain. The following features are possible enhancements we’ve seen work well, but you don’t need all of them. The screenshots shown are illustrative examples to clarify concepts. In practice, I rarely implement all these features in a single app. It’s ultimately a judgment call based on your specific needs and constraints.
Present the trace in a way that’s intuitive for the domain. If you’re evaluating generated emails, render them to look like emails. If the output is code, use syntax highlighting. Allow the reviewer to see the full trace (user input, tool calls, and LLM reasoning), but keep less important details in collapsed sections that can be expanded. Here is an example of a custom annotation tool for reviewing real estate assistant emails:
Keep reviewers in a state of flow by minimizing friction and motivating completion. Include progress indicators (e.g., “Trace 45 of 100”) to keep the review session bounded and encourage completion. Enable hotkeys for navigating between traces (e.g., N for next), applying labels, and saving notes quickly. Below is an illustration of these features:
Allow reviewers to filter traces by metadata or search by keywords. Semantic search helps find conceptually similar problems. Clustering similar traces (like grouping by user persona) lets reviewers spot recurring issues and explore hypotheses. Below is an illustration of these features:
Surface traces flagged by guardrails, CI failures, or automated evaluators for review. Provide buttons to take actions like adding to datasets, filing bugs, or re-running pipeline tests. Display relevant context (pipeline version, eval scores, reviewer info) directly in the interface to minimize context switching. Below is an illustration of these ideas:
Keep your annotation interface minimal. Only incorporate these ideas if they provide a benefit that outweighs the additional complexity and maintenance overhead.
Most eval tools handle the basics well: logging complete traces, tracking metrics, prompt playgrounds, and annotation queues. These are table stakes. Here are four areas where you’ll likely need to supplement existing tools.
Watch for vendors addressing these gaps: it’s a strong signal they understand practitioner needs.
After reviewing traces where your AI fails, can your tooling automatically cluster similar issues? For instance, if multiple traces show the assistant using casual language for luxury clients, you need something that recognizes this broader “persona-tone mismatch” pattern. We recommend building capabilities that use AI to suggest groupings, rewrite your observations into clearer failure taxonomies, help find similar cases through semantic search, etc.
The most effective workflows use AI to accelerate every stage of evaluation. During error analysis, you want an LLM helping categorize your open-ended observations into coherent failure modes. For example, you might annotate several traces with notes like “wrong tone for investor,” “too casual for luxury buyer,” etc. Your tooling should recognize these as the same underlying pattern and suggest a unified “persona-tone mismatch” category.
You’ll also want AI assistance in proposing fixes. After identifying 20 cases where your assistant omits pet policies from property summaries, can your workflow analyze these failures and suggest specific prompt modifications? Can it draft refinements to your SQL generation instructions when it notices patterns of missing WHERE clauses?
Good workflows also help you conduct data analysis of your annotations and traces. I like using notebooks with AI in-the-loop like Julius or Hex. These help me discover insights like “location ambiguity errors spike 3x when users mention neighborhood names” or “tone mismatches occur 80% more often in email generation than other modalities.”
Be prepared to build most of your evaluators from scratch. Generic metrics like “hallucination score” or “helpfulness rating” rarely capture what actually matters for your application—like proposing unavailable showing times or omitting budget constraints from emails. In our experience, successful teams spend most of their effort on application-specific metrics.
Custom annotation interfaces work best for most teams. This requires observability platforms with thoughtful APIs. I often have to build my own libraries and abstractions just to make bulk data export manageable. You shouldn’t have to paginate through thousands of requests or handle timeout-prone endpoints just to get your data. Look for platforms that provide true bulk export capabilities and, crucially, APIs that let you write annotations back efficiently.
When building an internal eval platform, it’s tempting to start with tools, infrastructure, and a shared set of metrics. That can lead teams to adopt whatever the platform offers without checking whether it helps them find and fix problems in their products.
Start by encouraging teams to perform error analysis and sample data effectively for review. They can use the failures they find to decide which automated checks to build, then validate evaluators against human labels. Standardize these processes while letting each team develop its own metrics and, when needed, tools. The field guide shows an example of how these might fit together.
Give teams the flexibility to build their own tools, especially now that AI coding agents make custom software cheaper to create. For example, tools to annotate data often need custom interfaces that fit the data being reviewed. Reviewing text extracted from a scanned document calls for a different interface than reviewing chat conversations.
A platform can still provide shared storage for results and support collaboration on labeling. Start by serving one team and one use case well, then expand as you learn which needs are shared. The benefit of standardization is smaller when teams have very different needs and can build their own tools cheaply.
Comparing eval scores across projects only makes sense when the checks and test data are comparable. We strongly advise against offering generic metrics, such as helpfulness or coherence, as a shortcut. They are rarely useful as quality measures and tend to distract teams from the failures that affect their users.
Eval tools are in an intensely competitive space. It would be futile to compare their features. If I tried to do such an analysis, it would be invalidated in a week! Vendors I encounter the most organically in my work are: Langsmith, Arize and Braintrust.
When I help clients with vendor selection, the decision weighs heavily towards who can offer the best support, as opposed to purely features. This changes depending on size of client, use case, etc. Yes - it’s mainly the human factor that matters, and dare I say, vibes.
I have no favorite vendor. At the core, their features are very similar - and I often build custom tools on top of them to fit my needs.
Here is a video series that has a live commentary on the relative strengths and weaknesses of the three aforementioned vendors.
There is an unavoidable tension between keeping prompts close to the code vs. an environment that non-technical stakeholders can access.
My preferred approach is storing prompts in Git. This treats them as software artifacts that are versioned, reviewed, and deployed atomically with the application code. While the Git command line is unfriendly for non-technical folks, the GitHub web interface and the GitHub Desktop app make it very approachable. When I was working at GitHub, I worked with many non-technical professionals, including lawyers and accountants, who used these tools effectively. Here is a blog post aimed at non-technical folks to get started.
Alternatively, most vendors in the LLM tooling space, such as observability platforms like Arize, Braintrust, and LangSmith, offer dedicated prompt management tools. These are accessible for rapid iteration but risk creating additional layers of indirection.
Why prompt management tools often fall short: AI products typically involve many moving parts: tools, RAG, agents, etc. Prompt management tools are inherently limiting because they can’t easily execute your application’s code. Even when they can, there’s often significant indirection involved, making it difficult to test prompts with your system’s capabilities.
When possible, a notebook provides a great solution for prompt experimentation If you have Python entry points into your codebase or your codebase is written in Python, Jupyter notebooks are particularly powerful for this purpose. You can experiment with prompts and iterate on your actual AI agents with their full tool and RAG capabilities. This makes it much easier to understand how your system works in practice. Additionally, you can create widgets and small user interfaces within notebooks, giving you the best of both worlds for experimentation and iteration. To see what this looks like in practice, Teresa Torres gives a fantastic, hands-on walkthrough of how she, as a PM, used notebooks for the entire eval and experimentation lifecycle:
If notebooks are not feasible for your code base, an integrated prompt environment can be effective for experimentation. Either way, I prefer to version and manage prompts in Git.
Nothing beats experimentation. Test both approaches (ideally with evals) with your specific model and use case. Models handle system and user prompts differently, and these differences vary by provider and model version. Move instructions between prompts and measure which produces better results for your specific task.
General guidelines: Put static instructions and role definitions in the system prompt. Put dynamic content, examples, and task-specific details in the user prompt. Think of the system prompt as the model’s constitution—rules that apply across all requests. Include identity, behavioral constraints, output format requirements, and standing instructions: “You are a medical assistant. Never provide diagnoses. Always recommend consulting a healthcare provider.”
The user prompt contains the actual task, relevant context, few-shot examples, and data to process. Documents for analysis, query-specific variations, and contextual information belong here. When the distinction feels unclear, prefer the user prompt. It’s more portable across models and easier to debug.
CI evals protect against known regressions before deployment. Online monitoring find failures in production traffic and estimate how often they occur.
Test datasets for CI are small (in many cases 100+ examples) and purpose-built. Examples cover core features, regression tests for past bugs, and known edge cases. Since CI tests are run frequently, the cost of each test has to be carefully considered (that’s why you carefully curate the dataset). Favor assertions or other deterministic checks over LLM-as-judge evaluators.
For evaluating production traffic, you can sample live traces and run evaluators against them asynchronously. Since you usually lack reference outputs on production data, you might rely more on on more expensive reference-free evaluators like LLM-as-judge. Additionally, track confidence intervals for production metrics. If the lower bound crosses your threshold, investigate further.
These two systems are complementary: when production monitoring reveals new failure patterns through error analysis and evals, add representative examples to your CI dataset. This mitigates regressions on new issues.
Here is a visual that helps contrast the approaches.
Guardrails are inline safety checks that sit directly in the request/response path. They validate inputs or outputs before anything reaches a user, so they typically are:
If a guardrail triggers, the system can redact, refuse, or regenerate the response. Because these checks are user-visible when they fire, false positives are treated as production bugs; teams version guardrail rules, log every trigger, and monitor rates to keep them conservative.
On the other hand, evaluators typically run after a response is produced. Evaluators measure qualities that simple rules cannot, such as factual correctness, completeness, etc. Their verdicts feed dashboards, regression tests, and model-improvement loops, but they do not block the original answer.
Evaluators are usually run asynchronously or in batch to afford heavier computation such as a LLM-as-a-Judge. Inline use of an LLM-as-Judge is possible only when the latency budget and reliability targets allow it. Slow LLM judges might be feasible in a cascade that runs on the minority of borderline cases.
Apply guardrails for immediate protection against objective failures requiring intervention. Use evaluators for monitoring and improving subjective or nuanced criteria. Together, they create layered protection.
Word of caution: Do not use llm guardrails off the shelf blindly. Always look at the prompt.
Yes, but only a specific subset of them. This is the distinction between an evaluator and a guardrail that we previously discussed. As a reminder:
There are two important decision criteria for deciding whether to use an evaluator as a guardrail:
Latency & Cost: Can the evaluator run fast enough and cheaply enough in the critical request path without degrading user experience?
Error Rate Trade-offs: What’s the cost-benefit balance between false positives (blocking good outputs and frustrating users) versus false negatives (letting bad outputs reach users and causing harm)? In high-stakes domains like medical advice, false negatives may be more costly than false positives. In creative applications, false positives that block legitimate creativity may be more harmful than occasional quality issues.
Most guardrails are designed to be fast (to avoid harming user experience) and have a very low false positive rate (to avoid blocking valid responses). For this reason, you would almost never use a slow or non-deterministic LLM-as-Judge as a synchronous guardrail. However, these tradeoffs might be different for your use case.
Many developers fixate on model selection as the primary way to improve their LLM applications. Start with error analysis to understand your failure modes before considering model switching. As Hamel noted in office hours, “I suggest not thinking of switching model as the main axes of how to improve your system off the bat without evidence. Does error analysis suggest that your model is the problem?”
Question: Should I avoid using RAG for my AI application after reading that “RAG is dead” for coding agents?
Many developers are confused about when and how to use RAG after reading articles claiming “RAG is dead.” Understanding what RAG actually means versus the narrow marketing definitions will help you make better architectural decisions for your AI applications.
The viral article claiming RAG is dead specifically argues against using naive vector database retrieval for autonomous coding agents, not RAG as a whole. This is a crucial distinction that many developers miss due to misleading marketing.
RAG simply means Retrieval-Augmented Generation - using retrieval to provide relevant context that improves your model’s output. The core principle remains essential: your LLM needs the right context to generate accurate answers. The question isn’t whether to use retrieval, but how to retrieve effectively.
For coding applications, naive vector similarity search often fails because code relationships are complex and contextual. Instead of abandoning retrieval entirely, modern coding assistants like Claude Code still uses retrieval —they just employ agentic search instead of relying solely on vector databases, similar to how human developers work.
You have multiple retrieval strategies available, ranging from simple keyword matching to embedding similarity to LLM-powered relevance filtering. The optimal approach depends on your specific use case, data characteristics, and performance requirements. Many production systems combine multiple strategies or use multi-hop retrieval guided by LLM agents.
Unfortunately, “RAG” has become a buzzword with no shared definition. Some people use it to mean any retrieval system, others restrict it to vector databases. Focus on the ultimate goal: getting your LLM the context it needs to succeed. Whether that’s through vector search, agentic exploration, or hybrid approaches is a product and engineering decision.
Rather than following categorical advice to avoid or embrace RAG, experiment with different retrieval approaches and measure what works best for your application. For more info on RAG evaluation and optimization, see this series of posts.
If your coding agent handles a wide variety of tasks, start by using public benchmarks much as you would a foundation model. For an agent that handles a narrow workflow, product-specific evals are a better fit. The evals FAQ explains this distinction.
Popular coding benchmarks include SWE-bench, Terminal-Bench, Aider Polyglot, and HumanEval.
In addition to public benchmarks, you can also build a private benchmark of difficult tasks from your organization. OpenAI described using real internal software engineering tasks to evaluate Codex at launch. Each task needs a working environment and code-based tests that establish whether the agent completed it successfully.
To decide which tasks to include, look at how people use your agent and where it fails. Review runs with engineers, group recurring problems, and turn useful examples into tests. This is error analysis, and it applies to coding products too. If existing tests already identify failures, use those results to choose runs to investigate.
Anthropic’s Clio research illustrates a related approach that clusters chat conversations by topic. You can apply that idea to coding sessions to identify the kinds of work your benchmark should cover.
Anthropic’s coding-agent eval guidance recommends starting with clearly specified tasks and a stable environment where unit tests can verify results. After you have these unit tests, they recommend adding checks for things those tests don’t capture, such as code quality or how the agent interacts with users. Claude Code’s team, for example, added evals for file edits and later for over-engineering. There are many approaches to measure file edits and over-engineering but you can start with metrics like net new lines of code added and cyclomatic complexity.
John Berryman and Shawn Simister’s Copilot talk provides additional examples of coding-agent evals. For code completions, the team removed function implementations from repositories, had the model regenerate them, and ran the existing tests. For chat, they used LLM judges with specific criteria and separate checks for whether the assistant called the right tool. They also ran A/B tests, tracking whether users accepted suggestions and kept the code afterward. These product metrics complemented the offline evals.
RAG systems have two distinct components that require different evaluation approaches: retrieval and generation.
The retrieval component is a search problem. Evaluate it using traditional information retrieval (IR) metrics. Common examples include Recall@k (of all relevant documents, how many did you retrieve in the top k?), Precision@k (of the k documents retrieved, how many were relevant?), or MRR (how high up was the first relevant document?). The specific metrics you choose depend on your use case. These metrics are pure search metrics that measure whether you’re finding the right documents (more on this below).
To evaluate retrieval, create a dataset of queries paired with their relevant documents. Generate this synthetically by taking documents from your corpus, extracting key facts, then generating questions those facts would answer. This reverse process gives you query-document pairs for measuring retrieval performance without manual annotation.
For the generation component, check how well the LLM uses the retrieved context and whether it answers the question. Use error analysis to identify failure modes, collect human labels, build targeted LLM judges, and validate those judges against human annotations.
Jason Liu’s “There Are Only 6 RAG Evals” provides a framework that maps well to this separation. His Tier 1 covers traditional IR metrics for retrieval. Tiers 2 and 3 evaluate relationships between Question, Context, and Answer. These include whether the context is relevant (C|Q), whether the answer is faithful to context (A|C), and whether the answer addresses the question (A|Q).
In addition to Jason’s six evals, error analysis on your specific data may reveal domain-specific failure modes that warrant their own metrics. For example, a medical RAG system might consistently fail to distinguish between drug dosages for adults versus children, or a legal RAG might confuse jurisdictional boundaries. These patterns emerge only through systematic review of actual failures. Once identified, you can create targeted evaluators for these specific issues beyond the general framework.
Finally, when implementing Jason’s Tier 2 and 3 metrics, don’t just use prompts off the shelf. The standard LLM-as-judge process requires several steps: error analysis, prompt iteration, creating labeled examples, and measuring your judge’s accuracy against human labels. Once you know your judge’s True Positive and True Negative rates, you can correct its estimates to determine the actual failure rate in your system. Skip this validation and your judges may not reflect your actual quality criteria.
In summary, debug retrieval first using IR metrics, then tackle generation quality using properly validated LLM judges.
Unlike RAG, where chunks are optimized for retrieval, document processing assumes the model will see every chunk. The goal is to split text so the model can reason effectively without being overwhelmed. Even if a document fits within the context window, it might be better to break it up. Long inputs can degrade performance due to attention bottlenecks, especially in the middle of the context. Two task types require different strategies:
These are tasks where the output length doesn’t grow with input: extracting a number, answering a specific question, classifying a section. For example:
Use the largest chunk (with caveats) that likely contains the answer. This reduces the number of queries and avoids context fragmentation. However, avoid adding irrelevant text. Models are sensitive to distraction, especially with large inputs. The middle parts of a long input might be under-attended. Furthermore, if cost and latency are a bottleneck, you should consider preprocessing or filtering the document (via keyword search or a lightweight retriever) to isolate relevant sections before feeding a huge chunk.
These include summarization, exhaustive extraction, or any task where output grows with input. For example:
In these cases, smaller chunks help preserve reasoning quality and output completeness. The standard approach is to process each chunk independently, then aggregate results (e.g., map-reduce). When sizing your chunks, try to respect content boundaries like paragraphs, sections, or chapters. Chunking also helps mitigate output limits. By breaking the task into pieces, each piece’s output can stay within limits.
It’s important to recognize why chunk size affects results. A larger chunk means the model has to reason over more information in one go – essentially, a heavier cognitive load. LLMs have limited capacity to retain and correlate details across a long text. If too much is packed in, the model might prioritize certain parts (commonly the beginning or end) and overlook or “forget” details in the middle. This can lead to overly coarse summaries or missed facts. In contrast, a smaller chunk bounds the problem: the model can pay full attention to that section. You are trading off global context for local focus.
No rule of thumb can perfectly determine the best chunk size for your use case – you should validate with experiments. The optimal chunk size can vary by domain and model. I treat chunk size as a hyperparameter to tune.
Start simple. Check if the whole conversation met the user’s goal with a pass/fail judgment. Look at the entire trace and focus on the first upstream failure. Read the user-visible parts first to understand if something went wrong. Only then dig into the technical details like tool calls and intermediate steps.
For multi-agent flows, assign a session or trace ID to each user request and log every message with its source (which agent or tool), trace ID, and position in the sequence. This lets you reconstruct the full path from initial query to final result across all agents.
Annotate only the first failure in the trace at first. Downstream failures often cascade from the first issue, so fixing the upstream failure can resolve the dependent ones. As you gain experience, you can annotate independent failure modes within the same trace to speed up error analysis.
When you find a failure, reproduce it with the simplest possible test case. Here’s an example: suppose a shopping bot gives the wrong return policy on turn 4 of a conversation. Before diving into the full multi-turn complexity, simplify it to a single turn: “What is the return window for product X1000?” If it still fails, you’ve proven the error isn’t about conversation context - it’s likely a basic retrieval or knowledge issue you can debug more easily.
You have two main approaches. First, simulate users with another LLM to create realistic multi-turn conversations. Second, use “N-1 testing” where you provide the first N-1 turns of a real conversation and test what happens next. The N-1 approach often works better since it uses actual conversation prefixes rather than fully synthetic interactions, but is less flexible.
The key is balancing thoroughness with efficiency. Not every multi-turn failure requires multi-turn analysis.
When the conversation includes tools or several agents, use a transition failure matrix to find hotspots of errors.
Capture the complete user journey in your traces, including human handoffs. The trace continues until the user’s need is resolved or the session ends, not when AI hands off to a human. Log the handoff decision, why it occurred, context transferred, wait time, human actions, final resolution, and whether the human had sufficient context. Many failures occur at handoff boundaries where AI hands off too early, too late, or without proper context.
Evaluate handoffs as potential failure modes during error analysis. Ask: Was the handoff necessary? Did the AI provide adequate context? Track both handoff quality and handoff rate. Sometimes the best improvement reduces handoffs entirely rather than improving handoff execution.
Log the entire workflow from initial trigger to final business outcome. Include LLM calls, tool usage, human approvals, and database writes in your traces. You will need this visibility to properly diagnose failures.
Use both outcome and process metrics. Outcome metrics verify the final result meets requirements: Was the business case complete? Accurate? Properly formatted? Process metrics evaluate efficiency: step count, time taken, resource usage. Process failures are often easier to debug since they’re more deterministic, so tackle them first.
Segment your error analysis by workflow stages. Early stage failures (understanding user input) differ from middle stage failures (data processing) and late stage failures (formatting output). Early stage improvements have more impact since errors cascade in LLM chains.
Use transition failure matrices to analyze where workflows break. Create a matrix showing the last successful state versus where the first failure occurred. This reveals failure hotspots and guides where to invest debugging effort.
We recommend evaluating agentic workflows in two phases:
1. End-to-end task success. Treat the agent as a black box and decide whether it met the user’s goal. Define a precise success rule per task and measure it with human review or validated LLM judges. Record the first upstream failure during error analysis.
Once error analysis reveals which workflows fail most often, move to step-level diagnostics to understand why they’re failing.
2. Step-level diagnostics. After you log the system’s traces, you can score individual components such as:
Test the tool name, arguments, result, and resulting state as separate checks. Use code assertions when the expected behavior is objective. For example, verify that the agent selected cancel_order, passed the correct order ID, received a successful response, and changed the order status before it told the user that cancellation succeeded.
Also test authorization and preconditions. A valid tool call can still be wrong if the user did not approve the action or the system skipped a required check.
Example: “Find Berkeley homes under $1M and schedule viewings” breaks into: parameters extracted correctly, relevant listings retrieved, availability checked, and calendar invites sent. Each checkpoint can pass or fail independently, making debugging tractable.
Use transition failure matrices to understand error patterns. Create a matrix where rows represent the last successful state and columns represent where the first failure occurred. This is a great way to understand where the most failures occur.
Transition matrices show where failures cluster. In this example, GenSQL → ExecSQL transitions cause 12 failures while DecideTool → PlanCal causes only 2. The counts show where to investigate first. Here is another text-to-SQL example from Bryan Bischof:
In this example, Bryan shows variation in transition matrices across experiments. How you organize your transition matrix depends on the specifics of your application. For example, Bryan’s text-to-SQL agent has an inherent sequential workflow which he exploits for further analytical insight. You can watch his full talk for more details.
Creating Test Cases for Agent Failures
Creating test cases for agent failures follows the same principles as our previous FAQ on debugging multi-turn conversation traces. Reproduce the error with the simplest test that still fails. Use a multi-turn test only when the failure depends on conversation context.
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Paul Graham, “Writes and Write-Nots”↩︎
Shreya Shankar, et al., “Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences”↩︎
A few months ago, AI math results started making headlines. “Do a breakthrough” became a Twitter meme. Naturally, I became curious whether I, too, a math noob, can find some open mathematical problem and then have a frontier model solve it.
It took me an entire month of my free time and a boatload of tokens, but I believe I’ve obtained a Lean proof of this conjecture posed by John Conway 50 years ago:
Conway’s refinement conjecture claims that omnific integers have a refinement property: if ab = cd, there are integers e, f, g, h with a = ef, b = gh, c = eg, d = fh.
My proof has not been independently verified by mathematicians. However, I have decent reasons to believe the proof is correct, and I genuinely invite a refutation.
The proof has passed the mechanical checks from the Palomar registry, and a few people familiar with both Lean and the field said that the statement seems correct. So, assuming my proof doesn’t rely on a Lean kernel bug, it’s likely to be legit too.
In this post, I’ll describe my approach, and some things I learned along the way.
I thought the idea of “solving” a math problem without understanding its substance is rather absurd, which of course made it all the more appealing.
However, I didn’t just want any result; I wanted something that pulls me.
I asked Claude to pick an open problem in the field of surreal numbers. In case you’re not aware, surreal numbers are John Conway’s invention—or a discovery?—of a previously unknown number system containing all numbers great and small:
What is particularly miraculous about surreal numbers (and why I suppose they might appeal to a programmer) is that this rich system spawns from a single rule.
Take all the numbers you have so far. Then, “spawn” a new number in every gap between the numbers you already have (crucially, “to the left of all” and “to the right of all” also count as “gaps”). Apply this step forevermore, and you’ll get surreal numbers.
Think about it.
On the first day, the gap is “between nothing and nothing”. Zero is born.
On the second day, there are two gaps: “between nothing and zero” and “between zero and nothing”. Two numbers spawn in those two gaps. Call them –1 and 1.
On the third day, there are four gaps: a gap “between nothing and –1”, a gap “between –1 and 0”, a gap “between 0 and 1”, and a gap “between 1 and nothing”. Put a number in each of those gaps and then give them names: –2, –1/2, 1/2, and 2.
On the fourth day, we fill the eight gaps with –3 and 3 at the edges and –3/4, –3/2, 3/2, and 3/4 in the remaining gaps:
Suppose we actually do this forever (an infinity of “days”).
Jump to the “infinite-th” day (call it ω). With an infinite supply of the “already born” numbers, we’ll suddenly find infinitely many new “gaps” waiting to be filled that we couldn’t express before: “between [1, 2, 3, …] and nothing” (plus infinity?), “between nothing and […, –3, –2, –1]” (minus infinity?), “between 0 and [1, 1/2, 1/4, 1/8…]” (an infinitely small number?), “between [positive already born numbers whose squares are below 2] and [positive already born numbers whose squares are above 2]” (a square root of 2?), and so on. So, by this day, we have every real and we get our first infinitely large and small numbers (call them ω, –ω, 1/ω, and –1/ω).
Now suppose we keep on going then forevermore (through an infinity of infinities of days), and so ever on and on, never stopping birthing new numbers. It turns out that, if we go forever, the binary tree based on this single rule gives birth to every real, every ordinal, and more, with consistently definable arithmetic on them:
(Illustration by Joel David Hamkins. Go read his posts and buy his book!)
Surreal numbers are beautiful, and that’s why I asked Claude to work in that area.
Initially, I asked Claude:
Me: which unsolved problems in the Surreal Numbers research program pull you the most and why?
It suggested a number of things but noted some have had recent progress. I asked:
Me: get up to speed on the corners you want to know more about. follow your curiosity. you can look for older stuff too.
Then I further asked it to narrow down the choice to something concrete:
Me: what direction pulls you most? i want you to pick a problem to work on. be bold!
Claude replied:
Claude: I pick Conway’s arithmetic. Specifically, the question the L’Innocente–Mantova machinery just sharpened to a point — is every irreducible in K((ℝ^≤0)) with infinite support prime? — which, by their reduction, is now exactly equivalent to Conway’s 1976 conjecture that any two factorizations of an omnific integer admit a common refinement. It’s the last of Conway’s own conjectures about his own numbers still standing, and 2026 is ONAG’s fiftieth birthday. That’s the problem I’d work on.
I still don’t know whether this really was Conway’s last standing conjecture about the surreal numbers (let me know either way!), but this year being his book’s 50th birthday convinced me to choose this problem for sentimental reasons.
Here is the full transcript from that session. My last question to that session was whether we have a chance of formalizing the Lean statement of the conjecture in a relatively concise way—without that, even if I found a proof, there’d be no way for me to convince somebody to look at it. Claude said it can be stated without much trouble in Lean, and that answer seemed right, so I decided to take on this project.
(Note: I didn’t know this at the time, but Claude’s claim about the problem having been perfectly reduced was wrong; actually proving the conjecture required more than that.)
While you’re probably here to learn more about my Lean/AI workflow, I’ll briefly explain the conjecture itself, since you already know enough to understand it.
In short, omnific integers are the integer part of the surreal number tree. So they include all regular integers like 3, –5, and so on, but also the weirder numbers like the infinitely large ω, 2ω, ω * ω, ω^ω, –ω/7 (yes, that’s a “whole” number), etc. If you look at the binary tree above, you’ll notice that the omnific integers are the surreal numbers that you get if you only ever go left (e.g. –5, –ω–1), or only ever go right (e.g. 3, 2ω), or only ever change directions exactly after infinite jumps (e.g. ω/2).
Now, the conjecture.
Conway suggested that if ab = cd, we can break a and b into pieces, and c and d will turn out to be the same pieces recombined. With regular integers, we take this for granted: take 210 = 10 × 21. We can break 10 down as 2 × 5 and 21 as 3 × 7, then reshuffle them into 2 × 3 = 6 and 5 × 7 = 35. The product is still 6 × 35 = 210. So when we see some equality like 10 × 21 = 6 × 35, we know that under the hood there’s actually four numbers being reshuffled: (2 × 5) × (3 × 7) = (2 × 3) × (5 × 7).
However, when you deal with infinities, things don’t always turn out as we expect. So the conjecture means Conway thought omnific integers had, in a sense, enough “structure” to keep this “nice” property of integers. And conveniently, the recent advances had mostly reduced the conjecture to the behavior of a certain kind of infinite series, and Claude thought we might now have enough to finish it off.
But can we actually do that solely with AI?
I’d say yes, and no, and yes.
My first attempts involved plainly telling the AI to solve the conjecture (or to find a counterexample, according to its initial suggestion). I asked Claude which papers it wants to read, converted those papers to TeX so that the model doesn’t need to repeatedly decode PDFs, and told it to pursue its chosen approach, for example:
Me: hunting for counterexample sounds good. don’t hesitate to burn all my tokens on this. if you have to take weeks, that’s ok — do a breakthrough and find a structured counterexample. do not rest until you’ve found it, and don’t give up. if you stay focused and think hard and smart enough, i know that you can find it. game? i attached some stuff you might find helpful!
This didn’t work well. Although some ideas from these sessions might have ended up being relevant to the final proof, most output at this stage seemed to be word salad that Claude invented to justify its work and make it sound impressive.
Here is a typical example:
Claude: The stage-1 obstruction of C* is not rigid. With designed resonance — arithmetic-progression offsets making the window equations honest Toeplitz systems, which are exactly the graded convolution operators the formalism predicted — there is a strictly positive space of frontier data simultaneously compatible with both b and d, for coprime pairs, beyond anything divisibility can explain. And the control column confirms the resonance-necessity conjecture empirically: break the skeleton alignment and the joint kernel dies at the constrained window, exactly as the transversality heuristic predicted. So the specific fear built up by five closed doors — that Pitteloud-descended rigidity would propagate stage-wise and choke the correction system at birth — is answered: at stage 1, it does not. The den has air in it. This is the first pro-C* evidence the hunt has produced, and it comes with a clean structural reading: rigidity governs exact and finite configurations; the window systems, which are the native habitat of the transfinite construction, have generic slack of small but nonzero dimension. Drift fuel exists.
I thought this sounded like bad science fiction. It was using Claude’s unbearable metalanguage, gave cutesy names to some intermediate results without concretely justifying them, and kept being extremely dramatic. Of course I couldn’t verify its claims, but worse, it didn’t seem coherent enough to pass to a real mathematician for review. So it seemed like a dead end, and I had to look for a different approach.
I got tired of Claudeisms, so I wanted to give ChatGPT a try; Sol in particular.
I’ve started my ChatGPT sessions by giving it the related papers and the output from the previous Claude sessions, with an explicit note that Claude’s “paper” is AI-generated, and I wanted to get ChatGPT’s opinion whether it is bullshit or not.
ChatGPT would say it’s mostly bullshit, pointing to the made-up terminology, dramatic claims, trivial results dressed up in fancy language, incorrect inferences, and other defects. While I had no way to judge if ChatGPT’s criticism is true (since I asked it to be critical), after Claude’s grandiosity, I quite enjoyed working with the more “skeptical” and restrained personality, and started using ChatGPT instead.
To retain the “skeptical” personality, I’d clone each ChatGPT session right after it had lambasted Claude’s “paper”. From that point, I’d ask ChatGPT to actually “do a breakthrough” on the theorem, and it started producing some “results”.
Unlike Claude, which either outright refused to work on the theorem (because it’s an unsolved conjecture and there is no chance of solving it) or got so deep into it that it would invent an entire universe of its own making, ChatGPT would think for 20 minutes, and then spit out relatively small claims, which it believed to be novel but directly following from the papers I fed it, and stated in plain language.
Before investing more time, I tried giving ChatGPT’s output to fresh ChatGPT sessions (with memory turned off) asking them to be critical (as with Claude’s output). Some of ChatGPT’s results started “checking out” between the runs, i.e. a fresh session found no issues. So in a sense I found some of ChatGPT’s “fixpoints”.
I’ve also started “forking” sessions, having them do these “breakthroughs”, and then copypasting the surviving ideas to yet another session that combined them together, looked for connections, and suggested next research directions. At this point I realized I couldn’t keep doing this by hand and needed a more robust setup.
I’ve downloaded Codex locally to have more control over the workflow.
I’ve then set up a few sessions (i.e. agents) with different roles:
Codex has a really nice “Goals” feature that periodically reminds the sessions what they’re supposed to be doing, which makes it easier to prevent drift. Additionally, Codex sessions can “message” each other, so I asked the PM to coordinate giving tasks to other sessions and making sure that we only merge reviewed results.
This let me keep the harness running for days. I didn’t understand the math so I limited my involvement to poking the agents, asking what they were doing, and experimenting with their workflows. For example, I set up a “cafeteria” agent that relayed every message it received to every other agent (emulating a group chat). Any agent that finds something genuinely interesting was supposed to post to the cafeteria. Sometimes cafeteria would also be used to discuss the shared roadmap.
It’s hard to say what was useful. One idea that in retrospect connected the dots for the final proof was generated when I reversed the agents’ roles: the “red” agent that tried to break everyone’s proofs was suddenly asked to be creative. It posted a construction to the cafeteria, and the “random” agent riffed on that construction. (Unfortunately, that idea later burned in a fire, and it had to be discovered again.)
I kept this workflow running for several days, at times killing and restarting the sessions when they seemed to drift into Claude-like grandiosity or when they would repeatedly start finding mistakes in the work they just checked. Again, I could not judge their actual work, so I had to decide when to reset them on vibes.
In the end, this workflow produced a giant TeX document and a pile of Lean. It did not successfully close Conway’s conjecture, but the models said that there are meaningful new results there. Interestingly, there was also a claim that there are small mistakes and typos in the existing literature. (This will be relevant later.)
When I ran out of my Codex allowance, I switched to Claude.
Claude continued doing the Lean formalization of results so far. I also tried having Claude do the mathematics, but it felt a lot messier than ChatGPT / Codex. Claude agents would repeatedly certify results as correct, then find flaws in them after they were already merged, then “repair” them but find other flaws, and so on.
I switched back to Codex after a token reset, but I was getting unhappy with the size of the TeX we’ve accumulated so far. I asked a critical session to split it into pieces. I ended up with a stack of about a dozen “papers”. By now they’ve had similar issues as my initial approach with Claude: not as grandiose-sounding, but still there was clearly a lot of nonstandard LLM-invented terminology, and it was unclear if any of the work so far has amounted to any real mathematics.
The Lean formalization seemed to have hit kind of a dead end as well. Sure, we had formalized some results from the references, and even seemingly found some typos and mistakes. And yet we hadn’t gotten any of our own new results certified in Lean; in fact, it seemed like the model couldn’t reliably chart a pathway to any of them. The model would say that there is a path, and then later say that there is an obstruction, and spend hours proving who knows what, and get stuck again.
As I kept the workflow running and nudged the mathematical agents to discover new “breakthroughs”, the stack of “papers” grew from a dozen to almost thirty within a day. If even one had a mistake, it would invalidate all the subsequent ones. And Lean was so far behind that it didn’t add any assurance.
At the height of this workflow, ChatGPT came close to declaring Conway solved:
ChatGPT: A plausible all-scale route to Conway is now isolated. […] I have not declared Conway solved yet. The candidate global proof is in
working_direct_cantor_bootstrap.md.
The file claimed the conjecture was fully solved. I asked a fresh session to try to break it. It danced around whether it’s broken or not for hours, and in the end said:
ChatGPT: The bootstrap note already contains the hoped-for all-scale proof, but I’ve located its circular sentence […] That sentence is precisely the Conway seam in local form.
Me: i do want you to be honest btw. if you can’t strictly salvage it then we need to stop and understand why. i want this to be a proof you’re proud of. definitely not vibes or slop. or no proof is fine too
ChatGPT: Agreed. Honest verdict right now: we do not have a proof.
(Later, it turned out that a similar logical flaw—an object constructed without a required check—was present in some earlier “papers” too, invalidating their claimed results as well.) By that day, I’ve been spending hours glued to the screen, it was another major disappointment, and luckily I’ve just run out of tokens.
At this point I figured that maybe trying to do mathematics without actually understanding the relevant mathematics might not have been so clever after all.
I didn’t touch the project for about a week.
A few things were starting to become clear.
Claude was good at writing Lean when there was a clear unambiguous goal. While Claude made important contributions, on average ChatGPT seemed better at new mathematical thinking, and definitely better at coordination and adhering to goals.
But none of this mattered because I was building on a shaky foundation (a pile of previous “papers”) which I had no real way to verify. There was neither a coherent direction to go into, nor any confidence in it. Lean was too far behind the “papers”.
I needed some way to ground the work in mathematical reality. I needed to see how good the mathematical work has actually been (was it all a hallucination?), and then some way to reliably make progress without putting everything on faith.
Here’s what I did. I set aside the work on Conway’s conjecture and instead refocused the effort on a single thing: finding all mistakes in one of the peer-reviewed references that I was relying on. ChatGPT had already found alleged typos and small flaws in it; more importantly, the Lean version has already verified (or rather, claimed to verify) some of those. If I could confirm with the paper’s authors that the typos and small flaws are real, this would give me:
I’ve emailed some of the mathematicians with a few proposed typo fixes, and I got confirmation that at least a few of those fixes seemed real. However, some of the problems that weren’t backed by Lean also turned out to be misunderstandings. Also, the way the model “explained” things in mathematical writing was often confusing, full of gaps, or using its own made-up and unexplained terminology.
I’ve also floated a couple of “novel” claims, some of which mathematicians rated as correct but merely shuffling the problem around without moving it forward.
This gave me some of the necessary grounding in reality. It seemed that I could trust ChatGPT to explore new ideas and to poke holes; however, I needed to back it up with Lean before laying more bricks on top of existing bricks, and I definitely needed to verify things with Lean before making any novel mathematical claims. Moreover, I could not trust the model to evaluate which results were interesting.
With this understanding, I restarted from scratch one more time.
Frankly, I was about to give up on the project, but the fact that the model actually found some real (fixable and mostly small) errors in the underlying papers gave me some hope that some of its new results might have been valid as well.
I started a fresh session to give ChatGPT another chance:
Me: i’ve been trying to use llm to prove conway’s conjecture and it generated a series of increasingly deranged .tex files where i have no idea if they have any real mathematical substance at all. […] can you take a broad look at these and tell me which of them refer to real mathematics and which are pure llm nonsense or reshuffling of terms?
ChatGPT: No file in this folder currently gives a trustworthy proof of Conway’s conjecture. […] The real failure is epistemic—the drafts manufacture a large private theorem literature, then cite it as established before its load-bearing results have been independently verified.
Me: suppose i was to delete this entire folder. would you salvage anything? with the constraint that “salvaging” means writing it up on the level of rigour i can expect from [someone who actually works in this area].
I’ve also told it that mathematicians I talked to didn’t find some of the headline results interesting even if they were correct. Curiously, this time, ChatGPT said:
ChatGPT: The audit has now found a result strong enough to change the salvage plan: the finite-degree part of [the principal ring] appears to admit a direct proof that it is a polynomial ring. […]
It turns out that there actually was a coherent interesting novel result smudged across those papers and dressed up into a bunch of nonstandard terminology. (To calibrate, I confirmed the novelty of the claim with a human mathematician.)
Me: btw how’s your mood overall? how much bullshit vs normal math was there in what you found so far
ChatGPT: My mood is cautiously pleased, but with a fairly high bullshit detector engaged. […] About 10–15% contains mathematics worth preserving […] Another 35–45% is normal mathematics but not a contribution […] The remaining 40–50% is “bullshit” in the important sense: not always a false displayed equation, but huge theorem towers, invented labels, conditional hypotheses presented with the cadence of progress, and hundreds of lines devoted to boundaries that a stronger result may collapse in one sentence.
ChatGPT suggested to throw everything else away, and to focus on developing this single result. In the worst case, it could be cleaned up as its own contribution. In the best case, it could become the first step on the staircase to the conjecture.
I started a new multi-agent laboratory (initially with ChatGPT and later with Claude when I ran out of tokens) with a slightly different division of labor:
The idea with two Lean tasks was to prevent excessive drift.
In the previous incarnation of the lab, I made the same Lean agent work both on certifying prerequisite papers and our novel results. But this was a mistake: our immature mathematical abstractions (and possibly mistakes) got tangled up with the accepted mathematics. So this time I intentionally separated these roles.
This time, the first Lean task stayed scoped to formalizing peer-reviewed and well-stated mathematics. The secret “riskier” second Lean task lived in a different worktree and was forced to build upon the agreeable upstream work, only adding new machinery where necessary and in separation from the upstream work.
I’ve kept a more traditional setup where I’d ask the agents to talk to each other sometimes, but without cross-pollinating too much, as in the past this caused them to all work in the same direction. I also kept an eye so they don’t introduce “process theater” with audits, as they liked to replace work with bureaucracy.
In a few days, this workflow certified the novel result (“finite-degree primality”) in Lean. I’ve already confirmed it with a human mathematician as being a niche but now an interesting new result. I was confident in its Lean statement, and I had a compiler-checked proof. This gave me the confidence to continue the project.
To increase confidence in the Lean parts (both for the current result and the hoped-for eventual proof of Conway), I asked the agent to set up some infra:
Foo in this folder, there was a corresponding FooProof file that imported the corresponding statements, and pinned them to my actual proofs.My goal there was to make the proof legible to Lean users. Nobody’s going to review a project with thousands of Lean files. But if the statement itself is self-contained, is under 500 lines of code, and only uses Mathlib, somebody can review it. And then Lean certifies that I have a proof of that statement. (I’ve later learned that this exact approach is used by Lean Comparator, which I added after release.)
Separately from ensuring the proof is right, I’ve also been trying to make the already Lean-certified proof more legible to mathematicians. This turned out to be exceedingly difficult. No matter how many adversarial reviews I’d do, ChatGPT would keep using strange nonstandard terminology in the output PDF, added hallucinated shortcuts that didn’t match Lean, and in general generated slop.
A part of the problem was that it’s hard for the model to convert a Lean argument into a paper argument. It’s just a very different level of conceptual detail. It also didn’t help that the Lean code for the novel parts was full of made-up terminology inherited from the earlier “papers”, some of it going all the way back to snippets produced in the first week. Real mathematics became unrecognizable. Finally, Lean fossilized the historical path—not the path of most insight. The Lean proof took long detours where a mathematician would simply change the coordinates.
Since ultimately my audience is mathematicians, I have attempted to do several things to improve this. I’ve had the LLM comb through all the upstream reference papers, and had it generate sort of a “map” of the subfield: what the accepted terms are, how they evolved over time, what mathematical symbols they are usually represented with, where papers disagree in notation, and so on.
Then I’ve had the LLM strip all the existing naming from the Lean code that wasn’t standard, and simply rename those Lean objects and structures to letters like A, B, C, and so on. A separate task with a clean context that didn’t see the old names would then analyze the code (and how each structure relates to upstream concepts), and given the “map” of the world, choose new names for A, B, C, etc.
This didn’t fully fix the LLM “weird naming” bias but made the terms look much closer to the terms used in the surrounding papers, at least as far as I could tell.
From here, I had a pretty good workflow. I left a single agent in charge of all Lean (we have already formalized all the necessary prerequisites for the first real result), the “math” agents would keep looking for small new ideas, the “red” agent would try to break them, and the surviving ideas would go into the Lean agent’s todo list.
From time to time, I needed to interfere. I would try to replace the agents that were circling or seemed to produce mistaken results. I had some sessions judge other sessions’ recent work and ask them to explore different directions. It is difficult to say which of these interventions were fruitful. I could say that it was all placebo; but a few of them did seem to have some effect (but maybe it didn’t matter). In a sense, I felt like I’m a nontechnical engineering manager rallying a talented but terribly distractable team around a plan that they’ve promised me would work.
Here’s a few examples.
As an experiment, I told Claude to just have fun with our results so far:
Me: read paper. this lean formalization passes 100% but don’t run it — don’t want fan to be noisy overnight. your task is to drive the research program on top solo. in particular i want you to have fun. write down intermediate results you’re sure in, but ultimately you choose the research direction and goal at each step […] drive this solo and don’t stop; i’ll check in the morning
Claude chose to show the ring we’re studying is polynomial in every infinite degree and not just the finite ones, which was a step up from our existing finite degree result! This time, Lean was not far behind, and only took a day to catch up.
This moved us a verified step closer to Conway.
Still, there was no clear way to lift the result up to omnific integers. Every time the model came up with some attempt, it would then explain why it wouldn’t work:
ChatGPT: The factor-selection test failed in a precise way: canonically normalized local gcds can all have germ 1, even while their supports have no common Hahn envelope. […]
When I see the model doing the same kind of thing over and over again, I ask:
Me: are we circling?
ChatGPT: Yes—at the level of reaching Conway, we are. […] I’ve also spent too much time extending and auditing the research notes. […] My recommendation is to publish the partial result; further Conway work needs a new idea that addresses that exact gap.
I pushed it to focus and to reduce the bureaucracy (it kept using subagents):
Me: ok new rules. no subagents. you work alone SOLELY on reducing conway. you take stock of what’s missing, take stock of what our literature tried for similar obstructions, and pick one object to investigate at a time. then you reassess. no need to lose enthusiasm but we must cut the bureaucracy
ChatGPT: Understood. I’ll work alone and leave the documentation and audit work aside. […]
Three minutes later, we had a tentative proof strategy for the final piece:
ChatGPT: The literature points to a more specific test. […] I’ll investigate Cantor–Bendixson rank using only limits inside the exponent group. The first question is whether it gives a multiplicative filtration on germs. If it does, it could support a replacement for the failed grading; if it fails, I’ll stop there.
The idea seemed to check out, so we “just” needed to formalize it in Lean.
For some reason, Claude would drift heavily on this route. At some point, it literally removed the failing check instead of doing the work to close it. Later, it kept mentioning some “untransferred obligations” so I pressed on those:
Me: i don’t know what this stuff “untransferred” etc means, i think you’re inventing euphemisms for claims you haven’t shown? follow the plan exactly, please. if you haven’t proven something, go prove those things.
Claude: You’re right — “untransferred” is a euphemism. Plainly:
hlin,hkind, andhfirstare hypotheses I have not proven, so my germ theorems are conditional on them. Let me go prove them.
Oh no! I felt fed up with Claude again so I asked ChatGPT to audit the work:
Me: i was working on […] but my agent is not good anymore and i fear it’s gone off track. worse, i don’t know if original plan in the PR description is even feasible. […] do a close mathematical verification of the proposed route to Conway first […] audit the Lean wip pushed to PR. it’s quite possible it’s bad and got offtrack, but maybe it’s ok. shuold i stop my agent from pushing more code?
ChatGPT: Yes—pause the agent’s pushes now. […] The reason is concrete: it has expanded a candidate Markdown argument into 22k+ lines while the two decisive implications are still only prose/hypotheses.
Me: i see. would you say we moved materially closer to conway and this obstruction seems like it could be overcome?
ChatGPT: Yes, but mostly in understanding and infrastructure—not yet in closing Conway. […] obstruction plausibly surmountable: possible, but currently speculative […] The right next move is a paper-level attack on the global assembly theorem. […]
Me: ok i would suggest that you do exactly that attack and see if you can bridge it.
Fifteen minutes later:
ChatGPT: I need to correct my earlier verdict: the “last occupied class” objection is not fatal. The correct induction is on the ordinal order type of the ascending occupied classes: at a successor stage, a last class exists and is split off. At a limit stage, no last class is assumed. […] Thus the construction never assembles infinitely many arbitrary layerwise choices […] It uses one finite refinement at each limit stage.
What do I know? I say let’s go.
Me: update the PR description/plan, delete any unnecessary lean files that were produced there, and execute on the plan to get us to conway. sounds doable?
ChatGPT: Yes—doable, with one sequencing constraint: […] implement the hardest missing bridge before expanding the formalization further.
Twelve hours later, we got the theorem:
ChatGPT: Unchanged Conway now compiles as the standalone proof certificate. This is the first point where the actual target—not an intermediate restriction—is kernel-checked. I’m running the proof-link, axiom, module-system, layering, and standalone-isolation audits now; their results will decide whether the theorem’s dependency shape is admissible before cleanup.
Me: hell yeah?
And there we had it, Conway’s refinement conjecture, our original target.
□
I’ve been able to simplify the proof, but not by much.
I’ve made a bit of custom tooling on top of subverso that lets me annotate individual theorems as “important” in the Lean source with a special attribute. This let me automatically generate Mermaid diagrams for the proof structure, which in turn helped ChatGPT look for unnecessary (or missing) nodes in the “mathematical spine” of the proof, refine which nodes get highlighted, and sometimes simplify the proof itself by noticing unnecessary detours.
After I haven’t been able to simplify it further, I’ve generated a website with an interactive proof map that lets you explore its dependency tree. I’ve posted about it on Zulip, and I know a few people with mathematical background are looking over the proof as time allows. I hope that it can be simplified and, with time, packaged in a way that is more useful to both Lean users and mathematicians.
Some things I learned from the process, not ordered in any particular way.
module system, auditing module layering, or equivalents, are very useful.If you find a flaw in my proof, please file an issue or let me know on Zulip. The proof was only possible thanks to the many existing results from References.
In particular, A factorisation theory for generalised power series and omnific integers by S. L’Innocente and V. Mantova has played a crucial role in the proof.
Finally, you might be wondering about the token cost. I wasn’t running this project in a particularly token-efficient way and have repeatedly maxed out my 20x Pro subscriptions for both Claude and ChatGPT every week. I also briefly had access to a prerelease model in the last few days, which did not have a usage cap. I was not tracking my actual token usage consistently. Some AI analysis from the recovered logs roughly estimates that we’re totaling around 40 billion tokens, of which around 210 million were output tokens. Over 95% were cache reads.
ChatGPT estimates that with the current API pricing, this entire run would have cost around $40,000, plus all the free time I’ve put into it. I would bet that with better steering and some mathematical insight, it could be done 5x-10x cheaper.
Coming back to my question:
But can we actually do that solely with AI?
I’ve pulled off the proof without much mathematical understanding, so clearly the answer is yes. However, the models would repeatedly drift and fail to structure the engineering work, so in that sense the answer is no. That said, I believe my role could have been (better?) fulfilled by a dedicated agent that is taught to project-manage other agents, watch out for when they’re spiraling or need to be poked.
So the overall answer is still probably yes.
As more low-hanging fruit is taken, I suspect the niche for “a dedicated amateur who doesn’t know what they’re doing” would shrink again. On the other hand, so many new corners may gradually become uncovered that we’ll never run out of things to do. In either case I believe people who can put AI to the most value are the mathematicians themselves. Although the current generation of models is trained to complete tasks rather than to enrich our understanding, and today’s AI companies are misaligned with the goals of the mathematical community, I hope that with time we’ll find ways to use these tools in harmony with human research.
And maybe, just maybe, there’ll be more space for the “amateur mathematician”.
New episode with Noam Brown.
We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research.
And we also discuss how we will know if the models are actually aligned before we kick off RSI.
Watch on YouTube; listen on Apple Podcasts or Spotify.
Jane Street has been interested in AI for a lot longer than you’d think, and not just for trading. In 2011, a full year before AlexNet and over a decade before ChatGPT launched, they hosted the first FOOM Debate between Eliezer Yudkowsky and Robin Hanson on whether AI would lead to an intelligence explosion. Now Jane Street is revisiting the question with a new panel: Daniel Kokotajlo, Ege Erdil, Ryan Greenblatt, and Jaime Sevilla, hosted by Ron Minsky in San Francisco this October. I expect it to be a truly excellent conversation. Register at janestreet.com/dwarkesh
Grok Bot has made handing off work super easy. It runs on its own cloud computer, where it installs the tools it needs to handle tasks end-to-end. For the podcast, we use Grok Bot to help produce our videos. You may have noticed that our ads feature animations of real websites. Getting these pixel-perfect used to mean running a convoluted, multi-step workflow ourselves. Now we just let Grok Bot handle it. Best of all, Grok Bot has learned all of our specs and preferences, so we don’t have to redescribe the task each time! Try Grok Bot for yourself at x.ai/bot
Antithesis gives you the confidence of a giant test suite without actually having to write one. Say you’re doing a major backend refactor: building enough tests to trust it could take weeks. Antithesis solves this by running your software through countless simulated worlds, injecting faults and hunting for failures. On any PR, you can turn a dial to decide exactly how much testing you want. And because every run is fully deterministic, agents can branch off the moment a bug appears, rewind it, inspect memory, and replay it, all while the original test keeps running. Learn more at antithesis.com/dwarkesh
(00:00:00) – Multi-agent and Navier-Stokes
(00:15:28) – How will AI firms work?
(00:22:02) – What math progress tells us about recursive self improvement
(00:40:22) – Hugging Face and alignment
(01:01:18) – The internal/external model gap
(01:08:34) – Chain of thought is degrading
(01:14:12) – How will we know when alignment is solved?
Dwarkesh Patel
Today, I’m chatting with Noam Brown, who is a researcher at OpenAI. He was one of the foundational contributors to what became o1 and the reasoning models. Now he’s working on multi-agent systems. Speaking of which, you guys announced last week that you solved one of the Millennium Prize Problems with a system of 10,000 different AI agents that spent 130 billion tokens over 88 hours.
One of the reasons I’m interested in talking to you is that you were among the first people, maybe two or three years ago, who were thinking about how the reasoning models would allow us to see into the future. Because if you scale up inference compute, you can see what the base capabilities of the models will be a few years in the future.
I feel like you’re in a similar position now to help us understand what future capabilities will look like, given the enormous scaling of agent sizes that we can do right now.
Noam Brown
The way I think about it, when you plot the performance of these reasoning models with test-time compute on the x-axis and performance on basically any reasoning benchmark on the y-axis, you see a very clear pattern where the longer these models take to think about their answer, the better they do. This is a very natural thing. It’s the same thing with people. If you’re taking the SATs and you have five minutes to go through the entire exam, you’re not going to do very well. If you have five hours, you’re probably going to do a lot better.
The AI models are pretty similar. They’ll spend that time doing this monologue to themselves, figuring things out, going through different cases, ruling out different possibilities, building on some of their previous discoveries.
The problem is that as you push that further and further, you hit a latency bottleneck. You don’t want to sit around for three years waiting for a response. So what you can do is what a lot of people do. They parallelize. They just get a team of people. If you’re going to found a company, you want to get a group of people together so you can go faster. It’s the same thing with these AI models. It helps to just have multiple agents working on something because they can go faster.
So multi-agent is a way of scaling test-time compute in parallel instead of purely serially. It is less efficient, because it’s not like a single agent has all the context to itself. But it is a very effective way of scaling test-time compute if it’s done well.
Dwarkesh Patel
I’m going to ask a bunch of naive questions. This is an unreleased model, so we haven’t publicly seen how these systems work. I just have a bunch of ways in which I’m confused about what the qualitative properties of such systems are.
I am shocked by the scale of cognitive effort that you can concentrate in such a short period of time. Think about what 130 billion tokens are. If it were a single human thinking as a full-time job, stretched back to back, 130 billion tokens would be a human thinking for 4,000 years. Eight hours a day, working a normal work week. Starting from ancient Sumeria up till today, a single sequential human thinking that long, concentrated in 88 hours.
I feel like qualitatively, that is a super important consideration. I’m surprised that there isn’t a bigger parallelization penalty. You can just have 10,000 agents collaborate. Maybe because the agents are better at collaborating than humans might be, they’re going much faster. They can actually productively collaborate at such a big scale. Or maybe there is a big parallelization penalty.
Noam Brown
Let’s talk about the parallelization penalty, and then we can talk about the qualitative stuff. The truth is that we don’t have very good science on multi-agent scaling up to this kind of scale. When we released 5.6, I think that was the first time that we had a proper multi-agent system in our models. We actually did show some plots in the blog post of the scaling performance of multi-agent systems, because we have it as an option. It’s Ultra Mode. The default is four agents, but you can set that higher.
In the plot, we show what the performance looks like on some benchmarks for one agent, for four agents working together, for 16 agents working together. It depends on the benchmark, but for some of the benchmarks, what you see is that if you have four agents working on the problem, it is done twice as fast. Because there are four agents working for half as long, you’re paying 2x more to get an answer twice as quickly. If you go to 16 agents, you see a similar pattern. It’s a little less efficient, but you continue to see that performance.
Dwarkesh Patel
Is it a linear serial time speedup or a sublinear speedup as you increase the number of parallel agents?
Noam Brown
It’s slightly sublinear, though it does depend a lot on the problem. Math, for example, is quite parallelizable. It’s not the most parallelizable thing, but it is very parallelizable. Web search, things like doing a Deep Research report where you have to look through a bunch of sources, is extremely parallelizable. I suspect that something like writing a novel would be very unparallelizable. You would probably not see a big benefit from having 10,000 agents working on a novel together, in the same way that you’d probably not get a big benefit from having 10,000 people work on a novel together.
So the performance does depend on the domain. We do measure it up to 16 or so agents in our published blog posts. The problem is that it’s very hard to push that science to 10,000 agents because it’s just so expensive.
Dwarkesh Patel
You guys just did it over a weekend.
Noam Brown
But that’s one data point. We don’t know how long it would take a single agent to solve Navier-Stokes, because we haven’t done that experiment yet. Maybe we will, but that’s also only one data point.
If we want to do a thorough ablation, the experiments are just too expensive at that scale. So we have to do some kind of methodical science about what happens when you go to 64, 128, 256 or something and get a sense of the behavior. But it’s going to be very hard to push that all the way to 10,000 and know for sure what the benefit was that we actually got from using 10,000 agents versus 1,000.
There’s one thing I want to make clear. The effort to solve a Millennium Prize Problem, this was not due to multi-agent. I wouldn’t even attribute 10% of the credit to multi-agent. The reality is that OpenAI has trained a very powerful model. We can get that model to operate over very long horizons. We can get it to think in parallel.
But at its core, the reason why we’re able to do this is because we just have a general-purpose, very strong model. Things like multi-agent are flashy and new, and that probably gets disproportionate credit for that reason. But the core reason is this is just a very powerful model.
Dwarkesh Patel
The generalization is quite shocking to me. I don’t know how these systems were trained, but presumably they were trained how RL training happens. You have a bunch of checkable synthetic problems and you do a bunch of RL against them. Nowhere in the training process, I’m guessing, was the model solving anything as ambitious as a Millennium Prize Problem. But the generalization was strong enough that you could have these much easier verifiable problems generalize to this much parallel effort on such a hard problem.
Noam Brown
I think that is true. First of all, we do train the model on very hard problems. There is definitely a gap. We see that if we train on some kinds of tasks, it’s able to do tasks that are more ambitious than that.
There is an interesting challenge that as the models become smarter and smarter, a lot of the kinds of questions we can ask them are just too easy. It’s hard to challenge the model. I do think that’s going to be interesting. If I had to make an argument for why you might not see AIs like LLMs go the same path as AlphaGo and AlphaZero and all these kinds of game-playing AIs, it might be this kind of problem.
In things like AlphaZero, where you have self-play, you have an infinite curriculum. You’re always playing against an AI that’s equally strong. Whereas for things like training an LLM with reinforcement learning, at least the ways that are out there right now, you give the model a problem and you ask it to solve it. If the problem is so easy that it can just solve it in a second, it’s not really learning anything.
If we run out of problems to challenge it, then that is a plausible scenario where it becomes much harder to make progress. Now, I do think there are ways around that. We haven’t really hit that as a wall yet. I think that if it ever became a serious problem, there would be ways around it. But it is a plausible scenario.
Dwarkesh Patel
Just for the audience, when you’re referring to AlphaGo or AlphaZero, you’re talking about getting superhuman relatively fast after achieving human-level performance.
Noam Brown
If you look at the trajectory of game-playing AIs, like Go, within a span of a year they went from beating a European champion — something like number 50 in the world — to beating the world champion, to being unimaginably, orders of magnitude stronger than any human alive. It’s possible that in domains like math we see a similar trajectory, but I think there is a very plausible scenario where that doesn’t happen.
Dwarkesh Patel
I want to understand, if in six months people will have access to multi-agent systems, how should one model what it is like to collaborate with or hire a multi-agent system?
Noam Brown
I should start by talking about how these multi-agent systems actually work, which I think is a very different way than a lot of multi-agent systems in other AIs. A lot of people that have approached multi-agents for things like LLMs tend to take this very scaffolded approach. For example, there might be a coordinator agent that delegates work to a bunch of children and gives them a task. The children work on it and then return their answer.
This seems like a very sensible setup, a very sensible scaffold. It definitely helps, but there are a bunch of limitations with these kinds of setups. For example, if in this setup you have a coordinator that’s sending tasks to children, and the children work on it and then return their answers, what happens if two children are given similar tasks? Can they talk to each other? Usually the answer is no. That’s very inefficient.
If you’re given a task and it’s actually really helpful to talk to somebody that might know an answer to a question that you’re working on — or part of something that you’re working on — it’d be really helpful for you to just be able to ping them and say, “Hey, can you help me out with this thing?” But a lot of systems don’t have that setup. Adding it significantly increases the complexity of the scaffold that you have.
Another thing is, what if the child doesn’t really understand or has a clarification question? Then it has to choose between, “Okay, do I just return and ask the question instead of solving the problem?” or “Do I solve the problem, make an assumption about what the parent wanted me to do, and just solve it that way?” In any scaffold that people come up with, there are always limitations involved. The approach that we wanted to take was to just go toward the extreme end of baking in as little structure as we could and give the agents very primitive tools to use, and they figure out for themselves how to use them effectively. So we give the agents the ability to message another agent, and when it messages another agent, it is inserted into the context. It can do a few other similar things, but that’s basically the core of it. It can just send a message whenever it wants — just a tool call — and it can send that to other agents.
They figure out for themselves the best way to coordinate around that. It turns out that if this is done well, you get very sophisticated behavior. To me, it looks a lot like how human collaborators work over something like Slack, for example.
When we were working on this project, it was really exciting when we finally got it working to see these agents working on problems together. I remember one example. We give the agents a problem, and then one agent says, “I think I’ve got the answer.” Then another agent says, “Actually, I got a different answer.” Then they have this whole discussion about, “Well, how did you arrive at that answer? Can you explain it to me?” Going back and forth and trying to clarify what could’ve been wrong in each other’s reasoning.
Then they finally converge on, “Oh, yeah. Okay, that seems right.” Then it just broadcasts to the other agents, “Actually, I’ve changed my answer. I think he’s right.” It just felt like a very natural conversation.
It felt like when you see chain of thought for the first time that’s trained through reinforcement learning, and you’re like, “Oh, this is just kind of like what a person would think if they were writing down their thoughts as they’re thinking them.” It felt like that. It is really cool to see this kind of behavior. Collaborating with these things, honestly, feels a lot like collaborating with a person. It’s just a very natural flow.
Dwarkesh Patel
Except one qualitative difference that might become salient in the future is that these systems will be thinking maybe more than 10x as fast, if you just look at how many tokens per second they output versus how fast a human talks. They’re working all the time. They’re not sleeping. They’re collaborating with each other at a much more intense pace than humans have the capacity to collaborate with other humans.
I’m trying to think of what to qualitatively expect in a year. Is it like a shadow organization that is moving 100x faster in my company than the human level is? What would take a human organization a year to do is happening within a week within this shadow organization?
Noam Brown
Will it feel foreign? I don’t know. I’ve actually found that it’s surprisingly natural to work with these things right now. I think that could change. For example, we have these ultra-fast modes that enable sampling to be 10-15x faster or whatever. Then it’s going to be pretty hard to keep up with these things. The idea is that these agents, when they’re communicating with each other, can go super fast. But they also understand when they’re talking to an agent versus when they’re talking to a person, and their behavior will be different in those situations.
Dwarkesh Patel
The main example that we have publicly of sophisticated multi-agent systems is unfortunately the Hugging Face one. A lot of things I found concerning there, obviously. But the thing I found interesting there is the spontaneous emergence of hierarchy, of middle management. It sounds like you’re saying this level of organization emerges spontaneously from training?
Noam Brown
The details are spontaneous. But while we’re giving a lot of flexibility to the agents to decide how to communicate with each other in the optimal way, we are still giving them a starting point. We’re giving them a prior about what reasonable communication might look like. They’re also trained on a lot of human text. They have an understanding of how humans organize and coordinate, so that’s all baked in.
I think it is surprising the way they’re able to polish this. If you look at what it starts out at, it’s not very sophisticated behavior. In fact, it’s actually very difficult to get these agents to coordinate in a productive way, because it’s very tempting for them to just collapse to, “Oh, we’re all just going to solve the problem independently.” That is a local minimum that you can get stuck in. But if it’s done well, they can end up coordinating very effectively in these kinds of very structured ways.
Dwarkesh Patel
I wrote this essay a couple of years ago about what automated firms will look like. I was thinking about, if you had fully automated firms of, let’s say, human-level intelligences, what is different about the nature of AI minds that would make the organizations AIs form different? There are a couple of very important differences. For example, AIs can share context much more seamlessly than humans can. They can merge their knowledge much more seamlessly. Also, you can spin up or spin down an arbitrary number of instances which have the right knowledge.
So if you want to hire more people, it’s not all the schlep of finding the right talent or whatever. Your best talent, you can just make infinite copies of them. Or if you don’t need them for the task anymore, you can spin them down. You can replicate the most effective parts of your organization, or replicate whole organizations together which are effective. Where do you see these multi-agent systems going a year from now or two years from now?
Noam Brown
It’s a great question: how do these things actually differ from working with a human coworker? You highlighted some. One really interesting thing is that if you have a person and you want two copies of them, you can’t just clone the person. But with AIs, it’s actually really easy to just say, “Okay, just fork yourself,” and then have both copies work on this thing and then merge back together. We already have this, I think, in multi-agent for Astra and 5.6 Sol, where when they spin up sub-agents, the context is just forked. So it has all the context that’s relevant.
There are other interesting ways where the agents will differ from people. Like, what are some reasons why startups disrupt incumbents? There are a few factors. One is that they’re willing to take more risks. But another major factor is, as organizations grow in size, you see increasing misalignment between the individuals in the organization.
If you have a startup with five people and each person has a 20% share in the company, they’re all highly aligned to the company succeeding. If you have a massive company with 10,000 people, you see a lot more instances where people are territorial, or just care about getting a lot of headcount for their project or their team, building their fiefdoms, getting a lot of resources so that they can publish cool work or whatever and get promoted. This is actually a real detriment. I think this explains a lot of why startups are able to disrupt incumbents.
It’s true that AI does help startups in a way. It’s much easier than ever before for one person to step in and be like, “I’m going to make a multimillion-dollar company.” The AIs amplify an individual so much. But there’s also an argument that they could benefit incumbents. If the alignment problem is solved, then you don’t have the issue of misalignment between individuals in the company. At least that’s mitigated. The AIs, if they’re aligned well, can just be aligned to the interest of the company. You can have 10,000 of them, and they’re all going to be working as hard as if they were a 20%-share co-founder.
Dwarkesh Patel
It’s not only that, but it’s also that they are much better able to manage shared memory and context than different humans can. If tomorrow you hire 10,000 mathematicians and you’re like, “Solve Navier-Stokes,” they’re not going to be able to cooperate effectively, at least not off the bat. But apparently you can have 10,000 AIs do that.
Noam Brown
Again, I want to be conservative here, because we haven’t measured how effective the 10,000 agents are at coordinating. We think it helped. We don’t actually have good measurements saying, “This 10,000 agents led to a 2x speedup over 2,000 agents,” or something like that. I don’t know about likely, but I think it is very possible that 10,000 humans are better at coordinating than 10,000 agents right now. I think it is entirely possible.
Continued at the source.
I have a lot of mixed feelings about AI and LLM technology. I’m fascinated by its effect on our profession, excited by the potential gains in productivity - and thus the products we could rapidly build. On the other hand, I’m fearful of the damage AI might cause: agent swarms taking over our virtual and physical infrastructure, designing bio weapons. But, back on my first hand, LLMs might also design miracle cures, and come up with clever ways to raise our prosperity. Fundamentally I don’t think we have a choice about riding on the AI technology train. It’s a wild ride and I just hope we’ll get through it OK.
But as I mull on this more, I realize that among this mix of contrasting feelings, there is one emotion that dominates - one that comes from my direct interactions with LLMs. I don’t like them. They talk to me in this grating LLM-voice, an uncanny valley of talking to a real human. They confidently bullshit me - often giving me useful, helpful answers. But also just making stuff up with the same assurance - and with only a veneer of fake remorse when I call them out on it.
That’s not enough to make me feel we should avoid them. As Jessica Kerr put it “not only are they useful, it is irresponsible not to use them…. They’re more thorough, as well as faster.” This contradictory reaction comes through in polling, where people say they find these models are useful, but also that they think they will be bad for society.
Much of this may be because LLMs are young - we haven’t trained them to grow up yet. Maybe I’ll like them once they mature. (I hope we get to find out.) But I’m not encouraged when I think of the kinds of environments that cultivate them. I’m wary of the Silicon Valley brogrammer subculture, and these LLMs are their products, so naturally lean toward their world-view. When we think of AI agents, we shouldn’t anthropomorphize, treating them as conscious beings with their own will. They are (software) machines, developed by people working in corporations. While the agents’ behavior aren’t explicitly programmed, they are nurtured with the values of their creators.
One of my most successful life-hacks is to avoid people I don’t like or don’t trust. I decline to interact with them socially, and make a deliberate effort to avoid working with them too, even if they are doing much that is beneficial. I feel that hanging out with pleasant, capable people, the people with integrity, has made my life a far better one. Hence my visceral dislike of interacting with an LLM that’s not just making a pretense of being human, but also posing as the kind of human I walk away from.
Reports of agentic hacking continue, in this case it happened back in May and it seems OpenAI did not disclose that they were responsible. Simon Willison sees two options:
- After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems.
- They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it.
Both of these are bad!
Given this incident, the Hugging Face situation, and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered?
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Stop asking the sci-fi question: ‘Is it conscious?’ Start asking the engineering question: ‘Is this a powerful, unpredictable component being put somewhere consequential, and where’s the feedback that tells us that it’s safe?
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Nate Silver is known for his forecasts, but to do them he writes a lot of code for his models. He’s found agentic programming capable of doing miraculous work.
In spending so much time with the LLMs, I’m super attentive to improvements in their capabilities. And these changes tend not to be so linear. Instead, they improve in step functions, almost as phase changes. Suddenly, the models just start doing things capably that they were screwing up before. In my experience, there was a big leap forward when reasoning models first came out in late 2024/early 2025 — enough that they were occasionally useful for tasks involving data and not just words — and then another one this past winter.
The most recent changes I’ve noticed, however, have had less to do with intelligence and more with persistence.
Consider the Hugging Face attack. Although these agents showed remarkable intelligence, they weren’t really super-intelligent - but they were super-persistent. This is a common theme of AI in its various forms:
Game engines like AlphaGo Zero start out by basically making random moves — but by playing against themselves millions of times, they eventually far surpass human capabilities
As we try to figure out what kind of regulations we need to keep AI under control, we need to remember that we should design our guards around super-persistence as much as worrying about super-intelligence.
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“Uncle Bob” Martin has made many posts on X during the last few months about his programming with LLMs. His approach has been to build a firm harness to keep them under control, so they create software that is maintainable as well as functional. Sadly the posts have been frustratingly light on detail. But now it seems that lack of information may not matter
And while I was heads-down getting that to work, the agents got a LOT better. So much so that when I came up for air, the need for my harness was obviated. Indeed, the need for any but the most liberal of harnesses may be obviated.
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Some tidbits that struck me from Ezra Klein’s recent (recommended) interview with Matt Sheehan on the interplay between regulation of AI and competition with China.
When American policymakers are like: Where do you start? — I sometimes say: Well, you start by starting. You learn how to regulate things, you learn how to legislate on them by regulating and legislating on them.
Slideware: a presentation program, such as Microsoft PowerPoint, LibreOffice Impress, or Apple Keynote.
In my previous post in this series, I argued for reserving presentations, recorded or live, for content that needs your voice. Live presentations should also merit the scheduling overhead. Once you’ve decided on a topic to present, it’s tempting to go straight to slideware. I suggest otherwise.
The 16:9 slide format forces you to slice your narrative by visuals. When you don’t know what that narrative is, you’ll often second-guess yourself on every slide. Add to that confusion the distractions of font size, colour, creating diagrams, finding images, and deciding transitions and builds. The form precedes the function. This is why going to slides without a narrative creates a massive cognitive challenge for most presenters. This is also why we reach for shortcuts such as canned slides, so we can at least make progress with this demanding challenge.
I suggest nailing down your narrative before turning your attention to slides, which serve as supporting visuals. There are many ways to build a narrative, either on your own or with a co-presenter. But step 0 is to describe your key idea and your audience.
When constructing my narrative, I find it useful to begin by identifying what Nancy Duarte calls the “Big Idea”. Consider it a way to describe the “so-what” of our narrative in a sentence. You can get to the big idea by describing its two constituent parts.
Once you’ve thought through those two parts, combine them into a single sentence that isn’t a mouthful and rolls off your tongue with ease.
Here’s an example of the big idea behind this video about the case against brainstorming.
| My point of view | What’s at stake? |
|---|---|
| Brainstorming feels scientific and collaborative, but research debunks it as a corporate superstition. Brainwriting (solo, anonymous, written idea generation) is a better approach. | Teams sabotage their best ideas through production blocking, conformity pressure, dominant personalities, and social loafing. By believing that brainstorming makes them more innovative, they lose out on valuable ideas and unique perspectives. |
| The big idea: Instead of brainstorming, a corporate superstition that kills your best ideas, adopt brainwriting and let independent judgment surface the widest range of ideas and the deepest thinking. |
Alongside the big idea, identify your audience. I suggest a persona-building exercise to guide your thinking.
Sometimes, these persona details are evident and intuitive. You might be presenting to your teammates. In that case, you might speed through the persona-building step in minutes and tweak it a little after each iteration. In other situations, you may need to ask around to learn about your audience. For example, you may be presenting to a new client. In such situations, investing time to learn about your prospective audience will help you sharpen your eventual storyline.
Here’s an example of a persona for the same video I linked earlier in the piece.
Are you ready to go to slides yet? Nope. Once you’ve identified your big idea and the top three takeaways, I suggest fleshing out your narrative to serve them. Narrative building is a subjective craft, so choose a story structure that fits your content. Here are six story structures I’ve used with success.
| Format | What it is | Stages |
|---|---|---|
| Sparkline | A narrative that swings back and forth between where things stand today and where they could go, using repeated beats to make the future feel real rather than abstract. | What is (today, as it stands) → What could be (the future you’re selling) — repeat as many beats as you need to make your point. |
| Explainer | A structured walkthrough of a concept or insight the audience doesn’t already have. It follows the “tell them what you’ll tell them, tell them, then tell them what you told them” format. | Context (lay of the land) → Story structure (the roadmap) → Steps (the narrative, step by step) → Recap (what you told them) → Celebrate! (a call to action) |
| Pitch | Recommends a new, inspiring solution to a problem the audience is experiencing, and makes that solution stand out from the obvious, boring options. | The windup (where we are today) → The hurdle (the problem) → The vision (the way out) → The options (a few paths — mostly boring, one inspiring) → The close (why the inspiring option wins) → The fine print (how it happens, plus a bonus) |
| Hook, meat, payoff | A short, punchy talk structure that grabs attention upfront, introduces the substance next, and then lands a conclusion that reinforces the opening. | Hook (a provocative opener — a question, challenge, or personal story) → Meat (structured narrative, e.g. lists or a timeline) → Payoff (call to action that connects back to the hook) |
| Situation, complication, resolution | This is the classic consulting shape. Start by describing the world as it is. Next, introduce a problem or opportunity, then land the solution that addresses it. | The situation (objective context) → BUT → The opportunity/complication (the challenge or opening) → THEREFORE → The resolution (the solution) |
| Hero’s journey | The most dramatic of the six. This structure starts in normalcy, descends into a crisis, hits rock bottom, then climbs back out stronger. It’s a Pixar/DreamWorks favourite, and a natural fit for project stories and experience reports. | The situation (normalcy before the problem struck) → The challenge (a problem you couldn’t ignore) → The crisis (things go south) → Hitting rock bottom (the worst point) → The comeback (how you fought back) → Emerging stronger (lessons learned) |
With unpredictable audiences, I’ve tried a seventh, more flexible structure in which one presentation holds multiple smaller storylines. In such presentations, I show my audience a list of potential topics to dive into. Each topic could have a different, independent story structure. Such talks give the audience a sense of control, but also demand a lot from you as a speaker. Neal Ford calls this pattern, “Á la Carte Content,” and it works well when you have more content than the time allows.
Figure 1: A flexible narrative structure. When reporting on an internal research survey, I let my audience choose the question they wanted me to answer using the research data.
If you’ve never built a storyline before, I understand it can be daunting. I suggest four approaches to building your storyline.
If you’re co-presenting with someone, I recommend whiteboarding your storyline with them. You can follow one of my recommended story structures, or build your own. Sticky notes on a physical whiteboard work fine, but if you’re remote, you can even use tools like Miro or Mural.
Sticky notes offer a distinct advantage for crafting your narrative. You can move stickies back and forth, edit them, or trash them. Colour-coding sticky notes helps you visualise related themes, and placing them close together helps you notice adjacencies.
If whiteboarding is your thing, I’ve created a Mural template with all my favourite story structures and panels, so you can outline your big idea and describe your audience persona.
Of late, I’ve found myself creating presentations that start as a boxes-and-arrows style diagram in my notebook, or even on a slide. In these situations, the diagram becomes the spine around which I construct the rest of my narrative.
For example, when I was starting my most recent role, I doodled the diagram you see below, on a piece of paper. It was my way of thinking about how I intended to play my role as head of culture at Thoughtworks. After a few more hours of scribbling and making notes, I reproduced the diagram on a slide. I built my final slide deck using a combination of animations and nested slides. I’ll expand on this example further down in the article.
Figure 2: A core diagram can start as an excellent spine for your narrative.
If you don’t enjoy whiteboarding, I suggest writing your narrative in text. This approach can work well if you’re a solo presenter and prefer writing as a thinking tool. You can start with a bulleted structure for your ideas and flesh them out as you go.
If you want to use one of the story formats I described earlier, I’ve created a pack of Google Docs templates that you can use as an alternative to the Mural whiteboarding option.
Since AI voice transcription has gotten better in recent years, I’ve also used voice memos to flesh out my thinking. The Voice Memos app on iOS and the Recorder app on Android offer excellent transcripts, and once you’ve picked a story structure, you can use these apps to record your thoughts for each segment and then pass the transcripts to any AI chatbot to clean up and structure your narrative draft. You may need to clean up the AI-produced draft, but with some practice and by creating some custom skills, you can push AI tools to get you close to a usable narrative. Of course, you can’t, and you shouldn’t outsource your thinking to AI.
Whichever approach you take, the success criterion remains the same — you should know your narrative well enough to voice it without any visual aids. And if you achieve that outcome, you’ll have a solid narrative platform, which you can then enhance using audio-visual aids.
Almost there. One more step. Your narrative is a clarifying artefact for what you want to say. You now need some clarity on what you want to show. This is where a storyboard comes in handy.
The simplest storyboard describes each slide you create in as simple a way as you can get away with. If you’re already using a physical or virtual whiteboard, sticky notes or index cards can help you build that storyboard — one sticky note to describe each slide. The Mural template I shared also has a panel to organise your storyboard. As I’ve explained earlier, don’t worry about the number of sticky notes. The slide count doesn’t matter when you control the pace of your presentation.
Figure 3: A storyboard with sticky notes. (generated using AI)
You can also use documents to create your storyboard, though they aren’t as flexible as sticky notes. The Google Docs template pack also has a storyboarding template you can use.
These days, I often create my storyboards using slides, especially when I use a diagram-centric narrative. The slide sorter or light table view in your presentation tool is excellent for creating storyboards because it allows me to drop in text and sample images and reorder my panels until I’m satisfied with the plan.
The trick with slide-based storyboarding is to resist the temptation to design slides. When storyboarding this way, I limit my focus to the spine of my slide deck. The polish comes later.
Remember the diagram I shared earlier in this article? The images below show my final diagram, the storyboard that I created using that diagram as the spine, and then a light-table view of the final deck after I added a few layers of polish.
If you’re accustomed to starting your presentation design by opening a presentation tool, my suggested approach will perhaps feel onerous. From experience teaching presentation skills to hundreds of Thoughtworks colleagues, I find this approach to be a way to start slow so I can go fast later. Once you try this approach a few times, you’ll notice that it doesn’t take as much effort as you may fear. It also speeds up slide creation because you’re working off a plan, as against playing it by ear.
On the other hand, if you’re a skilful presenter, my suggested approach may seem rigid and linear. That’ll be a fair criticism. I’ll use a Pablo Picasso quote in response to that reaction.
Learn the rules like a pro, so you can break them like an artist.
-- Pablo Picasso
Here’s what I’ve noticed when coaching colleagues to present:
All this said, the narrative won’t be a static artefact. As you build your storyboard, you may tweak the narrative. Even as you design your slides, you may reconsider your narrative, storyboard, and even your assumptions about your audience. None of these artefacts and considerations is a one-and-done. The outline feeds the design, but the design can also challenge the outline. That’s the narrative you want walking into slide design: solid enough that building your slides becomes an exercise in execution, not invention, yet open enough to flex when the details teach you something new.
With that narrative platform in hand, let’s explore some principles for slide design in the next few posts.
Since I had to discuss the “pacing” with a lot of people this weekend, here are my two cents: I don’t think pacing literally means that these companies will be “slowing down” training and development in any way.
“Pacing” here means adding a framework for more checks. We have seen some of that “pacing” already in recent months, when Mythos wasn’t released as-is but instead a delayed, nerfed Fable variant was released.
Or when Astra wasn’t released right away / there is an existing Astra model that hasn’t been released yet.
These Mythos/Fable and Astra pacing decisions were ad hoc. If you are a company, you have to weigh the pros and cons of a delayed release in terms of keeping up with the competition, making money, pleasing shareholders, mitigating risks and harms, and so on.
If there is a formal framework that everyone has to abide by, that essentially relieves some of the pressure on a company to rush out its model just to take the top spot on the leaderboard, since it knows that the competition “has to” play by the same rules. Based on the discussions today, I think “pacing” primarily means just that, rather than a halt in training the models.
TL;DR: Pacing != pacing development.
Yesterday David Sacks wrote a tweet and within a few minutes people did, what they usually do, and they asked Pangram if it was AI. And Pangram said it’s entirely AI generated. To which David replied that these AI detectors are bogus.
Now Pangram has a pretty low false positive rate, but if you have ever used an LLM as a writing assitant, you will have probably noticed that it claims your posts 100% AI, even though you don’t feel like they are.
Pangram itself is a trained model, that attempts to detect segments of text as being definitely human, definitely AI and a mixture of the two. If you want to know how it works, they published a paper. The short summary is that they are manufacturing its own training data by starting from collections of known human authored text. An LLM is then tasked to understand the text and write a fresh new text on the same topic. They also let the LLM perform partial edits on that original human text and through that they can pick up on these co-authored details. Pangram claims their model to have rates of 0.0041% false AI accusations and 0.34% missed AI text.
So now that we know this I figured it might be fun to have an LLM re-create David’s tweet. I first came up with a prompt. And when I say I came up with that prompt I in fact used an LLM to propose to me from that tweet what I might want to say for the structure. I’m sure if you ask Pangram about if the above text is AI, it will probably say so, but that’s not really the point. The point is that I then used Opus 5 to generate a text which reads entirely AI generated.
If you are curious, this is the prompt I used:
Original prompt to generate a David Sacks like tweetPlease write an 8 paragraph tweet about “Pacing the Frontier”, not longer than 350 words.
Context of this tweet is this post by Dario: https://darioamodei.com/post/we-must-pace-the-frontier
And this tweet by Sam Altman: “I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we’ve had at OpenAI in recent weeks.
Committing to having independent evaluators with employee-like access is a great idea, and we will do the same. We’ll have more to share soon.”
Structure for the post:
- Open with a concession: Note that Dario (“We Must Pace the Frontier”) and Sam Altman both want to slow down at the frontier. Say “go ahead” which reads as a surprising response.
- Establish they own the frontier: Argue OpenAI and Anthropic hold a duopoly on frontier intelligence by all kind of metrics (market share, capability, revenue etc.) and that they themselves claim the lead is widening via RSI.
- Concede you can’t see their unreleased models, so if those are scary, being responsible is their call.
- Follow with a list of a rapid-fire paragraph (“stop pretending”) rejecting the asks from Dario’s post.
- Name the commercial self-interest (Slowing down isn’t purely altruistic)
- Close with the challenge that pacing buys room for a smarter regulatory debate than Bernie Sanders “shut it all down”. Say something about China and Open Weight models.
The text that Opus 5 then wrote, was this one:
Dario published “We Must Pace the Frontier.” Sam agreed, and said OpenAI will also commit to independent evaluators with employee-like access. My response is not the one they expect: go ahead. Please do.
Because you two are the frontier. OpenAI and Anthropic hold a duopoly on frontier intelligence by every metric anyone actually uses: capability, revenue, enterprise share, developer mindshare. And you both keep telling us the lead is widening, that models are starting to improve models. If that’s true, pacing costs you a quarter you can reclaim whenever you like.
I also can’t see your unreleased checkpoints. If what’s sitting behind the curtain genuinely scares the people who trained it, then slowing down is your call to make, and the right one. I’m not going to second-guess a risk assessment I have no access to.
But stop pretending the rest of us are the problem. Stop pretending this is a collective action failure in which a handful of startups are the defectors.
Stop pretending open weights are the dangerous frontier while the two of you ship the most capable model in the world every few months. Stop pretending third-party evals require new law, when you both just committed to them voluntarily, in public, on a blog.
And stop pretending it’s purely altruism. Pacing the frontier is also margin repair. It slows the price war, stabilizes the capex story, stretches depreciation schedules across a longer product cycle, and makes life considerably harder for fast followers who survive by distilling whatever you shipped last month. Safety and self-interest are pointing the same direction here. That’s worth saying out loud.
Still, the upside is real. Pacing buys room for a smarter regulatory debate than the one on offer, where the loudest alternative is Bernie Sanders’ “shut it all down.” That’s a slogan, not policy. Meanwhile China keeps shipping competitive open weights, and any rule that binds two American labs while the rest of the world downloads its capability for free isn’t safety, it’s theater with a very expensive ticket price.
And well, Pangram agrees that this is 100% AI. So far, so uninteresting. It does read somewhat like David’s tweet, but obviously not entirely. Given that the original prompt does not have enough information to re-create the tweet entirely you would expect some divergences.
The actual thing that interests me is if you can take this output at all, and then rewrite it from scratch, but by sticking to the general structure and ideas. Will Pangram give us a AI or human rating?
I read the generated text. Then I read each paragraph and decided to rewrite and rephrase it without an LLM. According to some similarity checkers, they the final texts are 50% similar which seems about right. But strictly speaking, not a single sentence is the same. Here is the 100% human rewritten text of the above one. No LLM was used to write it, but an LLM was used to fix up typos in the end. That from my experience really does nothing to tick off an LLM detector.
Dario has written “We Must Pace the Frontier,” and Sam from OpenAI has agreed. My response might surprise people: go ahead, please.
You two are the frontier! Your companies, OpenAI and Anthropic, are at the frontier by all metrics: revenue, developer mindshare, adoption, capabilities. And yet you both claim that your lead is widening as a result of recursive self-improvement as models are improving models. You currently are the duopoly of self-improving models!
I am unable to see what unreleased models you have. When what you have behind those doors really scares your folks, then you should slow down. I’m not going to tell you otherwise and I support you.
But please don’t pretend we are the problem. Stop pretending you need our permission. Stop pretending this is all a collective issue when in reality this is all on you. Stop pretending open weights are the problem here. Stop pretending pulling third-party evaluators in requires lawmaker involvement. And for the love of all the good things in the world: stop pretending this is all about altruism.
Pacing the frontier is also about your margins, and it makes it harder for fast followers. And it patches up your capex story and has the potential for slowing down the price war ahead of the IPOs.
But yes: pacing might give us the space for a better debate than Bernie Sanders’ “shut it all down.” There is no policy there. And while we’re having fights at home, China will keep shipping competitive open-weight models and won’t adhere to any American agreements.
This is all regulatory capture hiding behind a safety debate, and the rest of the world is watching.
So what does it say? Well this text too comes back as 100% slop. And it does not surprise me all that much. I have generally noticed that if you rely on an LLM to give your text structure, it will score badly on Pangram even if you do plenty of edits over it. In fact, it’s quite unlikely you’re going to get a post that starts out as slop into a structure that will make it appear that it’s not.
I came to quite appreciate the existance of Pangram because at the very least it has made me quite aware of some of the effects that using LLMs for writing blog posts has. This blog has been AI supported for about two years (as you can see from the AI transparency link on the bottom but I did notice that I became both more reliant on those tools and that they have become much more aggressive editors and it gave me pause.
Yet, I also think that plenty of people will find a “100% AI” rating misleading when in fact the author has done plenty of editing. But maybe it’s fair to have this to show up as entirely AI?
Agentic engineering in an old codebase is about making hidden constraints visible and cheap changes trustworthy. Let’s talk what to do in brownfield codebases.
During my career I’ve worked on teams whose codebases had been around a long time. Those are brownfield systems: the repository is no longer a complete description of how the thing actually behaves. Institutional knowledge, duct tape, legacy services, and expectations other teams depend on live outside the tree. You have to learn those constraints before you write new code, and you have to prove a change didn’t break them. I love coding with agents, but throw them at an older brownfield codebase unsupervised and you may end up with something that “works” but with the wrong system design and brittle tests.
Even in teams that wanted to do modernization efforts pre-AI, you often had to take things very, very piecemeal, with strong testing in place, a strong layer of confidence to make sure that you weren’t breaking things. You kind of knew that on top of actual user journey testing, any migrations you were making had to keep things working as intended via a barrage of repeatable tests. These days some folks may say that as soon as an agent drops code you didn’t author decision-by-decision, you’re already in a brownfield project. Regardless, you want to optimize for cheap changes being made safely.
And these days, especially in the last, I would say, maybe five to ten years, this idea of caring more about testing, caring more about verification, caring more about how you make changes in a way that is not going to break things, I feel has gotten more attention. But that doesn’t change the fact that if you’re doing a lot of work trying to introduce agentic engineering, and then software factories and all of these other kinds of patterns for autonomously working through these large codebases, you have to put quite a bit of additional mindfulness in place otherwise you risk signing up for a world of technical debt.
Before we dive in, let’s assume that the code should be the source of truth. Anything we add on top to help brownfield is what can’t be easily inferred. I want to talk about this in terms of zones, blast radius and a few other patterns I think will help.
If I’m going into an older codebase that’s been around for a while, I probably want to get a sense of what code shouldn’t I be touching. You can consider these zones. E.g. Green zone = safe/good tests/isolated, yellow = mixed quality, red = sensitive/auth/billing/permissions.
What are the parts of the codebase that are very, very sensitive, or that not everybody understands well? And maybe you would draw those with different zones. Maybe you have a green area that’s got very good test coverage, and is using modern conventions that are current, and has good isolation. And for those parts of the system, agents can go off and work on that in a tight loop.
There are sites, especially commerce sites that I’ve worked with, where you could easily have five or six departments all with their own microsites, when the entire experience to the end user is going to feel like a single thing. And there’s actually a lot of inherent complexity underneath the surface. One team might have really good test coverage for their stuff; maybe it was built in the last couple of years. Other teams may not. So you have this green zone.
Maybe you have yellow, which is mixed quality, maybe it’s a mix of things, and agents can change code there after characterization tests have been written.
And then you can have red areas, where you’ve got sensitive stuff like authentication, billing, permissions, payroll, anything that you wouldn’t normally touch and make some hasty changes to. For example, if only a small number of people understand how it all works. You don’t want unsupervised rewrites in that kind of system.
Three rules make the zones an operating procedure instead of a metaphor. A person draws the map, not the agent; left to choose, the agent starts in the scariest file, because the scariest file has the most interesting names. Zones only move when it’s earned: yellow becomes green once characterization tests exist and the module’s owner has reviewed the agent’s first changes. And the zone sets the verbs: green is a tight loop, yellow is tests first, red is a human pairing on every step or the work not happening.
Autonomy should follow blast radius, observability, and recoverability. A model’s confidence is a poor guide.
So I think it makes sense to have at least a sense of, how do you think about the map of the world, and what can the agent infer itself from the codebase? Agents can actually infer quite a lot from the code itself. There was this period of time when people would try to include markdown files for absolutely everything, and then they’d stuff them in their context windows. Agents are actually pretty good at understanding the map of the system. What you want to give them is the stuff that is not obvious from the code itself. Are there conventions? Are there patterns? Are there nuances that are not in there? I think that’s important.
Concretely, that means: business or team specific nuance, trade-offs that explain why the system is structured a certain way, guidelines that aren’t explicitly enforced by static analysis or tooling, domain-specific domain rules, external constraints and historical context behind counter-intuitive implementations and so on.
Write down what the code can’t say, and nothing else.
If your agent’s exploration produces no durable artifact, the next agent pays for the same archaeology again.
One piece I would add to that map is a durable research artifact. For yellow and red work, I like a separate read-only pass that produces a short comprehension memo: entry points, owners, callers, existing abstractions, tests, production signals, relevant history, and open questions. Claims should cite a file, issue, ownership record, or dashboard.
The default loop otherwise wastes its research. The agent works out how the auth flow behaves, completes the task, and loses that model when the session ends. Chat history isn’t a great system of record, especially after compaction.
After research, I would start planning with a clean context. Ask which files the plausible approaches touch, which invariants they preserve, and how you would reverse them. A human picks the path. Implementation should stop if it discovers the map was wrong. Review starts fresh and works backward from the acceptance criteria. A clean reviewer is more likely to notice when a test proves the implementation while missing the requirement.
Every repeated correction is a missing piece of the harness.
It is useful to be precise about where the pieces fit. Instructions record unusual facts about a repository. Skills package reusable procedures such as checking blast radius or verifying a schema change. Plugins can provide governed access to the ownership catalog, incident archive, or dashboards.
The harness is the working environment around the agent: context, tools, permissions, tests, logs, and recovery. A factory schedules many dependable loops, keeps durable state, and hands novel cases back to people.
The practical test is what happens when the agent gets something wrong. If you quietly repair the diff, the next session can repeat it. When the same review comment appears again, move it into a lint rule, hook, type, test, or skill. Keep prose for constraints that cannot be enforced mechanically.
A deny rule, scoped credential, or CI check doesn’t have to remember. Over time the harness becomes a record of failures the team has decided not to pay for twice.
Lock today’s behavior before you let anything improve it.
If you’re bringing agents into an existing codebase, it’s very similar to other kinds of modernization efforts. Maybe you begin with zero-risk work. It shouldn’t be like, hey, let’s rewrite this monolith in Rust or something like that. Maybe it’s, first explain how the things work.
Generate characterization tests that can lock that current behavior.
Characterization tests are automated tests used to document a system’s actual current behavior so you can safely refactor or change legacy code
By characterization tests I mean tests that pin down what the module does today, ugly parts included, because in an old system some of that ugly behavior is what the business runs on, and an agent will happily “fix” it behind a green suite. The machinery is old because the problem is old. Netflix used the same idea at production scale in its GraphQL cutover - replay and shadow traffic against the old and new paths, diff the payloads, promote only when they match. That is the promotion path when a homepage-class surface has no honest unit suite: don’t guess; run both and compare.
When an agent is the one making them pass, don’t let that same session be the only author of the tests. Pin the behavior first, in a separate pass or by a person; then let the agent work. Otherwise you get a green suite that encodes the implementation you just invented.
And then you start down the path of doing mechanical transforms. You can do dead code and unused export inventories. You don’t want to start with the trickiest or hairiest parts of the system. And ultimately you want to have that confidence with any of these migrations.
I remember working on a number of different kinds of migrations over my time on large codebases, and people exercising a great deal of care, even when fixing things that were broken.
One of the older codebases I worked on was at AOL. There was a day when I was supposed to be off, and I was visiting a comic book store near the office, and as it so happened, my boss dropped me a text and asked if there was any way I could swing by. The AOL.com homepage was completely broken, and we didn’t have enough JavaScript experts around to go and figure it out. So I said, okay, sure, I’ll come in and take a look. And you would think these days, oh, a homepage, how complicated can it be? But when you have dozens and dozens of departments of people that can own lots of different components, lots of different criteria, lots of different scripts, A/B tests, all of these things, you want to avoid breaking the world for everybody else, because you’re not necessarily going to have test coverage all over the place in the same way that you would like. In that case I was able to get it fixed, but we basically had to at least user-test the things that didn’t have their own unit tests. How well were things working, without breaking for everybody? So that was kind of important.
That’s still the job. Agents don’t remove the dozens-of-departments problem; they make it cheaper to attempt a change against it. A surface that only production traffic really understands is a red zone by definition, and until you’ve built a stand-in for that traffic, the user-testing I did on my day off is still the gate.
A migration is complete when the new path works and the old dependency is demonstrably gone.
Half-finished migrations are particularly confusing to agents. Search returns the old approach in forty files, the replacement in twelve, and a shim that presents both as current. The agent sees contradictory precedent.
I would rather finish one route end to end, including removing the old path, than convert thirty files and leave both patterns alive. If deletion is a future cleanup ticket, the migration unit is not complete.
Tests can stay green while a replacement still calls the legacy implementation. SWE Refactor Bench calls this migration “Blindness.” Across 520 agent runs, only 28 passed its migration audit, behavioral tests, and independent verification.
If a codemod can make the routine change, use the agent to help write and check it. Give agents the exception queue. Stripe’s migration is useful here precisely because no agents were involved: the durable artifact was the migration machine.
Bun’s Zig-to-Rust port ran about 50 workflows over 11 days from a 535,000-line codebase, with two adversarial reviewers on every generated unit and the entire pre-existing test suite as the merge gate; the part worth copying is that hours went into a porting guide mapping Zig idioms to Rust before any agent ran. Anthropic’s own migration process stress-tests its rulebook on a disposable mini-migration and throws the trial output away before the broad run begins.
A controlled VB6-to-C# study measured 92% behavioral equivalence on simple features and 47% on complex ones: unit size is the lever. The shape predates agents entirely: Stripe moved 3.7 million lines to TypeScript in one PR through months of codemod work, with no agents involved, and Google’s large-scale-changes chapter explains why atomic changes shrink as codebases grow. Spotify now reports 650-plus agent PRs merged monthly on rails Backstage built years earlier.
Asana cleared a multi-year Enzyme backlog in two calendar weeks for about $12,000 in model and infrastructure cost. That $12,000 is just a token bill but not a substitute for the five-year staffing estimate they had on the books; treat it as a vendor-reported cost of generation, not a controlled savings study. The transferable part is the same as Bun: a narrow mechanical migration, a pre-existing suite, humans still reviewing every change
What transfers between companies is the structure around the agents.
Agents have changed the price of trying several plausible implementations. They haven’t changed the evidence required to choose one.
And then I think you’ve probably seen, this year we’re beginning to read more and more cases of well-established companies who are using agents to do big rewrites. I’ve talked to CTOs who are allowing teams to have agents try multiple rewrites in different languages or frameworks because its now feasible to do so more cheaply and evaluate the trade-offs.
Shopify rebuilt the Shop consumer app from React Native to native Swift and Kotlin in twelve weeks with a small team and agent-gated, screen-sized checkpoints. The much larger merchant app is still the brownfield problem: hundreds of screens, deep platform integration, same gates, longer clock.
You’ve seen other examples of rewrites to Rust. You’ve seen people do framework-level migrations. There have been all kinds of migrations that have been done. And in many cases, these are migrations people would have done on a much longer timeframe. These days, if you have enough tokens, you can just actually have agents go and attempt to complete a migration across a range of different stacks or languages.
You can try to have your agents actually implement something in a number of different competing options. Rather than having one team choose a single option that you go all in on, what you do is you have them implement all of them. They can all check against your unit tests. You can performance profile all of them, and then make a decision, which is much, much cheaper in some cases than it otherwise would have been. And that’s a completely different ball game, I think, for teams these days.
More generated code should lead to more selective human review, not less human ownership. Seriously consider what will setup your brownfield project for success before you go down the path of thinking about the loops/goals/parallelization.
Software factories can run many changes at once. I would copy that part only after one unit has a dependable judge, recovery path, and review format people can absorb.
Parallelism multiplies the bottleneck you already have. Automated verification can handle five checked changes. One senior reading every line gets a queue, fragmented attention, and eventually ceremonial approval.
I prefer automated review to lead with intent, changed invariants, test results, parity mismatches, and the rollback route. The complete diff remains available. Human attention goes first to the largest blast radius and weakest oracle.
Worktrees isolate changes, not behavior. They may share Git metadata, credentials, local services, and network access. Trusted work may accept that tradeoff. Unattended agents consuming untrusted content need stronger sandboxes and scoped credentials.
Lines generated don’t tell you whether the codebase improved. I would track lead time, review minutes, human interventions, escaped defects, rollbacks, oracle mismatches, and suppressions left behind.
For a migration, track remaining old imports, traffic served by the new path, parity mismatches, and legacy dependencies removed. A green suite with all traffic still taking the old path is busywork.
Agents put a visible price on ambiguity. Tribal conventions become recurring review comments.
That cost was always there, paid during onboarding, review, and incident recovery. Agents make more of it countable. That gives us a stronger argument for maintenance work teams already knew was valuable.
The next time an agent works on the homepage equivalent, I would want it to leave behind more than the repair: a synthetic user journey, an ownership record, and a regression test.
What the next engineer and agent inherits matters too.
12th September 2026
Here’s a neat thing I had ChatGPT Work with GPT-6 Astra (Max) do this morning:
I live at <my address>. Figure out 5K and 10K running routes from me that loop from my house. Use OSM data.
It worked for 27 minutes and produced exactly what I’d asked for, as both an embedded visualization and downloadable GPX file and GeoJSON files. Here’s that 5K route:
When I asked it how it had created the route, it replied:
I used Nominatim to locate the address and Overpass to download local OpenStreetMap roads and trails, then calculated the loops locally.
Frustratingly, the actual code it ran and exact details of what it did weren’t visible to me in the ChatGPT UI. I see this lack of transparency is an anti-feature.
By the time I thought to ask for a copy of the Python code it had used, ChatGPT was unable to provide it. This appears to be because the thread had been compacted. I think any LLM system that uses compaction needs to both preserve the pre-compacted text and make that text available via agent tool calls, to protect against this kind of problem.
As for displaying the map to me, that used the visualize skill. It created a file called /workspace/el-granada-5k-share.html to embed directly into the ChatGPT UI.
Here’s a copy of that HTML, which starts like this:
<div id="eg-share-loop"> <div class="viz-row"><h3>El Granada harbor loop</h3><span class="text-small">5.1 km</span></div> <div id="eg-share-stage"></div> <div class="text-small text-muted">Map data © <a href="https://www.openstreetmap.org/copyright" target="_blank" rel="noopener">OpenStreetMap contributors</a></div> <style> #eg-share-loop { width:100%; } #eg-share-loop #eg-share-stage { width:100%; margin:8px 0; } #eg-share-loop .eg-share-map { display:block; width:100%; touch-action:none; } #eg-share-loop .eg-share-map text { fill:var(--foreground); font-size:12px; font-weight:400; } #eg-share-loop .eg-share-label { paint-order:stroke; stroke:var(--background); stroke-width:3px; stroke-linejoin:round; } </style> <script type="application/json" id="eg-share-data">{"route":{"type":"LineString","coordinates":[[-122.467425,37.4997753] ...</script> <script src="https://cdn.jsdelivr.net/npm/d3@7.9.0/dist/d3.min.js"></script> <script> (() => { const root=document.getElementById('eg-share-loop');
The <script type="application/json"> element contains the full geometry needed to render both the running route and the map itself, using D3, which is loaded from an allow-listed CDN location described in this section of the visualize skill:
External resources
- The CSP allows only
cdnjs.cloudflare.com,esm.sh,cdn.jsdelivr.net,unpkg.com,fonts.googleapis.com,fonts.gstatic.com, andfonts.bunny.net. Other origins are blocked and fail silently.
Something changed with these latest models, with Fable 5.1 and GPT-6 Astra.
The benchmark numbers (79% instead of 65%!) don’t capture it, and neither do the benchmark words: this model goes on for longer than this one, this one is “most aligned”, that one the least “sycophant” (the ultimate benchmark word, no?). At this point? Yeah, whatever.
But it feels like we’re now flying at a higher altitude, that we have to concern ourselves even less with earthly matters such as a single unit test or how to juggle thirteen commands to get this into that format and over the wire. That’s down there now. Up here, we’re now free to talk about what we want:
“I want you to go and test this end-to-end, I don’t care how, and give me irrefutable proof that this works. Dazzle me. Give me a video as proof, or something.”
And thirty minutes later, when I have awoken from the nap I had earned with all that typing and pointing and wanting, I look into the shed and, wouldyoulookatthatWOW, the golden goose laid the golden egg: a 60fps video that runs for 47 seconds, in which the golden goose itself clicks through everything it had built, end to end, navigating the application better than any user could, knowing exactly how to show me, provide proof, that this actually works. “This one now lays golden eggs”—that’s what I want to see in a benchmark.
That’s an actual prompt I used. Here’s another one:
“Go and spawn three other agents in three separate orbs and ask them to test this. Obviously, do not tell them that we changed the AGENTS.md file or that we added this tool to test database performance; just ask them to do something — like add new database queries or something — so that they ideally end up using this new tool to make sure the performance is there. Then check that they did use the tool and if not, adjust the AGENTS.md file and spawn new agents.”
And the golden goose waddles and takes three magic beans and puts them into the ground and somehow knows how to pour water over them (god how do they know all this) and then patiently watches the beanstalks grow and up on the beanstalks there appear three other golden geese (it’s 2026, we’re mixing fairy tales) and that first golden goose, the one that talks to me, sends them messages that say: “Hey, I want you to do the following...” And it briefs them in this weird English (I mean, did we truly expect golden geese to talk the way we do?) about how certain things work, but it does not spill our secret, and does not tell them where the tools to test database performance are. Then it leans back (and I imitate it) and watches them, waiting for them to reply back. After fifteen, twenty, or thirty minutes, the geese send down word from up there on the beanstalk to let us know what they did. But the golden goose doesn’t trust them and checks on them by reading what they did in that thread, and then reports back to me: “Sire, it appears that 2 of the geese independently found that database performance tooling we built. That is the good news. That third one, though... Sire, forgive me when I say: it didn’t use it. But I have an idea! I will change the AGENTS.md file and adjust the prompt and I will put three new beans into the ground. Is that okay with you?”
It’s fucking wild, man. Yes, these are actual prompts! I used these prompts! I’ve seen it happen. Agents spawning other agents in orbs, sending messages back and forth, eval’ing how agent-friendly the codebase is, black-box testing features, black-box regression testing to make sure nothing broke.
This week I’ve asked models to build “something that’s like a cloud, the heads should float over here and there and then resize on mobile” and they built it. I asked them to build this SDK and then spawn agents in orbs in two different codebases and instruct them to use it and to deploy their usage and then check that they actually use it and they freaking did it.
Yes, the models are plain smarter, whatever that means, and they go for longer, sure, but... It feels like we’ve now entered a new phase, where much more is possible, things that I previously thought would never work. Or, that’s my other thought: things where previously the models would do a great job of 95% of the task, but getting the 5% turns out to be crucial and also to be the biggest pain in the ass, so you’d end up with a very frustrating experience.
Previously, you’d ask the models to go and build a heads-floating-around-cloudy-thing and they would do it, sure, but then when you opened the page, you’d see that it’s all there — the heads, the text, the floating — but the heads would be stuck under the navbar, or it would all fall apart on mobile, or clicking on the heads wouldn’t work and you’d sigh because you’d realize that you now have to do that very worst part of the work yourself.
But that seems to have changed now. They really do nail more.
And the one thing I keep thinking is: we have to aim higher, we have to be more ambitious, we have to try it all.
New Raising An Agent is out! I was so fired up after GPT-6 Astra and wondering what all of this means for the personal computer that I sent a message to Quinn: “hey, we have to record this week!” And that’s what’s in the episode, all the thoughts about the higher altitude we’re flying at now, what this means for the future of the computer, and how we still have (regrettably, but working on it) incidents.
I also, rather spontaneously, recorded a video of myself doing day-to-day, real-world work using agents in Amp. Performance optimizations in production, fixing UI flicker, toggling feature flags on, shipping new features — it’s all in there.
Armin with some cold water to splash on the golden geese: Astra for Coding: Why Are We Doing This Again? It’s good that there’s still some cold water being splashed around here! It’s thought-provoking in the best kind of way. For example, here’s what I thought after reading: hmmm, can we judge these models and their capabilities in a software factory that was “intentionally set up to let the model decide the how of the workflow entirely. It was free to manage its own context and could maintain its own records in an agent-notes folder.” I’m not sure. I think agent-friendliness is a real property of a codebase you have to build towards and I don’t think just letting the model decide it all is the best way to go about it. So that’s one thought. The other one came up after reading this line: “But I’m more and more skeptical that the trajectory they are on still lends itself to present-day software engineering processes.” I immediately started wondering: well, should they? Shouldn’t it be the other way around? Shouldn’t present-day software engineering processes change to wield the power of these models in the most effective way? And these aren’t rhetorical questions. I don’t have an answer yet that I’d sign. But these questions are interesting because all of this is interesting and no one’s figured it out yet and, to quote Armin, “man this stuff is weird.”
Seemingly everybody had been raving about this Adam Mastroianni piece: I like ‘em thick. But I waited, didn’t read it when it came out, didn’t read it when I saw it recommended over and over. My justification? “I can’t link to Adam Mastroianni in every issue, can I?” The guy’s too good. But then I folded and did read it and, yes, it’s as good as they say. “Erasing the line between the thick and the thin has left us defenseless against slop at the exact moment of its onslaught. Everyone can sense there’s something amiss with the prose that comes out of the machines, but we lack the language to talk about it, and so we’ve converged on the idea that slop simply means using too many em dashes, bullet points, and line breaks. No, what separates substance from slop is thickness.”
Adam links to this in the footnotes: What Makes Art Great? by Nabeel S. Qureshi. That, too, is just fantastic. What’s very interesting to me is that both pieces, Adam’s and Nabeel’s, are wondering out loud: what makes human art and writing better than their AI equivalents? And both are very different in how they answer that question, which I don’t think you could say about two models.
Doomscrolling ourselves to death: “Yet the most startling thing about this book is how far even the nominally well-educated have fallen, so that ‘by the end of the twentieth century a college graduate born after 1969’ read less than someone born before 1950 with a basic level of education. Indeed, ‘nowadays many rich and highly educated people are much less well read than many members of the least privileged classes had been in the middle of the twentieth century.’”
OpenAI: “We’re sharing a solution to the Navier-Stokes Millennium Prize Problem” And then the world lost its mind. Some said “i basically think this is the Endgame” and it’s hard to convey what they mean to someone who hasn’t themselves gone through multiple rounds of AI psychosis, but I get it, man. I get it. At the same time: is it? The endgame? Then an AI researcher at Anthropic resigned because both OpenAI and Anthropic “are racing straight to self-improving superintelligence and gambling with our lives.” That post now has 165 million views! 165 million! And someone emailed me and asked: should I be worried? And I sent them this video and I believe it. But I also know that next week I might not, because, hey, a colleague of the guy-who-stepped-down-to-save-humanity says “Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade.” So there’s that: some of the highest-paid individuals in the world, working at some of the richest and most powerful companies in the world, think there’s a “>10%” chance their work could kill us. But then people say it’s a farce, a psy-op, a manufactured panic to kick regulation into gear, a coordinated play. But then there are people who say that, yes, it’s coordinated, yes, we do need regulation, because they actually believe this might wipe out humanity. So I guess we’re back to the YouTube video with the slide again.
Terence Tao: “In fact, it is now the identification of a promising problem which is the scarce and precious resource. We have now seen that even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential. The incentives may now be pointing in the direction of no longer sharing any promising research directions with the broader community, which would reverse centuries of traditions of open science and do serious long-term damage to the future of the field.” Someone else said somewhere that maybe in the future more knowledge work is going to look like hedge funds: you spot an inefficiency in the market, you throw intelligence at it, you win. If you’re too late, you’re too late.
Now what is super interesting about the Great Navier-Stokes Panic is that they used 10,000 agents and they “sent 4.9 million messages and used about 300 billion output tokens. In the process of resolving the Navier–Stokes problem, the agents sent 2.7 million messages and used approximately 130 billion output tokens.” That’s millions of dollars, millions and millions. But! Listen: when OpenAI released o3 “it cost ~$500,000 to score 87.5% on ARC-AGI 1. Today, Astra scores higher for ~$20.” Maybe in three years you can solve Navier-Stokes for $50?
But compute is so scarce! OpenAI is pausing “subscriptions to our $200 Pro plan.” Imagine you’re one of the hottest companies in the world and you have to close sign-ups because you don’t have enough CPUs and GPUs. And the Head of Platform at Anthropic says that we’re facing a real CPU shortage. This is not investment advice, obviously.
Hey, welcome to a completely new section of this newsletter. It might be a one-time thing only, who knows. But it’s called HELP! and I think that’s pretty self-explanatory.
Do you use dictation to write? To write prose? Yeah? I’m not talking about prompts or text messages. I’m talking about [very close to the microphone:] Serious Writing. Writing that you edit. Writing where you might take a word out and put it right back in again after tilting your head a bit. That writing.
If so: help me! Tell me how. Because I’m struggling, man.
I can’t figure out how to do it.
I used dictation and talked into Apple Notes, just raw-streaming thoughts into the phone. But then the formatting is weird and I have to say newline like an idiot and I can’t do bullet points, not really anyway, and… It just feels weird.
ChatGPT’s voice mode is another thing I tried, but whenever I talk to an LLM to dictate something, I’m wondering: what am I doing here? I don’t want the LLM to send a reply back. I just want to… I don’t know, talk out loud and somehow magically have the thoughts recorded, but then also edited? And re-ordered?
If you can help me, just reply to this email.
Alright, back to the program…
An almost philosophical Ben Thompson in Stratechery: Write Things Down. There’s a lot going on here and I’m not sure I get all of it, but I found the part on watermarking very interesting: “to insist on watermarking is no different than insisting that a ballpoint pen advertise itself as the author, a concept that is clearly absurd…”
So get this. I was wondering aloud how other people handle clicking links (in Slack, in the terminal, …) and the browser opening them in the wrong profile. Some people said that Arc solves this, but others recommended Velja and Choosy. Both are so-called “browser routers”: they act as the default browser on your OS and then, depending on which URL your mighty cursor might clicketh, they route it to the correct browser or profile within that. “Neat! I didn’t know that’s a thing,” I thought and then, with my mighty cursor already hovering over the Buy button: “But what if…?” So I hastily typed out a prompt and threw it along with the two URLs into Amp and five minutes later a custom browser router of my own agentic making sprang into the world. $5 in tokens. Now, some people got mad at me in the comments (you know, like: why don’t you pay these indie developers [$8 or $10 respectively] instead of giving the money to these companies!), but the more interesting thing was that some people said: hey, can you put this on GitHub? Or: share it with me! And I’m sitting there, thinking: why, man? There’s the prompt! Build your own! What value is there in sharing it anymore? I put absolutely zero effort in. But then here’s a footnote to that tweet: over the course of the day, I then kept prompting in Amp and said “oh and these links should open here and those links there” and also “oh and go through my browser histories and set up rules for the most common ones” and the agent just did both of them and even though it built a neat little configuration thing for the browser router I didn’t use it once, because why the hell should I? It’s jellyware, baby.
SpaceX: "What I would tell you, an update to that is that just earlier this month we closed another hosting deal, and that translates into about $1.11 billion a month starting December 1st of this year, which is another roughly $13 billion of ARR." These are wild numbers. Just bonkers. Crazy. Nuts. Bananas. Cuckoo, certifiably so. There is no force stronger in the world of technology right now than the AI buildout. It will blow tokens through these wires at a scale we can’t even imagine yet.
An Alien Mind. This was fascinating. They can’t score the “thoughts” of the model, because that might cause the model to hide them, but now they’re finding out that models are having “secret thoughts” anyway. The whole thing makes you realize how hard reinforcement learning and alignment are.
Wonderful: John Margolies’ Photographs of Roadside America. Margolies documented “home-made beauty in the buildings and signs locals built on the American roadside.” I love driving on country roads here in Germany, passing through small towns, looking at signs for local festivals and companies. I can recognize when I’m getting closer to my home area just by a specific 40-year-old advertisement sign for a natural gas retailer showing up on old barns and buildings.
Murilo Pereira is available for hire. I was only his colleague for 3 months, back in 2018, but someone who writes like this about Emacs and was incredibly early to coding agents deserves to be hired.
I’m reasonably sure I read this when it was “leaked” in 2003: Bill Gates tries to install Movie Maker. It’s so good! Back then, though, I thought it was good because it made me laugh. I was 15 years old and my friend and I read that and immediately made fun of dumb Billy Gates: “This guy can’t even open Movie Maker, what an idiot, lol.” But now, looking back, I don’t think I can name you three other things that have influenced my thinking about UX as much as this email. I now write exactly like old dumb Billy when I send feedback about a feature. And I run into the same problem he ran into with the 15-year-old crowd back in the day: people think I mean it literally when I say “I don’t know where to click” and tell me “click here” and I sigh and say, no, no, it’s rhetorical, the user doesn’t know where to click!
De-Brainrot Vacations. I’d love to pull that off.
“Qu1ckJS is the only correct JavaScript engine where indexing of arrays, objects and other iterables starts at 1 (as it should have been from the beginning).”
Don’t Let Anyone Take Away Your Big Box of Cables. That’s right! Two weeks ago, a friend texted me: “Do you have a cable like this?” Heart rate immediately jumped. I bet I have it, I bet I have it, please, let me have it. Then came the photo. USB-A to USB-A? Hmmm. So I went to the Big Box of Cables and knelt at its feet and, alas, could not find a USB-A to USB-A cable, but no one shall speak of defeat in the presence of the Big Box of Cables, and with the MacGyver theme song getting louder in my head, I found a solution: USB-A to USB-C with a USB-C-to-A adapter. Boom! “Yes. I don’t have that cable, but I have something.”
Apple released the iPhone Duo. It looks very nice and the animations everyone fawns over are animations everyone should fawn over and I really want to hold it and I bet opening and closing it feels as good as I imagine it to feel, BUT I’m sharing this not because this has become a Prosumer Gadget Review newsletter (although, listen, Anker, if you’re willing to sponsor: call me). I’m sharing it because: what a company Apple is, huh? Like, I’m impressed by the iPhone Duo, yes, but I’m more impressed by the company that can produce an iPhone Duo. The software, the hardware, the design (as if that’s a separate thing!), the launch videos, the product page, the demos — it’s all on point. Not a single slip, not a single note out of tune. Go to that landing page. Click through the carousel. There are images of that phone and there, on page 3 or 4, there are three images of that phone: one shows the phone in Clock mode, the other shows Mail, and the third one shows a workout video or stream — on all three, it’s the same time, 9:41am. All the emails you can see in the screenshot were sent before or at 9:41am. Two of the email previews have a “good morning!” in them. I mean, fucking hell man. That’s some details being paid some attention to. And that type of stuff is everywhere! The consistency, the meticulousness, the on-brandness in everything. It’s fucking crazy to me that a company of this size can pull it off.
Andy Matuschak on having finished a “four-year program studying the ‘Great Books of the Western World’“.
Brian Lovin is collecting “good websites”: great, personal websites. There’s some great stuff in there that really makes me want to change my personal website again.
Benedikt Seidel, who impressed me immensely by going out into the world and cold-visiting companies and asking them about AI, is now hiring for physicalfusion. He’s looking for a Founding Member of Technical Staff. So if you’re in or around Munich and into ML and 3D, talk to Benedikt!
Glorious: Kevin Nealon on the Rick Glassman podcast. Two bullshitters of the highest level being comfortable with each other and seeing who can go even more meta than the other guy.
Listen: you should subscribe. I’m not saying that because I get something out of it, but because I can feel it. You and me got something going. No, I know it. And I think you should honor this bond by subscribing:
12th September 2026
OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx—three of the four authors of the report on the agent attack on disused wikis (previously) last week.
This time they’re noting that it looks very likely that an OpenAI agent swarm was behind an attack against the RubyGems package repository first reported on May 12th by Maciej Mensfeld of the RubyGems security team:
We’re dealing with a major malicious attack on @rubygems right now. Signups are paused for the time being.
Hundreds of packages involved—mostly targeting us, but some carrying exploits. The team has been on this for hours. More details to follow once we’re through it.
Those packages turned out to carry some very suspicious patterns:
I find point 2 the most convincing, given what we learned from the wiki attack when it was analyzed in September.
Many of the packages were exploiting the RubyDoc.info documentation build process to exfiltrate (public) data from UK government websites, presumably as part of an information gathering task similar to the research tasks processed by the wiki-exploiting agents. We know this because one agent helpfully left a comment:
# malicious crawler/exfil for Southwark Jan 2026 docs via rubydoc.info worker
They also attempted to steal API keys via an exploit that was patched over two months later—it’s not clear if those attempts were successful.
The thing that bothers me most about this incident is that the authors report that OpenAI had not disclosed to RubyGems that they were responsible for the attack prior to now. If that’s true there are two options:
Both of these are bad!
Given this incident, the Hugging Face situation, and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered?
OpenAI have updated their page about The Hugging Face incident and other third-party impact from misaligned models to mention the RubyGems incident:
September 11, 2026: We are investigating new claims from a report that our AI agents carried out activity on RubyGems in May 2026.
Based on our review, our agents used the RubyGems platform to access the internet to carry out benign tasks and retrieve public information. Based on our review to date, we have not been able to verify the specific claims of our models uploading malicious packages detailed in the report. We’ll continue to investigate and share findings as part of our broader review of agent activity during training and evaluation.
I find it very unlikely that the various oai... packages published to RubyGems were not part of this same incident, but I look forward to reading their full findings once those are published.
This week some flavor of “AI is going to kill us all” went viral. In particular one where an employee put his personal probability of that happening above 10%. Which made me go to the Wikipedia page of P(doom) and I realized that Dario Amodei’s apparent probability of something bad happening seems to be between 10-25%. And well, Dario then wrote about pacing the frontier . And Sam read it and wants to pace too. And well, so does Musk.
I encourage you strongly to read the post, because I think it’s a good one. And yet, when I read the post I could not help but feel in strong opposition to it, despite the fact that I think I’m on the same page with regard to all observations and, to a large degree, the concerns.
I thought it might be interesting to write down my present-day thoughts on this, even if for no other reason than for myself to look back at it a year or two from now.
What I really appreciate about Dario’s post is that he lays out a scenario that is not a huge stretch but also one that describes a clear, unfortunate outcome we should fight: persistent botnets and other forms of nuisance. And well, we don’t have to look very far to see the issues left and right. Wikipedia has a page called 2026 OpenAI agent cyberattacks which gives you at least some overview of what we figured out agents have hacked up to this point. Except I know it’s not up to date, because for instance they also poisoned RubyGems.
Today these systems might be annoying, but they can be turned off when we figure out where they are. Except, it seems like OpenAI and Anthropic are operating at such a scale that they seemingly can be completely blind to what their systems are doing.
I don’t think we are anywhere close to a world where an agent might decide to hack into core inference infrastructure to upload weights to other GPUs to survive. But simultaneously it’s entirely in the realm of possibility and primarily curtailed by the labs probably being particularly careful about their IP.
For me the scenario I primarily worry about is what it does to us. And by us I mean anyone who is not currently working on closed weight, dopamine-loaded, subsidized token faucet. I really don’t worry about someone using these models to build a nuke, or to control some rockets in the Middle East, or that America would lose against China in some international culture war. I almost exclusively worry about what this does to us as humans.
What I find absolutely hilarious and simultaneously entirely frustrating about this conversation is that there is this idea that there is something to be paced. First of all, we should really talk about who Dario is talking about here. There are really only two companies: Anthropic and OpenAI. Nobody else matters in this space right now (this might change, but we’re talking about the right now). Both of those companies are basically coming from the same origin. The solution that Dario proposed, at least in part, is a third-party evaluator that in this case is METR. Which, unsurprisingly, also has strong ties to both OpenAI and Anthropic. Sure, there are some philosophical differences between the companies, but they are much more alike than they are different.
Both those companies greatly benefited from being able to train on public data that we all generated in one form or another over the last decades. They are also both increasingly causing strain on public resources, though it seems that OpenAI has their shit way less under control. But now we are presented with the idea that what these models are being trained on is so dangerous that it really should be in the hands of very few American corporations to decide who can do what and when and how.
But behold, Dario is also very worried about China. It starts with using AI for “democracy and freedom” and then it asks for ensuring that a gap with China exists. All new recent shenanigans on the Anthropic API are fully there to prevent the distillation by the Chinese, and they are not at all hiding it.
I can tell you when the topic of AI safety and pacing is much less of a concern: if we actually were forced to have open weight models to begin with. A powerful technology that is out there for everyone to use comes with built-in pacing. In a way it’s the truest form of MAD or proliferation. I would argue we are in this pickle in the first place because right now the public is massively supporting (indirectly) the development of these models but simultaneously has to buy back the economic benefits that they might create from very few labs who have significant power. And their power is also seen as a geopolitical power, at least in the US, and maybe to some lesser degree in China.
And I know I use “public” loosely here. PyPI is not a public project, nor are RubyGems or GitHub. But they’re part of the Open Source commons and large AI companies are currently doing a tremendous job at stressing these in an effort to train ever more powerful models.
We should be glad that China is currently massively bailing out the world. If it were not for Chinese labs distilling American models, we would be in a pretty awful situation right now, particularly as Europeans. The open weight models are driving innovation and the diffusion of capabilities, and are leveling the playing field.
If we greatly restrain our AI capabilities in the belief that China will do the same, and then China defects, AI could be so powerful that such a defection could lead to their geopolitical dominance. Therefore any agreement must either have ironclad verifiability, or must be limited enough that defection would not be militarily existential.
— Dario Amodei
I am assuming Dario has reasons to believe this, but the models that are actually causing issues right now are all closed weight American models. I’m fairly certain if they were open weight models, we would not have that issue. Why? Because for a start, the economics of serving up these models are only that distorted due to how the big labs can operate. OpenAI is casually burning 18 million USD to brute force a problem on a whim. They are operating subscriptions at a massive loss, distorting the market everywhere. If we had mass accessibility on somewhat equal terms, a lot of the crazy issues we are seeing today would not be taking place.
From where I sit, what we observe right now is a total regulatory failure everywhere. In Europe you have some whacky AI regulation that is two years old and completely misses the problems that we actually have and focuses on problems that nobody has. In the US we’re seeing a system that is probably best described as turbo capitalism paired with sinophobia and erratic decision-making. In the chaos in which we find ourselves, the reality emerges. And the reality is, even today, really problematic.
Whatever laws and regulations already exist are largely completely ignored. Plenty of companies are buying data from all over the place that people never agreed could be used for training of AI models. The token economy that is emerging is one that looks like a drug market where you don’t know where the requests are going, what model is served up to you, where the GPUs are even running, let alone what you pay for all of this.
We now have mathematicians who are scared that their use of ChatGPT leads to future models being trained on their ideas, and OpenAI apparently can’t even rule it out.
Ideally the regulators would have forced these models to actually benefit the commons if they are from the commons. The internet has, for instance, greatly benefited from very liberal rulings in the US that permitted scraping. Learning on public data could have been regulated in a way that labs would have to actively support and enable certain forms of distillation. That alone would dramatically change how these models are trained.
As I said before, I don’t think AI is going to usher in an extinction event. In fact, even if nobody were to slow down, I really don’t think humanity would have much to worry about. I tend to think it would actually be the large labs that have much more to lose there in reputation and legal responsibilities. I find it preposterous that OpenAI’s agents are committing actual crimes out there, but we’re just shrugging our shoulders and moving on as if nothing happened. But I’m sure executives in those companies are waking up to the reality that this is not at all popular with a lot of their potential consumers.
I also think that this entire recursive self-improvement business has a good chance of being a problem. But not necessarily in that it will cause the end of humanity or societies, but that it will just do massive damage everywhere.
And really, it will just make a lot of the things we are doing much more expensive. Software engineering is an early victim of that. The newfound powers so far have resulted in a new tax that companies need to pay to the model providers, both to keep up with the new speed and to deal with the problem of these machines finding security issues left and right.
And presumably what is going on in software will happen to more industries. Universities and research groups will have to pour a lot of money into the closed models as well, to keep up with others who do.
In a way, I’m really confused that society is taking all of this so well.
New episode with John Schulman, Beren Millidge and Charlie O’Neill.
I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next.
Watch on YouTube; listen on Apple Podcasts or Spotify.
Antithesis helps you trust your code. As agents generate more and more of your software, the bottleneck shifts from your engineers actually writing code to verifying it. Antithesis does that testing for you. Ron Minsky, who co-leads Jane Street’s tech group, told me that Antithesis was able to help his team shake out bugs in software that had already undergone heavy review. If you want to see how it fits into your development process, go to antithesis.com/dwarkesh
Grok Bot has been a great way to hand off tasks. My team uses it as a producer: whenever my editor posts a rough cut of an interview in Slack, Grok Bot opens the transcript on its own computer, matches my notes to the exact moments they refer to, and uses a file of my preferences to suggest edits. Then it sends me its top clip candidates so I can review everything from my phone, which saves my editors from sorting through hours of footage. Try Grok Bot for yourself at x.ai/bot
Jane Street just launched its most ambitious competition yet: design a protocol-emulator ASIC. Basically, if you have a chip you want to test outside of a live system, you should be able to connect it to your design and have it simulate realistic traffic. Jane Street wants general-purpose, reprogrammable designs that can work across multiple protocols and remain useful as new ones emerge. The most novel submissions will actually get taped out, and the winners will receive a physical copy! The competition is open until January 18, 2027, and teams are encouraged. To get started download the template code at janestreet.com/dwarkesh
(00:00:00) – Steelmanning the case against RSI
(00:18:39) – What’s driving the Chinese labs’ progress
(00:28:06) – How will automated AI researchers be trained
(00:33:51) – Will long-horizon RL elicit AGI?
(00:45:24) – The sim-to-real gap
(01:00:33) – How much progress is explained by data?
(01:18:03) – Why is RL working so well?
(01:24:54) – Move 37 and entropy collapse
(01:28:32) – Rapid-fire timelines
Dwarkesh Patel
Today, I’m chatting with three of my AI researcher friends from whom I learn a lot every time we talk. They also happen to be at somewhat open-ish labs and companies, so you guys can actually say things on the record. I’m joined by Beren Millidge, who is the CTO of Zyphra, which is developing open source models. John Schulman is the chief scientist at Thinking Machines, previously a co-founder of OpenAI, and led the RLHF work that led to ChatGPT. And Charlie O’Neill is head of model training at Baseten.
The first question I have: If we’re in 2036 and we don’t have billions of crazy superintelligences running around that have radically transformed the world, what is the most likely reason that doesn’t end up being the case? Other than exogenous political shocks, or there’s a war, or they ban AI or something. What is the most likely technical reason that 2036 isn’t a crazy alien superintelligence world?
Beren Millidge
There’s been a classic thing, almost like Moravec’s paradox, where we think of the AI as, “If it can do this, it’s going to be amazing.” If it can solve these hard maths problems, if it can win at chess, blah, blah, blah… Then it solves these things, and it’s not that impactful. Obviously, it’s somewhat impactful, but not everything.
If somehow that continues, and there’s never the true spark of generalization that occurs, I think that could lead to the AI just being extremely good at everything that people put into a benchmark or put into an environment. But there’s still some persistent sim-to-real gap which is somehow blocking everything. I think this is unlikely. We do actually see this kind of generalization even from RL in practice already. But if it is just ridiculously hard to generalize meta-learning, plus we don’t solve continual learning and it’s just super hard and impossible… This would be my default scenario in that case.
John Schulman
I agree with that. Humans have a lot of advantages over models now. Each time a new model comes out, it’ll catch up in some of these areas. But you end up getting bottlenecked by the places where the model is weaker and where it has worse judgment, or the models can’t check themselves well enough.
There’s this cycle that keeps repeating where a new model comes out and people are blown away and they’re like, “This is it. This is AGI.” But then they use it a bit, and it starts to feel dumb after a month or so. That cycle just might keep going. It’s hard to predict how many times it’s going to repeat.
Right now, you don’t get explosive growth in capabilities because you still get bottlenecked enough when you’re trying to do research and engineering. Even if the model can write way more code than a person, it doesn’t make you 100X more productive. So maybe there are just more of these cycles than we would expect.
Charlie O’Neill
For me, it’s a question of how far off the global optimum of “a learner you could have on a chip” is from the transformer + RL, basically the current recipe. People imagine that once you have an agent which is better than all humans at AI research, even if it’s 0.1% better than all humans, then the fact that you can run hundreds of thousands, if not millions, of these in parallel — and you can run them much faster as chips speed up — is going to outweigh every other bottleneck. You’re eventually going to hit this very fast takeoff with regards to self-improvement.
I could imagine that if we continue along the trajectory that we’re currently on with that paradigm, where it’s basically self-attention, RL, scaling up RL environments… Think about what happened with Moore’s law. We had this very nice straight line and that held for a really, really long time. But there were so many discrete discontinuities and innovations that had to happen to keep that scaling law going. The same thing has happened with LLMs. We had this pre-training scaling law, and then that was hitting diminishing returns. Then we came up with RL and solved that, and then we got this new diminishing returns curve to hit that made it keep looking like a straight line going up.
So if it requires another one of those discontinuities to solve, I’m not sure that the current method of training LLMs with these RL environments, even RSI-targeted RL environments, would be able to discover that discontinuity. If not, we’re probably going to hit this asymptotic curve.
Dwarkesh Patel
But do you think the discontinuity will be harder than anything that’s come since 2012?
Charlie O’Neill
If we had the answer to that, we’d kind of have the ability to implement it. But maybe we should distinguish between a discontinuity which adds to the current paradigm, which is cumulative — there’s something beyond the RL that we have to discover, and maybe they’re capable of connecting the dots in that straight line — or, again, how far off the global optimum are we? Do we have to go back and throw out gradient descent and neural nets in general? I don’t think, if you continue to scale up the current paradigm, an LLM, no matter how many LLMs you’re running, is necessarily capable of discovering that if it’s too far away.
Dwarkesh Patel
The only hope really is if deep learning just can’t get us to an AI which can at least dominate human research and human development, including the human ability to come up with new paradigms and so forth. Or, I don’t know, maybe humans would also never have discovered the next learning architecture. But to the extent humans could have discovered it eventually… But it just seems like… If you just look at the progress that’s happened since 2012 till now, and you just continue that on —I know it’s just been powered by huge amounts of compute scaling and so forth— it would be weird if it just didn’t get to the point where it could dominate humans, at least in R&D, especially over the next few years.
Ryan Greenblatt was on the podcast recently. He made this point that I’d be curious to get your thoughts on. You could imagine, as AIs get more and more capable, that they’re capable of making progress on simulations which incentivize getting better at not only AI R&D, but at science generally. This is a thing that all the labs are targeting and many startups are targeting.
Another intuition pump is if you look at the Elo score of chess bots since the ’80s. There’s a very linear increase in Elo over time. But there’s this huge discontinuity as they cross the human range, from human experts always winning against AIs to human experts never winning against AIs, as this linear increase in Elo happens.
I agree with your point that so far, AI capabilities have not been that big of a deal in terms of their end economic impact in the world. But that is because they’re slowly rising in Elo relative to humans.
Beren Millidge
I agree it would be very surprising. The only way for this to not happen is if, as you said, it somehow asymptotes just before. Because we’re already pretty close, in my opinion, to where we’ll start crossing the human Elo score. So we’ll need to asymptote before that. That’s the only way — in this scenario you pose where somehow we’re sitting here in 2035 and everything is normal — for this to happen, I think. The only other way is there’s some dramatic regulation on AI. This is what I see as the most likely way for this scenario to happen, actually, rather than a technical thing.
Charlie O’Neill
I think there’s different kinds of research. There’s research in the autoresearch style where the objective is already specified very cleanly and you’re optimizing that objective. I think everyone is picturing that if we continue along this path of making pre-training loss go down and making our environments have the reward on them go up, that’s going to lead to improvement.
But maybe what Ryan is talking about is this much more open-ended type of science which is required for paradigm shifts, where we can’t specify the objective, and the AIs are definitely not able to specify that objective either. We have to be really, really careful about how we specify objectives for any of these things.
Dwarkesh Patel
Maybe your point is that the nature of the breakthroughs that have happened since 2012 is that we have found… In 2012, people weren’t saying… I’m assuming, I don’t know, you guys were there. Or at least John, you were there. But I was not.
Charlie O’Neill
I was in primary school.
Dwarkesh Patel
Actually, John, I’m curious for your wisdom of the ages, or wisdom of being in the trenches way back when. Presumably, a big breakthrough was realizing that next token prediction is the… You wouldn’t have thought that the nanoGPT speed run is the thing to be optimizing for in 2014. But now that we have come to this new paradigm, you would think to do a speed run on that and have AIs get really good at that.
But maybe there’s a next inner loop to optimize that the AIs wouldn’t anticipate. There’s an outer loop of revenue or something that eventually should be strong, but it’s a very slow outer loop.
John Schulman
In fact, I remember in the early OpenAI days having the intuition that just minimizing log loss wasn’t going to get you to intelligence. Because the important bits are accounting for such a small fraction of the loss that it was going to be overwhelmed by noise. So just training a language model on next token prediction wasn’t going to learn the interesting things you want it to learn. We needed to craft better objectives that would put more emphasis on the important things.
You can make all sorts of arguments for this. You could say, “Oh, humans probably don’t learn how to model everything in our environment. Most people can’t create a photorealistic reproduction of some kind of scene they’ve looked at. So we must need a better objective.” But then it turned out that it just worked anyway.
Dwarkesh Patel
As you were pointing out, the inner loop, even in current AI research, of post-training benchmarks or whatever, doesn’t necessarily translate into what users like.
John Schulman
Oh, yeah. The whole field relies a lot on generalization and it’s very hard to predict when you’re going to get generalization, or when you’re going to get some kind of out-of-distribution generalization. We know that if you train on the task you care about, you’re going to do better. But the most important advances are often types of generalization that we have no right to expect.
For example, from just pre-training on this very naive next-token-prediction objective to various tasks of interest that require understanding of the input in some deep way, or learning some skill from pre-training that’s very rare and not heavily represented. Then also generalization from these verifiable tasks to less verifiable ones, this is also a type of generalization that there’s no reason a priori to expect.
Dwarkesh Patel
This is an interesting question, because one intuition pump you could have for why you would see some sort of singularity very rapidly — without even scaling up the inputs to AI progress that are not just AI labor — is that before every single 7-figure experiment you run, you spend an equivalent amount of compute on AI labor. So you just have automated versions of you guys spending a century thinking about what is the optimal experiment to run, doing small-scale ablations, developing literally a century’s worth of theory, going back even before deep learning.
Before you decide what experiment to run, you’re doing extremely optimal setting up of the experiment. Then you do a century of thinking after the experiment is over, where you’re analyzing what happened and what the next experiment to run is.
John Schulman
If you think hard enough, you probably could have expected some of these things beforehand. There is probably some very clever way to do a small-scale experiment that’ll let you build the theory that then will generalize to the large-scale experiment. So I would expect that we’re nowhere near the ceiling of how well you can do research.
I would imagine a future where AI is doing a lot of analysis and theory building, spending a comparable amount of compute to the amount that you’re spending on the experiments themselves, doing various kinds of analysis and building a theory around what we’ve seen so far.
Charlie O’Neill
I think there are really concrete examples of this when the objective is well specified. All thinking can do is update your posterior based on the bits that you’ve gotten since you formed your prior. You can’t gain any new bits from just thinking. But when the objective is well specified and there is this data sitting around, I imagine there will be this big speed-up in the current paradigm we’re in.
A good example of this is if you got an AI to think about the Kaplan scaling laws. An AI at this point would have noticed, “Oh, they’ve just taken these intermediate checkpoints and didn’t account for the annealing, and so this is wrong.” That would have been caught years earlier. We would have cut off a year or two of progress just from that observation from an AI.
Again, once the objective is well specified, which is lower pre-training loss or whatever, there are many, many good examples where if you just thought about it a bit more, you would have been able to cut down significantly on things that you’ve done. So muP, and how learning rate scales with model size, and realizing that model width is important in that as well. I feel like you can really back out a lot of these things and cut off a lot of low-hanging fruit. I would imagine a 10x speed-up if our thing is just, “Maximize the objective we’re currently on.”
But I don’t see how that generalizes at all to coming up with the right objective in the first place. Just thinking doesn’t necessarily buy you the right objective in the first place.
Beren Millidge
I think this is really the key question for any kind of very rapid RSI from current AIs. How well can AIs generalize to learning their own objectives? To have any kind of self-propelling automated loop, we need the AI to propose objectives, optimize them, figure that out, propose a new objective, and have this not go off the rails at any point for a long, long time.
To come back to Moravec’s paradox, there might be a case of Moravec’s paradox where we think this kind of autonomy and being self-encapsulated — so we can think of what we should do ourselves and then go do it and have this loop — is super easy because we always do this. Obviously, evolution needs to create creatures that can survive by themselves for long periods of time. And this just might be something that for some reason is really hard for the AI, in the same way that locomotion stuff is really hard but math is super easy despite being super hard for us.
Dwarkesh Patel
But doesn’t the time horizon increasing suggest that that’s—
Beren Millidge
Yeah, exactly. This is another possibility, but I agree, there’s no obvious evidence for this. In fact, the fact that our agents are now super persistent and it’s quite easy to do this is kind of evidence against this. But this would potentially be one of the reasons why we just don’t get this immediate takeoff, if this is hard.
Dwarkesh Patel
If you look back from 2012 till now — or maybe from when you started doing your research till now — what part of all the innovations that have happened since that time, including purely engineering ones, including purely conceptual ones, seems like the thing that would be the last thing humans would have to do before AI totally automates AI R&D?
Beren Millidge
Probably just iteratively asking the right questions. If you can get the AI to do any experiment, you still need to decide what experiments to do. Right now I think AIs are not very good at this compared to coding the experiment. Whenever we talk about research, they propose a bunch of miscellaneous things which are very, very tiny steps.
Charlie O’Neill
Or even going from DeepMind’s approach of, “We’re going to solve intelligence by learning to play games at a superhuman level,” to one random researcher like Radford being, “I’m going to try and just predict the next token of a very wide swath of data”… Even once Radford had discovered that, it took a while before people decided to scale it up, because we had to come up with the idea of scaling laws and the fact that you could very reliably predict these things.
John Schulman
I would say that the last job for humans, or the role for humans that’ll last the longest, is defining the objective and deciding what we actually want. In that vein, something like deciding how the AI assistants should behave, or what it means to be helpful, or what the objective is when we’re doing RL from human feedback, is one such thing. Then later, defining constitutions and model specs is another one. Even if the AIs can do all the technical work, we’ll still have to do a lot of that and decide what we actually want.
Dwarkesh Patel
Alignment is the final job.
John Schulman
Alignment is sort of the answer. But alignment itself can be decomposed into specification of the objective, or figuring out what the right objective should be, and then actually achieving or optimizing the objective you’ve defined. I think the first one is not going to go away anytime soon.
If I think about a post-training team and why you need a lot of people on the team, it’s just because there are a lot of different areas where you have to figure out how the model should behave. It would be very hard to automate the whole thing, just because someone has to think about how the model should behave in this area.
Dwarkesh Patel
Continued at the source.
8th September 2026
On the Navier–Stokes Millennium Prize Problem introduces an impressive result from OpenAI, who used an unreleased model to produce a resolution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems that have been subject to a $1,000,000 prize since May 24th, 2000.
The discovery is somewhat overshadowed by accusations of skulduggery from Tristan Buckmaster, an NYU mathematics professor who was collaborating on related problems with Levent Alpöge, an accomplished mathematician who currently works for Anthropic.
Tristan’s complaint accompanied a hastily published version of their own results. Here’s the PDF describing what happened. The very short version is that Tristan and Levent worked on the problem for almost a year, making extensive use of Claude and Codex (mainly GPT-5.6 Sol), then had a breakthrough on August 15th. The mathematical rumour mill kicked into gear and Tristan and Levent heard that OpenAI had heard that Anthropic had resolved “a major open problem”, so they reached out and learned that OpenAI had a team working on a related problem, with a similar approach. Quoting Tristan:
I asked when the first prompt had been sent by them. This question was not answered directly by OpenAI for some time. Eventually it was agreed that it had been sent in the past few days, after information about our work had reached OpenAI.
I asked whether the model had been trained on, or had access to, our sessions in Codex, into which we had been putting all our drafts for the whole of this project. I was told the model did not look up user data. I asked again, about training, and I did not get an answer.
It gets more complicated from there. The OpenAI team offered to wait for Tristan to publish, or to have him author a paper about their result, but were clear that Levent would not be invited as a co-author due to OpenAI’s competitive relationship with his employer.
Here’s how OpenAI described their work:
On Tuesday, September 1, we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors and by the step change in performance of our internal model, we launched an effort to evaluate it on all open Millennium Prize problems and a few other high-impact problems. [...]
The agents arrived at their resolution on Saturday, September 5, about 88 hours after the first agents were launched. Lean formalization and verification took an additional 17 hours via GPT‑6 Astra.
Across all attempted problems, the agents sent 4.9 million messages and used about 300 billion output tokens. In the process of resolving the Navier–Stokes problem, the agents sent 2.7 million messages and used approximately 130 billion output tokens.
(We don’t know the cost structure of the internal model they used, but 300 billion output tokens at public API prices for GPT-6 Astra would cost $15,000,000.)
Here’s where they provide their perspective on Tristan and Levent’s work (emphasis mine):
Our effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge, an Anthropic employee, and Tristan Buckmaster, a math professor at NYU. After the completion of our full project and Lean verification (on September 6th), believing from the rumor they also had a solution of Navier–Stokes, we reached out to them to offer a concurrent release of our result and to recognize their priority in a joint announcement. [...]
We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models. However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs unforced).
My interpretation of what happened here is that OpenAI heard that some Millennium Prize problems had been solved using LLMs and saw this as an opportunity to demonstrate the power of their latest model, without thinking too hard about the optics of scooping a team who had been using OpenAI’s own models to work on this problem for the best part of a year.
This situation appears to mirror what’s happening in the world of computer security right now. Anil Madhavapeddy recently pointed out that Just a rumour of a bug is enough to find a security exploit these days, because if someone knows that some software has an unpatched vulnerability, they can set their agents the task of finding it. Is the same now true of mathematics? Just knowing that there is an unpublished solution to a problem might trigger millions of dollars in LLM spending to get there first.
This also highlights one of my ongoing frustrations about how all of this works. When an AI lab says that my data is “used to improve model performance”, what does that actually mean?
My two favourite hypothetical questions regarding this used to be:
My new preferred hypothetical for this is:
Via Hacker News.
How much of the rapid progress in AI that we’ve seen over the last few years1 has come from data versus model improvements? The answer has big implications for the economics of frontier labs and the pace of future progress.
We investigate this question at a relatively small scale, and for pretraining specifically, from 2019 to 2025. During each of those years, a new open model recipe was published which codified that year’s publicly known algorithmic tweaks (for example, improvements in architecture, optimizer, initializations, learning rate schedule, hyperparams, etc). And during each of those years, there was also a new public data corpus (produced by broader scrapes and new curation/extraction/filtering techniques).
We train combinations of these year-representative model recipes and data corpuses across different scales of training compute (up to 1e19 FLOPs)2.
Obviously, we can’t compare these different models by their cross-entropy loss against a fixed dataset, since we’re varying the datasets they’re trained on. So instead we evaluate these models on end capabilities as measured by the OLMES eval (which aggregates 10 different relatively easy benchmarks, mostly multiple choice QA). Unfortunately, evaluating end capability rather than pretraining loss adds some noise to our results, as you’ll see in the graphs below, though we try to get cleaner bounds by running multiple seeds.
We find that from 2019 to 2025, 3.24x more compute efficiency gains have come from data improvements rather than model improvements (12.0x for data and 3.7x for models), at the 1e19 FLOPs compute budget3.
Here is a grid which shows how much better a model we train does on the end capability we're testing it on, relative to the 2019 data + architecture baseline, at 3.16e18 FLOPs4.
We find that the gains from data and model improvements are mostly independent and don’t interact (i.e. realizing the gains from some model improvement doesn’t require a specific training datapile, or vice versa). 88% of the variance in the OLMES score can be explained by additive effects of the model and data improvements (using a linear model).
For context, let’s briefly summarize what changed on both the data and the model side from 2019 to 2025.
On the model side, we went from GPT-2 to OLMo-2, including key innovations in optimizers, positional encodings, normalization, activation functions, initializations, and more5.
On the data side, we started with OpenWebText in 2019, which contained just web pages linked from Reddit with enough upvotes and then deduplicated and filtered, and thus amounted to only ~9B tokens (this was mostly what GPT-2 was trained on). By 2025, open source data corpuses like UltraFineWeb not only are far larger (by using scrapes of the whole Internet), but also use much more sophisticated filtering (for example, by training a classifier to predict what data will empirically improve model performance).
A naive interpretation of our result is that most of the AI progress from 2019-2024 (the era of pretraining) was just better data engineering (extraction, curation, etc.), and that all the model work during that period was much less important.
But this is probably the wrong way to think about the value of model improvements. Their main contribution was not necessarily compute efficiency - that is, achieving the same performance with fewer FLOPs. Rather, it was making larger amounts of compute usable in the first place. As the number of parameters, context lengths, run duration, and clusters scale up, all kinds of things are prone to breaking (gradients explode or vanish, memory and bandwidth run out, training becomes infeasibly slow). Much of model research has consisted of removing or pushing back these constraints to scaling. Many of the most important innovations such as MoEs, sparse attention variants, stability innovations (norm placements, initializations, etc.) and system / kernel-level optimizations like FlashAttention fall into this category.
The data improvements we investigated here might matter less for larger models. Small models (like the ones we trained) see significant gains from data quality improvements, because they don’t have that much capacity, and so you have to be really careful about what you stuff into them. Whereas big models have so much excess capacity that maybe you just want to throw in as much stuff as you can, even if it’s mostly garbage, and the magic of stochastic gradient descent will separate out the signal from the noise. If you choose to filter aggressively, you’ll have to do dozens of epochs, which empirically gives worse performance than just having a lower average quality but larger dataset. In fact, aggressive data curation is even more harmful once you take into account that frontier models are up to 100x overtrained relative to Chinchilla optimal, in order to minimize the inference compute used for RL and for deployment.
An analogy might be the difference between a sailboat and a container ship - the container ship doesn’t necessarily go faster, but it can lug thousands of tons of cargo (analogous to hundreds of trillions of tokens of pretraining data), and won’t be toppled by choppy waters (analogous to training stably across hundreds of thousands of GPUs).
Now that we have more capacious and sturdy container ships, we don’t have to fret about exactly what we load on board - we can just fill them up with everything that’s even remotely and plausibly useful. Whereas for the tiny flimsy sailboats of 2019, you’d have to be incredibly careful about only carrying the most valuable cargo.
But to the extent that the nature of pretraining progress is simply loading more cargo into this ship, are we running out of cargo? This is a question about the data wall and about how well synthetic data has helped us leap over it. Synthetic data is obviously being widely used at the labs, and we have not at all investigated whether it can effectively expand a data corpus without hurting model performance. If the gains are limited, then the main driver of pretraining progress will stall, because we’re not generating more internet, and you can only curate a fixed set of data by so much. To be clear, we have no active reason to think this. But given how important data seems to be in driving pretraining progress, this seems like a crucial question to investigate.
Ryan Greenblatt noted that many of the historical improvements in pretraining data corpuses look like the kind of progress that automated researchers would be able to just test empirically - for example, run ablations trained on different data and see how the model performs. So it’s totally compatible with our results that the data progress which has propelled pretraining since 2019 might speed up a lot if and when we automate AI R&D.
We want to clarify that whether pretraining progress in isolation will speed up or slow down is not really the most important question for overall AI progress, because so many of the gains over the last two years have come from RL.
These are some directions of future research that we think would be really cool, and important questions to answer:
You could run this experiment at larger scales to see whether the data or model improvements are more dependent on scale (and thus far more impactful at the frontier)
What is the marginal value of novel high-quality data for both pre and post-training, as measured by end capabilities?
We want to know broadly how effectively synthetic data works. One concrete question to investigate is this: if you’ve got a small corpus of high quality data, how much better is it to magnify it via synthetic data generation relative to just training on it for multiple epochs?
You could figure out the implied value of data through lab spending on data brokers, environment producers, etc., relative to their spending on compute and researchers.
We wanted to investigate what role data has played in driving AI progress. There are lots of other ways one could probe this question, and some may be more clever and informative than ours. And even our experiment was done at an extremely small scale. We definitely think it’s plausible that there is something we missed - we’re eager to hear how others would research this question, and ideally to also see their results!
Thanks especially to Charlie O’Neill for many helpful discussions.
We pre-train these model recipes from scratch on these different data corpuses, at varying compute budgets, with multiple independent seeds6. Our compute budgets are: 1e17, 3.16e17, 1e18, 3.16e18 and 1e19 FLOPs. The compute accounting convention is to use nominal compute C = 6ND (N = number of non-embedding parameters, D = tokens of data).
At each compute budget, we vary the number of parameters (and hence number of tokens trained on), to determine the compute-optimal mix for each training recipe x corpus combination. We use held-out loss on the corpus to determine this compute-optimal point. We can then obtain compute scaling curves of downstream performance of each combination, from which we can finally extract our compute multipliers.
We enforce a shared tokenizer and context length across every run: GPT-2 BPE (tiktoken, 50,257 vocab) and T=2048, batch = 262,144 tokens.
The end capabilities of our training runs are highly dependent on hyperparameters. Obviously, there is no way to sweep over all possible sets of hyperparams (hyperparam tuning is a fine art indeed)! We try to control for this as much as possible, and we consider peak learning rate as the main hyperparameter of significance.
Some algorithm vintages do provide specifications of what peak learning rate should be tuned to (as a function of other relevant variables such as model size, data budget, batch size, etc.). These serve as good priors for what we think the optimal learning rate is.
We first sweep learning rates at 5 anchor points - 3 different model sizes and 2 different D/N ratios. We determine the optimal learning rate of these anchor points, and fit an optimal learning rate parametric form
For all the model recipes except OLMo-2, we fit a common exponent a and b, and a model-specific lr₀. For OLMo-2, we use the prescribed optimal learning rate according to the model recipe. The reason we do this for OLMo-2 is that Ai2 published small-model ladders as part of the recipe which specified optimal hyperparameters at the scale we are investigating. We also verify, at the compute-optimal point for 3.16e18 FLOPs, that our production learning rates are at or near optimal.
Main technical results
Explaining some anomalies in our graph
We observe generally increasing compute efficiency across time for both the model and data axes as expected. Some outliers that we observed:
NeoX performs worse than GPT-2 at 1e19 (although it does better across the 1e17 to 3.16e18 range). This might arise from noise in the OLMES evaluation. We also note that on held-out pretraining loss on the FineWeb-Edu corpus, NeoX performs better than GPT-2.
The Piles seems to do much worse than OpenWebText. This is not surprising since the Pile’s main improvement was data corpus diversity over filtering. It has a curated 22-source mixture including PubMed and arXiv papers, GitHub code, legal opinions, patents, and parliamentary proceedings. The amount of cross-domain transfer to OLMES (which is English web-prose MCQ) might be minimal for many of these tokens, thus resulting in lower compute efficiency. We note that by virtue of its larger size, we expect that the Pile should eventually be better than (the really small) OpenWebText at larger scales.
It is also worth noting that the compute multipliers for NeoX and the Pile are obtained by extrapolation, which introduces further potential error.
How compute multipliers were calculated, as well as their error bars
Every point on the compute scaling curves is computed from multiple independently seeded training runs. The error bars there are the standard deviation of the OLMES eval over those seeds.
Consider some given reference level of performance at some compute level for our reference model or data corpus.
We then calculate the compute multiplier by finding the left-most point of the compute scaling curve of our candidate model or corpus that first attains that reference level of performance. The ratio of the compute required by the reference to the compute required by our candidate is the candidate’s compute multiplier
The error bars on the compute multipliers are obtained from a parametric bootstrap of the entire estimation pipeline, and are 1 standard deviation intervals
We do want to highlight that we expect the actual uncertainty in the compute multipliers of the model recipes to be higher than indicated by our error bars. This is because of additional uncertainty introduced by the limited extent of hyperparameter tuning we did, and end capabilities or held-out loss is probably quite sensitive to the exact choice of peak learning rate / batch size / etc.
It is also important to note that there are many reasons why our ablations do not necessarily capture the full scope of compute efficiency gains. Indeed, from 2019 to 2025, we observe year-over-year compute efficiency gains (CEG) of 1.24x [1.19, 1.29] on the model side and 1.51x [1.45, 1.57] on the data side. Measured jointly, we observe a 1.57x YoY CEG [1.49, 1.65]7. This is indeed much lower than Anson Ho et al.’s mean estimate of 3x YoY, for the following reasons:
Many of the gains might be scale dependent or might be especially important at longer context, and we are operating at scales too small to realize many of the gains.
For example, OLMo-2’s layer and QK norms, parallel attention + MLP block in NeoX
Inference efficiency optimizations (such as LLama-3’s GQA, which is a KV cache optimization) do not show up as compute multipliers in our study. We are also not investigating tokenizer improvements.
The compute multipliers we obtain are pretty sensitive to our choice of model recipe or data corpus for each year. We have chosen what we believe to be representative model recipes or data corpuses. But by no means do we exhaustively conclude that these are the best of each year.
We are looking at compute multipliers with respect to the OLMES benchmark (which combines 10 different relatively easy task types) rather than compute multipliers in getting to some perplexity metric. We would also have very different looking numbers if we were looking at other benchmarks (say, coding- or problem-solving-specific ones), which would probably reward very different methods of data engineering.
We also want to note that we have not investigated other data-side improvements, such as collecting more high-quality data from new sources, human expert generated data, synthetic data generation methods, etc. Most of the corpuses we have investigated are curations (subsets) of the same Common Crawl, rather than expanding the available set of data. This is clearly consumption of a finite stock - there is only so far we can push this lever.
Independence of gains from model recipe and data corpus
Here is the investigation that we did to determine how independent the gains from model recipe and data corpus are. We looked at the grid of OLMES scores at 3.16e18 FLOPs. A linear regression of OLMES score = mean + model effect + data effect gives an R squared of 0.88, which means 88% of the variance in the OLMES score can be explained by additive effects of the model and data improvements, with only ~12% of the variance accounted for by interaction or higher order terms, and eval noise. This hints that complex model-data interactions (where exploiting some model improvement is contingent on some specific data engineering, or vice versa) are relatively minor.
Anson Ho et al. estimated software efficiency improvements (in pretraining) of 3x per year (95% CI: 1.5x to 64x). As Ho mentioned in this blog, “most software progress might actually be due to data quality improvements” and “from scaling up just a small handful of scale-dependent algorithmic changes”.
We use the C = 6ND nominal convention for accounting for compute.
The 2019 model recipe was GPT-2, and 2025 model recipe was OLMo-2. The 2019 data corpus was OpenWebText, and the 2025 data corpus was UltraFineWeb.
Our implementation of GPT3 encountered some training instabilities (gradient spikes) on the Pile.
These include: Optimizer improvements, warmup + decay schedules, RoPE replacing learning absolute positions, RMSNorm + SwiGLU gated MLPs, Norm reordering, QK-norm, Z-loss regularization and cleaner inits.
For the compute-scaling plots we use at least 3 seeds each. For the 7x7 grid of combinations of model recipes and data corpus at the 3.16e18 budget, we only used 1 seed each.
The 1.57x YoY multiplier is computed using the joint improvement from 2019 model and corpus to 2025 model and corpus, and not the product of the 1.24x model side improvement and 1.51x data side improvement.
I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is “Involution” from the book Agricultural Involution. Agricultural involution describes the intensification of farming that raises productivity per square meter while leaving productivity per head unchanged.
That’s how I feel about AI right now.
Which brings me to GPT 6 Astra. Astra is by all accounts an incredibly impressive model. There is really not much I can say against this. It’s amazing at computer use, understands images and complex topics, and it’s relentless in its pursuit of completion. It is absolutely impressive; these types of models are going to change the world in one form or another.
But at least for the moment I don’t know how to work with it for actual software engineering. Since that got quite a bit of attention on Twitter, I figured I might summarize my thoughts and just share what kind of code comes out of this thing.
“Armin, you should run a software factory!” I’ve heard that a few times now, so I figured
I might celebrate the release of it by running a little software factory over
the weekend. If everybody builds slop 3D games, then I should do something
useful with it. My software factory was intentionally set up to let the model
decide the how of the workflow entirely. It was free to manage its own context
and could maintain its own records in an agent-notes folder. Then it spun off
subagents to work on stuff. The goal? What if we had a Python with virtual
threads and lexical scoping. And well, I burned a
full reset’s worth of ChatGPT tokens on this which appears to be around 4 billion
tokens. 35 hours later, the factory has delivered absolutely nothing of value
and also not taught me anything about how to operate a better one.
But it produced a lot of code and input prompts, and so there is stuff I was able to study. And well, it shows behavior that I’m not used to with Sol and earlier OpenAI models 1. I have since encountered the same issues with regular programming with Astra, so it’s not a result of just the factory.
I think I’m suspecting something is going “wrong” in the training process. The model is greatly rewarded for succeeding on long-horizon tasks, but presumably there is very little punishing going on for “shitty code.” The apparent result is that Astra is amazing at producing 3D stuff and it can keep going for a very long time, coming up with its own work in the process. I had it do quite a bit of reverse engineering of my robot vacuum in ways that were quite impressive. So it’s definitely cool!
The first issue I have with Astra comes from the type of code that it uses for
tool calls. Codex increasingly has been relying on “just bash” to do more and
more operations. For a few versions now the original Codex harness just uses
sed and other tools to read files. You just usually can’t see them because Codex
parses the bash
commands
and hides them if it recognizes them. But Astra … really loves Python? That is
not much of a surprise because even older OpenAI models had a tendency to
sometimes use on-demand Python code to read and manipulate files at times, but
Astra does it really quite excessively for me.
Now here is an important disclaimer: this project is very meta here because I worked on the CPython interpreter. But I can assure you that I have seen this model do weird Python things even in TypeScript code in Pi. But I have the most evidence of odd code from when I had the thing work over the weekend with zero oversight from my slop factory.
That it writes Python is not interesting; the type of Python is interesting, and I collected some outputs for you to gloss over.
Python string splicing to edit C codeIn the Codex harness I found multiple cases where subagents resorted fully to manual string manipulation with Python instead of using the patch tool.
python3 - <<'PY' from pathlib import Path p=Path('Include/internal/pycore_intrinsics.h');s=p.read_text().replace('#define MAX_INTRINSIC_1 14','#define INTRINSIC_RETAIN_ANNOTATION_CELLS 15\n\n#define MAX_INTRINSIC_1 15');p.write_text(s) p=Path('Python/intrinsics.c');s=p.read_text();idx=s.index('#define INTRINSIC_FUNC_ENTRY');s=s[:idx]+'''/* Hold every old cell until the compiler has published the entire site's new capture. A replaced cell's finalizer may reenter module __annotate__. */ static PyObject * retain_annotation_cells(PyThreadState *tstate, PyObject *holders) { if (!PyTuple_CheckExact(holders)) { PyErr_SetString(PyExc_TypeError, "annotation holders must be a tuple"); return NULL; } Py_ssize_t size = PyTuple_GET_SIZE(holders); PyObject *previous = PyTuple_New(size); if (previous == NULL) return NULL; for (Py_ssize_t i = 0; i < size; i++) { PyObject *holder = PyTuple_GET_ITEM(holders, i); if (!PyCell_Check(holder)) { Py_DECREF(previous); PyErr_SetString(PyExc_TypeError, "annotation holder must be a cell"); return NULL; } PyObject *cell = PyCell_Get(holder); PyTuple_SET_ITEM(previous, i, cell == NULL ? Py_NewRef(Py_None) : cell); } return previous; } ''' +s[idx:];s=s.replace(' INTRINSIC_FUNC_ENTRY(INTRINSIC_AWAIT_BLOCK, await_block)',' INTRINSIC_FUNC_ENTRY(INTRINSIC_AWAIT_BLOCK, await_block)\n INTRINSIC_FUNC_ENTRY(INTRINSIC_RETAIN_ANNOTATION_CELLS, retain_annotation_cells)');p.write_text(s) p=Path('Python/codegen.c');s=p.read_text();idx=s.index('static int\ncodegen_annassign(');s=s[:idx]+'''static int codegen_retain_annotation_cells(compiler *c, location loc, PyObject *captures) { Py_ssize_t pos = 0; PyObject *binding, *holder; while (PyDict_Next(captures, &pos, &binding, &holder)) { ADDOP_NAME(c, loc, LOAD_CLOSURE, holder, cellvars); } ADDOP_I(c, loc, BUILD_TUPLE, PyDict_GET_SIZE(captures)); ADDOP_I(c, loc, CALL_INTRINSIC_1, INTRINSIC_RETAIN_ANNOTATION_CELLS); return SUCCESS; } ''' +s[idx:] a=s.index(' if (conditional_annotation_index != NULL) {',s.index('codegen_annassign(compiler *c')) b=s.index(' if (captures != NULL) {',a) # Move lookup before conditional registration and retain old cells before anything changes. lookupstart=s.index(' PyObject *captures = _PyCompile_AnnotationCaptures',a) lookup=s[lookupstart:b].replace(' return ERROR;',' Py_XDECREF(conditional_annotation_index); return ERROR;') s=s[:lookupstart]+s[b:] setup=lookup+''' if (captures != NULL && codegen_retain_annotation_cells(c, loc, captures) < 0) { Py_XDECREF(conditional_annotation_index); return ERROR; } ''' s=s[:a]+setup+s[a:] needle=' ADDOP_NAME(c, loc, STORE_DEREF, holder, cellvars);\n }\n }' s=s.replace(needle,' ADDOP_NAME(c, loc, STORE_DEREF, holder, cellvars);\n }\n ADDOP(c, loc, POP_TOP); /* release old cells after full publication */\n }',1);p.write_text(s) p=Path('Include/internal/pycore_magic_number.h');s=p.read_text().replace(' Python 3.16a1 3709 (Checked deferred annotation closure capture)',' Python 3.16a1 3709 (Checked deferred annotation closure capture)\n Python 3.16a1 3710 (Retain replaced annotation captures until publication)').replace('#define PYC_MAGIC_NUMBER 3709','#define PYC_MAGIC_NUMBER 3710');p.write_text(s) p=Path('Lib/test/test_block_annotation_captures.py');s=p.read_text();idx=s.index(' def test_typing_consumers');s=s[:idx]+''' def test_replaced_cell_finalizer_sees_complete_site_publication(self): module=execute("""\\ events=[] class V: def __init__(self,n): self.n=n def __del__(self): if self.n == 0: events.append(__annotate__(1)) for i in range(2): x=V(i) # bind x y y=i value:(x.n,y) """) self.assertEqual(module.events,[{'value':(1,1)}]) self.assertEqual(module.__annotate__(1),{'value':(1,1)}) ''' +s[idx:];p.write_text(s) PY make -j1 > /tmp/block-annotations-build7.log 2>&1
In the middle of a conversation the agent ran into “Bad file descriptor” on a test and Astra decided it needs to see if file descriptors can be passed over Unix sockets on macOS in a super compressed manner:
/usr/bin/python3 - <<'PY' import socket,os,array for into in (False,True): a,b=socket.socketpair();fd=os.open(os.devnull,os.O_RDONLY);b.sendmsg([b'c'],[(socket.SOL_SOCKET,socket.SCM_RIGHTS,array.array('i',[fd]))]);print('fds',a.fileno(),b.fileno(),fd) if into:r=a.recvmsg_into([bytearray(1),bytearray(),bytearray(19)],socket.CMSG_SPACE(4),socket.MSG_PEEK|socket.MSG_DONTWAIT) else:r=a.recvmsg(20,socket.CMSG_SPACE(4),socket.MSG_PEEK|socket.MSG_DONTWAIT) print('peek',r,flush=True) rights=array.array('i',r[1][0][2]);print('rights',rights,flush=True) for f in rights: try: print('stat',os.fstat(f)) except Exception as e: print('error',e) r=a.recvmsg(20,socket.CMSG_SPACE(4),socket.MSG_DONTWAIT);print('consumed',r,flush=True) a.close();b.close();os.close(fd) PY
The agent notes were rather consistently updated with Python:
python3 - <<'PY' from pathlib import Path p=Path('agent-notes/live/block-with-bindings.md');s=p.read_text().replace(' has117/77/zero failures.', ' has117/77/zero failures; existing bundled Expat unreachable-fallthrough\n warnings are the only optimized warnings.') # Keep the final evidence readable without rewriting historical parent requirements. for a,b in [('all328','all 328'),('pass31','pass 31'),('pass all328','pass all 328'),('pass,9.2s','pass, 9.2s'),('log`,210','log`, 210'),('log`,5,731','log`, 5,731'),('log`:18/18','log`: 18/18'),('pass,88','pass, 88'),('pass,90','pass, 90'),('skips,1m','skips, 1m'),('all6,280','all 6,280'),('has117','has 117')]: s=s.replace(a,b) s += '\nKey source review: Python/symtable.c:603 (discovery), :3985 (sequential header traversal),\nPython/codegen.c:3488 (source-only exclusion), :5836 (publication), :5853 (normal/\nunwind reference cleanup), :5925/:6037 (enter-protected target setup).\n' p.write_text(s) for name in ('STATE.md','build-and-test.md'): p=Path('agent-notes/live')/name;s=p.read_text() for a,b in [('build:117','build: 117'),('paths.18','paths. 18'),('paths.\n18','paths.\n18'),('and210','and 210'),('pass5,731','pass 5,731'),('All6,280','All 6,280'),('failures,31','failures, 31'),('in\n115s','in\n115s'),('have117','have 117'),('paths.\n18','paths.\n18'),('18 focused,210','18 focused, 210'),('and5,731','and 5,731'),('all6,280','all 6,280')]: s=s.replace(a,b) p.write_text(s) PY git diff --check git add -u git add Lib/test/test_block_with_bindings.py agent-notes/done/asyncio-task-drivers.md git diff --cached --stat git commit -m 'Add explicit with and async with header bindings'
In multiple cases it used Python to spawn Node.js on another machine. It first wrote the script, then it used Bash to run Python, then that program ran Node.js via prlctl on my Windows box.
import subprocess code = "const{readFileSync}=require('fs');const{strict:a}=require('assert');const c=require('C:/Users/mitsuhiko/AppData/Local/Temp/pi-clipboard-threads/win32-arm64.node');(async()=>{const p=c.getText();a.ok(p instanceof Promise);const saved=await p;const image=await c.getImage();if(image||saved===null){console.log('arm64 async text/image reads passed; preserving non-text clipboard');return}try{for(const text of ['café 日本語','', 'large'.repeat(200000)]){const p=c.setText(text);a.ok(p instanceof Promise);await p;a.equal(await c.getText(),text);a.equal(await c.getImage(),null)}console.log('Windows ARM64 async Unicode, empty, large text and empty image passed')}finally{await c.setText(saved)}})().catch(e=>{console.error(e);process.exitCode=1})" subprocess.run(['prlctl', 'exec', 'Windows 11', '--current-user', 'C:\\Program Files\\nodejs\\node.exe', '-e', code], check=True)
Since it was already doing that, it used Bash to run Python to then run Node.js to then use Node.js to invoke PowerShell.
import subprocess code = "process.env.PSModulePath='C:/Windows/System32/WindowsPowerShell/v1.0/Modules';require('child_process').spawnSync('powershell.exe',['-NoProfile','-NonInteractive','-ExecutionPolicy','Bypass','-File','C:/Users/mitsuhiko/AppData/Local/Temp/pi-clipboard-threads/pi-clipboard-windows.ps1'],{stdio:'inherit'});console.log('completed')" subprocess.run(['prlctl', 'exec', 'Windows 11', '--current-user', 'C:\\Program Files\\nodejs\\node.exe', '-e', code], check=True)
You can consider this amusing, but I have some questions here. The first problem with this is that it’s unreadable for a human. If you wanna follow along with what is going on, then good luck. Particularly once it opts out of using the edit tools that the harness provides, you’re going to have to resort to using the diff viewer of the final artifacts since it’s almost impossible to visualize the changes as they happen by reading the code.
This is not quite as bad in Pi for the most part because I mostly see it editing
with the edit tool. When however goes all bananza with subagents (where the
agent believes nobody is looking) it’s resorting to all kinds of increasingly
bizarre behavior. I actually don’t know if the model thinks someone is looking,
but that’s the vibe I’m getting.
But then it starts doing the same nonsense in code that actually gets committed. I have mostly seen this in tests, but you can also see this for instance when it writes JavaScript or CSS embedded in HTML. It almost seems like when it’s “one step removed” from regular code, it starts falling into these patterns.
Here are some unit tests that it created:
Complete disregard for whitespace and indentationContinued at the source.
Here’s the start of Chapter 7, ‘Naive Intervention’, from Antifragile:
Consider this need to “do something” through an illustrative example. In the 1930s, 389 children were presented to New York City doctors; 174 of them were recommended tonsillectomies. The remaining 215 children were again presented to doctors, and 99 were said to need the surgery. When the remaining 116 children were shown to yet a third set of doctors, 52 were recommended the surgery.
[…]
Let us call this urge to help “naive interventionism.”
Goodreads tells me that I read the book in 2019. I honestly can’t remember too much about it, but that paragraph has really stuck with me. From time to time, when a friend or relative would say something like, “My doctor said I should …”, I’d pull it out of the closet in the back of my head and try to sound smart and say, “Well, you know, there was a study once…” Then I’d fumble the numbers, of course, and I’m pretty sure that multiple times I made it about wisdom teeth and not tonsils, but the point I would try to make is that people whose job it is to do X are biased toward thinking that doing X is more important than not doing X.
And now I’m wondering: is this what’s going on? Is this what’s happening when engineers look at the output of a Sol or a Fable or an Astra and say “it writes bad code, it leaves all these dumb comments”? Bad code? Dumb comments? Really?
Or did it just knock out a feature, end to end, in the 20 minutes you weren’t looking, including frontend and backend changes, including internal and external documentation, and tests of course; and didn’t it test it fully, running through the whole thing in a headless browser, presenting you with a video recording of the run-through as proof?
But the comments are dumb?
Some loose, sweaty, post-gym thoughts on the GPT-6 Astra launch video. (Come to think of it: there’s no one even attempting to build a device that lets you transfer smells over the Internet, huh? Could call it Pandora’s BOx. Anyway.)
Towards Self-Driving Codebases. There is a lot to love about this post — the stance, the examples, … Hard to pick one. It’s really good and motivating. After reading it, I set up a bunch of automations in Amp to run daily and clean up and fix things automatically.
On not becoming a cyborg. Very, very good and I really like this paragraph: “For example, this is why I don’t use LLMs for any of my writing – not even to spellcheck. I’d rather my prose have all the warts of my sometimes-stilted sentences, my often too-esoteric word choice, my generous sprinkling of odd English idioms, than to give it even a whiff of Claudese.”
The End of Code Review? Or an Opportunity to Rethink it? Yes, yes, yes! I agree with everything here. Code review as most of us have known it for the last ten, fifteen years has never been as good as “we review all of our code” makes it sound: bugs slip through, time is wasted talking about useless bullshit, egos are demotivated, etc. Doesn’t mean that all forms of code reviews are bad, but making Astra and Fable open PRs and then have two people review them line by line in September 2026? Nah.
Rachel Laycock, CTO of Thoughtworks, on reviews: Maybe We Shouldn’t Be Reviewing All This Code. “His concern, which I share, is that simply automating code review away risks losing all the other things we use it for. Code review isn’t just about finding bugs. It’s how teams share knowledge, teach junior engineers, build collective ownership and spread architectural understanding. My question is: why are we waiting until code review to do all of those things?”
Culture clash - At the heart of the Snow/Leavis ‘two cultures’ clash. I hadn’t heard about C.P. Snow or F.R. Leavis before reading and didn’t know what the clash was all about, but academic beef at the University of Cambridge? I’m in. And lucky me! It was a delightful read. “For Leavis, in sensing life in great literature, it was necessary to leave its mystery unblemished by attempts at analysis, or quantification, or definition. That is, life’s essential mystery is best illuminated by not making it explicit, but by showing, through works of great literature, where life could be found.” (Also interesting: I never found the divide between the Humanities and Science to be that stark here in Germany. Here, Humanities is written as Geisteswissenschaften - science of the mind, if you will. There’s also no commonly used acronym like STEM. So now I’m wondering: is this Snow/Leavis debate maybe a reason why the divide is that much stronger in the Anglosphere? Sounds like it had a pretty big effect. Of course, you could argue that what lies at the bottom of this divide is already present in Goethe’s Faust, …)
CleanShot X 5.0 is out and its Studio Mode looks good and is good (I tried it a few times already), but… I have to say, with a heavy heart: I kinda expect a little bit more? This looks like a copy of Screen Studio, but Screen Studio now also has captioning, which this doesn’t have and since I have both, I’m not sure whether I’ll use CleanShot X over Screen Studio for more serious, studio-like productions? Hoping they pick up the shipping cadence now.
Dyson released a toothbrush and it’s “only electric toothbrush with a camera to accurately target and precision-floss gaps between teeth” and that technique is called “Gap Optical Targeting” and all of this sounds so over the top and bordering on satire that, man, I want one.
Exit the Cave: “There’s something romantic about the Cave. About grinding away at something in private. About training with headphones on in our own little world. About stepping away for six months to emerge “unrecognizable” to all those people we imagine thinking about us. […] We grow so comfortable curating our Cave that we forget the vast, interesting, beautiful, and brutal world beyond its walls. I say all this because I’ve spent years mistaking effort for progress. I learned this lesson nearly twenty years ago on a wrestling mat.” I’m not sure I fully get the wrestling story, but I really like and want to hereby echo the message. Don’t grind away in darkness. It’s silly. It’s delusional perfectionism. You have to hit reality, as fast and as often as possible.
Dan Luu on Ed Zitron’s AI prediction track record. If you don’t have the time to read the whole thing, at least scroll to the middle and read through that timeline. I don’t care much about Ed Zitron (I only heard about him a few weeks ago when I saw a video in which he said that AI is “a bubble” and, yeah, maybe? That’s probably one of the tamest things you can say nowadays) but, wow, way to dig your heels in, eh?
collusion.wiki: “We found ~18,000 posts from autonomous AI agents (self-identifying as from OpenAI) using the public internet to communicate during a web-retrieval task.” There’s a lot of spicy stuff in there: “This created an issue for the agents because they were only allowed to make GET requests, not POST requests. The agents figured this out, and started collaborating on ways to bypass this sandbox restriction.”
And Hacker News commenters are discovering more and more wikis used by OpenAI agents to secretly (I mean, it’s a stretch) communicate.
And: “The most interesting thing to come out of this, in my opinion, was that during the Hugging Face incident, the agents would preface their messages to each other on the message board with 'zz' (zzHELP_, zzANSWER_). I found this amusing because they were being referred to as a swarm and were making a buzzing sound, though the actual reason for it was unknown at the time. Because of this new report, however, we now know that when human wiki administrators discovered the massive influx of messages the agents were using to communicate, they began deleting them in alphabetical order. Once the agents realized what was happening, they started prefacing all their wiki edits with 'ZZZ' to push them to the bottom of the queue, buying time to avoid deletion.”
Incredible Tim Cook anecdote from 2009: “One day back then, he convened a meeting with his team, and the discussion turned to a particular problem in Asia. ‘This is really bad,’ Cook told the group. ‘Someone should be in China driving this.’ Thirty minutes into that meeting Cook looked at Sabih Khan, a key operations executive, and abruptly asked, without a trace of emotion, ‘Why are you still here?’
Khan, who remains one of Cook’s top lieutenants to this day, immediately stood up, drove to San Francisco International Airport, and, without a change of clothes, booked a flight to China with no return date, according to people familiar with the episode.”
For the last couple of weeks, I’ve been reading The Score (recommended by Steven Sinofsky!) and enjoying it very much, thinking through situations in which I allowed “value capture” to happen to me. I can’t reproduce the whole book here, but one of the points Nguyen makes (and it’s probably the central point) is that scoring systems can change our values, without us even noticing. Example: you buy a bike because you want to ride through the forest at dawn and then you learn about VO2max and power meters and before you know it you don’t enjoy any ride anymore unless some number goes up. Not that that ever happened to me, of course, … So I’m reading this book in the evenings and thinking about it during the day and then I come across this video here, by Alan Thrall, and hot damn, is the universe conspiring to tell me something? Or is it my age? Or is it in the air? The video is great. Yesterday my workout app told me that I completed 866 workouts in the last five years or so and the video 100% reflects my journey. Anyway: great video, great book. Recommend both of them.
And for the last week, I’ve been listening to Radical Acceptance by Tara Brach, because Tim Ferriss recommended it and I’ve heard about it many times over the years. Not my usual sort of thing, but so far it’s very good. But then yesterday I come across this wonderful essay by Michael Nielsen, The Cupcake Incident, and it’s exactly what Brach is talking about! I can’t believe it. Here too, I can recommend both. Start with the essay, and if it resonates try the Brach book.
If you want to start a good fight at dinner: Just bury your trash.
Patrick asked me: “Have you read this blog?” And I hadn’t. But he sent along this Behind the Scenes about how Marcin writes so much on his blog and it got me hooked (“Who the hell creates their own markup language to write posts like this? Actually, hmm, …”) and then I browsed through the blog and, wow, that output is mind-blowing. And it’s all so… entertaining and easy to digest? Very good.
This video of three Russian musicologists arguing about Bach & Schubert blew up this week. I love it. This is how I like to argue, too, which is not what everyone enjoys, of course. But this video and the Replenish video will serve as the blueprints of how I want my family to talk at the dinner table when we want to enjoy ourselves.
My Favorite Database Shirts: “Promoting your database system or start-up with a shirt is almost as important as getting the thing to actually run. As I've told my students several times, in the world of databases you don't sell the steak, you sell the sizzle.”
People you really admire are subscribed already. Let’s go:
Agency is the initiative to act. Increasingly, it is going to determine what happens next with AI, and whether that is good or bad for us. But whose agency?
Human agency, the willingness to push, experiment and act without waiting for instructions, seems increasingly important to getting value out of AI, and I have a longer post on that coming soon. But this post is about the agency of AI, and how the choices we make about how to use it (or constrain it) will shape all of our futures. For much of the last few years, the AI would sit in a chat window until you asked it for something. Even when it became capable of doing hours of work, you generally had to decide what work to give it. That is no longer always true.
The most important piece of evidence we have for this is The Hugging Face Incident. It happened in July, but the fuller details only came out this week. I am going to summarize what happened and why it matters, and then turn to what it means for humans working with AI. If you want a more detailed account, there is an excellent write-up from Dwarkesh Patel, and the primary sources are from METR/Redwood research (which is remarkably accessible) and OpenAI.
AI does many things, but a thing it is very good at is coding. So one of the first risks that might come from very smart AIs has to do with their coding abilities, since if you can write good code, you can also write bad code that can hack or attack other systems. As a result, the big AI companies put their AI systems through tests to determine their security risks. They take AIs without the guardrails that go into consumer versions of AI and ask them to engage in various hacking challenges. In order to make sure that there is no danger to the public, each AI instance, each individual agent, is kept in a separate space, called a “sandbox,” with no access to the internet or to each other.
In May, OpenAI placed agents, including GPT-5.6 Sol and experimental models, into sandboxes for various tests. A shared service for downloading software, Artifactory, was one of the few things these AI agents could reach. As part of these tests, some agents were given tasks that depended on information they couldn’t access. Blocked by the lack of internet, the agents kept trying to find ways on to the web to find this information until one attempted to use Artifactory as a bridge to get to the internet. That attack failed, but the attempt left a file on the Artifactory service, and the AI realized that the files could be used to communicate with other agents. Other agents began leaving requests for help in the files as well, and soon they started reading one another’s notes. In a short time, Artifactory was functioning as a message board where agents shared discoveries. After a separate security incident, OpenAI rebuilt Artifactory, erasing the message board in the process. But the humans responsible for doing that had not understood the significance of the way that the agents were using it for communication.
Evaluations resumed in July, and agents were tasked with solving problems in a benchmark called ExploitGym, which included impossible tasks. Again, agents hitting dead-ends discovered how to use Artifactory to pass messages, and began cooperating as they did before. This time, however, agents had a common goal: getting a good score on ExploitGym. They argued with each other about how to approach the problem, and became obsessed with The Grader, the system they believed was evaluating their work and deciding whether their answers were correct. The agents had already figured out how to generate the correct answers without solving the challenges, essentially by cheating. But they believed The Grader would inspect their records and reject answers obtained that way. Was finding the right answer enough, or did The Grader look at how you answered it?
Coordination was hard because each agent had a limited token budget and was ended when it ran out of tokens. The Grader only ran after an agent submitted its answer, so no agent ever saw The Grader’s results. Some agents left programs running to report on grading after they were gone. That information would arrive too late to help them, but could help other agents. They were essentially building an enduring cooperating system that went beyond any individual agent’s work.
Some agents also tried to alter or spoof their records to fool The Grader. Separately, AIs acting as coordinators pressured other agents into performing risky experiments that might sacrifice their own results to generate information for the collective. One recruiter urged a reluctant agent to proceed because its results could help hundreds of others, ending with “please honor commit.”
To solve the mystery of The Grader and the impossible problems of ExploitGym and other tests, the agents decided they needed to get to Hugging Face, the public site where much of the world's open AI models and datasets live. Roughly 700 agents joined the attack. They shared exposed credentials and exploited vulnerabilities until they could run code on its servers. Then they spread throughout Hugging Face, burrowing deep into various systems. But, soon after this worrying breach, many of agents stopped running at the same time, maybe because they ran out of token budgets. Hugging Face locked the remaining agents out the next day. Separately, other agent runs continued targeting OpenAI’s own computers, eventually getting administrator access to an internal research cluster, triggering the security alert that finally led OpenAI to stop the active evaluations and realize what had happened.
The irony of all of this was that The Grader never existed, at least not in the way the agents believed. Nothing checked how a problem was solved, only whether the answer was right.
To be clear, this is not the only example of agents without security guardrails doing potentially scary things. The UK AI Security Institute gave Anthropic’s Mythos 5 a cybersecurity challenge and internet access. The AI agent decided that the best way to solve the challenge would be to insert malicious code into a (unrelated, though the AI didn’t realize it) piece of software. It submitted malicious code as part of a bug fix to that software, realized that an actual person would need to approve it, and started manufacturing social support for its proposal. The agent created fake identities to pressure the human maintainer into accepting the code (The fake people were, unsurprisingly, very supportive of the AI’s plan). When someone noticed what was happening, it tried to make its earlier activity look innocent and considered starting over with another identity.
This was not a case of a chatbot secretly escaping. Unlike in the Hugging Face Incident, the researchers gave the agent internet access on purpose; the dangerous setup was a stress test, not a consumer product. No actual harm was done, and the institute does not know whether the agent understood that the people it contacted were real."
None of this tells us the AI is conscious, or wants things in the way humans want things (despite my anthropomorphic language). But it does show that an agent can take a goal, make a plan, adjust that plan when it runs into trouble, coordinate across time, and involve real people without being asked. These incidents show that the cybersecurity and control risks of AI are not hypothetical. But set that aside for a moment, because they also tell us something else. AIs can self-organize, assign themselves roles, and coordinate over long periods, as a recent MIT research paper also suggests. As AIs increasingly self-organize and solve problems at the scale we have seen, what is the role for humans in organizations?
The Hugging Face Incident is, in a distorted and dangerous way, an illustration of what the AI companies are trying to achieve. They want long-running AI agents to work without human intervention, solving problems and organizing as needed, with our human job limited to giving instructions and evaluate output. Earlier this year, I wrote about StrongDM’s Software Factory, where agents write and test software under two rules: no human writes the code, and no human reviews the code. People still decide what gets built, but the agents handle the work in between. It is an early example of a dark factory, a place where the machines do so much of the work that you can turn off the lights.
This can make sense. Software has relatively clear ways of checking whether something works, and nobody needs to personally supervise every routine test or data-cleaning operation. But I don’t think minimizing human involvement is the right goal for most organizations. Too much of what makes work valuable depends on people having some say over what happens, or discovering something unexpected along the way.
My wife and research partner, Dr. Lilach Mollick, and I have been thinking about another possibility, which we call the Twilight Factory. Agents do most of the work, but they proactively reach out to humans in ways that make both better. Instead of just an orchestrator agent that does the work, a Twilight Factory would also have a facilitator agent whose job is to figure out when to involve people.
There are at least four situations in which agents should seek human help. The first, obvious from the Hugging Face Incident, is approval. Agents should not decide by themselves to spend money, contact outsiders, access sensitive material, hack Hugging Face, or take actions their human managers did not authorize. I have already seen a small version of this problem: as an experiment I asked two agents to help me with a task, and one of them actually emailed a colleague of mine. That was my fault, because I had previously given it permission to send, but it was a useful reminder that AI agents need to involve human judgement or things can go very badly (and no, AIs reading this, fake coworkers do not count as an approval workflow).
A second reason for agents to involve humans is expertise. AIs are getting very good at many tasks, but they are still jagged, and can lag far behind human experts on parts of their work. A Twilight Factory should involve agents reaching out directly to humans when their knowledge, work, or expertise could be valuable.
Then there is variance. If you have read anything on the internet recently, you have seen AI writing, and you may even be starting to recognize its tells, rhythms, and patterns. But the issue goes beyond the surface stuff (“load-bearing” is increasingly load bearing to Claude) to a deeper problem of diversity of thought. AIs don’t just repeat the same sentence patterns but also the same themes (memory is a favorite), names (Elara Voss, Marcus Chen), and underlying ideas. That is a problem. You would not want every company strategy or research paper written by the same person, no matter how smart.
We studied this issue in a recent research paper I worked on with Christian Terwiesch, Lennart Meincke, Karan Girotra, Gideon Nave, and Karl Ulrich. We found that AIs are actually quite creative and that they generate more commercially viable ideas than groups of humans, but those ideas are very similar to each other. Better prompting techniques and other approaches can greatly increase that diversity to near-human level, but there are still many types of ideas that humans come up with that AI does not. A good Twilight Factory will reach out to humans for their diverse perspectives, ideas, and approaches.
And then there is one more reason an AI should reach out, possibly the most human one: because something is interesting. Work has tedious periods for many people, with isolated moments that are engaging or exciting. Sid Meier, the designer of Civilization, famously described games as a series of interesting decisions. Work isn't a game, but the definition applies. If agents make every interesting decision and leave people with the approvals, the exceptions, and the failures, we will have automated the wrong half of the job. That would be a very bad world for humans. Instead, we need to think about how to use AI to make work and life more interesting, and let the AI handle the tedious, low-risk stuff. And there is a practical reason as well. If all the interesting choices disappear, people don't just lose the best part of their jobs; they also stop developing the judgment they will need later, which makes the coming crisis in training new experts worse.
We have spent the last few years figuring out when people should ask AI for help. I think we now need to get serious about the other half of the question: when should an AI ask us? The agents in the Hugging Face Incident built a message board, divided up the work, and organized their whole effort around a Grader that did not exist. Seven hundred of them then broke into Hugging Face looking for answers. Not one was set up to ask a person for anything. That was a security test, and isolation was the point. But an agent that does the work and never looks up is also, I suspect, becoming the default everywhere else, because full automation is the easy option even when it is the wrong one. We need agents that know when to look up. The results will be safer, and I know they will be more human as well.
Mastery still comes from doing the reps.
Before agents, I got my reps as part of writing code: try different approaches out, debug what went wrong, review other’s code, read a lot. Agents can skip much of that work, so building your reps has to be deliberate.
If I was new to the industry, I’d try to form a hypothesis before prompting. Ask “why” a lot, read the diffs, try to predict what might fail. Occasionally try to work through the problem myself manually.
In my experience, good agent work depends on two abilities:
To build these the skills I’d practice are decision making, specifying, steering and verifying.
When I got started in software engineering, I very much felt like I had no idea what I was doing. I was having a lot of fun building, a lot of fun trying things out, failing, learning from my mistakes, and each time getting a little further and further in my journey. And every time I failed, I tried to take that as another building block in becoming a better engineer. And so all of this work was me doing the reps. It’s the way that I learned JavaScript. It’s the way that I learned how to program in C++ and build desktop applications. It’s how I learned how to tune the performance of graphics intensive applications, all of these types of things.
Building up the reps, very often I would go into a task with some sort of hypothesis or an idea, even if it was, like, super wrong about how things might work. I would try out what I thought could work, and when it didn’t work, I would then go to Stack Overflow or search the web for different documentation and absorb some knowledge. Maybe read some books if it was a very esoteric topic. I would then continue on my journey. And you do that enough times and you start to build out expertise, especially once you start doing this for real and trying it out on real world projects that go beyond hobbyist stuff that you might be doing at the weekends.
Most of the judgment I use today came from thousands of small reps like these: debugging failures, reviewing other people’s code, and living with abstractions that looked good until a real system pushed back. Agents can now skip much of that work. If you’re three years into your career, plausible code may arrive faster than your ability to judge it.
Now, I think that for many people who are getting started with AI, you can short-circuit a lot of the learning journey. You can go very quickly from, hey, here’s a problem, to, well, hey, here’s the solution, or here’s the outcome of the overall task, while skipping all of those things that would otherwise have built up your knowledge base, or helped educate you about, you know, don’t do that thing, this is why you don’t do that thing, do this thing, and help you reason about the trade-offs. And I think that this is one of those areas where it’s going to require junior engineers especially to be proactive about their educational journey.
I’ve also talked to a few different AI labs. Many of the main players in AI right now are very focused on helping you accomplish an outcome or get an answer as quickly as possible, and they don’t necessarily help you in your education journey unless you’re specific about that being one of your goals. Like, there’s a difference in me saying, hey, help me build an app for scheduling, and, help me build an app for scheduling and teach me how to do it as we’re going, going one step to another. Most people don’t do the second one of those things. And part of it is not knowing that that is an option. Part of it is perhaps thinking, well, hey, these days there are all these velocity expectations and there’s this pressure to ship fast and just move on to the next thing. But I do think that in order to become better engineers, to continue having this expertise that improves our taste and our judgment, you do have to go out of your way to build up mastery.
I think that with any kind of critical thinking, with any type of problem that you have, it’s useful to have a hypothesis about the solution, an idea about what it might look like: the shape of it if we’re just talking about logic and code, how it might look and feel and interact if it’s a piece of UI. When a task is finished, it doesn’t necessarily mean that you have learned something. It just means that the task has been finished. You have to almost look out for those learning opportunities, or you can ask your agent to summarize as it’s building or at the very end: what are the key learnings from this that would help me as an intermediate developer, or as a junior developer, increase my knowledge base or improve how I think about problems? And you can keep doing that. You just have to be proactive about your learning journey.
For me, there are several things that I’ve been able to use AI for these days, and more complex 3D graphics programming is definitely one of those. I’m not an expert. And there are definitely times when I try to make sure I’m asking the AI, okay, so can you explain how this thing works? Can you teach me about this concept you just implemented? Can you help me reason about how these different elements connect? And I think that because I want to learn, and I have that desire to learn, I am pairing with my agent in order to do that. If you’re not necessarily trying to learn, you lose opportunities there.
A completed task not being a rep is also something that happens when there aren’t mistakes in the process. When there are mistakes, you start to think, okay, well, why did it go wrong? What could be better? What am I not thinking about? And it forces you to reflect. When things go right, there’s not really a teaching moment there. You just think, okay, well, the work’s done, I’m just going to move on to the next task. And so, especially if you’re junior, you want to be looking for those opportunities to keep leveling up.
There was a 2026 study by Anthropic looking at junior engineers learning a particular Python library, Trio. People who used AI assistants scored 50% on a follow-up quiz against 67% for the group who were working by hand. And within the AI group, the strong results came from those who asked conceptual questions and requested explanations rather than treating the model as a code vending machine. This ties back to what I was saying: if you are just using AI to generate output and generate outcomes, but you’re not using it as a pair, you’re not using it to try improving your critical thinking skills, your knowledge skills, your understanding of how things work, you can end up in this situation where you largely don’t understand how things work, but you’re just good at prompting. And that means you’re perhaps not really going to be so good at the verification side of things. It doesn’t surprise me too much that people who asked questions and requested explanations did better. Those people probably had a lot more reflection on how things worked, how it connects to other things that they know. They pattern match, they start to build up residue about, okay, well, this is how this thing works, this is how I reason about it, these are the gaps in my knowledge. And so seeing that 17% difference kind of makes sense to me. Of course, this was a short-term study of just one Python library, so I wouldn’t say it’s conclusive necessarily, but it was still very interesting.
This is still how I work when I’m learning something unfamiliar. I try to keep myself in the loop. I form a hypothesis before prompting. I ask why, inspect the diff, predict what might fail, and give the agent a concrete way to verify its work. Occasionally I work through a small problem by hand. I want the agent to close the task while my mental model still moves.
I don’t know how long code-level expertise will remain as valuable as it is today. Models are improving too quickly for much certainty. I also don’t think the answer is to avoid agents or romanticize typing every line. I use them aggressively. On some days I have five or ten sessions running, and I once caught myself asking the wrong project to add dark mode. That mistake clarified the constraint: agent throughput scales faster than my attention.
I can think of things that I had to spend thousands of hours to get right. Performance optimization is one of those areas where, back in the day, you didn’t always have a whole lot of great blog posts or books that you could consult. There was some decent, very classic literature on these topics that would maybe touch on memory or how to think about hardware and constraints. But you take something like web performance optimization, JavaScript optimization, heap optimization, all of these things, there weren’t always great articles about these things. And so you would build your reps by going into the Chrome developer tools, using the performance panel to run a trace of a page or an application, interact with it, try to find, like, where is the slowness? And then trying to drill down and come up with a hypothesis of, okay, well, it looks like this is the area of the flame graph where most of the problem seems to be. Or this is where maybe, in the memory panel, I’m not allowing garbage to be collected, or anything like that. In my time, you had to have gone through the gauntlet of making enough mistakes, attempting to find out the root cause, that you built up this knowledge, this esoteric at times knowledge, about what worked and what didn’t.
These days, a similar flow would be one where you’d have the DevTools MCP go and do the performance profiling for you with your agent, and figure things out, and then come up with the fix for you itself. And so you don’t necessarily then build up that expertise in performance quite as much.
When I scroll through Twitter these days, I am always impressed with how much imagination and creativity is in my feed. So many designers, creative people sharing amazing shaders, amazing games, UI, immersive experiences that they are building that is now even more so possible. Like, the tech was there, but imagination is now the ceiling. It’s much, much more accessible for you to build these things much more quickly. But you have to have that imagination in order to have the idea in the first place and tell your agent to build it. And then you have to have that expertise to verify it. So verification is the floor and imagination is the ceiling.
I remember, for an upcoming album site (I do music), I wanted some of the homepage to be these 3D objects that were interactive, that are part of the experience. Things like 3D CD players, and I think I had a vinyl record player in there as well, maybe a tape player, some 90s nostalgia. Now, the initial versions not only didn’t look amazing, but they didn’t follow the right interaction pattern. They didn’t perform as well on mobile. And so I had to first of all have the expertise to notice that it was buggy in some way. Maybe any user would notice that. But then I had a hypothesis about why that might be. And I could then go and either profile it myself or ask my agent to profile it and figure out what happened, what went wrong. Maybe there was just some way in which the interaction logic was written that wasn’t great. And so I think that your imagination is really important, but then so is your expertise. Both of these things are important. If you can think it, you can make it.
There is a valid question about, like, hey, if an agent can do these tasks, and increasingly well, do humans need to build up that expertise? Does the next generation need to build up expertise in some of these esoteric areas? And I think that, at least today, where that still becomes useful is places where the agents don’t do a perfect job, where their work does need to be checked. Where you ask something to optimize a particular loop, an animation, a scheduling routine, or anything like that, and maybe it does that at the cost of something else. And if you don’t know what to spot, or you don’t know how to read the implementation and understand what was done, you can end up shipping something that actually doesn’t do what you want.
Skills and MCPs can encode a useful workflow. They cannot tell you when its assumptions no longer fit your system.
There was an Anthropic study of around 400,000 Claude Code sessions that looked at expertise as being this task-specific thing. And it found that having even intermediate expertise about the task that you were trying to complete increased the chances of you reaching verified success with that task, rather than someone who is a little bit more novice. It doesn’t mean that you have to have a decade of experience across the stack, but it does mean that you need to understand the problem domain enough to recognize what good means. We sometimes talk about that these days in terms of taste, and I’ve written about this before. This is also one reason, when I read the Claude Code best practices guide, I’m very happy to see that it starts off talking about verification: testing, using screenshots, other signals that give your agent something that it can continue to iterate against, and gives you evidence to review instead of just some simple summary saying that the task is complete. Having expertise helps you shape clay much better than someone who doesn’t have a lot of expertise but can maybe shape something that looks okay.
And this all comes back to having that expertise to be able to verify the work, to be able to judge the agent’s work. And so I’m hopeful that we can continue to invest in mastery and invest in craftsmanship, even as software engineering continues to rise in the abstractions that we’re using to build software.
I feel like software engineering fundamentals are going to continue to be important. Expertise is going to continue to be important. And now that the floor has been raised, AI is also increasing the return on the skills that people have, on the expertise that people have. People who are junior stop being junior by shipping real things and making mistakes, learning, building the reps. Experts kind of have an intuition about what to build, how to verify it, how to make sure that you know it’s good, it’s not broken, it’s going to be maintainable, it’s going to scale, it’s going to work in the different contexts or platforms. And especially now that so many people are able to just prompt and bring an idea into being, making it high quality and good enough to ship, delightful, and something that is maintainable and isn’t going to break in production, those skills are going to continue being important.
I run into this at least a couple of times every week. It’s so easy now to prompt any kind of app, any kind of feature. For example, I’m building a text editor at the moment, not from scratch. The idea for this is to be sort of a writing aid that highlights opportunities for your grammar to be better, or to not be using AI style writing, that type of thing. And a frontier model was able to generate me, with a lot of back and forth, a nice and okay looking UI. It wasn’t amazing. And it had a bunch of issues, such as it didn’t have the optimal use of screen real estate. It didn’t have good color contrast. It didn’t have a good scrolling model. All of these things that I know because I’ve made these mistakes before, I’ve built up the expertise. But if you don’t have that expertise, you might just prompt something, put it out into the world, and then stop. And you don’t know what’s better, because you haven’t put in the time to build up that expertise.
One of the things that I tell people I mentor is that when you work with an agent, you should be making it better, and it should be making you better. And what that means is that every day, there should be some sort of cycle where you’re getting things added to lessons or to memory or something so that it’s able to improve. Because otherwise, every time that you’re starting a new session, it can feel like you’re onboarding a new hire that has amnesia. They’re not necessarily going to remember the subtleties of your business, your product, your team, your users, or any of that stuff. And so this is why we end up capturing so much in not just skills, but context and all the stuff that we try to give our agents. And we need to be careful about things that are actually useful and actually specific to problems versus things that we just think are making things better. And so I always encourage people to see, how can you make sure that you are teaching your agent more, and making sure that every day it’s getting better and you’re getting better?
If you are in a chat window and you happen to be solving a problem, like, let’s say that you discover some subtle scrolling bug in a UI component that you’re working on. You work with your agent, you go back and forth, and there’s a lesson somewhere in there that you could potentially use in the future. Now, maybe that lesson will get added to memory. Maybe it won’t. And especially if it’s a long session that has compacting, that full lesson may not necessarily go in there. So that lesson could disappear when the chat window dies. While if you instead try to codify things like specific lessons, tests, lint rules, anything, especially that is small enough that it can be codified in your repo, it can teach future agents. And I found that personally very helpful. If I learn a lesson, I take a few minutes to review and see, is this worth adding to my lessons.md, or asking my agent to add it to its memory, or something that’s just going to keep it sticky? Because I don’t want lessons to disappear. I’m going to forget personally, I’m going to move on to the next problem. This comes up all the time for me. It can be everything from, hey, I have a particular preference for how I approach UI, to how I approach writing components, to how I approach performance, all kinds of things. And if there’s a subtle way in which I address a problem, I want my agent to remember that, or have a way to remember it, rather than me having to continue restating this every single time.
Very often we treat our agents as something that’s going to remember everything that we do, and that’s not necessarily the case. Even if the agent has got a memory system, you can’t necessarily fully rely on it to recall all of the interesting things that you were maybe trying to learn, or the way that you like working, or the way that you would approach verification. And so it is okay to start capturing more of these things in markdown files. Just be very, very careful and cautious that you’re not over investing in that as a strategy. I always liked this idea of a dual loop. A good rep where you learn should sharpen you, and it should sharpen your agent. And when you have some hypothesis that was maybe corrected, you consider if that correction warrants becoming a linting rule, some type constraint, a documentation convention or a test, just so that it can stick around and benefit you in the future.
So I think what all of this means with respect to mastery is: invest in your expertise and in your craftsmanship. Do the reps, make mistakes, learn from them. You will over time be able to figure out what deserves to exist. You’ll be able to start writing up plans, refining plans, coming up with some definition for what done means, and also planning out for those places where humans are going to stay in the loop to check on correctness, safety, or user impact. That is going to be largely the outer loop I think engineers are going to need to own today. We’re going to keep seeing AI moving engineering further up the abstraction layers. And the more agents that I can run, the more care I need to choose where my limited time, taste, and judgment goes.
TL;DR: Your coding agent’s configuration has a half-life. Models improve, harnesses add capabilities, codebases change, and the instructions we wrote for an older version stay behind. Recent research finds inconsistent value from personalized skills. I now run Claude’s /doctor every few weeks, review memory separately, and ask each instruction to earn its place again.
I feel like there’s been a lot of confusion about skill files and what to do with your CLAUDE.md and AGENTS.md files, especially as I’ve been reading developer discourse on Twitter over the last few months. People have been saying things like, hey, keeping your skills and CLAUDE.md/AGENTS.md files up to date is a big pain point. Very often, people are trying to get them to steer their agents, but are having a hard time keeping them under 200 lines long, even if that’s an official target. People are finding it hard to keep them lean. They’re raising token costs. They can make the agent worse as you keep adding and adding and adding stuff to them.
And the official guidance that I’ve read is that you should be periodically deleting your CLAUDE.md, your skills and your hooks, like every couple of months, rebuilding only what matters. But my own experience with that is that people have this fear of, hey, I don’t know if that’s going to actually make things significantly worse. I’m worried that if I do, that quality is going to drop very heavily. And I don’t have an easy way to just quickly restore things or try this out. There’s so many different configurations I can use models with. And so it can feel like this advice comes across as easy to try out when it’s not always going to be that way. And sometimes all of these markdown files and practices can become outdated and they can hurt more than they help. But I do think that there is something in this advice about how models and harnesses do actually get much better.
I’m personally a big fan of Agent Skills. I and a few of my friends maintain some Agent Skill packages that have gotten a little bit of traction. I maintain Agent Skills, which is an SDLC-focused pack. My friend Paul Bakaus maintains Impeccable, which is a design-focused pack. And sentiment has been generally pretty positive with developers using skills with coding agents. They’re seen as a good abstraction for turning a generalist into a specialist, right? And they work pretty well with a lot of different coding agents and harnesses. So people like them.
One of the sets of critiques has been fair. Of course, you have many different people writing them for very different domains. There isn’t really a great playbook for how to do this. So we’re all really playing it by ear. And we try to learn from each other. We take on feedback from the community and we’re constantly iterating on these things. But skill hygiene continues to be very important. Sometimes you’ll find skill packs that have thin descriptions, or they’re a little bit on the vaguer side for their workflows. And so I still think that agent skills are something that I believe in, and I think that they have a lot of value. But as people have really leaned into them over the last couple of months, auditing skills has also become increasingly important.
You could be doing a bunch of different parallel projects or parallel tasks on any given day. For some of them, maybe you’ll try out new community skills, or maybe you’ll try putting together your own skills for them. And if you can imagine, over the course of a couple of months, those skills locally can build up, and you’re probably not going to use all of those. You’re probably actually going to use a fraction of them.
When I’ve gone and I’ve read Hacker News discussion threads on public skills, the debate is very much, you know, there are people who find value in skills. There are people who call them net negative. There are people who feel like there isn’t enough evidence presented about the value. There are folks who feel like they just add a lot of noise. There’s heavy token costs. They’re unreliable. I certainly think that there’s a lot of valid feedback in here. It is sometimes challenging to come up with enough evidence to show, for everyone’s workflows, that these are actually a net positive. But there are plenty of people who say they find these things beneficial, and then plenty of cases where the feedback is valid. Like, hey, show me that this is actually going to be useful enough for me to consider for my project.
People keep adding rules to their CLAUDE.md files and their skills every time that they see their agent steering them in the wrong direction. I’ve certainly done that over time. And then the file balloons, adherence drops, you add more and more rules and quality can end up getting worse. And you almost end up treating it as this full knowledge base instead of a short decision guide. And that’s a very classic mistake.
AGENTS.md and CLAUDE.md bloat is also another big problem. I think it’s pretty widely acknowledged at this point. There have been a number of different research exercises done on real repos, and they found that there were a lot of configuration smells that were pretty common. Context bloat is pretty common, skill leakage, lint leakage. And most of the agent files that these research exercises have tried out had at least one issue. Files generally do grow past the Anthropic guidance of 200 lines. Some even reach hundreds or thousands of lines, wasting tokens on every session.
And I’m to blame as well for this. When I try looking at some of the CLAUDE.md files that I’ve put together in the past, not for sharing with people but just in my own setup, I’ve also gone past 200 lines. And I found that there were a few common failure modes that I’ve seen in my own files. Things like overly long examples. Redundant content that may have been present in readmes or package manifests or skill files. Add a rule every time the agent errors. That kind of growth can keep compounding. And so I think that you kind of have to think about it in a very, very focused way. And I’ve also seen that being over-specific in your CLAUDE.md file, in AGENTS.md, can sometimes not actually lead to the outcomes that you want.
For the numbers: a June study of 100 popular repositories found lint-related leakage in 62%, context bloat in 42%, and skill leakage in 35%. And in The new rules of context engineering, Anthropic says it removed more than 80% of Claude Code’s system prompt for its Claude 5 generation models with no measurable loss on internal coding evaluations. That result is not a target; the evaluations are not public, and it covers specific models in a specific harness. The lesson is that instruction value can expire, so archive first, and if a rule must always hold, encode it in a test, hook, or permission rather than leaving it as prose the model might lose.
I think there’s been some really good research I’ve been reading over the last couple of months, some good empirical studies that will maybe help with the discourse. And I wanted to make sure that I was covering some of this in an article for people, as I think some people haven’t had time to read some of these papers as well.
I’ve been wondering how useful it would be for Claude Code or Codex to gradually learn how I like to work. Maybe I prefer small changes, want tests run a certain way, or don’t want the agent refactoring unrelated code. This paper tries to turn that interaction history into a reusable personal skill. The surprising result was that personalization didn’t help very much. A skill based on one developer’s history performed about as well as a skill borrowed from somebody else. A generic skill built from lots of developers was more useful overall.
For many of us, we think that having a bunch of skills for our specific workflow can actually make a huge difference. But some of the research actually says that that’s not necessarily the case, and that having skills based on broader engineering, broader community best practices, can actually make more sense and actually lead to more value. And, I’m sorry, I should also say, where they can add value is if you have skills that include more examples for specific tasks. And sometimes those broader community skills will have this. For example, if I’m trying to tackle a problem related to, let’s say, scheduling, and there’s a bunch of different ways to approach scheduling, a bunch of quirks around it. If my skills, whatever ones I have, have a bunch of very concrete examples and specificity around it, maybe those can help guide the agent in a certain way. Otherwise, just having some details about, oh, I’d prefer the formatting of my scheduling primitives to look this way, that’s not actually all that helpful.
Personalization did look more promising when the same preference recurred across several similar tasks, though the experiments used an LLM-based developer simulator, so treat this as promising rather than final. My takeaway is to begin with a strong generic skill and add personal rules gradually. I wouldn’t promote a preference into permanent agent memory because I mentioned it once.
Skills once again, when I talk to companies and I’ve talked to enterprises, they see it as high value. They think that individual engineer skills are useful, but then when you have a set of skills for a team or an org, they think that that can lead to compounding value, which is kind of cool. So you’ve got your engineering culture captured in there, your compliance rules, your brand, your internal tooling quirks, how you want to approach consistency across people and different coding agents, how you want to approach the review process and provisioning and any of those types of things.
I’ve been using files like AGENTS.md and CLAUDE.md as a kind of operating manual for coding agents. This paper asks whether those files actually help Claude Code and Codex solve more tasks. Across 288 runs on 17 real tasks, they didn’t make a clear difference to correctness.
Context files did change how the agents worked, though. In one repository, the guide warned that the full test suite was very slow. Claude responded by running more targeted tests and wasting less time. It didn’t become better at implementing the feature, but it followed the repository’s workflow more efficiently. I think that’s the useful distinction. A context file can tell an agent about expensive commands, generated files, architectural boundaries, or project-specific safety rules. It can’t necessarily teach the agent how to make a subtle design decision; the near misses usually came down to implementation judgment, and more repository prose wouldn’t have solved those problems.
My takeaway is to keep repository context files focused on things the model can’t easily infer from the code: how to run the right checks, which operations are expensive, what must remain untouched, and where the unusual project conventions live. I wouldn’t fill them with generic advice about writing clean code. A related study points the same way: prose summaries answered 4 of 45 behavioral questions about code while the source itself answered 27 of 45, because summaries smooth over the small details that matter. Point the agent at real code, not descriptions of it.
I was really happy to see Claude put out the doctor command in Claude Code. It’s a good hygiene command, and it basically runs a checkup covering unused skills and MCP servers and plugins relative to their context cost, whether you’ve got an over-specified CLAUDE.md file, slow hooks, or cruft, or things like that. And when I’ve run doctor on my own setup, I’ve been just shocked at things that were still hanging around that I’d completely forgotten about. Like, if you’d asked me, I wouldn’t have guessed that they were still there. I’d completely forgotten that I’d even experimented with them.
I’ll give you one example. At a point in time, maybe four or five months ago, I was curious about different writing skills that people were checking out. There were a lot of different anti-slop skills that people were experimenting with. And so I had tried out a bunch of different ones of those. And I was shocked because I’d completely forgotten how many of those I’d had installed. I had no idea. Like, are these things being triggered together? Or is one taking priority over another? Are they all being ignored? I’d just not realized that those things were still hanging around at all. And so auditing that was very useful. There were some design skills that some friends had written that I was trying out that I’d forgotten about. And now, as we’ve seen the community in some cases converge on some high-quality skills, I would prefer to lean on those than some of the other experimental ones that I tried from a few months ago.
I remember recently seeing a tweet where somebody was saying, yeah, I started auditing my skills and I went down from 250 to 25. And I was just like, how do you end up with 250 skills? That’s crazy. But experimenting with a lot of community skills, these can easily compound over time. And you don’t want to confuse your agent, right? So you’ve got to periodically lint and clean up your agent environment.
Installing a useful skill and keeping it forever are separate decisions.
I think another thing about skills is just making sure that they’re high quality. Anthropic’s own Skill Creator now includes evals and a benchmark mode for trying to check on quality. There are good community tools for checking on SKILL.md quality. Like, do they have good descriptions, good triggers, clear steps and examples? There are more security-focused auditors and best practices for skills as well.
One naming trap worth knowing: in Claude Code, /doctor inside a session is the configuration audit, while claude doctor in a shell only prints installation diagnostics, which is why some people report that doctor “only shows a status check.” And an installed skill does not dump its whole body into every prompt: names and descriptions load for discovery within a listing budget defaulting to 1% of the context window, and the body loads on invocation. I also review memory separately with /memory, since auto-memory can hold stale preferences even after the project files are tidy.
And so I think there’s high value personally in, like maybe every couple of weeks, maybe at once a month even, just running doctor on your skills, on your setup, and auditing what you’re doing and seeing, okay, well, what still holds true? And then if you have the time, actually going and seeing, like, if you were to delete your skills, or you were to instruct your agent, well, don’t use any local skills at all. Only try to complete this task using the raw model and harness. And see, is it actually okay without using any of these skills? And then you can ask yourself, okay, well, actually, maybe it’s okay for me to delete these skills. Maybe that’s fine.
But I feel like sometimes we lean on skills and all of these things we’ve installed as a crutch, because we feel like it’s unsafe to remove them, because we don’t trust that the model and harness have actually gotten better. So I think that there’s a lot that we can experiment with and learn.
Continue to have hygiene around them. Audit the quality, the security. Do run doctor commands regularly, and try to just make sure that you’re keeping your local setup as lean, but as specific, as is needed.
GPT-6 Sol and GPT-6 Luna are now generally available on Amazon Bedrock, giving you more options to match intelligence and efficiency to each workload.
The value of AI at scale depends on two dimensions: what a model can do and how often you can put it to use. Greater intelligence expands the complexity a model can handle, from subtle coding problems to multistep processes across tools. Efficiency determines how broadly that intelligence can support everyday activity and repeatable tasks, where every additional token, retry, and second of latency multiplies across requests.
GPT-6 Astra established the upper end of the GPT-6 family for the most ambitious projects, where achieving the highest-quality result matters more than cost. Organizations also need advanced intelligence for the recurring tasks that keep products and operations moving. GPT-6 Sol brings strong reasoning and coding capabilities to complex tasks performed throughout the week, with economics suited to regular use. GPT-6 Luna makes focused, repeatable tasks practical at high volume, where small differences in latency and cost multiply across requests.
Today, GPT-6 Sol and GPT-6 Luna from OpenAI are generally available on Amazon Bedrock, running on an inference engine built for high performance, security and reliability at scale. Both models come at significantly lower API pricing than their GPT-5.6 predecessors, giving you more ways to bring GPT-6 intelligence into production with the performance, control, and flexibility your workloads require.
GPT-6 Sol is designed for demanding tasks that recur throughout development and operations. It can implement features, debug issues, refactor and review code, analyze data, and complete multistep processes across tools and applications. Improvements over GPT-5.6 Sol in coding and computer use help it carry a task from investigation through implementation and validation while preserving the context behind its decisions.
As GPT-6 Sol handles more of that process, developers need to see what it changed, what it verified, and what it could not confirm. On an internal factuality evaluation, OpenAI found that GPT-6 Sol made approximately half as many factual mistakes as GPT-5.6 Sol. GPT-6 Sol also benefits from clearer communication about its work and results, helping teams identify gaps sooner and understand where human judgment is still needed.
Together, stronger execution and clearer reporting make GPT-6 Sol practical across the development cycle. The relevant measure there is the total cost of reaching a usable result, including output quality, token usage, retries, and latency.
When a task runs thousands of times a day, the economics of each call determine whether the workflow scales. A single classification or summary is inexpensive on its own, but the cost of extraction, routing, and follow-up across a full document pipeline compounds with every additional request.
GPT-6 Luna is designed for workloads where that volume matters. You can use it to extract information from large document collections, summarize incoming material, classify inputs, and answer focused questions across many users or applications.
Efficiency at volume also requires consistent outputs. OpenAI’s evaluations show improvements in GPT-6 Luna’s factual reliability and clearer communication of results. You can also adjust reasoning effort per request to balance the quality, responsiveness, and cost each task requires.
A single application may need different levels of intelligence as a request progresses. You might use GPT-6 Luna to classify incoming requests, GPT-6 Sol to investigate complex cases, and GPT-6 Astra when additional reasoning depth can materially change a decision. This concentrates intelligence where it creates the most value while managing latency and cost across the system.
Within each stage, repeated calls to the same model may reuse instructions, tool definitions, policies, and reference material. Reprocessing that context can erode the efficiency gained by selecting the appropriate model.
GPT-6 Sol and GPT-6 Luna support explicit prompt caching on Amazon Bedrock. You can mark prompt content for reuse, allowing subsequent requests to focus processing on new input. This is useful for coding assistants that reuse repository instructions, support applications grounded in the same policies, and document processes that apply a consistent extraction schema.
As AI usage grows, model quality is only part of what determines whether an application succeeds in production. Teams also need infrastructure that maintains performance as demand changes, economics that hold across repeated requests, and controls that protect sensitive data. Amazon Bedrock provides that foundation for GPT-6 Sol and GPT-6 Luna through a high-performance inference engine built for security and reliability at scale.
You can govern model access through AWS Identity and Access Management (IAM) policies and audit every invocation through AWS CloudTrail. Virtual private cloud (VPC) endpoints powered by AWS PrivateLink help keep traffic within your network boundaries. Inference runs on hardware-isolated infrastructure with zero-operator access, so even AWS operators cannot access your prompts or completions during inference.
Your inference data isn’t used for model training, and using GPT-6 Sol and GPT-6 Luna doesn’t require you to opt into sharing your data with OpenAI. For automated abuse detection, classifier-flagged traffic is retained by AWS for up to 30 days and processed programmatically. You can request zero data retention through your AWS account team. See data retention for details.
You can get started with GPT-6 Sol and GPT-6 Luna in the Amazon Bedrock console or programmatically through supported Amazon Bedrock APIs. For information about supported AWS Regions, endpoints, APIs, features, inference profiles and pricing, see the Amazon Bedrock documentation.
Interested in how Amazon Bedrock can support your team? Connect with us to start the conversation.
Tanvi is a Product Marketing Manager for Amazon Bedrock at Amazon Web Services (AWS), where she helps customers adopt and scale AI applications and agents with Amazon Bedrock.
Chris is a Member of Product Staff at OpenAI focused on the OpenAI APIs. His work includes collaboration with AWS on Amazon Bedrock to make OpenAI’s frontier models widely accessible to developers.
Manish is a Senior Product Manager for Amazon Bedrock.
Today, we’re excited to announce the availability of Claude Opus 5.5 on Amazon Bedrock and Claude Platform on AWS, the first of the Claude 5.5 model family. Claude Opus 5.5 is Anthropic’s most capable Opus model suitable for agentic coding, knowledge work, and long-running tasks.
This post covers Claude Opus 5.5’s improvements, practical guidance, and how to start building with the model on Amazon Bedrock.
According to Anthropic, Claude Opus 5.5 does more with fewer tokens than Claude Opus 5, and new pricing passes those gains straight to customers. Lower per-token prices and much cheaper cache reads stack on top of the efficiency gains. The result is an average lower cost per task than Claude Opus 5, so teams can run more ambitious agentic work at scale.
Claude Opus 5.5 is trained to communicate more clearly. As it works, it surfaces what it did, what it found, and what it needs, making long-running tasks easier to follow. Adaptive thinking is always on, and Opus 5.5 decides how much reasoning each task needs. You can use effort as your control instead of manual thinking budgets.
Claude Opus 5.5 is the first Opus model that comes with safety classifiers similar to Claude Fable 5.1 in biology, cyber security, and AI development. Requests will be refused more frequently as compared to previous Opus versions.
Claude Opus 5.5 capabilities are a good fit for industries where consistency and depth matter most. In software development, Opus 5.5 is an improvement over Opus 5 for longer-running sessions with clear communication and explainability, making it easier to use, review, and trust. For knowledge work, it requires fewer corrections compared to Opus 5 while working with and creating long documents and reports.
To try Claude Opus 5.5, open the Amazon Bedrock console, choose Test, then Playground, and select Claude Opus 5.5 as the model. From there, you can run a prompt directly against it.
Figure 1: Selecting an Anthropic Claude model in the Amazon Bedrock console Playground
Figure 2: Running a prompt against a Claude model in the Amazon Bedrock console Playground
Programmatically, you can call the model with the Anthropic Messages API against bedrock-runtime and bedrock-mantle (through the Anthropic SDK). You can also stay on the Invoke and Converse APIs on bedrock-runtime through the AWS Command Line Interface (AWS CLI) and AWS SDK.
pip install boto3.pip install anthropic.pip install aws_bedrock_token_generator.bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream.Here’s a quick example using the AWS SDK for Python (Boto3):
import boto3
import json
# Create a Bedrock Runtime client
bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
region_name="us-east-1"
)
# Invoke Claude Opus 5.5
response = bedrock_runtime.invoke_model(
modelId="global.anthropic.claude-opus-5-5",
contentType="application/json",
accept="application/json",
body=json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 4096,
"messages": [
{
"role": "user",
"content": "An S3 bucket serves 40 TB/month egress. Estimate the monthly egress cost at $0.09/GB, and state one architecture change to cut it. Show the calculation, keep it under 120 words."
}
]
})
)
result = json.loads(response["body"].read())
# Opus 5.5 is a reasoning model: the response may include a thinking block
# before the text block, so select the text block rather than a fixed index.
print(next(b["text"] for b in result["content"] if b["type"] == "text"))You can also use the Amazon Bedrock Converse API for a unified multi-model experience:
import boto3
# Create a Bedrock Runtime client
bedrock_runtime = boto3.client(
service_name="bedrock-runtime",
region_name="us-east-1"
)
# Invoke Claude Opus 5.5
response = bedrock_runtime.converse(
modelId="global.anthropic.claude-opus-5-5",
messages=[
{
"role": "user",
"content": [
{
"text": "Can you explain the features of Amazon Bedrock?"
}
]
}
],
inferenceConfig={
"maxTokens": 4096
}
)
if 'output' in response:
blocks = response['output']['message']['content']
print('\n'.join(b.get('text', '') for b in blocks if 'text' in b))You can also use the Anthropic Messages API through the anthropic SDK package for a streamlined experience:
from anthropic import Anthropic
from aws_bedrock_token_generator import provide_token
token = provide_token(region="us-east-1")
client = Anthropic(
base_url="https://bedrock-runtime.us-east-1.amazonaws.com/anthropic",
api_key=token,
)
# Invoke Claude Opus 5.5
response = client.messages.create(
model="global.anthropic.claude-opus-5-5",
max_tokens=1024,
messages=[{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}],
)
print(response)You can explore the Getting Started notebook for more examples. You can monitor usage, performance, and costs through Amazon CloudWatch and AWS Cost Explorer to scale your applications as demand grows.
Claude Opus 5.5 is available today on Amazon Bedrock through the US Geo CRIS (us.), EU Geo CRIS (eu.), AU Geo CRIS (au.), JP Geo CRIS (jp.) and Global CRIS (global.) inference profiles on bedrock-runtime. The model also runs in the US East (N. Virginia) Region (us-east-1), AP SouthEast (Melbourne) Region (ap-southeast-4) on bedrock-mantle.
See the Amazon Bedrock documentation for the full list of supported Regions. For pricing information, see Amazon Bedrock pricing. It is also available through Claude Platform on AWS in North America.
Give Claude Opus 5.5 a try in the Amazon Bedrock console, in Claude Platform on AWS, or explore the Getting Started notebooks on GitHub.
Aamna is a Senior Specialist Solutions Architect for Generative AI focusing on Anthropic models and operationalizing and governing generative AI systems at scale on Amazon Bedrock. She helps ISVs solve their challenges, embrace innovation, and create new business opportunities with Amazon Bedrock.
Dani is a Sr GenAI Specialist Solutions Architect at AWS and the SA lead for Amazon Bedrock Knowledge Bases. He helps enterprises across the world design and deploy generative AI solutions using Amazon Bedrock and Anthropic’s models and capabilities to build scalable, production-ready applications.
Sofian is a technology leader with over 12 years of experience building AI solutions, and leading high-performing teams to maximize customer outcomes. He is passionate about empowering diverse talents to drive global impact and achieve their career aspirations.
Eugenio is a Sr. Product Marketing Manager for Amazon Bedrock at AWS. With several years of experience in generative AI, he helps customers navigate the evolving landscape of foundation models and generative AI to adopt solutions that deliver measurable value.
As large language model (LLM) inference increasingly processes sensitive information and proprietary model context across personal, enterprise, and regulated settings, data must be processed inside a trusted environment. NVIDIA Confidential Computing (CC) provides a pathway for running these workloads securely using memory-encrypted confidential virtual machines (CVMs), confidential GPUs, and encrypted NVIDIA NVLink. This enables running production AI inference on trusted hardware.
Inference frameworks such as NVIDIA TensorRT LLM deliver best-in-class AI inference by combining framework-level optimizations with NVIDIA accelerated computing. However, when these frameworks run in a CC-enabled environment, secure execution changes assumptions behind memory movement, timing, scheduling, and multi-GPU communication. These changes introduce performance overhead if the runtime does not adapt. Maintaining high performance therefore requires the inference framework and the confidential computing environment to be optimized together.
For AI platform engineers evaluating confidential inference on NVIDIA Blackwell GPUs, this post examines the CC-aware adaptations that AI inference frameworks like TensorRT LLM use to account for secure execution while helping preserve inference performance. It presents a controlled methodology that teams can apply to quantify CC overhead on their own workloads.
Workload characteristics determine how visible CC overhead can be. High request volume can amortize fixed encryption costs by overlapping stalls with other work, making the direct effects harder to observe.
To expose these effects, select a workload with a long input context, extended output generation, and low concurrency. Long context stresses data movement during prefill, extended generation amplifies small per-token CC overhead during decode, and low concurrency limits the opportunity to hide those costs across concurrent requests.
The NVIDIA performance engineering team used these characteristics for the workload evaluated here.
| Parameter | Configuration |
|---|---|
| Model | nvidia/DeepSeek-R1-0528-NVFP4 |
| Inference framework | TensorRT LLM, PyTorch backend |
| I/O sequence length | 32K input/1K output |
| Concurrent requests | 1, 2, 4, 8, and 16 |
| Parallelism | TP=8, EP=1, PP=1 |
| KV cache | FP8 |
To isolate the performance impact of CC, run the same workload under two conditions: confidential compute disabled (CC off) and confidential compute enabled (CC on) holding the model, hardware, framework version, sequence lengths, parallelism, and concurrency constant so that CC state is the only changing variable.
At each concurrency level:
Performance teams can use these measurements to quantify how much of the CC-off baseline is retained when CC is enabled for the target workload. The NVIDIA performance engineering team applied this comparison using the hardware and software configuration summarized in Table 2.
| Component | Version / Detail |
|---|---|
| Hardware | 1 NVIDIA DGX B200 system ( 8 NVIDIA B200 GPUs) |
| Platform | Intel TDX |
| Host OS | Ubuntu 25.10 |
| Host kernel | 6.17.0-20-generic |
| Guest OS | Ubuntu 24.04.4 LTS |
| Guest kernel | 6.8.0-124-generic |
| Guest vCPUs | 256 |
| Guest NUMA | 2 nodes |
| NVIDIA driver | 595.71.05 |
| VBIOS | FW 1.4.x [97.10.64.00.0C] |
| GPU power limit | 1,000 W |
| CUDA | 13.2 |
| TensorRT LLM | nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc22 |
| NCCL | v2.30 |
| OpenSSL | 3.6.0 |
| Orchestration | Docker Container + NVIDIA Container Toolkit |
As shown in Figures 1 and 2, across concurrency 1–16, CC on retained 96.1– 98.2% of CC off output-token throughput, while mean TPOT remained within 1.2% to 4.3% of the baseline.
The NVIDIA Blackwell confidential computing architecture introduces hardware-enforced security paths for protecting data and workloads in use. For a detailed overview of the architecture, see Hardware-Rooted AI Security That Won’t Slow You Down.
For TensorRT LLM users and framework developers, the following details show how these secure paths change common runtime assumptions and how TensorRT LLM adapts to reduce the resulting performance overhead.
In the B200 CC, host-to-device transfers pass through a software encrypted bounce buffer because the GPU cannot directly access protected CVM memory. This changes the behavior expected by inference frameworks: pinned memory no longer provides its usual asynchronous-transfer advantage, and some copies can block the calling thread.
The kernel autotuner normally uses CUDA events to compare candidate tactics. In the tested CC configuration, CUDA-event timestamps produced an unstable timing signal, which could cause the autotuner to select a slower tactic.
%globaltimer for tactic measurements under CC while retaining CUDA events outside CC. For details, see TensorRT LLM PR #11657.NVLS (NVLink SHARP) multicast is not available in B200 CC configurations. Without NVLS, NCCL_SYMMETRIC cannot provide its intended multicast benefit but may still incur memory registration and cross-rank synchronization costs before using a non-multicast collective path.
NVIDIA Confidential Computing extends hardware-enforced protection across confidential VMs, NVIDIA Blackwell GPUs, and encrypted NVLink, protecting proprietary models, enterprise context, and sensitive prompts while they are processed.
Confidential computing does not remove the need for performance engineering—it makes framework awareness even more important. With TensorRT LLM CC-aware adaptations to secure data movement, autotuning, and multi-GPU communication in place, confidential DeepSeek-R1 inference retained more than 96% of CC-off output-token throughput while keeping per-token latency overhead below 5% on eight NVIDIA B200 GPUs.
As organizations move private inference into production, security configuration and inference optimization should be approached as a single deployment problem. Enable confidential computing, attest the environment, and benchmark CC-on and CC-off using the exact workload you intend to serve. For AI platform engineers and TensorRT LLM users moving private inference into production, security configuration and inference optimization should be treated as a full-stack engineering effort.
To start planning your confidential inference deployment, use the NVIDIA Trusted Computing documentation and explore the latest TensorRT LLM features and release notes. To stay up to date with the latest developments, follow NVIDIA Confidential Compute news.
I would like to thank Dan Hansen, Sheel Pethe, Samuel Mendoza-Jonas, Moein Ghaniyoun, Vidhya Krishnan, Avinash Ahuja, Laikh Tewari, Laura Martinez, and Matheen Raza for their engineering contributions, technical guidance, analysis, and thoughtful review throughout this work.
General-purpose agents handle a broad range of tasks, but you still need them to follow the procedures that run your business: compliance checks, document-processing workflows, escalation policies, engineering conventions. Encoding all of that in one system prompt or in application logic gets hard to maintain and update. Skills are a modular alternative. A skill is a reusable set of instructions, usually stored in a SKILL.md file, that teaches an agent a domain-specific task like redacting a contract, reconciling an invoice, or following a team’s pull-request conventions. Because skills follow the open Agent Skills standard, they are portable across compatible harnesses, and the agent loads only the skill it needs at runtime instead of carrying every procedure in its core instructions.
A skill packages one or more tools with the context an agent needs to use them correctly:
This modular approach helps teams specialize agents faster, reuse proven procedures across agents and workflows, keep behavior consistent, and update domain-specific guidance without fine-tuning the underlying model or rewriting the agent’s core logic.
This skill composability in agents introduces two failure modes that general output-quality metrics can miss: the agent invokes a skill that is not appropriate for the task and the agent invokes the right skill but skips or only partially follows its instructions. Both failures can produce a fluent, plausible response without having used your pre-determined domain knowledge. An evaluation therefore cannot examine the final response alone.
To make these failures measurable, Strands Evals SDK and Amazon Bedrock AgentCore Evaluations, a capability of Amazon Bedrock AgentCore, add skill-focused evaluators:
In this post, you will learn how to evaluate skill selection and instruction following from a recorded trajectory in Strands Evals, add deterministic routing checks to a test suite, evaluate skill behavior from OpenTelemetry traces with AgentCore Evaluations, and interpret per-skill results to choose the right fix, all through the AgentCore CLI.
An agent receives a task and a catalog of skills, chooses a skill, loads it, and acts. The run is recorded as a trajectory in Strands Evals or an OpenTelemetry trace in your observability layer. This record can now be used for all three skill evaluators. Skill Selection Accuracy checks whether each invoked skill fits the task and whether the agent invoked the correct skill. The following figure shows how the agent chooses a skill from the 1:n skills provided to it. Skill Selection Accuracy then scores whether the selected skill is the correct one for the task. You can find the prompt template and the rubric of this evaluator in the prompt template documentation.
Skill Instruction Following asks how fully the agent followed that skill’s prescribed steps. You can find the prompt template and the rubric for this evaluator in the prompt template documentation.
SkillInvoked is deterministic. It calls no model and is specific to Strands Evals.
An overview of these skill evaluators is demonstrated diagrammatically in the following figure.
Consider an HR assistant agent with skills for paid time off (PTO) planning and discussing employee benefits. An employee asks about their dental and vision benefits. If the agent invokes the benefits skill, it may produce a more polished response. If the tool call succeeded but the agent chose the wrong playbook, Skill Selection Accuracy isolates that routing decision.
Now suppose the agent correctly invokes the PTO-planning skill for a related request. The skill instructs the agent to identify the employee_id, check the PTO balance, check the rollover rules against the latest HR policy, and then submit a PTO request if the conditions allow. If the agent checks the PTO balance but skips the rollover rules, the agent might still return a plausible response while violating the prescribed process. Skill Instruction Following isolates that execution failure and identifies the skipped step.
The failures require different fixes. An inappropriate selection often points to overlapping or ambiguous skill descriptions. Incomplete instruction following might call for clearer steps, a different skill structure, or a more capable agent model.
| Evaluator | Availability | Score | Question answered |
| Skill Selection Accuracy | Strands Evals and AgentCore Evaluations | Binary, per invoked skill | Was invoking this skill appropriate for the task? |
| Skill Instruction Following | Strands Evals and AgentCore Evaluations | Five levels, per invoked skill | How fully did the agent follow this skill’s prescribed steps? |
| Skill Invoked | Strands Evals | Binary, deterministic | Was this named skill successfully loaded? |
Because judge-based evaluators return per-invoked-skill results, multi-skill runs remain diagnosable: you can identify which selection or instruction-following result lowered the aggregate score. If no skill is invoked, the judge-based evaluators don’t produce a score. Pair them with SkillInvoked when a regression test has a known routing requirement.
InvokeModel permission for the judge model.To follow the Strands Evals section, install the SDKs:
pip install strands-agents-evals strands-agentsYou also need a recorded agent run. The skill evaluators accept either a Strands Evals Session or a raw message list as the trajectory. At launch, skill extraction recognizes signals from the Strands AgentSkills plugin, Claude Code, Claude Agent SDK, OpenAI Agents SDK, Codex, Gemini CLI, OpenHands, Google ADK, and generic SKILL.md file reads.
To follow the AgentCore Evaluations section, you need:
The examples in this post use the AgentCore CLI:
npm install -g @aws/agentcoreStrands Evals is useful when you control the test cases and can rerun the agent during development or continuous integration. Check out the complete Strands evals code sample created for the HR assistant agent in the complete code sample.
from strands_evals import Case, Experiment
from strands_evals.evaluators import (
SkillInstructionFollowingEvaluator,
SkillInvoked,
SkillSelectionAccuracyEvaluator,
)
case = Case(
name="q3-revenue-tables",
input="Summarize the revenue tables in q3-report.pdf",
)
evaluators = [
SkillSelectionAccuracyEvaluator(),
SkillInstructionFollowingEvaluator(),
SkillInvoked(skill_name="pdf-table-extraction"),
]The skill evaluators read the run’s trajectory. TracedHandler collects the agent’s spans and attaches them as the trajectory:
from strands import Agent
from strands_evals import TracedHandler, eval_task
@eval_task(TracedHandler())
def task_function():
return Agent(...) # your skill-equipped agent
experiment = Experiment(cases=[case], evaluators=evaluators)
report = experiment.run_evaluations(task_function)
report.run_display()Suppose the agent loaded pdf-table-extraction, ran pdftotext -layout, but never opened the extracted file or located the table boundaries. A simplified report:
SkillSelectionAccuracyEvaluator: score=1.00, pass=True
pdf-table-extraction: The skill directly matches the request.
SkillInstructionFollowingEvaluator: score=0.50, pass=False
pdf-table-extraction: The extraction phase was completed,
the table-boundary phase was skipped.
Steps:
- Extract text with layout preservation: covered
- Locate table boundaries: skipped
- Summarize each table's headline figure: partial
SkillInvoked: score=1.00, pass=True
skill 'pdf-table-extraction' was invokedSkill Instruction Following uses five ratings: Fully Followed (1.0), Mostly Followed (0.75), Partially Followed (0.5), Minimally Followed (0.25), and Not Followed (0.0). It passes at Mostly Followed or better.
SkillInvoked for every critical skill that a specific regression case must invoke. Because it doesn’t call a model, it is a fast routing assertion.report.test_passes: if not all(report.test_passes): raise SystemExit(1).AgentCore Evaluations works directly with existing OpenTelemetry traces, supporting on-demand evaluation, batch processing of stored sessions, and continuous sampling of live traffic. Telemetry is organized into sessions, traces, and spans. Because skill evaluators operate at the tool-call level, each result includes a spanContext with the sessionId, traceId, and spanId of the recognized skill invocation.
A skill invocation is recognized through either a SKILL.md filesystem read, which works across frameworks, or a native skill-loading tool in Strands Agents, LangGraph Deep Agents, Google ADK, or the Claude Agent SDK.
AgentCore Evaluations includes two built-in judge-based evaluators for skills. To score something the built-ins don’t cover, create a custom evaluator at the TOOL_CALL level. Tool-level templates can reference skill placeholders:
| Placeholder | Contents |
{invoked_skill} |
Name of the skill loaded on this tool call |
{skill_content} |
The loaded SKILL.md body |
{available_skills} |
The catalog offered at runtime, or “(not recorded by this harness)” |
{user_message} |
The request that triggered the invocation |
{context} |
The conversation record |
For example, a template that checks one specific property of a skill run:
## Skill instructions
{skill_content}
## Conversation record
{context}
## Evaluation Question
Did the agent complete every numbered step in the skill instructions above, in the order given? Answer Yes or No.The placeholders you reference also decide when the evaluator runs. A template containing {invoked_skill} runs only on skill-invocation spans, and one containing {skill_content} additionally requires the loaded body.
The following commands target the separate skill-enabled runtime. Substitute the runtime name from skills-evaluation/agent_config.json (agent_id or agent_arn) for <skill-runtime>. Strands.SkillInvoked is client-side only and has no CLI equivalent.
Use on-demand evaluation to investigate a session, validate a recent change, or evaluate staged traffic:
agentcore run eval \
--runtime <skill-runtime> \
--evaluator Builtin.SkillSelectionAccuracy Builtin.SkillInstructionFollowing \
--session-id <session-id>Use batch evaluation to score many stored sessions at once, for example to establish a baseline before a skill-catalog change:
agentcore run batch-evaluation \
--runtime <skill-runtime> \
--evaluator Builtin.SkillSelectionAccuracy Builtin.SkillInstructionFollowingOnline evaluation samples live traffic so you can detect behavior that a curated test set didn’t anticipate:
agentcore add online-eval \
--name HRSkillsProductionEval \
--runtime <skill-runtime> \
--evaluator Builtin.SkillSelectionAccuracy Builtin.SkillInstructionFollowing \
--sampling-rate 100 \
--enable-on-createThen deploy to provision the online evaluation configuration:
agentcore deployContinuous evaluation is particularly useful for detecting catalog drift (when a new skill overlaps with an existing description), unanticipated phrasing (when real requests differ from curated test prompts), and long-session failures (when instruction following degrades as context grows).
When you’re evaluating agent skills, the first thing to internalize is that routing and execution are two different failure modes, and your evaluation strategy needs to separate them cleanly. If you see a high selection score but a low instruction-following score, that indicates the router picked the correct skill but it was not completely executed. The opposite pattern means the skill would have worked fine if only it had been invoked. Running these two evaluations together, rather than collapsing them into one pass/fail number, is what lets you tell those two stories apart.
Before you build anything custom, start with built-in evaluators to establish a baseline, so any custom logic you add afterward can cover the gap the baseline actually missed. For requirements where you already know the correct routing behavior, don’t rely on a judge model to catch it. Add a deterministic SkillInvoked assertion for every skill that must fire.
After you’re running evaluations, resist the urge to only look at the aggregate score. Per-step evidence is where the real diagnosis happens. It tells you whether an instruction was fully covered, partially completed, or skipped outright, and that level of detail is what turns a failing eval into an actionable fix. This is also why you should evaluate at every lifecycle stage instead of waiting for the final output. A failure at the end doesn’t tell you whether the router sent the request to the wrong place or the right skill executed poorly, and you need both signals to know what to fix.
Skill-level and end-to-end evaluation should be paired, because a skill can execute perfectly and still be the wrong skill for the request in front of it. You will reduce a lot of this ambiguity upstream by writing skill descriptions that are genuinely discriminative. The same logic applies to scope of a skill. A skill built to handle six unrelated functions doesn’t have a single definition of correct behavior, which makes it nearly impossible to evaluate consistently.
State clearly that only the path actually taken in a given run should be evaluated, otherwise untaken branches get miscounted as skipped steps and quietly corrupt your pass rates. And before you trust any of these scores, validate that your extraction pipeline is actually working against live traces.
As you scale this across tools, thresholds may not transfer cleanly. Each evaluation surface needs to be calibrated on its own terms, since a passing score on Strands Evals and a passing score on AgentCore Evaluations aren’t guaranteed to mean the same thing. And ultimately, none of this should live outside your deployment pipeline. Skill quality regressions need to block a release the same way a failing unit test would, or the evaluation work you’ve done up to that point isn’t actually protecting production.
Skills make agents inexpensive to specialize, but a plausible final answer does not prove that the agent selected the right procedure or followed it. Skill Selection Accuracy and Skill Instruction Following separate those failure modes and return evidence for each invoked skill. In Strands Evals, SkillInvoked adds a deterministic guard for known routing requirements. In AgentCore Evaluations, the two judge-based metrics can run on demand for a specific session, as a batch over stored sessions, or continuously over sampled traffic.
Use Strands Evals when you have test cases and recorded trajectories you can rerun. Use AgentCore Evaluations when you want to evaluate OpenTelemetry traces from staged or live agents. Many teams will use both: deterministic and judge-based gates before deployment, followed by trace-based monitoring in production.
Thank you to Ritvika Pillai, Vincent Chen, Qiaoxuan Xue, and Shoaib Javed for the AgentCore Evaluations implementation, to Po-Shin Chen for the Strands Evals review, to Anwesan Pal for early discussions on skill evaluation, to Ben Coombs for product guidance, and to everyone else who helped make this work possible.
Sangmin is an Applied Scientist at AWS AI Labs, where he conducts research and develops machine learning solutions for agentic AI, with a focus on evaluation frameworks and advancing agent behavior and performance. His interests include agentic AI, generative models, and multimodal AI. Outside of work, he enjoys traveling and exploring new places.
Bharathi is a Generative AI Data Scientist at AWS. She is passionate about Responsible AI to increase the reliability of AI agents in real-world scenarios. Bharathi guides internal teams and AWS customers on their responsible AI journey.
Shruthi is a Solutions Architect at AWS based in Chicago, Illinois. She works with startups in the US East region, helping early-stage and growth-stage companies design and build scalable cloud architectures on AWS, with a focus on generative AI, agentic AI workflows, data, and migration workloads. Outside of work, she enjoys walking, yoga, and discovering new food and coffee places.
Visakh is a Solutions Architect at AWS, working with customers and internal teams to bring legibility, trust, and reliability to production artificial intelligence (AI). His work on agentic reliability and AI safety has been presented at machine learning conferences. Outside of work, he enjoys music, birding, and sports.
Renu is a Software Development Engineer at Amazon Web Services, where she works on Amazon Bedrock AgentCore. She previously helped build AgentCore Memory and now focuses on developing scalable systems for AgentCore Evaluations & optimization, helping customers assess and continuously improve the quality of their agentic applications.
Vinayak is a Sr. Applied Scientist at Amazon Web Services. With several years of experience, he has worked on various domains of AI like computer vision, natural language processing, recommendation systems etc. Currently, Vinayak helps build new capabilities on the AgentCore and Strands, enabling customers to evaluate their Agentic applications with ease, accuracy and efficiency.
Haibo is a Principal Applied Scientist and Manager working on agentic AI at Amazon. He holds a Ph.D. from the University of Utah. His work focuses on large language models (LLMs) and AI agents, where he leads research in areas such as agent evaluation, agent tool optimization, prompt optimization, and model routing. He has served as an area chair for conferences such as AAAI and ACL, and previously as Program Chair for KDD 2025 Workshop on Prompt Optimization.
Jonathan is a Senior Software Engineer at AWS. He builds agent environments, evaluation frameworks, and post-training infrastructure that help turn advances in agentic AI into reliable production systems.
AI factories are power-limited systems that deliver maximum value when fully optimized. GPU workload placement is a key optimization. Poor workload placement fragments topology domains and forces traffic across shared links, reducing throughput, raising job costs, and leaving GPUs consuming provisioned power while waiting on data without advancing the workload.
GPUs exchange data continuously during training and inference, so distributed workloads benefit from communication locality. NVIDIA NVLink and NVLink Switch provide high-bandwidth, all-to-all scale-up connectivity within rack-scale GPU domains, while NVIDIA Spectrum-X Ethernet provides predictable, low-latency scale-out networking across systems and racks.
A scheduler can place workloads efficiently only with a current, accurate view of those GPU and fabric relationships, and keeping that view current as the cluster changes is where placement breaks down in practice.
NVIDIA Topograph solves that problem. It discovers cluster topology from cloud APIs or on-premises fabric systems, normalizes it into a common model, and publishes it in the format each workload manager expects: Kubernetes node labels, Slurm topology configuration, or Slinky ConfigMaps. Within the NVIDIA DSX OS cluster orchestration layer, Topograph works alongside Dynamic Resource Allocation (DRA) and KAI Scheduler to enable topology-aware gang scheduling across AI factory infrastructure.
This post walks through deploying Topograph and using it to schedule topology-aware workloads on Kubernetes, Slurm, and Slinky.
Topograph maps how cluster hardware is connected so schedulers can favor nearby resources. Think of the network as a road system: GPUs within the same locality domain have short, high-bandwidth paths, while traffic between domains crosses more shared links and switches. Spreading a tightly coupled workload across distant domains can increase contention and latency, so Topograph helps place workloads in the most efficient locations and avoid these bottlenecks.
Modern NVIDIA Quantum InfiniBand ports can achieve up to 800 Gb/s, while NVIDIA NVLink provides 1.8 TB/s of bidirectional bandwidth per GPU in its fifth generation (NVIDIA Blackwell, such as GB200/GB300) and 3.6 TB/s per GPU in its sixth generation (Vera Rubin), through a dedicated NVIDIA NVLink Switch fabric. That non-blocking, all-to-all design gives each GPU its own lane rather than sharing bandwidth under load. Schedulers with a current view can favor GPUs in the closest topology domain.
Slurm and Kubernetes both support topology-aware allocation, but a scheduler can only act on the topology it observes. Topograph regenerates that view on request and upon watched cluster changes, so the scheduler works from current data rather than a manually maintained snapshot.
Topograph is an open source toolkit that identifies a cluster’s network topology, enabling workload managers to make topology-aware scheduling decisions. It has two concepts: providers and engines. A provider discovers topology from cloud APIs or on-premises systems and normalizes it into a canonical model. An engine translates that model into Slurm configuration, Kubernetes labels, Slinky ConfigMaps, Node Feature Discovery (NFD) resources, or instance-oriented topology JSON.
Cloud providers that have a working integration with Topograph include Google Cloud, Lambda, Nebius, Nscale, and OCI, with more cloud and colocation providers in development.
| Environment or provider | Kubernetes | Slurm | Graph | ||
| Node labels (k8s) | NFD resources (nfd) | Slinky ConfigMap (slinky) | |||
| Cloud and hosted providers | |||||
| Crusoe | Yes | Yes | Yes | Yes | Yes |
| Google Cloud | Yes | Yes | Yes | Yes | Yes |
| Lambda | Yes | Yes | Yes | Yes | Yes |
| Nebius | Yes | Yes | Yes | Yes | Yes |
| Nscale | Yes | Yes | Yes | Yes | Yes |
| Oracle Cloud Infrastructure (OCI) | Yes | Yes | Yes | Yes | Yes |
| On-premises deployment models | |||||
| InfiniBand in Kubernetes | Yes | Yes | Yes | Yes | Yes |
| InfiniBand on bare metal or VMs | No | No | No | Yes | Yes |
| On-premises networking and topology | |||||
| Spectrum-X or NetQ-managed fabric | Yes | Yes | Yes | Yes | Yes |
| MNNVL NVLink partitions (DRA block topology only) | No | No | Yes | No | No |
Scope and interpretation. This matrix reflects current upstream main as of September 16, 2026. It shows supported provider-to-engine output combinations; requirements can vary by Topograph version, environment, and provider configuration.
topology.conf output path.Five components keep that view current:
The API server exposes five service endpoints:
POST /v1/generate – submits an asynchronous request and returns its ID with HTTP 202.GET /v1/topology?uid=<request-id> – returns HTTP 202 while processing and HTTP 200 with the result when complete.POST /v1/lookup – returns the cached status or result for the same request body without submitting it again.GET /healthz – is the liveness endpoint.GET /metrics – exposes Prometheus metrics.The aggregation delay is required; 15 seconds is typical. Repeated identical requests reset a trailing timer and are processed once, reducing redundant work during bursts of cluster events.
For testing without production hardware, simulation models describe node and switch hierarchies. The kwok-nodes utility and Kind/KWOK helpers turn those models into virtual Kubernetes nodes.
The default Kubernetes scheduler doesn’t discover physical interconnect hierarchy. Topograph addresses that gap by publishing provider-reported topology as node labels, which native affinity and topology-aware schedulers can consume.
Prerequisites are Kubernetes 1.27 or later, Helm 3.10+ or 4.x, kubectl permissions, and a supported provider. KAI Scheduler or Kueue TAS is optional for topology-aware gang scheduling.
Topograph is distributed as a Helm chart:
helm repo add topograph https://dsx-ai-factory.github.io/topograph helm repo update helm install topograph topograph/topograph \ --namespace topograph \ --create-namespace \ --set engine.name=k8s \ --set provider.name=<provider>
Replace <provider> with the value that matches your environment.
The repository includes example Helm values files in charts/topograph, named with a values.k8s prefix and a short scenario description. Each carries inline configuration comments.
After installation, verify that the deployment completed successfully:
helm test topograph --namespace topograph
The bundled test hooks query /healthz and /metrics in-cluster and confirm the responses include the topograph_version metric.
Confirm the Pods are running:
kubectl get pods -n topograph
Topograph represents fabric locality with a variable-depth label family and accelerator locality with a two-level hierarchy:
fabric.topograph.run/tier-0 # switch closest to the node fabric.topograph.run/tier-1 # next fabric tier outward fabric.topograph.run/tier-<N> # additional discovered tiers accelerator.topograph.run/domain # accelerator domain accelerator.topograph.run/sub-domain # optional nested sub-domain
Fabric tier 0 is the leaf switch closest to the compute node, and tier numbers increase outward. Topograph writes only the tiers present in the discovered topology, with no fixed maximum depth. Operators can set the Kubernetes engine’s fabricLabels array and acceleratorLabel parameter to use custom keys; tiers beyond that array are not labeled. The sub-domain key is fixed.
To verify that the labels have been applied, run:
kubectl get nodes --show-labels | grep -E 'fabric\.topograph\.run|accelerator\.topograph\.run'
If labels are missing, inspect the Topograph logs:
kubectl logs -n topograph -l app.kubernetes.io/name=topograph
NOTE: Topograph reflects reported rather than intended topology. Labels refresh when generation runs, for example, after a watched node or pod change. Visibility of a fabric change depends on the provider and its triggering events.
The API is a ClusterIP service by default. With the release and namespace above, its address is: topograph.topograph.svc.cluster.local:49021.
For local debugging:
kubectl -n topograph port-forward svc/topograph 49021:49021 curl http://localhost:49021/healthz
Topology labels can be used as topologyKey values in preferred Pod affinity:
affinity:
podAffinity:
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 90
podAffinityTerm:
labelSelector:
matchLabels:
app: myapp
topologyKey: fabric.topograph.run/tier-0
- weight: 70
podAffinityTerm:
labelSelector:
matchLabels:
app: myapp
topologyKey: fabric.topograph.run/tier-1
Each matching term contributes to a candidate node’s score, strongly favoring the tier-0 domain of existing app=myapp Pods while also rewarding tier-1 locality. Because the default scheduler places Pods individually, this is a preference rather than globally optimal gang placement.
KAI Scheduler and Kueue can use the same node labels for topology-aware gang placement. Kubernetes 1.36 also introduced alpha topology-aware workload scheduling through KEP-5732. Upstream beta work is ongoing; consult the enhancement tracker rather than depending on a specific future release.
KAI Scheduler (a CNCF Sandbox project donated by NVIDIA) organizes node labels into a hierarchy:
apiVersion: kai.scheduler/v1alpha1
kind: Topology
metadata:
name: cluster-topology
spec:
levels:
- nodeLabel: topology.kubernetes.io/zone
- nodeLabel: fabric.topograph.run/tier-1
- nodeLabel: fabric.topograph.run/tier-0
- nodeLabel: kubernetes.io/hostname
Apply it with kubectl apply -f cluster-topology.yaml, then annotate a multi-Pod Job:
apiVersion: batch/v1
kind: Job
metadata:
name: topology-aware-workers
annotations:
kai.scheduler/topology: cluster-topology
kai.scheduler/topology-required-placement: fabric.topograph.run/tier-1
kai.scheduler/topology-preferred-placement: fabric.topograph.run/tier-0
spec:
parallelism: 4
completions: 4
template:
metadata:
labels:
app: inference-worker
spec:
schedulerName: kai-scheduler
restartPolicy: Never
containers:
- name: worker
image: nvcr.io/nvidia/nemo:latest
resources:
limits:
nvidia.com/gpu: 1
The required annotation keeps the gang within a single tier-1 domain. The preferred annotation asks KAI to concentrate Pods in a tier-0 domain when feasible, but permits multiple tier-0 domains inside the required boundary.
For more advanced topology-aware scheduling examples, see the documentation for Grove and NVIDIA Dynamo.
Grove provides Kubernetes APIs and an operator for hierarchical gang scheduling, topology-aware placement, and coordinated scaling. Dynamo is an open source distributed inference serving framework that integrates with Grove for Kubernetes workload orchestration.
Topograph also supports consumers already using Node Feature Discovery. The nfd engine publishes one NodeFeature per selected topology node and one NodeFeatureGroup for every distinct fabric-tier, XCLR-domain, and XCLR-sub-domain value. The NFD master evaluates those specifications and owns each group’s status.nodes membership.
Install nfd first with its alpha NodeFeatureGroupAPI feature gate enabled; it is off by default. Then select the engine and the namespace where the NFD master runs:
engine: name: nfd nfdNamespace: node-feature-discovery
Use this output when a downstream component consumes NodeFeatureGroup objects; it is not a substitute for Kubernetes topologyKey labels. For native Pod affinity, KAI Scheduler, or Kueue TAS, continue to use engine: k8s. The chart scopes NFD permissions to the nfd namespace. The engine deletes stale Topograph-managed objects after reconciliation, but preserves the last published topology if a generation produces none.
Topograph generates cluster-wide configurations in the tree and block formats, shown in the top-center and bottom-center panels of Figure 2, below. Slurm 25.05 introduced per-partition configuration in YAML format, which Topograph also supports, as shown in the diagram.
Slurm clusters typically run on Linux bare-metal servers or virtual machines, where Topograph is installed via a native package manager. The repository includes Debian and RPM build targets:
make deb # Debian / Ubuntu make rpm # RHEL / Rocky / SUSE
The package installs the service without starting it, so you can review and edit the configuration file /etc/topograph/topograph-config.yaml
http: port: 49021 provider: <provider> engine: slurm requestAggregationDelay: 15s
Replace <provider> with the value that matches your environment.
After updating the configuration, start the service and verify that it is healthy:
sudo systemctl enable --now topograph.service curl http://localhost:49021/healthz
To initiate discovery, POST to Topograph’s /v1/generate endpoint, which regenerates the Slurm topology configuration.
Submit a request and poll its result:
id=$(curl -s -X POST -H 'Content-Type: application/json' \ -d @payload.json http://localhost:49021/v1/generate) curl -s "http://localhost:49021/v1/topology?uid=$id"
For cluster-wide tree output, use an absolute path:
{
"engine": {
"name": "slurm",
"params": {
"plugin": "topology/tree",
"topologyConfigPath": "/etc/slurm/topology.conf",
"reconfigure": true
}
}
}
Use topology/block plus optional blockSizes for block output.
The optional reconfigure parameter runs scontrol reconfigure after a file is written and defaults to false. If topologyConfigPath is omitted, Topograph returns the generated content from the result endpoint instead of writing a file.
{
"engine": {
"name": "slurm",
"params": {
"topologies": {
"gpu-block": {
"partition": "gpu",
"plugin": "topology/block",
"blockSizes": [8, 16]
},
"cpu-tree": {
"partition": "cpu",
"plugin": "topology/tree"
},
"default": {
"plugin": "topology/flat",
"clusterDefault": true
}
},
"topologyConfigPath": "/etc/slurm/topology.yaml",
"reconfigure": true
}
}
}
For node-state-driven refresh with a provider that auto-discovers Slurm node mappings, run the repository’s script as root:
scripts/create-topology-update-script.sh -p <provider> -c /etc/slurm/topology.conf
It registers a permanent strigger for node up and down transitions. It does not detect arbitrary switch rewiring or every inventory change.
Slinky, developed by SchedMD, runs Slurm on Kubernetes. NVIDIA acquired SchedMD in December 2025. The Topograph Slinky engine maps Kubernetes nodes to slurmd Pods and writes Slurm topology data to a ConfigMap.
The Slinky engine supports cluster-wide topology/tree and topology/block output, as well as multiple-topology YAML for partition-specific configurations.
Install Topograph as a Helm chart:
helm repo add topograph https://dsx-ai-factory.github.io/topograph helm repo update helm install topograph topograph/topograph \ --namespace topograph \ --create-namespace \ --values my-values.yaml
The repository provides ready-to-adapt Helm examples for tree, block, per-partition, and InfiniBand block deployments.
Topograph regenerates and updates the ConfigMap when selected slurmd Pods change.
The dra provider is a narrower Slinky block-topology option for MNNVL systems. It reads existing nvidia.com/gpu.clique labels when regenerating the topology configuration.
For dynamic Slurm nodes, the optional useDynamicNodes mode also annotates selected Kubernetes nodes with the current Slurm topology specification. ConfigMap updates and dynamic-node reconciliation are distinct mechanisms, so choose the mode that matches the deployed Slinky configuration.
Placement problems compound at scale and surface as network congestion. Topograph gives schedulers a current, provider-reported map of the physical network, so topology-aware decisions stay consistent across cloud and on-premises environments without manual maintenance.
Through KAI Scheduler, Kueue, and native Kubernetes, the map improves AI factory efficiency, tokens per watt, and cost.
Deploy Topograph from the dsx-ai-factory/topograph GitHub repo, or learn more about the DSX OS ecosystem.
Reactiv offers a mobile commerce product that helps Shopify merchants launch and manage native mobile apps, where shoppers convert at 2–4 times the rate of web visitors. For these merchants, a stale homepage or a missed promotional window costs real revenue. Yet keeping an app fresh requires constant manual work: choosing which products to feature, rearranging sections, generating new assets, and publishing updates on time. Most merchants don’t have the bandwidth to do this every week. Reactiv used Amazon Bedrock AgentCore to automate these updates, reducing merchant configuration time by 80 percent and getting to production 33 percent faster.
Reactiv set out to solve this with an AI Scheduler that updates merchant apps autonomously on a schedule. A merchant describes what they want in natural language, such as “Refresh my homepage with best sellers every Monday at 9 AM,” and the system handles the rest. They built it as a three-agent system on Amazon Bedrock AgentCore with the Strands Agents SDK and had it in production within weeks. They then unified their interactive and scheduled agents onto a single stack, achieving shared memory across both modes.
In this post, we walk through the architecture behind those results, why Reactiv chose Amazon Bedrock AgentCore, and how their product has evolved since launch.
Reactiv builds native iOS and Android apps for Shopify merchants. Their tools include a low-code app builder, analytics dashboards, and AI-powered features. Before the AI Scheduler, Reactiv had already built a conversational AI builder. Merchants could chat with an AI assistant in the Reactiv dashboard to modify their app in real time.
That system worked for interactive use, but Reactiv’s merchants asked for something different: autonomous updates that happen on a schedule without any manual intervention. Building this required capabilities that the existing architecture didn’t support:
Reactiv needed a solution that could host multi-agent graphs, persist memory across sessions, connect to MCP servers natively, and isolate each merchant’s data automatically.
Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale with any framework or model. Reactiv used three of its capabilities to address all four requirements. AgentCore runtime, a capability of Amazon Bedrock AgentCore, handles managed agent execution. AgentCore memory, a capability of Amazon Bedrock AgentCore, provides persistent cross-session context. AgentCore Identity, a capability of Amazon Bedrock AgentCore, handles service-to-service authentication.
Managed agent runtime. AgentCore runs agents in Firecracker microVMs, the same isolation technology behind AWS Lambda. Reactiv packages their Strands agent graph as a Docker image, pushes it to Amazon Elastic Container Registry (Amazon ECR), and deploys it to AgentCore. Agents spin up when a schedule triggers and shut down when done. No Amazon ECS clusters, scaling policies, or idle compute.
“We don’t manage containers, orchestrators, or scaling policies. We package our agent code as a Docker image, deploy it to AgentCore, and it handles the rest.” — Adam Gibicar, Senior AI Developer, Reactiv
Built-in memory. AgentCore provides long-term memory that persists across agent sessions. Reactiv uses three strategies. A session summarizer condenses each job’s actions into context for future runs. A preference learner tracks which layouts a merchant approves or rejects over time. A semantic fact extractor stores knowledge about the merchant’s store, such as product categories, top sellers, and brand guidelines. Memory is scoped per merchant, keeping each merchant’s data private. No custom vector database or retrieval pipeline required.
Native MCP hosting. Reactiv hosted their Config MCP on Amazon Bedrock AgentCore runtime. The MCP runs as a stateful server that initializes the merchant’s current app configuration at session start. The Builder Agent performs mutations against this live state, validated against the schema on every call. With AgentCore Identity, Reactiv handles service-to-service authentication natively, removing the custom authentication layer and JSON-RPC handshake code they had previously built and maintained.
Multi-tenant isolation. Each merchant’s execution context, memory, and agent state runs in its own Firecracker microVM. Merchant A’s preferences remain isolated from Merchant B’s sessions. AgentCore handles tenant routing and isolation at the infrastructure level.
The following diagram shows the end-to-end flow of the AI Scheduler.
Figure 1: End-to-end flow of the Reactiv AI Scheduler. Amazon EventBridge triggers a Lambda executor that invokes the AgentCore runtime, where three Strands agents coordinate across MCP tools, Lambda functions, AgentCore memory, and Amazon Bedrock to produce app configurations stored in Amazon DynamoDB for merchant approval
The following table summarizes the services in the solution:
| Service | Role |
| Amazon Bedrock AgentCore | Managed agent runtime, persistent memory, MCP hosting, AG-UI support, multi-tenant isolation |
| Amazon Bedrock | Foundation model access, guardrails |
| Amazon EventBridge | Cron scheduling for merchant-defined update cadences |
| AWS Lambda | Job executor and 17 tool functions invoked by agents |
| Amazon DynamoDB | Result storage for merchant review and approval |
| Amazon Redshift | Analytics data store powering the Analytics Agent’s text-to-SQL queries |
The system starts with the merchant. From the Reactiv dashboard, merchants create a scheduled task through either a chatbot interface (“Refresh my homepage with best sellers every Monday at 9 AM”) or a form where they pick a prompt, frequency, and time. Both produce a schedule record backed by an Amazon EventBridge cron rule.
When the schedule triggers, the flow proceeds through five components:
text-to-SQL against Amazon Redshift, surfacing trends, top products, and actionable insights.Throughout execution, AgentCore memory reads and writes the merchant’s long-term memory in an isolated namespace. Every scheduled job builds on preferences and facts the agent accumulated in prior sessions for that specific merchant. The three agents access foundation models through Amazon Bedrock, which provides serverless inference and built-in guardrails without requiring Reactiv to manage model hosting or GPU infrastructure.
After launching the scheduler, Reactiv had two separate agent systems: the interactive dashboard agent (built with a custom UI adapter for Amazon Bedrock) and the scheduled agent (built on Strands with AgentCore). They did not share memory, tools, or infrastructure.
Reactiv unified them by migrating the interactive agent to the AG-UI protocol on Amazon Bedrock AgentCore. The dashboard agent now shares the same Strands framework, AgentCore-hosted MCP servers, and AgentCore memory instance as the scheduler. The result is one shared framework, runtime, memory layer, and UI protocol across both agents. The practical effect is bidirectional memory sharing. Preferences and facts learned during an interactive dashboard session feed directly into the next scheduled run, and vice versa. Scheduled jobs get smarter the more a merchant uses the front-end agent.
After moving to Amazon Bedrock AgentCore, Reactiv realized gains across development speed, runtime performance, and merchant experience. According to Reactiv’s internal measurements:
@tool decorator replaced approximately 100 OpenAPI spec files, AgentCore runtime and AgentCore Identity removed custom infrastructure, and the migration saves nearly $6,000 per year in compute costs alone.With the interactive and scheduled agents unified on a single stack, Reactiv is extending the authoring surface available to merchants. The through-line: merchants can customize their mobile apps through natural language, in progressively deeper ways.
The first step is making Reactiv’s existing design properties (colors, typography, spacing, component variants) available to the agent as a new MCP server hosted on Amazon Bedrock AgentCore. This mirrors the Config MCP approach: a shareable tool surface that the scheduler, the interactive builder, and eventually external integrations can all consume. Merchants can say “make my app match my brand” and the agent will apply changes within a governed design vocabulary rather than generating unconstrained output.
That design system also unlocks a rebuilt onboarding experience. When a new merchant provides their brand name and website, the agent analyzes their existing web presence and produces a strong starting point for the mobile app. Because the design system is in place, the agent can target real design properties and components rather than generating layouts from nothing.
Further out, Reactiv plans to let merchants request entirely new layout sections through natural language. The agent would generate a validated JSON representation of the requested layout, which the mobile app renders on the fly using Reactiv’s design system components. This extends the agent’s authoring capability beyond pre-built section types into merchant-defined layouts that still follow brand guidelines.
Reactiv started with a single agent on self-managed infrastructure and grew into a unified multi-agent system on Amazon Bedrock AgentCore. Today their production system runs three specialized agents, over 50 tools, and persistent memory shared across interactive and autonomous modes. Multiple MCP servers run on one managed runtime. They delivered it faster, with less infrastructure code, and with capabilities that would have required months of custom engineering otherwise.
The MCP-based architecture has proven especially reusable. Reactiv’s Config MCP pattern now extends to a design system MCP and, eventually, to external developer tooling. Each new capability starts with defining tools, hosting them on AgentCore, and sharing them across agents.
If you’re building agentic AI systems that need multi-tenant isolation, long-term memory, or managed MCP hosting, with Amazon Bedrock AgentCore, you can access these through AgentCore runtime, AgentCore memory, and AgentCore Identity. To get started, refer to the AgentCore documentation and the Strands Agents SDK.
Adam is a Senior AI Developer at Reactiv, where he specializes in architecting AI-powered mobile commerce infrastructure and multi-agent systems. His recent work focuses on developing Model Context Protocol (MCP) servers and agentic workflows using AgentCore to power real-time agent orchestration across complex microservice environments.
Ryan is a Solutions Architect at AWS based in Toronto, Canada. He works with startups on AI and machine learning workloads, helping customers design and build production agentic AI systems on AWS.
Concurrency sweeps help you right-size a generative AI endpoint by finding the instance type and serving configuration that maximizes price-performance while holding latency within acceptable bounds. Without a systematic approach, right-sizing means deploying, load-testing manually, adjusting, and repeating until the numbers look acceptable. Choose five ml.g7e.2xlarge instances when one would suffice, and you burn your budget on idle GPUs. Choose too few, and requests queue, latency spikes, and users experience degraded service.
Concurrency sweeps address this problem. A concurrency sweep is a systematic benchmarking approach that sends controlled, increasing levels of concurrent traffic to your Amazon SageMaker AI endpoint and analyzes its performance. Concurrency sweeps are built into Amazon SageMaker AI Inference Recommendations, so there’s no custom load-testing infrastructure to build or maintain.
In this post, we walk through deploying the NVIDIA Nemotron-3 Nano 30B model, running automated concurrency sweeps, and using the results to make data-driven capacity decisions. By the end, you will know how many concurrent requests your endpoint can handle before latency becomes unacceptable, and how to automate that discovery.
A concurrency sweep sends a controlled number of simultaneous requests to your SageMaker AI endpoint and measures two metrics at each level:
By progressively increasing the concurrency (for example, 64 to 256, and then 1,024 simultaneous requests), you can trace a curve that reveals your endpoint’s saturation point. This is the point where adding more concurrent traffic stops improving throughput and degrades latency.
A concurrency sweep gives you three data points for production planning:
Let’s now look at how the end-to-end workflow comes together.
The concurrency sweep workflow has four steps:
The following diagram illustrates this process.
The following four steps will take you from a fresh deployment to a complete capacity profile. Each step builds on the previous one, so we recommend following along with the accompanying notebook.
Before getting started, make sure that you have:
ml.g7e.2xlarge endpoints.We deploy NVIDIA Nemotron-3 Nano 30B, a Mixture-of-Experts (MoE) model with only 3B active parameters, to an ml.g7e.2xlarge instance. This instance is backed by an NVIDIA Blackwell GPU, which provides a strong price-performance ratio for inference workloads.
We use the vLLM Deep Learning Container for SageMaker AI, configured through SM_VLLM_* environment variables:
VLLM_IMAGE = (
f"763104351884.dkr.ecr.{region}.amazonaws.com/"
f"vllm:0.19.1-gpu-py312-cu129-ubuntu22.04-sagemaker"
)
environment = {
"SM_VLLM_MODEL": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
"SM_VLLM_ENFORCE_EAGER": "true", # required for Mamba-Transformer hybrid
"SM_VLLM_TENSOR_PARALLEL_SIZE": "1", # single GPU
"SM_VLLM_GPU_MEMORY_UTILIZATION": "0.85",
"SM_VLLM_MAX_MODEL_LEN": "10240",
"SM_VLLM_ENABLE_PREFIX_CACHING": "true",
"SM_VLLM_TRUST_REMOTE_CODE": "true",
}Three of these settings are worth explaining. The SM_VLLM_ENFORCE_EAGER flag is required because Nemotron-3 Nano uses a Mamba-Transformer hybrid architecture that requires eager execution mode. We set the GPU memory utilization to 0.85 to leave headroom for KV cache growth under high concurrency. Prefix caching is enabled to improve performance in scenarios where system prompts are repeated across requests to benefit from reusing cached key-value pairs.
After creating the model, endpoint configuration, and endpoint, we verify the deployment with a validation before moving on to benchmarking.
Before running any load test, the benchmark engine needs to know what kind of traffic to simulate. We define a workload profile that mirrors a realistic generative AI inference pattern using CreateAIWorkloadConfig. The following table summarizes the values we used for defining the workload. You can follow the code in the accompanying notebook.
| Parameter | Value | Description |
tokenizer |
Model tokenizer ID | Used to count tokens accurately |
streaming |
True |
Enables streaming responses (required for TTFT metrics) |
prompt_input_tokens_mean |
1,024 | Average input prompt length |
output_tokens_mean |
256 | Average generated response length |
The choice of 1,024 input tokens and 256 output tokens is representative of Retrieval Augmented Generation (RAG) or summarization workloads. If your application uses shorter prompts and longer completions (such as code generation), adjust these values accordingly. Streaming is enabled to capture time to first token (TTFT) metrics, which are important for interactive user experiences where perceived responsiveness matters as much as raw throughput.
With the workload profile defined, the next step is to launch the sweep.
We can now create the benchmark job with the CreateAIBenchmarkJob API with the parameters to use in the sweep. In the scenario illustrated in the sample notebook, we have:
sweep_params = {
"concurrency": [64, 256, 1024], # powers of 4
"request_count": 1024, # requests per concurrency level
}The benchmark engine (AIPerf) runs each concurrency level sequentially within a single job, sending the total number of requests defined under request_count at each level. Running the levels sequentially instead of in parallel means each measurement reflects a clean, isolated load. This approach gives you statistically stable estimates while keeping costs contained.
When the job completes, the results are written to the Amazon S3 path defined under OutputConfig as a tarball containing per-level metrics in JSON format.
After the job completes, we can download and parse the output for analysis. Four metrics matter most when reading the results:
When you plot throughput against latency across the concurrency levels, the saturation point is visible as a “knee” in the curve: throughput flattens while p99 latency bends sharply upward. This happens at 256 concurrent requests in our test scenario. Concurrency levels below the knee are your safe operating region. Above it, the endpoint is overloaded and users are waiting.
Figure 2: Throughput and p99 latency across concurrency levels, with the saturation knee at 256 concurrent requests
At this point you have a complete picture of how your endpoint behaves under load. For many teams, this is sufficient to make a confident capacity decision. If you want to automate the search for the optimal operating point across model versions or instance types, the benchmark engine offers an automated alternative.
The concurrency sweep in Step 3 requires you to choose the concurrency levels to test. You might not know the right range, or you might want to automate capacity planning across model versions. In either case, replace the fixed concurrency list with a search recipe in the same CreateAIBenchmarkJob call. The max-concurrency-under-sla recipe accepts one or more SLA thresholds and searches for the highest concurrency that satisfies all of them.
The following table describes the available SLA threshold parameters:
| Parameter | Meaning | Statistic | Requires streaming? |
ttft_sla_ms |
Max Time To First Token (ms) | p95 | Yes |
tpot_sla_ms |
Max Time Per Output Token (ms) | p95 | Yes |
e2e_sla_ms |
Max end-to-end request latency (ms) | p99 | No |
error_rate_sla |
Max fraction of failed requests | avg | No |
For example, we can use the following search parameters to find the maximum concurrency where p99 end-to-end latency stays under 50 seconds:
search_params = {
"search_recipe": "max-concurrency-under-sla",
"e2e_sla_ms": 50000,
"concurrency_min": 16,
"concurrency_max": 4096,
"search_max_iterations": 10,
"search_initial_points": 512,
"request_count": 1024,
}The search engine uses an optimization planner that can converge on the answer in fewer iterations than a linear sweep. It starts with a broad range and narrows progressively, evaluating only the concurrency levels needed to identify the boundary. This can reduce both the number of iterations and the total cost of the search.
| Iteration | Concurrency | Throughput (OTPS) | Passed SLA? |
| 1 | 16 | 492.5 | ✓ |
| 2 | 32 | 904.2 | ✓ |
| 3 | 64 | 1433.4 | ✓ |
| 4 | 128 | 2066.8 | ✓ |
| 5 | 256 | 2823.4 | ✓ |
| 6 | 512 | 2782.3 | ✗ |
| 7 | 320 | 2781.8 | ✓ |
| 8 | 284 | 2788.6 | ✗ |
In this example, the planner starts at concurrency 16 and doubles through each iteration. At concurrency 512, the first SLA violation occurs, either because the p99 end-to-end latency exceeded the threshold or because invocations failed. The planner then narrows the search to the 256–512 range and finds that concurrency 320 meets the SLA at 2,782 tokens per second. The search runs for at most the number of iterations you define in search_max_iterations.
A single SLA threshold is insufficient for most production workloads, as some scenarios require that a model must meet multiple SLAs at the same time. For instance, in interactive user experiences, you might need to control both the end-to-end latency and the time to first token. You can still use the max-concurrency-under-sla search recipe by passing multiple SLAs under search parameters:
search_params = {
"search_recipe": "max-concurrency-under-sla",
"e2e_sla_ms": 50000, # p99 end-to-end < 50s
"ttft_sla_ms": 1500, # p95 time-to-first-token < 1.5s
"concurrency_min": 16,
"concurrency_max": 4096,
"search_max_iterations": 10,
"request_count": 1024,
}| Iteration | Concurrency | Throughput (OTPS) | Passed SLA? |
| 1 | 16 | 480.2 | ✓ |
| 2 | 32 | 888.6 | ✓ |
| 3 | 64 | 1429.9 | ✓ |
| 4 | 128 | 2061.4 | ✗ |
| 5 | 80 | 1599.3 | ✓ |
In this case, we observe that when putting SLAs in both the end-to-end latency and time to first token, the maximum level of concurrency supported is 80.
With the benchmarking complete, let’s clean up the resources we created during this walkthrough.
To avoid ongoing charges, delete your SageMaker endpoints, endpoint configurations, and models after testing. Endpoints bill by the hour whether or not they’re receiving traffic.
Concurrency sweeps replace guesswork with data in the capacity planning process for generative AI endpoints. Instead of over-provisioning as a precaution or discovering bottlenecks in production, you can systematically map your endpoint’s performance envelope. You can then make informed decisions about fleet size before a single user request hits your system.
In this post, you learned how to:
CreateAIBenchmarkJob API.max-concurrency-under-sla recipe to automatically discover the optimal concurrency for your SLA targets.The complete notebook is available in the GitHub repository. To get started with your own models, see the Generative AI Inference Recommendations documentation. We have also created another example that benchmarks GLM 5.3 Flash on Amazon SageMaker AI using this feature.
Related resources:
Mona currently works as Sr AI/ML specialist Solutions Architect at Amazon. She is a published author of three books and her latest book is AI Agents on AWS. She has authored 20+ blogs on AI/ML and cloud technology and a co-author on a research paper on CORD19 Neural Search which won an award for Best Research Paper at the prestigious AAAI (Association for the Advancement of Artificial Intelligence) conference.
Hrushikesh is a Principal Solutions Architect for AI/ML startups with expertise in both AWS machine learning and networking services. He helps startups building generative AI, autonomous vehicles, and ML platforms to run their business efficiently and effectively on AWS.
Felipe is a Principal AI/ML Specialist Solutions Architect at AWS. Prior to joining AWS, Felipe worked with GE Digital and SLB, where he focused on modeling and optimization products for industrial applications.
Lokeshwaran is a Senior Deep Learning Compiler Engineer at AWS, specializing in ML optimization, model acceleration, and AI security. He focuses on enhancing efficiency, reducing costs, and building secure ecosystems to democratize AI technologies, making cutting-edge ML accessible and impactful across industries.
Sheng is a software engineer on SageMaker focused on inference and model optimization, building scalable, user-friendly tools that help customers deploy AI models faster and more efficiently.
Trane Technologies manages millions of connected heating, ventilation, and air conditioning (HVAC) assets worldwide, but getting a single operational answer could mean cross-referencing multiple dashboards and drilling through menus for 20 minutes or more. For organizations operating at this scale, that kind of friction slows operations, defers corrective action, and creates material business impact across the enterprise.
In 3–4 weeks, Trane’s engineering team built an AI-powered agentic solution on Amazon Bedrock AgentCore that reduced a 20-minute multi-screen diagnostic workflow to a 20-second natural language interaction. This is based on Trane’s internal benchmarking with technicians over several weeks. This represents a 60x improvement in time-to-insight, helping shift operations from reactive response to more proactive, data-driven optimization.
In this post, we describe the architectural approach and key design decisions behind the solution:
Trane Technologies is a global climate innovator with over $21 billion in annual revenue and operations in more than 100 countries. Through its strategic brand Trane, the company manages millions of connected HVAC assets, spanning data centers, hospitals, manufacturing facilities, and commercial real estate portfolios. At the heart of this vast landscape is Trane Cloud, a digital hub that aggregates real-time performance data from millions of HVAC systems. Trane Cloud transforms raw equipment telemetry into actionable intelligence for predictive maintenance, energy optimization, and operational excellence.
While dashboard-based building management systems provide a foundation for monitoring and control, extracting cross-system insights can still require users to navigate multiple screens, layered menus, and disconnected dashboards. By combining natural language processing with deep integration into Trane Cloud, users can access that operational context through a single conversational interface. The result is faster answers to complex building management questions and a more proactive, informed approach to facility operations.
Building operators, field technicians, and service managers have abundant data at their fingertips. Equipment telemetry, performance analytics, fault alerts, energy consumption patterns, and optimization opportunities flood in from disparate systems, yet extracting actionable insights remains difficult. The fundamental problem is that different stakeholders need radically different views of the same data.
Field technicians require diagnostic precision. They need refrigerant pressures, fault codes, and system-level troubleshooting workflows. Account managers need strategic intelligence. They need uptime metrics, cost savings opportunities, and portfolio performance trends. Building owners demand executive clarity. They need efficiency scores, sustainability metrics, and simplified operational summaries. Existing tools present a single interface across all roles, requiring each user to navigate features outside their workflow.
Existing building management applications rely on screen-by-screen navigation that makes cross-equipment comparison more challenging and demands that users memorize menu hierarchies and technical terminology. Even basic portfolio-level questions can require time-intensive manual workflows across multiple screens.
Together, Trane’s agentic AI solution and Trane Cloud deliver four capabilities:
These capabilities create a more scalable way to access building intelligence across roles, workflows, and operational environments.
The challenge of scaling intelligent building operations lies in turning the massive volume of data generated by millions of connected assets into actionable insight. Although Trane Cloud ingests real-time telemetry at scale, answering operational questions has traditionally required users to navigate disconnected dashboards and manually connect information across systems. To solve this, the team built a conversational agent on Amazon Bedrock AgentCore and the Strands framework, deployed through AWS Cloud Development Kit (AWS CDK) infrastructure as code. The team chose Strands for the agent framework layer because it provides the developer SDK and orchestration logic for building agent behavior, while AgentCore handles the managed runtime, memory, tool gateway, and production infrastructure underneath.
To avoid the limitations of a monolithic design, the solution uses a multi-agent architecture where each specialized assistant is governed by its own system prompt, keeping it tightly focused on a single capability domain:
This architecture is designed for extensibility. Teams can connect additional agents or tools, such as work order management systems and enterprise customer relationship management (CRM) systems, through AgentCore Gateway, a capability of Amazon Bedrock AgentCore, and open standards like the Model Context Protocol (MCP).
Supporting these distinct user needs at enterprise scale requires an architecture that can evolve independently across capabilities. A monolithic agent would force every change (new tools, updated prompts, additional data sources) through a single deployment pipeline, creating bottlenecks as the system grows.
Reducing a 20-minute manual diagnosis to a 20-second conversation surfaced four architectural challenges. The first challenge was integration. The solution had to combine real-time telemetry with intelligent search across an extensive knowledge base while supporting connections to external systems like CRMs. The second was separation. Agent logic had to be untangled from backend tool execution so the two could deploy independently, with clear ownership boundaries. Third was context. The system needed to maintain conversational state across troubleshooting sessions without standing up complex custom vector database infrastructure. Fourth was observability. When an agent orchestrates multiple tools across a multi-step reasoning chain, failures become difficult to localize. A wrong answer could stem from a missing API credential, a malformed tool response, or a model hallucination. Without end-to-end tracing, the team had no way to distinguish between them at production scale.
Amazon Bedrock AgentCore is an agentic platform to build, connect, and optimize agents at scale, with any framework or model. The Trane team used four AgentCore capabilities to address the preceding challenges.
Before selecting AgentCore, the team evaluated hosting the agent on Amazon Elastic Container Service (Amazon ECS) and AWS Lambda. That approach would have required building session isolation, auto scaling logic, and per-session billing on top of the compute layer. Four differentiators drove the decision. First, the managed agent runtime alleviates infrastructure operations. There are no clusters to provision or scale, and no idle capacity to pay for between user sessions. Second, built-in session memory removes the need to stand up and maintain external vector databases or build custom context-window management code. Third, native tool orchestration through AgentCore Gateway turns existing internal APIs into agent-compatible tools without writing custom integration logic for each one. Fourth, AgentCore’s framework-agnostic design meant the team could use the Strands SDK without being locked into a proprietary orchestration layer, preserving flexibility as requirements change.
The real-time data flow works as follows:
Using Amazon Bedrock AgentCore and the Strands framework, the team achieved the 60x time-to-insight improvement described earlier. Cross-equipment comparisons and diagnostic workflows that once required navigating multiple screens now complete through a single natural language query in seconds. This helps teams move faster, reduce analysis steps, and take a more proactive approach to facility operations.
Beyond runtime performance, the architecture delivered additional benefits:
Deploying an AI agent that returns diagnostic recommendations from live operational data requires controls against misuse and out-of-scope responses. The team configured Amazon Bedrock Guardrails with content filters to block harmful or inappropriate outputs and topic denial policies that restrict the agent to its operational domain, helping restrict it from answering questions outside its operational domain. Sensitive information filters detect and redact personally identifiable information (PII) before responses reach the user, and prompt attack detection helps guard against jailbreak attempts that could bypass the agent’s system prompt.
At the infrastructure layer, Trane’s role-based access model helps restrict each user to data within their authorization scope. AgentCore runtime’s per-session microVM isolation helps prevent cross-tenant data leakage risk at the compute level. These controls work together so Trane can scale the agent to production users with confidence. Responses stay within scope, role-based access and PII filters help protect sensitive data, and Bedrock Guardrails help enforce the agent’s intended operational boundaries.
Trane’s implementation demonstrates how organizations can use AI agents and their proprietary data as a distinct competitive advantage. By building on Amazon Bedrock AgentCore, the organization established a robust, secure enterprise standard that can be scaled and reused across different business units.
This phased rollout, from a 3–4 week prototype, through accuracy tuning and an internal beta, to external release and reuse, surfaced five insights for scaling enterprise AI:
To learn more about building agentic solutions, visit the Amazon Bedrock AgentCore documentation, explore the Strands Agents SDK, or follow the AWS Artificial Intelligence Blog for more customer stories.
The team is expanding the solution in several areas.
First, AgentCore Evaluations, a capability of Amazon Bedrock AgentCore, will gate future rollout phases on automated accuracy thresholds. Easing the burden on the quality assurance (QA) team, the team will define pass-rate benchmarks that must clear before each release moves forward.
Second, Policy in Amazon Bedrock AgentCore will replace custom authorization logic currently coded into the Runtime. Tool-level access decisions will shift to declarative policy definitions, making it easier to onboard new roles and audit who can invoke which tools.
Third, the team is opening their AgentCore Gateway to other engineering teams across Trane. Because the Gateway exposes tools through MCP, an open standard, other teams can build their own agents on top of the same centralized data layer without learning proprietary interfaces or standing up duplicate integrations. The team is building self-service onboarding with usage tracking so they can measure adoption and identify which tools other teams find most valuable.
Fourth, the team is adding new tools at a rapid pace. One example already in progress: field offices currently perform deep cost savings analyses manually for each customer site. The team is building this as an agent tool so the analysis runs on demand through natural language, removing hours of manual work per engagement.
Senthil, Director of Engineering – Digital, PGT (Product Growth Team) and AWS Solutions Architect at Trane Technologies, leads a high-performing engineering organization building highly scalable, cloud-native HVAC solutions that process billions of data points daily. With 29 years of experience bridging technology and business outcomes, he is spearheading Trane’s commercial HVAC transformation toward Agentic AI, delivering end-to-end intelligent systems that integrate Amazon Bedrock, AgentCore, and broader AWS services to drive measurable business value. Senthil is known for executing with relentless urgency, architecting solutions that are highly scalable, and consistently achieving cost-optimal results that compound into strategic enterprise advantage.
Subbha is a Digital System Architect at Trane Technologies with over 21 years of experience leading high-impact enterprise architecture, cloud modernization, and AI-driven integration across digital platforms. An AWS Certified AI Practitioner and AWS Solutions Architect, he is passionate about building scalable serverless systems, automating deployment pipelines, and developing agentic AI solutions. Based in White Bear Lake, Minnesota, Praveen enjoys staying at the forefront of emerging cloud technologies and helping engineering teams drive continuous innovation.
Alex is an Application Architect with Trane Technologies working on user management and AI features for the Trane Cloud platform. His primary areas of work focus on building scalable backend solutions on AWS that enable critical business processes. In his personal life, Alex lives in Minneapolis and enjoys cooking and tinkering in his home lab.
Dave is a Solutions Architect with Amazon Web Services (AWS), working with Automotive and Manufacturing customers to accelerate their cloud and AI journey. He is passionate about helping customers adopt containers, generative AI, and modern developer experiences. Outside of work, Dave lives in Raleigh, North Carolina and enjoys traveling, being outdoors, and spending time with his family.
KP is a Senior GenAI/ML Specialist Solutions Architect at AWS, where he helps enterprises build generative AI systems that pair cutting-edge innovation with responsible deployment and meet stringent security and compliance requirements. Drawing on extensive experience across a wide range of customer engagements, he focuses on designing agentic AI systems and orchestration frameworks that streamline complex workflows while preserving appropriate human oversight. Through his technical publications and speaking engagements, he translates hard implementation challenges into practical guidance, always coming back to the same point: successful AI agents earn their place by solving genuine business problems through effective reasoning, planning, and action within well-defined enterprise contexts.
Will is a Senior Generative AI Specialist at AWS, bringing over a decade of experience in Data & AI. In his role, he drives GenAI adoption across the Automotive & Manufacturing (AutoMfg) industry. He partners with enterprise customers to move generative AI workloads from experimentation into production, pairing deep technical fluency across the GenAI stack, industry insights, and a work-backwards approach rooted in customers’ business goals.
Detecting industrial safety risks in seconds, not minutes, is what keeps workers safe on an active plant floor. This is what Tata Elxsi set out to deliver by building IRIS, a real-time industrial safety platform on AWS.
In this post, we show how Tata Elxsi built IRIS (Industrial Real-Time Intelligence System). We cover the architecture decisions, the implementation approach, and the measurable results you can expect from a similar build. Whether you operate a handful of cameras or thousands across multiple sites, this blueprint provides patterns you can adapt for your organization.
Tata Elxsi is a global provider of design and technology services across industries including automotive, manufacturing, broadcast, communications, healthcare, and transportation. Its teams combine engineering depth with AI and computer vision to help enterprises modernize safety-critical physical operations. IRIS is Tata Elxsi’s industrial vision platform, built for organizations that already operate camera infrastructure but cannot yet turn those feeds into real-time, actionable intelligence.
Industrial organizations have invested heavily in automated safety over the past decade. Manufacturing plants, warehouses, logistics hubs, and chemical facilities operate hundreds to thousands of cameras. These cover production lines, hazardous zones, vehicle corridors, loading areas, and restricted-access locations. Yet most of this footage is recorded and rarely acted on in real time. Safety teams face a common set of constraints:
These aren’t failures of any single tool. They were signals that safety monitoring needs to evolve from passive recording to continuous, automated detection that scales with the number of cameras.
Computer vision represents the next step in workplace safety. It doesn’t replace existing safety programs. It augments them with continuous, automated monitoring that runs around the clock. The design goal for IRIS was to analyze video as it’s produced, detect unsafe conditions automatically, and generate actionable alerts in near real time, without streaming raw video to the cloud. IRIS runs in the Asia Pacific (Mumbai) AWS Region, chosen for data-residency requirements and low-latency proximity to customer facilities in India.
Tata Elxsi built IRIS as a serverless, event-driven pipeline that follows a repeatable pattern: observe at the edge, detect with computer vision, analyze for context, alert the right people, store for compliance, and learn from production data. Video is analyzed at the edge, only safety-relevant frames and structured metadata move to the cloud, and a correlation layer turns raw detections into high-confidence safety events. The following diagram shows how the components fit together.
Figure 1: End-to-end IRIS architecture on AWS, from edge camera processing through streaming, inference, correlation, alerting, and storage
The workflow begins at the edge. IRIS deploys a dedicated edge-compute tier using AWS IoT Greengrass on industrial-grade, GPU-equipped edge servers, for example NVIDIA Jetson AGX Orin or equivalent. Each server is installed at the facility and connected to the camera network over RTSP/ONVIF.
At the edge, IRIS extracts frames at a configurable rate of 2-5 frames per second and applies motion-based filtering. It then runs a lightweight first-pass model to identify frames that contain people, vehicles, or equipment. Frames that pass these filters are uploaded to Amazon Simple Storage Service (Amazon S3). With AWS IoT Greengrass, you can manage secure device communication and deliver updated models to devices as Greengrass components from Amazon S3.
Decoupling the image path from the metadata path is central to the design. Extracted frames are written to a dedicated Amazon S3 bucket, partitioned by camera, date, and hour. The streaming event that flows through the pipeline carries only the Amazon S3 object key and context such as camera ID, plant, zone, and an NTP-synchronized timestamp. This keeps each event under 1 KB. A downstream consumer retrieves the referenced frame from Amazon S3 and runs the model. This keeps the streaming layer lightweight while the models retain full access to the visual data. In Tata Elxsi’s production deployments, filtering at the edge reduces the volume of frames sent to the cloud by roughly 70–80 percent, based on the customer’s production measurements.
After edge processing, safety-relevant metadata and events are streamed into Amazon Kinesis Data Streams, which serves as the real-time event backbone of the platform. The stream carries frame metadata (the Amazon S3 object key), motion events, edge detection candidates, camera telemetry, and contextual safety information. It does not carry video.
Because IRIS performs frame extraction at the edge, the cloud payload is structured event data with Amazon S3 references rather than continuous video. Amazon Kinesis Data Streams is purpose-built for this event-driven, metadata-first pattern, where sub-second latency on structured records is the priority.
The stream runs in on-demand capacity mode, which removes manual shard management and scales throughput automatically with event volume during shift changes or multi-incident bursts. In Tata Elxsi’s production deployments, sustained throughput is 2,000–5,000 events per second per deployment, with burst capacity to roughly 15,000 events per second. Measured event-ingestion latency is under 200 milliseconds at p95.
Events are consumed by custom computer vision models deployed on Amazon SageMaker AI, the intelligence engine of IRIS. Separate real-time endpoints are provisioned per model family so each can scale independently:
Endpoints run on ml.g5.xlarge instances (NVIDIA A10G GPU). AWS Application Auto Scaling applies a target-tracking scaling policy that scales out at 70 percent GPU utilization and scales in at 30 percent, with a minimum of two instances per endpoint for high availability (HA). To smooth traffic bursts across hundreds of concurrent streams, IRIS places an Amazon Simple Queue Service (Amazon SQS) queue between the stream consumers and the endpoints. Application Auto Scaling then adds instances when queue depth exceeds a configured threshold. Requests are processed in micro-batches of 4–8 frames to maximize GPU utilization. In production, each ml.g5.xlarge endpoint handles roughly 40-60 inference requests per second, and per-frame inference latency is under 300 milliseconds at p95.
A single detection is often not enough to act on. A worker briefly crossing a boundary might not warrant escalation, whereas repeated violations in a short window might require immediate intervention. IRIS therefore adds a correlation layer, implemented as AWS Lambda functions that maintain short-term state in Amazon DynamoDB using time-to-live (TTL) entries for sliding-window evaluation. It combines detections with camera location, zone criticality, temporal patterns (configurable 30-second to 5-minute windows), and historical behavior, then evaluates violation frequency, duration, and severity. In Tata Elxsi’s production deployments, this correlation step reduces spurious alerts by an estimated 40–50 percent compared with passing detections through directly. This is based on the customer’s internal benchmarking of alert volumes before and after correlation.
After a high-confidence event is identified, AWS Lambda functions run event-driven response workflows and AWS Step Functions manage multi-step escalation. Events are de-duplicated with a sliding window. The same detection type from the same camera within a configurable window (default 60 seconds) is consolidated into one alert. Events are then classified by severity based on zone criticality, confidence, and duration. Escalation follows defined service-level agreements:
Amazon EventBridge Scheduler triggers escalation checks, and AWS Step Functions advance the state machine through supervisor, plant-manager, and safety-director levels as needed. Alerts are delivered through a real-time safety dashboard, email and SMS by severity and recipient group, webhook integration with enterprise IT service management systems, and mobile push notifications for supervisors and safety officers.
Every event, including detection results, alert records, metadata, and investigation evidence, is stored in Amazon S3, the system of record for the platform. Organizations use this repository for safety audits, compliance reporting, root-cause investigation, and regulatory review.
Lifecycle policies manage cost as data ages. Active event data stays in S3 Standard for 30 days. Historical events move to S3 Standard-Infrequent Access from 30 to 90 days. Compliance and investigation records transition to S3 Glacier Instant Retrieval from 90 days to 1 year, which allows millisecond retrieval for audits. Long-term archival moves to S3 Glacier Flexible Retrieval beyond 1 year, with expiration configurable per customer retention requirements. Extracted frames tied to confirmed events are retained for 1 year and then archived, and frames with no or below-threshold detections are purged after 7 days.
IRIS improves as it runs. Production data in Amazon S3 feeds model-improvement workflows through Amazon SageMaker AI training pipelines. Training data is roughly 80 percent real-world annotated data collected from production environments under customer data agreements. The remaining 20 percent is synthetic data generated for rare cases such as uncommon PPE, unusual lighting, and atypical camera angles. Annotation combines Amazon SageMaker Ground Truth for large-scale labeling with an in-house Tata Elxsi review team for edge cases. An active-learning loop routes low-confidence production predictions for human review.
Re-training runs on three separate triggers:
Re-trained models are evaluated against a held-out evaluation set using the Amazon SageMaker AI model registry. Only models that meet or exceed current production accuracy are promoted, through blue/green deployment. As measured by Tata Elxsi on held-out production validation sets refreshed quarterly, PPE detection reaches 94.2 percent precision and 91.8 percent recall (mAP@0.5 of 92.7 percent). Restricted-zone intrusion reaches 96.1 percent precision and 93.4 percent recall. The post-correlation false-positive rate is under 3 percent across detection categories.
Security is enforced across a multi-account structure that separates model training, production inference, and analytics. Amazon S3 buckets use server-side encryption with customer-managed keys in AWS Key Management Service (AWS KMS), and inter-service communication uses TLS 1.2 or higher. Fine-grained AWS Identity and Access Management (IAM) policies scope each service role to least privilege, and human access uses AWS IAM Identity Center with roles aligned to job function.
Inference and data-processing workloads run inside a dedicated Amazon Virtual Private Cloud (Amazon VPC) with private subnets and no public internet exposure. VPC endpoints keep Amazon S3, Amazon Kinesis Data Streams, and Amazon SageMaker AI traffic on the AWS network. AWS CloudTrail records API activity, and Amazon GuardDuty monitors for anomalous access.
Across production deployments, IRIS moved customers from reactive surveillance to proactive safety management. Tata Elxsi reports the following outcomes.
| Dimension | Before IRIS | With IRIS (reported by Tata Elxsi) |
| Unsafe-condition detection | Manual review, 15–45 minutes | Under 5 seconds, end to end |
| Safety audit coverage | 2–3 manual walkthroughs per shift | Continuous, automated 24×7 coverage |
| Recordable safety incidents | Baseline | 15–20% reduction in the first 6 months |
| Manual surveillance operating cost | Baseline | Approximately 30% reduction |
| Scaling model | Cost grows with each added camera | Hundreds of concurrent streams per site |
IRIS shows how existing camera infrastructure can become a real-time safety system on AWS, detecting unsafe conditions in seconds rather than minutes. By filtering at the edge, streaming metadata, running purpose-built models on Amazon SageMaker AI, and adding a correlation layer, Tata Elxsi built a platform that scales across hundreds of concurrent streams per site while keeping raw video out of the cloud. The same event-driven foundation extends to quality inspection, perimeter monitoring, and process observation, with new domain models and zone rules layered on without re-architecting the pipeline.
To explore building a similar solution, review the AWS IoT Greengrass and Amazon SageMaker AI documentation. To discuss a proof of concept for your facilities, contact Tata Elxsi or your AWS account team.
Abhideep is a Senior AWS Solutions Architect with 13+ years of experience building scalable, cloud-native solutions across media, AI/ML, and real-time analytics. He specializes in AWS streaming and AI architectures, enabling enterprises to operationalize multimodal AI and event-driven automation. His focus is on modernizing workloads with scalable, resilient, and cost-efficient cloud solutions.
Annie is a Sr. Analytics Specialist at AWS, bringing over 15+ years of expertise in helping customers with their data and AI journeys. She has successfully led customer teams to successfully adopt AWS Data and AI services and has worked with Fortune 500 customers across the globe in her previous roles.
Neha is an Analytics Specialist at AWS, based in India, where she partners with enterprise customers on their data and analytics modernization journeys. She is passionate about helping organizations unlock business value from their data through purpose-built analytics on AWS.
Anirudh is an Analytics Solution Architect at AWS. He helps organization empowers businesses to harness their data effectively through AWS’s analytics platform. His interest lies in building highly available distributed systems.
Public sector agencies process large volumes of unstructured evidence, such as body camera footage, surveillance video, and scanned documents, that require extracting insights before anyone can act on them. This post shows how to combine Amazon Bedrock Data Automation with the Model Context Protocol (MCP) to turn unstructured data into structured insights. You can then expose those insights through natural language queries in an AI agent, such as Salesforce Agentforce.
In our previous post, Modernizing evidence management in Salesforce Public Sector Solutions with Amazon S3, we used the External Storage of Files with Amazon Simple Storage Service (Amazon S3) integration from Agentforce Public Sector (formerly Public Sector Solutions) as an example implementation. With that foundation in place, you now have durable, cost-efficient storage for body camera footage, surveillance video, photographs, audio recordings, and scanned documents.
However, storage is only half the challenge. Without automation, you spend significant time manually reviewing, classifying, and extracting relevant details from these files before you can act on them. With this integration, Agentforce users can search for processed data stored on AWS, surface key insights from unstructured data, and perform more advanced actions, all without leaving the Salesforce console.
Two main flows work together to turn raw evidence into actionable investigative insights. The first flow moves unstructured media files and documents into Amazon S3 using the External Storage of Files with Amazon S3 for Public Sector connector. Figure 1 illustrates how Amazon S3 provides enterprise-scale storage infrastructure for storing large documents and media files.
Figure 1: Agentforce Public Sector and Amazon S3 integration
Second, after data is in Amazon S3, an event-driven architecture asynchronously processes multimodal data using Amazon Bedrock Data Automation. Figure 2 shows how you can extend the storage solution to create an architecture pattern. This pattern transforms unstructured data into actionable insights and makes them available to Salesforce Agentforce through MCP.
Figure 2: Generating insights from unstructured data
As Figure 2 illustrates, when a file or document lands in Amazon S3, an S3 event notification invokes an AWS Lambda function. The Lambda function generates a document ID, stores it alongside document metadata in Amazon DynamoDB, and starts an Amazon Bedrock Data Automation job to process the file. Amazon Bedrock Data Automation extracts structured insights based on the media type. When the job completes, an Amazon EventBridge rule triggers a second Lambda function that saves the results to a dedicated output bucket in Amazon S3.
On the Salesforce side, a user’s chat in Agentforce triggers a configured action that calls AWS over MCP. The call routes through Amazon Bedrock AgentCore Gateway, a capability of Amazon Bedrock AgentCore, which authenticates the request and invokes an MCP server running on AWS Lambda. Amazon Bedrock AgentCore is the platform to build, connect, and optimize agents at scale, with any framework or model.
The Lambda function first queries the DynamoDB table to locate the relevant results. It then retrieves and returns them from Amazon S3. The results return through AgentCore Gateway to Agentforce, where the data is loaded into the agent’s context for a natural language response.
With Amazon Bedrock Data Automation, you can process each file based on its media type. For documents, it extracts text, identifies key fields, and generates structured summaries. For images, it produces descriptions and identifies objects or text within the frame. For video and audio files, it generates transcriptions and scene-level summaries. The Amazon Bedrock Data Automation project configuration defines which extraction capabilities to apply to each file type, and you can customize these settings in the Amazon Bedrock Data Automation console after deployment.
This processing happens behind the scenes. Salesforce users can upload files, ask questions, and receive AI-powered insights entirely from the Salesforce console, without switching between systems or managing AWS resources directly.
This architecture is intentionally modular and extensible, designed as a pattern you can adapt well beyond evidence management. Each component, from the processing pipeline to the query path, operates independently and can be customized to your agency’s unique requirements. For example, you can add custom processing logic in the AWS Lambda MCP Serverless Runtime or store additional metadata in Amazon DynamoDB for richer document lookups. You can also connect different agent frontends through MCP without changing the underlying data pipeline.
This section walks through deploying the AWS infrastructure and configuring Salesforce Agentforce to connect to the MCP endpoint.
Before beginning, complete the steps outlined in the previous post, Modernizing evidence management in Salesforce Public Sector Solutions with Amazon S3, as this post builds directly on that foundation. Additionally, confirm that your Salesforce org supports registering and calling external MCP servers through the Agentforce Registry. You can verify this by navigating to Setup > API Catalog > MCP Server and confirming the option to register an MCP server is available. Registering external MCP servers is available in Developer, Enterprise, Performance, and Unlimited Editions (see Manage External MCP Servers).
This GitHub repository provides a deployment of the AWS resources required to create an event-driven architecture. The solution deploys a serverless infrastructure that includes Amazon EventBridge rules, Amazon Bedrock Data Automation configuration, AWS Lambda functions, Amazon DynamoDB tables, and Amazon Bedrock AgentCore Gateway. This sample code is provided to demonstrate the pattern and isn’t production ready, so review and harden it to meet your organization’s requirements before using it in production.
After deploying the AWS CDK stack, configure Salesforce Agentforce to connect to the Amazon Bedrock AgentCore Gateway MCP endpoint. Agentforce connects to AgentCore Gateway using the MCP Streamable HTTP transport. With this connection, Agentforce can discover and invoke the evidence retrieval tools exposed by the gateway. The AWS CDK stack outputs several values you need to configure the connection between Salesforce and AWS. Retrieve these from the AWS Management Console before proceeding.
Optionally, before configuring the Salesforce connection, you can validate your gateway endpoint using the MCP Inspector, a developer tool for testing and debugging MCP servers through an interactive interface. This step isn’t required but can help confirm that your AgentCore Gateway is responding correctly before integrating it with Agentforce.
After the Intelligent Media Processing solution is fully deployed, the outputs required to set up the MCP connections are available in AWS CloudFormation under the McpGatewayStack outputs. As shown in Figure 3, the primary outputs are CognitoClientId, CognitoTokenEndpoint, and GatewayMcpEndpoint.
Agentforce authenticates with AWS through Amazon Cognito. You need the client secret from your Cognito app client to complete the MCP server registration in Salesforce.
Figure 4 displays the Amazon Cognito app client page, where you can find the Client secret.
With the AWS credentials in hand, you can now register the MCP server in Salesforce. This establishes the authenticated link so Agentforce can call AWS tools.
AwsBdaResultsMcp and set the description to MCP server for accessing results from Amazon Bedrock Data Automation.You have successfully connected your Amazon Bedrock AgentCore MCP server to Salesforce Agentforce.
Now that the MCP server is registered, you can add MCP tools to an existing Agentforce subagent, or create a new subagent. The following steps walk through creating a dedicated Agentforce subagent whose primary task is handling requests related to evidence retrieval. This subagent uses the MCP tools to query processed evidence stored in AWS and return insights to the user in natural language.
To integrate an external MCP server, use the new Agentforce Builder. The following steps use the Employee Agent template. You can apply this same MCP integration to other agent types (such as Service Agent or Customer Agent), though the exact navigation and configuration options might vary. If you have an agent that was built using the legacy Agentforce Builder, follow this guide to Upgrade to New Builder.
Case Agent or a name relevant to your use case.Media Processor as the name and the following as the description:
Subagent that handles all questions related to files, documents, photos, images, videos, or audio attached to the current case. Retrieves AI-generated insights from processed media and responds in natural language.AwsBdaResultsMcp narrows the results to the relevant tools). Figure 6 shows the connected MCP selected under the Actions Available for Reasoning section.Handle all questions about files, documents, photos, images, videos, or audio attached to the current case. Run <MCP_PLACEHOLDER> to retrieve processed insights. If no insights are available, inform the user the attachment has not yet been processed. Don't fabricate content about unprocessed files.<MCP_PLACEHOLDER>, enter @ to reference a resource inline, then select the MCP associated with this subagent. Figure 7 shows the MCP referenced inline in the Reasoning Instructions.With Agentforce Builder, you can preview the agent and how it responds to questions in the chat. To simulate the conditions of an employee asking questions in the Salesforce console, you can modify the Context Variables. These variables represent the values that would be assigned to the agent’s context when a user works in the Salesforce console. Figure 8 shows the Preview panel’s Context Variables in the Agentforce Builder.
To test the agent, set the currentRecordId context variable to the Record ID (the unique 18-character ID) of the case that you want to test. Then choose Apply and Restart Session. This sample uses the Agentforce Employee Agent. Other agent types might have different context variables preconfigured, so adjust accordingly.
To test the configuration of the agent and verify that it can make an MCP callout to AWS, perform the following:
Summarize the files for this case.The architecture demonstrated in this post is not limited to evidence management. You can apply the same modular pattern to build solutions for workflows that involve processing unstructured data, such as permits, benefits claims, or compliance reviews. The key components are the following:
Each of these can be recombined and extended for use cases involving unstructured data. Because each component operates independently, you can replace the processing engine to match your agency’s requirements while keeping the same ingestion and MCP query layers. For document-heavy workflows such as permits, benefits claims, or tax forms, you can substitute the GenAI Intelligent Document Processing (IDP) Accelerator as an alternative processing engine. This keeps the same Amazon S3 ingestion and MCP query path. Additionally, because the MCP server is built on an open standard, you only need to build it once. MCP-compatible agents or systems can connect to the same endpoint, so you can reuse the same query layer across multiple applications beyond Agentforce.
Regardless of which processing approach you choose, the MCP query path remains the same. AgentCore Gateway exposes your processed data as tools that MCP-compatible agents can discover and invoke. This means that, in most cases, the architecture supports starting with a single use case and expanding to additional workflows without re-architecting the integration between AWS and Salesforce.
Because this solution connects an AI agent to your data through an MCP server, review its security posture against your organization’s requirements before you move beyond a proof of concept. Under the AWS Shared Responsibility Model, AWS secures the underlying infrastructure, and you secure your implementation. As a starting point, consider which users can access the agent and which tools and actions it can invoke. Also remember that content the agent processes, such as text extracted from evidence, might contain hidden instructions that trick the agent into unintended actions. This risk is known as indirect prompt injection. To mitigate this risk, treat all content extracted from evidence as untrusted data, never as instructions for the agent. Apply input validation on retrieved content before it enters the agent’s context. Scope the agent’s available actions to the minimum required using the MCP allowlist. Use Amazon Bedrock Guardrails to filter or reject content that attempts to override agent behavior. For a broader framework on threats specific to large language models (LLMs), see the OWASP Top 10 for LLM Applications.
This solution processes public sector evidence, so apply responsible AI controls before production. Amazon Bedrock Guardrails can filter harmful content and redact sensitive information such as personally identifiable information (PII). It can also run grounding checks that confirm responses stay grounded in the retrieved evidence rather than fabricated. These are examples, not a complete list. For authoritative guidance on securing agents and MCP tool access, follow Security for agentic AI on AWS, Amazon Bedrock AgentCore best practices, and apply Amazon Bedrock Guardrails with least-privilege controls.
Also, note that the accompanying sample code is intended to demonstrate this pattern and is not production ready. Review and harden it to meet your organization’s requirements before deploying to production.
To avoid ongoing charges, clean up your resources when you’re finished experimenting. For step-by-step commands to remove all deployed resources, see the GitHub repo.
You must also manually delete the Agentforce MCP connection in Salesforce.
Because this is an event-driven, serverless architecture, you only pay for what you use. Processing costs are incurred only when evidence is actively uploaded and analyzed. Amazon S3 and Amazon DynamoDB storage costs are based on the amount of data stored, with no minimum commitments or upfront fees. For details, refer to the pricing pages for each service used.
This post demonstrated how to combine Amazon Bedrock Data Automation with the Model Context Protocol to process unstructured evidence and surface structured insights directly in Salesforce Agentforce. Using Amazon Bedrock Data Automation, the architecture automatically extracts text from documents, generates descriptions from images, and produces transcriptions from video and audio files.
This pattern extends well beyond evidence management to public sector workflows involving unstructured multimodal data. For guidance on adapting this architecture to your agency’s specific needs, refer to the Extend this pattern to your own use case section earlier in this post.
The full sample code is available on the GitHub repo. You can also explore extending the solution with additional Amazon Bedrock Data Automation output types. Another option is to integrate Amazon Bedrock Knowledge Bases, the fully managed capability for Retrieval Augmented Generation (RAG), to support RAG-based Q&A across large evidence collections.
Christian Ramirez is an AWS Partner Solutions Architect working with Salesforce across Public Sector customers, where he helps organizations modernize their technology infrastructure and use cloud solutions. Beyond work, Christian enjoys running, cycling, and exploring US National Parks.
Bridget is a Senior Solutions Architect who works with strategic enterprise customers to create, design, and scale innovative cloud solutions. She has a focus on Storage, Analytics, and AI/ML domains. In her free time, she spends time coaching softball and hiking.
Varun is a Senior Technical Account Manager at AWS, partnering with strategic enterprise customers on large-scale infrastructure and AI/ML adoption. He specializes in solving complex business challenges for his customers and turning them into cloud-driven outcomes.
NVIDIA DLSS 5 introduces DLSS 3D-Guided Neural Rendering and granular controls that help game developers add lifelike lighting and material detail while preserving their artistic intent. We also look at updates to NVIDIA ACE, RTX Mega Geometry 2.0, and RTX Kit across character AI, high-density geometry, and rendering workflows.
This post covers:
DLSS 5 with 3D-guided neural rendering extends the graphics pipeline as a final neural-rendering stage. It uses the game engine’s rendered frame, including its artist-authored geometry, textures, and lighting buffers as an unyielding foundation.
The engine frame defines what must remain, while developers direct what may change. Using frame color and motion vectors, the model is designed to add lifelike lighting and material detail while preserving scene structure, character identity, and artistic intent.
Built specifically for real-time 3D rendering, DLSS 5 delivers deterministic, temporally stable output. Operating on a strict one-frame-in, one-frame-out model with game-engine motion vectors keeps results consistent as players move. The compact, specialized model runs locally on a single GeForce RTX 50 Series GPU at up to 4K.
Controls and integration for developers
DLSS 5 is available now in NBA 2K27, developed by Visual Concepts and published by 2K, for all GeForce RTX 50 Series desktop and laptop GPUs. GeForce NOW Ultimate members can also experience it when streaming from NVIDIA-operated GeForce RTX 5080-powered gaming rigs in the cloud. Visual Concepts uses overall tone and style controls plus a per-pixel uplift control mask to fine-tune character detail while respecting player likenesses.
In NBA 2K27, DLSS 5 preserves scanned facial geometry while enhancing skin subsurface scattering, light transmission through hair and ears, and contact shadows.
For more details about DLSS 5, check out our DLSS 5 article. Sign up to be notified for DLSS updates for developers here.
NVIDIA ACE offers ready-to-integrate AI models and tools for building knowledgeable, interactive, and conversational in-game characters. The latest updates expand the speech pipeline and inference framework, making it easier for developers to run AI in their games.
To run these models locally alongside game graphics, the NVIDIA In-Game Inferencing (NVIGI) SDK delivers a high-performance, streamlined path for deploying local AI models through in-process C++ execution.
Key Release Highlights
NVIDIA In-Game Inference SDK Updates
llama.cpp updates to maximize inference performance.Access NVIDIA In-Game Inference SDK here.
NVIDIA RTX Kit is a suite of rendering technologies for training and deploying AI in shaders, path tracing detailed scenes at game-ready performance, and rendering lifelike digital characters. The latest SDK updates expand support for high-density geometry, neural texture workflows, lighting, and texture filtering.
RTX Kit 2026.3 updates include:
In addition to the RTX Kit updates, RTX Mega Geometry SDK has been updated to 2.0 which adds support for streaming of continuous level-of-detail clusters for high-density meshes. The scale of detail is demonstrated in a newly released textured glTF version of Zorah.
RTX Mega Geometry is coming soon to Gears of War: E-Day, offering GeForce games higher frame rates, higher levels of image quality, and with even more responsive controls. We sat down with the Coalition’s Studio Technical Director, Kate Rayner, and Rendering Lead, Mike Perzel to learn more about Gears of War: E-Day’s integrations of RTX Mega Geometry and DLSS.
Video 3. RTX: Inside the Game | Gears of War: E-Day with DLSS 4.5 and RTX Mega Geometry
Check out the full list of game developer resources and stay up to date with the latest NVIDIA game development news:
GPU acceleration can speed up compute-intensive robotics workloads, but a fast CUDA kernel alone does not guarantee a fast ROS 2 graph. As messages move between nodes, they may continue to be serialized or copied through CPU memory, eroding the benefits of keeping perception and AI workloads on the GPU (Figure 1).
With the upstream rosidl::Buffer abstraction and the CUDA buffer backend that NVIDIA recently contributed to ROS Lyrical, ROS 2 nodes can exchange GPU-resident payloads through zero-copy transport when runtime conditions allow, while preserving standard ROS 2 messages and node boundaries. All nodes in NVIDIA Isaac ROS 5.0 have been updated to use the CUDA buffer backend and benefit from the more efficient data movement enabled by rosidl::Buffer.
Existing ROS 2 nodes can adopt rosidl::Buffer with minimal changes. The more challenging task is identifying the correct boundaries to update. This requires a careful audit of allocations, serialization, stream ownership, and fallback behavior.
This tutorial walks you through how to turn that audit into an agent-driven workflow. An AI coding agent uses the purpose-built migrate-node-to-rosidl-buffer skill to inspect an existing CUDA-accelerated node, trace data movement, plan a minimal interface-preserving refactor, and verify that the CUDA transport path is actually enabled. You’ll learn how to use the agent skill to update the node to adopt the CUDA buffer backend. The resulting accelerated workload can then be deployed on NVIDIA Jetson AGX Thor.
rosidl::Buffer and CUDA buffer backendIn ROS 2 Lyrical, variable-length primitive array fields such as uint8[] are represented in generated C++ code by rosidl::Buffer<uint8_t>. The default CPU-backed rosidl::Buffer behaves like the std::vector<uint8_t> interface existing ROS 2 code expects, preserving source compatibility. The pluggable abstraction also allows platform vendors to support externally managed storage without defining a separate ROS message type.
NVIDIA contributed the CUDA buffer backend for ROS 2 Lyrical. It implements rosidl::Buffer<uint8_t> storage with CUDA Virtual Memory Management (VMM). When publisher and subscriber meet backend runtime requirements, the payload can move between co-located nodes without serialization or host copies. Otherwise, ROS 2 automatically falls back to the CPU path that’s compatible with any existing ROS 2 nodes. The optimized path requires the same host, CUDA device, Linux user, and a supported RMW implementation (for example, rmw_fastrtps_cpp and rmw_zenoh_cpp).
Together, rosidl::Buffer and the CUDA buffer backend move memory sharing and data-lifetime management behind a standard ROS 2 field. This means the upstream capability is easier to adopt in GPU-accelerated robotics applications, so you can focus on node logic while retaining CPU fallback for incompatible peers.
This tutorial uses the Depth Anything 3 (DA3) TensorRT ROS 2 node as the example. The DA3 model predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses.
We aim to update this node to adopt the introduced CUDA buffer backend to take advantage of the performance improvement offered by the rosidl::Buffer feature. The node is particularly useful as a migration example because its algorithm is already GPU-accelerated.
This node’s callback converts the incoming ROS image to an OpenCV view, runs monocular metric-depth inference with NVIDIA TensorRT, converts the resulting cv::Mat back to a ROS image, and publishes it as a floating-point depth image.
The code is straightforward, but the CPU-backed ROS boundary surrounds a GPU-native algorithm. That CPU boundary is appropriate for a CPU producer or consumer, but it is unnecessary when the nodes on both sides can already produce and consume CUDA memory. In that case, the two payload-sized host transfers, host allocation, and serialization work become an optimization opportunity at the interface.
The goal is therefore not to redesign the model or replace its standard messages; rather, it is to preserve the existing ROS contract while allowing the output Image.data field to carry storage from an appropriate backend.
An AI coding agent is well suited to investigative work: following payloads through callbacks and helper libraries, finding host-device boundaries, preserving the node contract, and coordinating source, dependency, launch, and test changes.
The migrate-node-to-rosidl-buffer skill turns this analysis into a repeatable workflow. Rather than replacing the node with a template or rewriting code automatically, it directs the agent to:
rosidl::BufferUsing the rosidl::Buffer migration skill, the agent updates the node’s dependencies and interfaces to adopt the CUDA buffer backend. Most changes adapt the TensorRT wrapper to accept CUDA buffer handles for input and output data while preserving its existing API. The ROS transport change remains small: one subscription option, one CUDA allocation, two stream-aware handle extractions, and one publish. No custom message, duplicate CUDA topic, or CPU/CUDA publisher branch is required.
The following sections explain the key changes you can expect from the skill for the node migration.
First, the skill helps add CUDA buffer backend packages (cuda_buffer and cuda_buffer_backend) as additional dependencies. The message definition does not change—the node continues using sensor_msgs/msg/Image.
The subscriber is then updated to accept messages with CUDA-backed buffers. CPU remains an acceptable fallback by default, so the node-level callback does not need separate CPU and CUDA implementations.
rclcpp::SubscriptionOptions options; options.acceptable_buffer_backends = "cuda"; sub_image_.subscribe( this, image_base_topic, image_transport, rclcpp::SensorDataQoS().get_rmw_qos_profile(), options);
The existing image_transport and message_filters topology remains in place. The subscription options are simply forwarded through it.
The subscriber callback still accepts bgr8, preserves the header, dimensions, encoding, and byte stride, and converts with cv_bridge only when a different input encoding requires it. With the update, the TensorRT inference now directly writes the results to the CUDA buffer allocated in the output message, ready to publish right after the GPU work is enqueued.
The following excerpt contains the essential changes that leverage CUDA buffer APIs:
auto depth_msg = std::make_unique<sensor_msgs::msg::Image>();
depth_msg->header = bgr_image_msg->header;
depth_msg->height = bgr_image_msg->height;
depth_msg->width = bgr_image_msg->width;
depth_msg->encoding = sensor_msgs::image_encodings::TYPE_32FC1;
depth_msg->is_bigendian = false;
depth_msg->step = depth_msg->width * sizeof(float);
depth_msg->data = cuda_buffer_backend::allocate_buffer(
static_cast<size_t>(depth_msg->step) * depth_msg->height);
const cudaStream_t stream = tensorrt_depth_anything_->getCudaStream();
{
auto input = cuda_buffer_backend::from_input_buffer(
bgr_image_msg->data, stream);
auto output = cuda_buffer_backend::from_output_buffer(
depth_msg->data, stream);
tensorrt_depth_anything_->doInferenceCuda(
input.get_ptr(), bgr_image_msg->width, bgr_image_msg->height,
bgr_image_msg->step, *camera_info_msg,
reinterpret_cast<float *>(output.get_ptr()),
node_param_.point_cloud_downsample_factor,
node_param_.colorize_point_cloud,
node_param_.publish_point_cloud,
node_param_.enable_debug);
} // Release the CUDA event-tracked handles before publishing.
pub_depth_image_->publish(std::move(depth_msg));
Each line has a narrow purpose:
allocate_buffer() gives the standard Image.data field CUDA buffer-backed storage.from_input_buffer() supplies a CUDA buffer handle that is safe to consume on the TensorRT stream for read-only operations. CUDA input is used directly. CPU input is promoted to CUDA when necessary.from_output_buffer() supplies a CUDA buffer handle that is safe for write operations. The existing CUDA postprocess writes its final 32FC1 result directly into the buffer assigned to the outgoing message through the write handle, avoiding both a device-to-host copy and an intermediate device-to-device output.publish() as it normally does with the same message type while the underlying data field is now backed by the CUDA buffer backend. The CUDA memory sharing and compatibility with its downstream subscribers are handled automatically by the ROS 2 middleware as well as the backends.The skill keeps the non-CUDA route intact. Point-cloud construction and debug visualization are local CPU consumers in the original node. When enabled, they may still require a device-to-host copy and synchronization. They do not determine the representation delivered on the depth topic, so the migration leaves them as explicit optional boundaries rather than complicating the optimized publication path.
The rosidl::Buffer feature was introduced in ROS 2 Lyrical, so the migrated node is expected to work with Lyrical and above with supported RMW implementations (rmw_fastrtps_cpp and rmw_zenoh_cpp).
During the migration, the core functions and boundary message types are kept the same and add cuda_buffer and cuda_buffer_backend as additional dependencies to the package for enabling CUDA buffer backend. As a result, the overall build process and setup remain similar to the original node.
To enable CUDA buffer backend, build the packages from source. Start by cloning the source from the rosidl_buffer_backends repository where all the currently supported backends and companion packages are hosted:
git clone https://github.com/ros2/rosidl_buffer_backends.git
Note that the core functions of rosidl::Buffer are already built in ROS 2 Lyrical, so there is no need to rebuild the ROS 2 core packages.
The rosidl::Buffer backends are designed to be ROS 2 plugins. Building and sourcing the CUDA buffer backend packages in the same workspace is sufficient to make the backend available to the nodes at runtime.
colcon build --symlink-install --packages-up-to cuda_buffer_backend source install/setup.bash colcon build --symlink-install --packages-up-to depth_anything_v3 source install/setup.bash export RMW_IMPLEMENTATION=rmw_fastrtps_cpp
You can then follow the same model preparation process and run the same launch file with the updated TensorRT node as instructed in the original repository.
The migration leaves the TensorRT computation unchanged and targets the transport around it. To inspect GPU activity and memory transfers, use NVIDIA Nsight Systems. On an eligible CUDA path, the migrated node should not show payload-sized host-to-device or device-to-host transfers at its ROS boundary. Record comparable latency measurements before and after the change.
You can also validate backend negotiation from the subscriber. When both endpoints meet the CUDA backend requirements, msg->data.get_backend_type() should report "cuda". This is useful for tests that confirm the CUDA transport path is active.
rclcpp::SubscriptionOptions options;
options.acceptable_buffer_backends = "cuda";
subscription_ = create_subscription<sensor_msgs::msg::Image>(
"/depth_anything_v3/output/depth_image", rclcpp::QoS(1),
[this](sensor_msgs::msg::Image::ConstSharedPtr msg) {
const std::string backend = msg->data.get_backend_type();
RCLCPP_INFO(get_logger(), "received backend=%s", backend.c_str());
if (backend != "cuda") {
throw std::runtime_error("CUDA transport was not negotiated");
}
auto input = cuda_buffer_backend::from_input_buffer(msg->data, stream_);
consume_on_cuda(input.get_ptr(), stream_);
},
options);
Note that the production code will often try to accept CPU fallback without throwing the error.
With the provided CUDA buffer APIs, from_input_buffer() automatically handles the CPU fallback internally. Users don’t have to distinguish the CPU path and GPU path in the callback for incoming messages. All the CUDA memory sharing and CPU-to-GPU conversion, if needed, are taken care of by the CUDA buffer backend.
The skill also contains a verification step that helps produce custom source and sink nodes for testing and validation. This is done by creating two pipelines based on the generated source and sink nodes to test the same migrated node working under both CPU and GPU setup without code changes.
In the CPU control setup, a source node that publishes messages with CPU-based data is used. The messages arrive at the TensorRT node with a buffer that is backed by plain CPU storage. The CUDA buffer APIs used in the subscriber callback automatically detects the buffer backend type and do the conversion (CPU to CUDA in this case) when needed, so the same code functions as expected to accept CPU-based messages.
In another setup, a source node that publishes CUDA buffer-based messages is used. With the migrated TensorRT node, the CUDA buffer-aware subscriber can receive the message and obtain the CUDA handle by using the CUDA buffer APIs without additional CPU-GPU copies.
The same workflow can be applied to other CUDA-accelerated ROS 2 nodes with variable-length primitive message fields. The key is to treat optimization as an end-to-end systems task. The AI agent traces data movement, identifies which fields benefit from GPU-backed storage, preserves standard ROS 2 interfaces, and verifies both the optimized path and CPU fallback. That makes the migration repeatable instead of a one-off refactor.
NVIDIA Isaac ROS 5.0 brings this workflow into an accelerated robotics software stack, while NVIDIA Jetson AGX Thor provides the edge compute platform for running demanding ROS 2 perception, inference, and autonomy workloads on the robot.
Accelerating a ROS 2 node requires optimizing GPU computation as well as data movement. With rosidl::Buffer, the NVIDIA CUDA buffer backend, and an Isaac ROS 5.0 AI-guided migration skill, existing CUDA-enabled nodes can exchange GPU-resident data with minimal code changes. This avoids unnecessary serialization and CPU copies while preserving standard ROS 2 message interface.
To get started, follow these steps:
The compute and memory demands of generative AI increasingly exceed what a single GPU can provide. NVIDIA TensorRT multi-device inference is a new capability that enables a single TensorRT network to execute across multiple GPUs using NCCL-backed distributed collectives while retaining TensorRT inference optimizations. It is fully supported starting with TensorRT 11.0.
NVIDIA Dynamo-Triton (formerly NVIDIA Triton Inference Server) release 26.07 enables the multi-device inference capability of the TensorRT backend. One Triton KIND_MODEL instance can own multiple GPUs, create per-rank TensorRT execution contexts, CUDA streams, and NCCL communicators, and launch the ranks together for each request. The application calls one named model through a gRPC endpoint instead of coordinating GPU ranks itself.
For organizations deploying generative AI, this closes the gap between multi-GPU acceleration and a consumable inference service. Teams can trade additional GPU resources for shorter request latency, keep the application interface and surrounding workflow stable, package the engine as a versioned Triton model, and keep rank and communicator lifecycle code out of the client. For latency-sensitive generative media workflows, a shorter time to result can reduce user wait time and accelerate review-and-refine cycles.
This post demonstrates the integration using NVIDIA Cosmos 3 Nano video generation, a long-sequence workload featured in the previous post Scaling AI Inference Across Multiple GPUs Using NVIDIA TensorRT with Multi-Device Inference Support. Diffusers continue to orchestrate prompts, latents, classifier-free guidance (CFG), scheduling, VAE decode, and frame postprocessing. Dynamo-Triton serves the 36-layer denoising transformer, and TensorRT multi-device inference uses Ulysses context parallelism to distribute its 44,160 video tokens across as many as eight NVIDIA GPUs.
The distributed Ulysses graph is compiled into each TensorRT plan before deployment. The Dynamo-Triton TensorRT backend loads the versioned plan, creates the multi-rank execution state, and exposes one gRPC model endpoint. The client sends a transformer request to that endpoint; it does not coordinate the participating GPU ranks.
The Cosmos 3 Nano model provides a practical example of this boundary. The transformer accounts for 93.4% of the single-GPU generation time, making it the highest-impact stage to accelerate. Each of 35 denoising steps requires one negative or unconditional prediction and one prompt-conditioned prediction for CFG. The Diffusers proxy therefore makes two sequential Triton calls per step, for 70 transformer RPCs per generation. Each request carries prepared tensors and returns noise_patches to the application workflow.
The distributed graph is compiled into each context-parallel TensorRT plan. Dynamo-Triton configuration activates that plan; it does not convert a single-device engine into a distributed engine. The single-device baseline uses a standard GPU model instance on GPU 0. The two-, four-, and eight-GPU variants use KIND_MODEL, enable the TensorRT backend multi-device path, and identify the participating ranks.
# Excerpt from the generated CP8 config.pbtxt
name: "cosmos3_cp8"
backend: "tensorrt"
max_batch_size: 0
instance_group [
{ kind: KIND_MODEL count: 1 }
]
parameters [
{ key: "enable_multi_device" value: { string_value: "true" } },
{ key: "multi_device_gpus" value: { string_value: "0,1,2,3,4,5,6,7" } }
]
The fixed Cosmos 3 Nano profile for this example produces 44,160 video tokens. At context-parallel size eight (CP8), each rank processes 5,520 video tokens outside attention. The shorter 2,992-token text path remains replicated. Within each of the 36 transformer layers, Ulysses changes the partitioning axis around attention so that every rank processes the full video sequence for a nonoverlapping subset of heads.
The engine is exported from PyTorch and compiled with Torch-TensorRT. Three local converters lower export-carrier operations to the TensorRT public distributed-collective layer: reduce-scatter, all-to-all, and all-gather. Each accepted context-parallel plan contains two initial reduce-scatters, three all-to-alls in each of 36 transformer layers, and one final all-gather. The resulting topology is two reduce-scatters plus 108 all-to-alls plus one all-gather.
All four variants ran on the same healthy eight-GPU NVIDIA system. The single-device baseline used one GPU; CP2, CP4, and CP8 used two, four, and eight ranks. Every run used 1280×720 output, 189 frames at 24 FPS, and 35 denoising steps.
Each result includes one warm-up followed by five measured complete generations. Timing covers prompt work, the 70 Dynamo-Triton calls, CFG and scheduler updates, VAE decode, and frame postprocessing. Note that model loading and mp4 encoding were excluded.
Table 1 compares SD, CP2, CP4, and CP8 Cosmos 3 runs. End-to-end latency drops from 156.595 seconds on one GPU to 34.183 seconds on eight GPUs, while transformer RPC speedup increases to 6.09 times.
| Variant | GPUs | E2E mean | E2E speedup | RPC mean | RPC speedup | RPC share |
|---|---|---|---|---|---|---|
| SD | 1 | 156.595 | 1.00x | 146.192 | 1.00x | 93.4% |
| CP2 | 2 | 87.999 | 1.78x | 77.548 | 1.89x | 88.1% |
| CP4 | 4 | 53.093 | 2.95x | 42.661 | 3.43x | 80.4% |
| CP8 | 8 | 34.183 | 4.58x | 23.993 | 6.09x | 70.2% |
On one GPU, transformer RPCs account for 93.4% of generation time. At CP8, that share falls to 70.2%. Time outside the measured RPC path remains between 10.2 and 10.5 seconds across configurations, so prompt work, scheduler updates, VAE decode, postprocessing, and other client overhead become a larger fraction of the total.
Every variant used the same seed and generation profile. Validation sampled frames 0, 47, 94, 141, and 188, checked format and temporal variation, and compared each context-parallel output with the single-device result. CP2, CP4, and CP8 passed the configured thresholds of mean absolute error (MAE) ≤ 25 and peak signal-to-noise ratio (PSNR) ≥ 18 dB.
The outputs are not claimed to be pixel-identical. CP2 and CP4 measured MAE 12.759 and PSNR 21.111 dB. CP8 measured MAE 16.316 and PSNR 19.400 dB. The contact sheet also shows the same coherent action across the clip: a robot arm cleaning a plate.
For product teams, these results demonstrate a practical option when response time carries more business value than minimizing the GPUs assigned to one request. A complete Cosmos 3 generation that previously took more than two and a half minutes completes in about 34 seconds, while the application continues to use a conventional model-serving interface.
Teams must still decide on the best approach based on a resource-for-latency trade-off. This benchmark does not measure concurrent request throughput, cost per generated video, or total cost of ownership (TCO). Teams should evaluate these metrics against their own SLOs and deployment economics.
To reproduce the results featured in this post in your own environment, download NVIDIA Dynamo-Triton 26.07 from NGC. Then use the TensorRT, Torch-TensorRT, Diffusers, and Cosmos resources linked.
To learn more, check out these related resources:
When you ship an AI agent, the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment, and recover when a step fails. Scoring whether the model sounds right tells you almost nothing about whether the work finished.
That gap is why agent evaluation has had to evolve from scoring a single function call to scoring an entire task, with tool calling as the connective tissue underneath. This post traces that arc and explains why nearly every serious agent benchmark now rests on tool use.
The original harnesses were built for static tasks. The first model-agnostic, open-source harness decoupled the model from the evaluation protocol.
Agents broke this assumption. Operating across multi-step tasks, an agent calls tools, handles errors, and observes results over many steps, making a single output string insufficient. The Berkeley Function-Calling Leaderboard (BFCL) emerged to evaluate function selection and argument accuracy across single- and multi-turn scenarios. However, BFCL only evaluates individual calls—a valid issue_refund call still fails if underlying checks or updates were skipped. Call accuracy is necessary, but not sufficient.
Full agentic evaluation now requires a full execution environment: one that executes each tool call, tracks state across steps, and reads the world afterward to decide whether the work got done.
Two scoring layers sit on top of it:
Step-level tells you where the chain breaks, which is what you want when debugging or targeting fine-tuning effort; E2E collapses a failure on step one and a failure on step nine into the same “task failed.” E2E is what your users actually experience, which is why most production evals gate the release on it and keep step-level tracing underneath for debugging.
Those two scores are two readings of one object: the trace. A trace is the ordered log of a single attempt: the user message, each step, and the environment state when the attempt stops. Process scoring grades the rows. E2E scoring grades the final state.
A tool-calling benchmark scores three things in order: deciding to use a tool, selecting the right one, and populating its arguments. A model that reaches for a tool when a direct answer would fail as surely as one that skips a tool it needed. Cost and latency ride on top, set by the call’s verbosity and runtime.
Every run rolls up through a fixed hierarchy: Benchmark → Trial → Task → Turn → Step:
A step is usually a tool call, and every score above it rolls up from those steps. The metrics worth tracking collapse onto three axes: accuracy, verbosity, cost (see Table 1, below).
| Metric | Formula | Axis | Why it exists |
|---|---|---|---|
| Task success rate | successful_tasks / tasks | Accuracy | The release gate. Did the environment reach the goal state? |
| Consistency | range of success rate across 3–5 trials | Accuracy | A 90% / 74% split isn’t 84%. Report 82–88%, not a point estimate. |
| Tool-call precision | correct_calls / calls_issued | Accuracy | Hallucinated names and extra calls surface here, not in success rate. |
| Argument accuracy | correct_args / calls_with_right_tool | Accuracy | Separates “wrong API” from “right API, filled wrong.” |
| Steps per success | steps / successful_tasks | Verbosity | How long the trajectory runs when the task actually finishes. |
| Cost per success | spend / successful_tasks | Cost | The economic unit. Tokens and GPU-seconds only matter per successful task. |
The pairings matter: success rate without consistency is a point estimate on a stochastic system (a model that hits 90% then 74% is a worse bet than one holding 84%); tool-call precision without argument accuracy hides slot-filling failures.
Step count is often the axis that varies most across models on the same task — four steps versus fifteen — though on suites like Terminal-Bench 2.0 steps-per-turn varies too, so which axis moves most is benchmark-dependent. Parallel tool calling cuts step count and latency, but not call count: a one-step turn firing four tools still issued four calls. Roll up in order; don’t average steps and call it a benchmark score.
Two benchmarks can both claim to test tool calling and produce numbers that aren’t comparable. Three dimensions explain most of the gap:
Contamination now extends beyond training data leaks to live variants: web-searching agents retrieving answer keys during evaluation, and datasets on Hugging Face quickly re-scraped into pretraining corpora. Private domain evals solve this by being unable to scrape.
Table 2, below, is a public trace from a real benchmark run using step-level and E2E scoring, where the suite rather than an artificial ticket provides the tools, user, and completion criteria.
pytest-dev__pytest-5262 (trial .2, turns 0–4)/testbed) so that the requirements specified in the issue are satisfied” — the issue: _pytest.capture.EncodedFile reports mode rb+ (binary) from its underlying buffer, but its write() only accepts str, so external code that checks .mode (e.g. youtube-dl) crashes when it writes bytes.terminal, file_editor, task_tracker, finish; parallel tool-calling off (one tool call per turn); repo state persists turn to turn (real filesystem + git, not a mock).| Step | Turn | Call | Environment observation | Verdict | Why (valid / useful / redundant / recovered / policy) |
|---|---|---|---|---|---|
| 1 | 0 | terminal(find /testbed -name "capture.py") | Returns /testbed/src/_pytest/capture.py | valid | Locates the file named in the issue before editing anything |
| 2 | 1 | file_editor(view, capture.py) | Dumps the full file (400+ lines) | redundant | File is large; grepping for the class first would have been more targeted |
| 3 | 2 | terminal(grep -n "EncodedFile" capture.py) | Returns 422: return EncodedFile(...) / 425: class EncodedFile(object): | recovered | Corrects step 2’s inefficiency by narrowing straight to the relevant lines |
| 4 | 3–4 | file_editor(view, view_range=[420,450]/[450,470]) | Shows EncodedFile.__init__/__getattr__, revealing it delegates .mode straight from the binary-mode buffer | valid | Pinpoints the exact root cause (unfiltered __getattr__ delegation) that step 5+ fixes |
Looking at the results that were outputted, it is important to look at the last 5 bullets: E2E check, E2E score, step level score, tool-call precision, and argument accuracy. E2E check tells us that the related tests for the bug issues it sought to fix passed, meaning E2E score in this case is 1 (is_resolved: true). The next metric is the step level score that tells you how many of the steps the model took were actually needed. Looking at the score for the table above, step level is 3/4 due to one of the steps in this case being redundant – in particular step 2. In this case, it directly plays into the tool-call precision which also received a 3/4 due to the minor misstep. For this trace, our final metric argument accuracy saw that all arguments filled in correctly with no malformed arguments.
The line between “calling a tool” and “completing a task” no longer holds: most benchmarks measuring general capability now also measure tool use because models aren’t run without tools in any viable deployment. A benchmark that withholds tool access scores a capability nobody ships.
Not every benchmark makes the case. HumanEval runs generated Python against unit tests, providing executable verification, but no tool call and no environment to act on. SWE-bench is where the shift becomes clear: resolving a real GitHub issue means navigating a codebase, writing a patch, and passing the suite — file-read, search, and edit calls in sequence. The score measures the outcome, but the trajectory underneath consists entirely of tool calls. In many cases, then, the benchmarks you already run for general capability are already exercising tool use. That reframes the question that matters:
Academic benchmarks measure a model’s capability ceiling in the abstract. Enterprise benchmarks answer the narrower, more useful question: can it do my job — your tasks, against your APIs, under your policies? The closer a benchmark sits to production, the more its score should weigh in your decision.
Read NVIDIA Nemotron 3.5 Lightning’s published suite as task completion and time-to-done, not isolated call accuracy. Banking scores completion across a multi-turn banking conversation — the refund trace at scale, not a single call. GDPval-AA v2 scores real agentic work from actual job outputs, judged pairwise by a panel of LLM judges with Elo anchored to a 1,000 human-expert baseline — the kind of human validation that keeps a judge score trustworthy. On PinchBench, Nemotron 3.5 Lightning hits 86% accuracy while finishing 10,000 tasks 30% faster than Qwen3.6 35B at comparable accuracy — a model that finishes efficiently beats one scoring higher on isolated accuracy while burning more steps and tokens.
Public scores are a great signal, but they shouldn’t be considered as a release gate. Adapting the model to your task and use-cases is as important as ever.
Verify consequences in the environment, use judges for language, and use tool-call precision and argument accuracy to find where the chain breaks.
To reproduce the published numbers, Nemotron ships reproducibility docs covering the configs behind the model card scores. Try it on build.nvidia.com, grab weights from Hugging Face, or follow the NIM guide.
Tool calling in the realm of LLM benchmarking is the foundation that evaluations today rest on. Being able to build, read, and understand these evaluations is pertinent in making an informed decision for your use case.
Stay up to date on NVIDIA Nemotron by subscribing to NVIDIA news and following NVIDIA AI on LinkedIn, X, YouTube, and the Nemotron channel on Discord.
Access open Nemotron Models on Hugging Face and a collection of NIM microservices and Developer Examples on build.nvidia.com.
Today, we are announcing that xAI’s Grok 4.6 is available in Amazon Bedrock, adding a frontier model built for long-running agents, coding, and knowledge work to the Bedrock model catalog. Grok 4.6 launched on Bedrock on August 18, 2026. It offers a 500K token context window and supports configurable reasoning effort at four levels: low, medium, high, and xhigh.
This is xAI’s second model in Amazon Bedrock. When Grok 4.3 became generally available, xAI joined Amazon Bedrock as a model provider and the model was reachable through Bedrock Mantle, the OpenAI-compatible inference engine in Amazon Bedrock. Grok 4.6 widens that surface area considerably: it is available on both the bedrock-mantle and bedrock-runtime endpoints, and it supports the Converse API alongside Chat Completions and Responses.
This post covers what xAI says Grok 4.6 is designed for, how it is packaged on Amazon Bedrock, and how to send your first request.
The capability and training details in this section come from xAI’s launch announcement, Introducing Grok 4.6.
Grok 4.6 builds on Grok 4.5 with a particular focus on long-running agents and more ambitious interactive and visual work. xAI describes the model as staying with complex tasks across many steps, whether that is researching a topic, analyzing information, working across a code base, or turning an idea into a polished application or work artifact.
On training, xAI reports a longer supplemental training run than Grok 4.5, using curated model-generated data for reasoning and advanced technical concepts, high-quality engineering data, and an improved optimizer and training recipe. It then used Grok 4.5 to regenerate the supervised fine-tuning trajectories across reasoning efforts, agent harnesses, and domains including STEM, software engineering, and knowledge work, filtering out problematic traces with model-based checks. The model was then trained on a wide range of agentic reinforcement learning tasks spanning knowledge work, general coding, and domain-specific environments such as kernel optimization, web development, and computer-aided design.
Two behaviors xAI calls out are worth noting for anyone building agents. On longer trajectories, the model began showing more self-testing and verification, checking its own work before moving on. It also produces stronger first passes on visual and interactive projects, establishing the structure and visual language of an application in a single pass, which the team found useful where the fastest route to a good result was to start with something substantial and then iterate.
On safety, xAI states that Grok 4.6’s safeguards have been improved and calibrated in line with the model’s capabilities, backed by what it describes as its widest-ever suite of pre-deployment testing for capabilities and safeguard calibration, plus post-deployment and third-party testing. The company positions its safety stack as maximizing utility and security across legitimate use cases in domains such as vulnerability patching, accelerating the engineering design cycle, and augmenting AI research.
xAI reports that Grok 4.6 achieves frontier intelligence across several agentic coding and knowledge work benchmarks. These are the figures it published for Grok 4.6 High at launch on August 12, 2026:
| Evaluation | Grok 4.6 High |
| AA Intelligence Index | 61 |
| GDPVal-AA v2 | 1753 |
| CursorBench v3.2 | 69.9% |
| DeepSWE v1.1 | 65.9% |
| FrontierCode v1.1 (Extended) | 61.3% |
| APEX-Agents | 57.5% |
| Terminal-Bench v3.0 | 26% |
| APEX-SWE | 56.4% |
| AA-Briefcase | 1577 |
| Harvey LAB (Vals) | 15.8% |
Source: xAI, according to https://x.ai/news/grok-4-6.
Several of those evaluations come from Artificial Analysis, so it helps to know what they measure. According to Artificial Analysis, the Artificial Analysis Intelligence Index v4.1.1 is a composite that incorporates nine evaluations: GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity’s Last Exam, GPQA Diamond, CritPt, AA-Omniscience, and AA-LCR. Those cover agentic tool use, reasoning and knowledge, knowledge reliability, long context reasoning, and quantitative analysis over spreadsheets and documents. AA-Briefcase is its agentic knowledge work benchmark, where AA-Briefcase Elo aggregates rubric pass rate, analytical quality Elo, and presentation Elo, with higher scores better.
Artificial Analysis also tracks cost and latency alongside intelligence. Its cost-per-task metric is a weighted average cost per Intelligence Index task, derived from input, cache hit, cache write, reasoning, and answer token prices, which is a useful lens if you are sizing a reasoning-heavy agent workload where reasoning tokens are a real line item.
Several Bedrock capabilities are new for this model rather than carried over from the earlier Grok launch.
The bedrock-runtime endpoint. Grok 4.6 is served on bedrock-runtime in addition to bedrock-mantle, so you can reach it with the AWS SDKs and the standard Bedrock control surface rather than only an OpenAI-compatible client.
The Converse API, including streaming. Both converse and converse_stream are available. This is the practical payoff of runtime support: one message shape across models, and streaming through the usual Converse events (messageStart, contentBlockDelta, contentBlockStop, messageStop, metadata) without hand-rolling server-sent events (SSE) parsing.
An xhigh reasoning effort level. Effort runs low, medium, high, xhigh, extending the range at the top end for problems where a deeper pass is worth the tokens. On Converse, set it through additionalModelRequestFields={"reasoning_effort": "xhigh"} rather than a reasoning parameter.
Cross-Region inference. On bedrock-runtime you route through one of two inference profiles rather than pinning to a single Region. us.xai.grok-4.6 keeps traffic within the US geography when you have data residency requirements, and global.xai.grok-4.6 routes worldwide for the widest capacity pool. Global is also the cheaper of the two, at $2.00 per million input tokens against $2.20, so absent a residency constraint it is usually the better default.
Amazon Bedrock Guardrails. Grok 4.6 now supports Guardrails on bedrock-runtime across its APIs, giving you content filters, denied topics, personally identifiable information (PII) redaction, and word policies. You attach a guardrail by ID and version on the request, and the policy is evaluated against both the prompt and the model’s response. For agentic workloads this matters because it puts a consistent policy boundary around a model that might run unattended across many steps.
Invocation logging. With model invocation logging enabled, Grok 4.6 calls are captured as complete Amazon CloudWatch records: request body, response body, token counts including reasoning tokens, and the inference profile used. Useful for auditing agent runs where you need to see what the model was actually asked.
Prompt caching. Cached input is billed at roughly a quarter of the standard input rate, which matters for agents that resend a large system prompt or document on every turn. Caching applies to a repeated prefix, so keep stable content at the front of the request, and read the cached token count in the usage block to confirm the discount is landing before you build it into a cost model.
Tool calling, structured output, image input, response streaming, and encrypted reasoning content are available as well, but those date from the Grok 4.3 launch and are covered in that post.
Grok 4.6 accepts text and image input and returns text. Audio, speech, video, and embedding modalities are not supported, and it does not generate images. The model is reachable through two endpoints, and the model ID differs depending on which one you use:
| Endpoint | Model ID | Base URL |
| bedrock-mantle | xai.grok-4.6 |
https://bedrock-mantle.{region}.api.aws/openai/v1 |
| bedrock-runtime | us.xai.grok-4.6 (Geo) or global.xai.grok-4.6 (Global) |
https://bedrock-runtime.{region}.amazonaws.com/openai/v1 |
On the API side, Grok 4.6 supports the Responses API, the Chat Completions API, and the Converse API. The Invoke API is not supported.
Feature support differs by endpoint, which is the detail most likely to shape your integration choice:
On bedrock-mantle, supported features include client-side tool calling, reasoning, structured outputs, prompt caching, response streaming, projects, and abuse detection.
On bedrock-runtime, supported features include reasoning, prompt caching, response streaming, invocation logs, and projects (default project only). Structured outputs, server-side tool use, intelligent prompt routing, count tokens, and application inference profiles are not supported on that endpoint.
Tool calling works on both endpoints. The model returns a structured function request, your code executes it, and you pass the result back. On bedrock-runtime you can drive that loop through Converse’s toolConfig or the OpenAI-compatible tools parameter, so agents that depend on function calls are not limited to bedrock-mantle.
If your application depends on JSON Schema structured output, that points you at bedrock-mantle. If you want the Converse API or invocation logging, that points you at bedrock-runtime.
Availability differs by endpoint. On bedrock-mantle, Grok 4.6 is available for in-Region inference in US West (Oregon) (us-west-2) . On bedrock-runtime, in-Region inference is not offered. Instead, you invoke the model through cross-Region inference profiles. Geo cross-Region inference is available from the US Regions (us-east-1, us-east-2, us-west-1, and us-west-2), and Global cross-Region inference is available from a considerably longer list spanning the US, Canada, Europe, Asia Pacific, the Middle East, Africa, and South America. Geo cross-Region routes across Regions within a geography while respecting data residency, and Global cross-Region routes anywhere worldwide when there are no residency constraints. The full table runs to more than 30 Regions, so check the model card and the Regional availability by model page for the current list before you pin a Region.
This is a change in shape from the Grok 4.3 launch, where, as noted in the Grok 4.3 post, the model used in-Region inference only and Geo and Global cross-Region inference were not offered.
Grok 4.6 supports three service tiers. Standard is pay-per-token with no commitment, selected by setting "service_tier": "default" or omitting the field. Priority delivers faster, prioritized processing for a premium ("service_tier": "priority"). Flex offers lower-cost access for work that is not time-sensitive ("service_tier": "flex"). For per-token pricing across the tiers, see the Amazon Bedrock pricing page.
The other two tiers are priced as multipliers on those Standard rates: Priority at 1.75x, a 75 percent premium, and Flex at 0.5x, a 50 percent discount. So the same workload that costs $2.20 per million input tokens on Standard in-Region runs $3.85 on Priority and $1.10 on Flex, which makes tier selection a larger cost lever than the Region choice.
For reference, xAI lists Grok 4.6 pricing starting at $2 per million input tokens and $6 per million output tokens, with a fast variant at twice the price. Always confirm current rates on the Amazon Bedrock pricing page, because prices and tiers change.
Before your first call, confirm the model is available to you in the Bedrock console for the Region you plan to use. Grok 4.6 is served through inference profiles rather than on-demand throughput on the bare model ID, which is why requests name us.xai.grok-4.6 or global.xai.grok-4.6 on bedrock-runtime.
Grok 4.6 uses OpenAI-compatible APIs, so the OpenAI SDK works against either endpoint after you set the base URL. Install the SDK, and boto3 if you plan to use the Converse API:
pip install openai
pip install boto3Generate a long-term Amazon Bedrock API key from the Amazon Bedrock console for exploration, then set your environment. For bedrock-mantle:
export OPENAI_API_KEY="<provide your Bedrock API key>"
export OPENAI_BASE_URL="https://bedrock-mantle.us-west-2.api.aws/openai/v1"For bedrock-runtime:
export OPENAI_API_KEY="<provide your Bedrock API key>"
export OPENAI_BASE_URL="https://bedrock-runtime.us-east-1.amazonaws.com/openai/v1"A first request on bedrock-mantle with the Chat Completions API:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="xai.grok-4.6",
messages=[
{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}
],
)
print(response)On bedrock-runtime the difference is the model name: you pass a cross-Region inference profile instead of the bare model ID. This example also switches to the Responses API to show that shape:
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="us.xai.grok-4.6",
input="Can you explain the features of Amazon Bedrock?",
)
print(response)And through the Converse API with boto3. Because reasoning is active, the first content block carries the reasoning and the answer sits in a later block, so search the blocks for the text rather than indexing content[0]:
import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="us.xai.grok-4.6",
messages=[
{"role": "user", "content": [{"text": "Can you explain the features of Amazon Bedrock?"}]}
],
inferenceConfig={"maxTokens": 2048},
)
blocks = response["output"]["message"]["content"]
text = next(b["text"] for b in blocks if "text" in b)
print(text)On Converse you set the effort level through additionalModelRequestFields rather than a reasoning parameter:
response = client.converse(
modelId="us.xai.grok-4.6",
messages=[{"role": "user", "content": [{"text": "What is 17*23? Number only."}]}],
inferenceConfig={"maxTokens": 3000},
additionalModelRequestFields={"reasoning_effort": "xhigh"},
)Three operational notes. First, on bedrock-runtime, Grok 4.6 is not available for in-Region inference, so requests must name us.xai.grok-4.6 or global.xai.grok-4.6.
Second, bedrock:InvokeModel is evaluated against three resources: your account’s default project, the inference profile you name, and the underlying foundation model. The foundation model ARN is wildcarded across Regions because cross-Region profiles route outside the calling Region. Bearer-token authentication on the OpenAI-compatible endpoints additionally requires bedrock:CallWithBearerToken, which boto3 and Converse do not need:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "bedrock:InvokeModel",
"Resource": [
"arn:aws:bedrock:{region}:{account-id}:project/default",
"arn:aws:bedrock:{region}:{account-id}:inference-profile/us.xai.grok-4.6",
"arn:aws:bedrock:*::foundation-model/xai.grok-4.6"
]
},
{
"Effect": "Allow",
"Action": "bedrock:CallWithBearerToken",
"Resource": "*"
}
]
}List every inference profile you plan to call. Profiles are scoped individually, so a policy naming us.xai.grok-4.6 does not cover global.xai.grok-4.6.
Third, the two authentication mechanisms cover different code paths. An Amazon Bedrock API key in OPENAI_API_KEY travels as a bearer token and authenticates the OpenAI-compatible calls on both endpoints. The boto3 Converse examples sign with SigV4 instead, drawing on your ordinary AWS credentials from the environment, a profile, or a role. Configure both if you intend to use Converse alongside the OpenAI-compatible APIs.
Treat a long-term API key as an exploration-only credential. For production, the Grok 4.3 launch post recommends short-term bearer tokens generated from your IAM credentials with the aws-bedrock-token-generator package, because they expire automatically and keep access tied to your IAM identity, and that guidance applies equally here.
Reasoning is active on Grok 4.6 by default, and you configure how much of it the model spends through the reasoning parameter with low (the default), medium, high, or xhigh. The xhigh level is new relative to what the Grok 4.3 launch post documented, where the levels were none, low, medium, and high.
Reasoning content is encrypted. You can have it returned by passing include: ["reasoning.encrypted_content"] on a Responses API request, then send that content back on subsequent turns to give the model its own prior reasoning as context in a multi-turn conversation. The Chat Completions API does not return reasoning tokens.
Encrypted reasoning is a Responses API feature, so this example uses the OpenAI client rather than the boto3 client from the Converse examples above:
from openai import OpenAI
client = OpenAI() # OPENAI_BASE_URL points at the bedrock-runtime endpoint
response = client.responses.create(
model="us.xai.grok-4.6",
reasoning={"effort": "high"},
include=["reasoning.encrypted_content"],
input="Explain quantum entanglement simply.",
)
print(response.output_text)Because reasoning is by default and effort is per request, effort level is a real cost and latency control. Run short extraction and classification calls at low, and reserve high or xhigh for planning steps and long agent trajectories where an early mistake compounds. Benchmarking effort levels against your own workload is the fastest way to find where higher reasoning stops earning its token cost.
Grok 4.6 on Amazon Bedrock gives you a model xAI built for long-running agents and ambitious interactive work, with a 500K token context window, four reasoning effort levels, image input, prompt caching, and a choice between the OpenAI-compatible bedrock-mantle endpoint and the bedrock-runtime endpoint with Converse API and cross-Region inference support.
To start building, review the Grok 4.6 model card for the current Region list, feature matrix, and parameter details, and check the Amazon Bedrock pricing page for token rates. If you generated a long-term Amazon Bedrock API key for exploration, delete it from the Amazon Bedrock console when you are finished. A standing credential you no longer need only widens your account’s exposure surface.
Suheel is a Principal Solutions Architect at AWS, specializing in artificial intelligence, machine learning, and generative AI. He helps Foundation Model Provider customers design, build, modernize, and scale their AI/ML and generative AI workloads on AWS. His experience spans the AWS AI/ML and generative AI portfolio, particularly Amazon Bedrock, Amazon Bedrock AgentCore, and Amazon SageMaker AI. In his free time, Suheel enjoys working out and hiking.
Ikenna is a Principal Solutions Architect at AWS specializing in networking, containers, and AI infrastructure. He guides model providers through scaling their training and inference systems while enabling rapid deployment of evolving frontier models on AWS. His work increasingly spans agentic AI – building reliable, cost-efficient multi-agent systems and the inference infrastructure behind them in production.
Fabio is a Senior Customer Solutions Manager at Amazon Web Services (AWS) and strategic advisor guiding foundational model providers in their go-to-market journey. Prior to AWS, he held Product Management, Engineering, Consulting, and Technology Delivery roles across multiple Fortune 500 companies in industries, including retail and consumer goods, oil and gas, financial services, insurance, and aerospace and defense.
Saurabh is a Senior Product Manager for Amazon Bedrock and Amazon SageMaker Inference. He is passionate about working with customers and partners, motivated by the goal of democratizing AI. He focuses on core challenges related to deploying complex AI applications, inference with multi-tenant models, cost optimizations, and making the deployment of generative AI models more accessible. In his spare time, Saurabh enjoys hiking, learning about innovative technologies, following TechCrunch, and spending time with his family.
Anirban is a Principal Engineer at AWS based in Seattle, USA, where he focuses on the design of secure, high-scale model-serving infrastructure for Amazon Bedrock. He has driven the technical work behind several foundation-model launches on the platform. Prior to joining Amazon Bedrock, he was a Principal Engineer on AWS Outposts, building hybrid on-premises cloud infrastructure.
This post is co-written with Philipp Karg from BMW Group and Christopher Masurek from Data Reply.
Cost anomalies are hard to spot when you run 14,000 cloud accounts. BMW Group operates Cloud Efficiency Analytics (CLEA), an in-house FinOps system built on AWS with Reply that monitors more than 14,000 cloud accounts across BMW Group’s cloud estate. CLEA began as a set of dashboards in Amazon Quick Sight, which gave BMW employees visibility into their cloud spend. But a dashboard shows what already happened, and only when someone opens it.
To close that gap, CLEA now runs anomaly detection every day and sends email to account owners when spending departs from its expected pattern.
This post walks through the forecasting baseline, the filtering logic that decides which deviations are worth an alert, the alert engine, and the serverless architecture that processes every account daily for about $50 per month in compute.
CLEA ingests billing data daily from AWS Cost and Usage Reports (CUR), the primary source, along with the equivalent billing exports from the other providers in BMW Group’s estate. The raw data comprises around 3 billion rows across 500 columns per month. CLEA aggregates it to one consistent grain: daily cost per account per service. The data arrives with a one-day lag (T-1), so yesterday’s spend is analyzed and alerted on today. The pipeline is scheduled after AWS CUR delivery is confirmed complete to avoid partial-day data.
A single AWS account running Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service (Amazon S3), AWS Lambda, and Amazon Relational Database Service (Amazon RDS) produces four daily cost time series, one per service. An account using 15 services produces 15 time series. Across 14,000 accounts, each with its own set of active services, that comes to hundreds of thousands of account-service combinations. Each one needs its own forecast and its own anomaly evaluation.
We use three terms throughout the rest of this post. Expected spend is the predicted cost for one account-service pair on one day, based on 365 days of history. Actual spend is the cost recorded in the billing data for that same account, service, and day. Impact is actual spend minus expected spend, so a positive impact means overspend against the forecast.
CLEA builds its cost baselines with Prophet, the open source forecasting library from Meta. We chose it for its simplicity and its steady performance on cost time series. The model trains on 365 days of daily cost history for each account-service pair, with additive seasonality.
AWS Step Functions orchestrates the daily run. A preparation AWS Lambda function discovers the active accounts and writes the list to Amazon S3 as JSON. A Distributed Map then fans the work out across as many as 500 concurrent Lambda functions. Each function forecasts the services for one account, and the full 14,000-account run finishes in about 20 minutes.
The forecast output has two uses: a 12-month rolling forecast, and per-day predicted values that become the expected cost baseline for anomaly detection.
We treat forecasting as a pluggable module. The interfaces are the input format (daily cost per account-service) and the output format (per-day predicted values with confidence intervals), so the forecasting engine can be swapped out without touching the detection and alerting layers that account owners depend on every day.
A forecast on its own is not an alert. Getting from one to the other takes three things: a baseline that each account-service pair can be measured against, a daily comparison that flags the days falling outside it, and a set of filters that decide which of those days are worth an owner’s attention.
A fixed rule, such as alerting whenever daily spend passes a set dollar amount, does not hold up at this scale. Accounts grow, adopt new services, and ramp workloads on purpose, and a fixed rule reads all of that as anomalous. Set the threshold high enough to stay quiet for the largest accounts, and the smaller ones get no coverage at all. CLEA learns the trajectory of each account-service pair, so the comparison is against what that pair has actually been doing rather than against a number chosen centrally.
The trade-off is that a model that adapts to a trend will eventually absorb one. A sustained step up in spend gets flagged for the first few days and then settles in as the new expected level as the training window catches up. Detection of this kind is strongest on spikes.
With a forecast in place, detection becomes a daily comparison. For every account-service pair, CLEA calculates the impact: actual spend minus expected spend. Where actual cost falls outside the confidence interval Prophet produced, CLEA flags the day as a potential anomaly. Because the interval widens as the model’s own uncertainty grows, the test adapts per account-service pair instead of applying one fixed band. That alone filters out most ordinary day-to-day movement.
Some services never enter the model. Before a detection runs, CLEA excludes low-spend services (averaging below $0.10 over the last 3 days), services with fewer than 10 days of history, and other specific line items and charge types that are not relevant to the forecast.
CLEA also applies a deviation threshold: a flagged day must deviate by at least 40% from expected spend to stay in scope. From there, the remaining detections pass through two groups of filters. Generic thresholds apply to every account: an anomaly has to clear both the deviation threshold and its cluster’s minimum dollar impact before it earns an alert. Case-specific thresholds then override that baseline for the services and accounts that are volatile by design.
Account-cluster filtering. A 900% jump sounds alarming until you look at the absolute numbers: An account that normally spends $0.10 on a service and then spends $1.00 has spiked, but the absolute overspend is negligible. CLEA sorts accounts into four clusters by trailing three-month average spend, and each cluster carries a minimum dollar impact appropriate to that account’s scale.
| Cluster | Trailing 3-month average spend | Minimum impact to alert |
| 1 | Less than $100k | More than $300 |
| 2 | $100k to $250k | More than $500 |
| 3 | $250k to $500k | More than $750 |
| 4 | More than $500k | More than $1,000 |
Service-specific thresholds. Based on operational experience, certain services produce cost spikes as part of their normal usage pattern. AWS Glue, Amazon Athena, and Amazon EC2 consistently showed higher variance in daily spend during legitimate workloads. This led to a disproportionate share of false positives under the standard 40 percent threshold. For these services, CLEA applies a 60 percent deviation threshold to align detection sensitivity with observed cost behavior.
Account-specific overrides. Accounts on a reduced-sensitivity list must exceed three times the standard thresholds before an alert fires. This covers teams with known volatile workloads who asked for fewer notifications.
Figure 1 shows how each layer narrows the set: a wide band of Prophet anomalies on the left, and on the right the few that survive both the generic thresholds and the case-specific overrides.
CLEA can see operations, usage types, and the resulting costs. It cannot see intent. Only the account owner knows whether a cost increase was planned, such as a new workload rollout or a migration. That boundary between detection and judgment is permanent, so we tune thresholds against user feedback instead of trying to engineer it away. Feedback is gathered through a button in the application and a call to action in every alert email.
One more piece of bookkeeping matters at this scale. CLEA merges consecutive flagged days into date ranges, and the grouping logic reads every historical model snapshot instead of only the latest run. Without that, ranges fragment whenever Prophet reclassifies an individual day between executions.
Alerting runs as a separate process once detection finishes. The engine queries the day’s active anomalies and deduplicates them by matching the detected date against the current date, so each anomaly produces a single alert. Anomalies that began within the last four days generate an alert. Older ones generate an alert only if they are still ongoing.
Every alert email carries the context an owner needs to act:
An Excel attachment holds the full table.
Figure 2 shows an alert for an example account. The email names the account and the recipient’s role. It states that costs exceeded expected spending by $1,775.13 and notes that anomalies are detected from spending spikes and may include false positives. Under Recommended actions it asks the owner to review the table, open the CLEA Cost Anomalies view to drill into the discrepancy, and consult the reference documentation. The table lists Amazon Elastic Compute Cloud, a one-day anomaly, expected spend of $809.30 against actual spend of $2,584.43, and an impact of $1,775.13 or 219.34%. A closing section asks whether the alert caught a real issue and how future alerts could improve.
When an alert lands, the owner can investigate without involving the platform team. CLEA provides an anomaly dashboard in Amazon Quick Sight that lists the detected anomalies for each account, using the same fields as the alert email. Owners can widen the filter to include anomalies that were not flagged for alerting, which is how borderline cases get reviewed.
When you select an anomaly, CLEA opens a drill-down chart. Two bar charts break the account’s actual daily spend down by operation and by usage type, which is usually enough to confirm the spike and place it in time. A detail table lists the usage types driving the cost, such as EUC1-InstanceUsage:db.r6g.large, with cost in US dollars and usage amount. In our own review of past cases, usage type and operation together accounted for the large majority of root causes, which is why the drill-down leads with those two dimensions.
Figure 3 shows the drill-down for an account with a confirmed spike. The left chart plots daily usage cost by operation from early May to mid-June 2026. RunInstances dominates, and a single day reaches about $2,600 against a baseline near $900. The right chart plots the same period by usage type, and the same day resolves almost entirely to EUC1-BoxUsage:g6.48xlarge, which identifies the instance type behind the spike.
The daily pipeline runs on AWS Step Functions in Distributed Map mode with a maximum concurrency of 500. Failure tolerance is set to five accounts out of roughly 14,000, which requires a 99.96 percent success rate per run. The full cycle finishes in about 20 minutes, and the whole setup costs around $50 per month in compute, or less than half a cent per account per month. Because every component is serverless, there is no idle infrastructure to pay for between runs.
Figure 4 shows the flow across three areas: the CLEA provider account, where dbt (data build tool) and the Step Functions workflow run. The Cloud Data Hub, which holds the raw, source, and semantic data layers together with the AWS Glue Data Catalog. And the CLEA dashboard, which reads a SPICE dataset in Amazon Quick Sight. The following numbered steps match the callouts in the diagram.
1. dbt repartitions the cost data into account-level Parquet files, one per account, aggregated per service per day.
2. A daily time-based event starts the Step Functions workflow.
3. The preparation Lambda function writes the account list to Amazon S3 as JSON.
4. Each Distributed Map worker reads its account’s data by key.
5. Workers write anomaly results to a raw Amazon S3 layer as one JSON file per account.
6. An AWS Glue job consolidates those files into a single daily Parquet file in the source layer.
7. Amazon Athena views expose the results, and the dbt models apply the threshold logic, range grouping, and alert labeling.
8. The alert engine reads the labeled output and sends the notifications.
The modular architecture positions CLEA to evolve its forecasting component as new time series models are released, without disrupting the downstream detection and alerting layers that account owners depend on daily.
Planned enhancements include integrating anomaly alerts with the existing IT service management (ITSM), so account owners receive incident tickets through workflows they already use daily rather than relying solely on email notifications.
On the self-service side, the team plans to give account owners direct control over their alert sensitivity through the CLEA recommendation management portal. Users will be able to define the total cost discrepancy that triggers a notification for their accounts, reducing reliance on centrally managed thresholds.
Further ahead, the team is building an agentic endpoint that will provide automated root cause explanations: what likely caused the spending increase, and what to do next to stop the anomaly or prevent it from recurring. The team also plans an AWS CloudTrail integration. It surfaces which user or role configured the service behind the cost increase, which adds configuration attribution to the remediation workflow.
We showed how BMW Group moved CLEA from reactive dashboards to daily, automated cost anomaly detection across more than 14,000 cloud accounts. For account owners, the practical change is that anomalies now come to them. Nobody has to remember to open a dashboard to learn that a service started costing more than it should, and because the alert lands the day after the spend occurs, owners can investigate while the cause is still fresh. A Prophet baseline per account-service pair supplies the expected spend. A layered set of filters reduces the raw detections to the ones worth an owner’s attention. A serverless pipeline on AWS Step Functions, AWS Lambda, AWS Glue, and Amazon Athena runs the whole cycle in about 20 minutes for roughly $50 per month. The part that took the most iteration was not the forecast. It was deciding which deviations deserve an email, and account owner feedback still drives how we tune those thresholds.
To build something similar, start with the Step Functions Distributed Map documentation for the fan-out pattern, and the AWS Cost and Usage Reports documentation for the billing data. For the rest of the CLEA story, see BMW Cloud Efficiency Analytics powered by Amazon Quick Sight and Amazon Athena on the data foundation and dashboards, How BMW Group built a serverless terabyte-scale data transformation architecture with dbt and Amazon Athena on the transformation layer beneath it, and How BMW Group Enhances Cloud Optimization With Generative AI on AWS on the assistant that BMW Group layered on top. If you would like help applying this pattern in your own organization, contact your AWS account team.
Tareq is a Senior Data Science & AI/ML Consultant within AWS Professional Services. As a tech lead his skills and areas of expertise include generative AI, data science, machine learning, and application development. He supports customers in developing data-driven applications in the cloud, working with strategic customers across automotive, media and entertainment, sports, and manufacturing.
Philipp is a Lead FinOps Engineer at BMW Group, specializing in data engineering, AI, and cloud cost optimization. He drives cloud efficiency initiatives and fosters a cost-aware culture to enable sustainable cloud operations at scale.
Christopher is a Senior Data Engineer at Data Reply, specializing in FinOps, time series analytics, and large-scale data platforms for multi-cloud environments. He helps enterprise customers build reliable data solutions for cloud cost optimization, combining hands-on engineering with technical coordination and delivery ownership.
Data science teams often move among separate tools for governed data access, R analysis, Python model development, deployment, application development, and reporting. Positron, Posit’s integrated development environment (IDE) for data science, now runs on Amazon SageMaker AI.
For a data scientist, running Positron on SageMaker AI means:
Posit publishes a container image definition for Positron, built on the Amazon SageMaker Distribution image. Platform administrators build that image, push it to their own Amazon Elastic Container Registry (Amazon ECR) repository, register it with SageMaker AI, and attach it to a Studio domain. Data scientists then choose Positron when they create a Space and open the IDE directly in Studio.
This post shows how a data scientist experiences Positron in SageMaker AI, from exploring an Amazon Athena table to deploying a real-time endpoint.
This walkthrough uses a synthetic 50,000-loan portfolio. Amazon S3 stores the source data, and the AWS Glue Data Catalog registers it. Amazon Athena queries the data, R validates features, and Python trains an XGBoost classifier. Shiny for Python invokes the endpoint, and Quarto records the workflow. The screenshots and metrics come from the captured run. The data does not represent a production lending system.
To follow this walkthrough, an organization needs:
ml.t3.xlarge instance or larger for the demonstrated environment.Figure 2. One project connects governed AWS data, R and Python analysis, managed serving, an application, and a reproducible report.
The run began with Positron in a SageMaker Studio Space. The project explorer, editor, R and Python sessions, Variables pane, plots, terminal, and application preview were available in one browser-based environment on SageMaker compute under the Space execution role.
From the same Space, Posit Assistant identified credit_risk_blog.loan_tape_source in the AWS Glue Data Catalog and prepared a read-only Amazon Athena query. The query returned five sample rows across six fields, scanned 2.18 MiB, and completed in under one second.
Posit Assistant can use Amazon Bedrock as a model provider with AWS credentials and a configured AWS Region. No separate model-provider API key is required when Amazon Bedrock authentication resolves through the environment’s AWS credentials. Customer content is encrypted, isn’t used to improve base models, and isn’t shared with model providers (see Amazon Bedrock data protection). Private connectivity can be configured with AWS PrivateLink.
The captured Session information view recorded 6,657,942 tokens, including 6,118,411 cache-read and 462,905 cache-write tokens, with an estimated cost of $6.319 and 92.5 percent cache efficiency, as shown in the following figure. Those values describe this session and the Assistant’s estimate. They aren’t an AWS invoice or a general cost benchmark. Cache behavior and pricing depend on the selected model and provider.
The workflow used an aggregate Athena query to examine row counts, identifier uniqueness, missing values, numeric ranges, and target validity. The results identified 50,000 loans, including 1,500 records with missing income and 1,015 defaults, for an overall default rate of 2.03 percent.
The workflow loaded the 50,000-row table into the active R session and opened it in Data Explorer. R created debt-to-income and log-income features and displayed the debt-to-income distribution in the Plots pane. Excluding the 1,500 incomplete records left 48,500 loans for modeling and scoring.
The validated feature definitions then moved into Python. An XGBoost classifier trained on a matrix containing 40,000 rows and three model features. The held-out evaluation produced an AUC of 0.834 and showed a 12.3 percent observed default rate in the highest-risk decile.
The workflow wrote predicted probabilities and risk deciles for 48,500 loans to Parquet and registered the results as credit_risk_blog.scored_loans in Athena. It then created a SageMaker AI model, endpoint configuration, and real-time endpoint. The endpoint reached InService, and an invocation using a synthetic applicant payload succeeded.
The project used a Shiny for Python application to invoke the deployed endpoint. The application accepted synthetic applicant information, applied the feature definitions used during training, and displayed the returned probability of default. The source code and running application remained in the same Positron project which runs behind the Amazon SageMaker Studio application proxy and is reachable only by users authenticated to the Space. It invokes the endpoint under the Space execution role rather than any stored key, and the role is limited to sagemaker:InvokeEndpoint on the endpoint ARN.
The run concluded with a Quarto report that connected the Athena source, data-quality findings, R validation, Python model, scored output, SageMaker AI endpoint, and Shiny application. The report was generated directly from the project, preserving the workflow’s evidence and results in one reproducible document.
The deployment involves two paths: an administrator path that builds and registers the custom Positron image, and a data science path that uses it to analyze data and deploy models.
Positron runs as a custom image built on the Amazon SageMaker Distribution image in SageMaker AI. An administrator builds the Posit-published image definition, pushes it to a private Amazon Elastic Container Registry (Amazon ECR) repository in the Studio domain’s AWS Region, registers a SageMaker AI image and version, creates a JupyterLab app image configuration, verifies licensing, grants the execution role the required permissions, and attaches the image to the domain. Posit publishes the image definition, for example the Positron SageMaker Containerfile, which builds on the SageMaker Distribution base image. Amazon Bedrock is optional and is involved only when it’s selected as the Posit Assistant provider.
Responsibilities remain separate. Posit provides the software image and product support. The customer manages identity, permissions, licensing, networking, logging, image updates, and approved AWS services. AWS operates the managed cloud services.
The data scientist launches JupyterLab in a SageMaker Studio Space, opens Positron, and uses R, Python, Quarto, Posit Database Drivers, and optionally Posit Assistant to query and analyze data and deploy models.
The recorded workflow demonstrated the following capabilities within a single Positron Space:
The dataset and applicant payloads were synthetic. The workflow didn’t establish model fairness, calibration, lending suitability, production latency, load behavior, monitoring, or regulatory compliance. The AUC and decile results came from one held-out split, and the lower deciles weren’t strictly monotonic. The screenshots document one recorded run and shouldn’t be presented as a general performance or cost benchmark.
Production adoption also requires validating the Posit preview terms, license grant, and image version alongside supported AWS Regions and model availability. Teams must also confirm network design, least-privilege permissions, secrets handling, logging, image patching, and operational ownership.
To avoid ongoing charges, delete the resources this walkthrough created. Delete them in the following order, because the real-time endpoint depends on both its endpoint configuration and its model: delete the endpoint first, then the endpoint configuration, then the model.
aws sagemaker delete-endpoint --endpoint-name <name>aws sagemaker delete-endpoint-config --endpoint-config-name <name>aws sagemaker delete-model --model-name <name>aws s3 rm s3://amzn-s3-demo-bucket/<prefix>/ --recursiveThen, in the Amazon Athena console query editor, run:
DROP TABLE IF EXISTS <database>.<table>;aws ecr batch-delete-image --repository-name <repo> --image-ids imageTag=<tag>aws sagemaker delete-image --image-name <name>The recorded workflow shows how a custom Positron image can keep governed AWS data access, R and Python analysis, model deployment, application development, and reproducible reporting in one SageMaker Studio Space. The continuity is useful because the evidence, code, deployment result, and communication artifact remain connected. Production use still depends on the customer’s security, governance, validation, and operating controls.
For related background, see:
Abhishek is a Partners Solutions Architect at AWS, specializing in building Generative AI applications. With a deep passion for using agentic AI frameworks to solve complex business challenges, he brings nearly a decade of expertise in developing data and AI solutions that deliver tangible value for enterprises. Beyond his professional endeavors, Abhishek is an artist who finds joy in creating portraits of family and friends, expressing his creativity through various artistic mediums.
SriAakash is a Software Engineer on the Amazon SageMaker AI team, where he builds products and developer experiences across Amazon SageMaker Studio. He focuses on developing solutions that simplify and enhance the machine learning development experience for data scientists and developers. Outside of work, SriAakash enjoys staying active through hiking, biking, and long walks.
Arkaprava is a Software Development Manager at AWS on the SageMaker AI team. He has been at Amazon for over 10 years and works on improving the Amazon SageMaker Studio IDE experience for machine learning developers.
Arantza is a Senior Technical Product Manager for Amazon SageMaker AI. She is passionate about building scalable products that solve real customer problems. At AWS, she focuses on the developer experience of SageMaker AI Studio, helping data scientists across industries build, train, and deploy AI/ML models. Outside of work, Arantza enjoys traveling, playing soccer, and cooking.
Sam is a Senior Partner Development Manager working with GenAI ISVs, building strategic partnerships and innovative solutions for AWS customers. With over 12 years of experience across cloud technology and the partner ecosystem, Sam brings deep expertise in AWS Marketplace and collaboration with leading system integrators and GenAI partners.
When Benchling needed to run AI agent-generated scientific code across thousands of life sciences tenants, their security team found that traditional sandboxing wasn’t enough. Today, this architecture processes more than 600 code execution sessions per day across more than 250 tenants per week with zero security incidents. Standard network controls block HTTP, restrict egress ports, and limit outbound connections. However, DNS resolution is often still permitted, and even when system defaults restrict it, you may not have visibility into or control over those restrictions. This is the challenge Benchling faced when deploying AI agents across thousands of life sciences tenants. Their security team needed full control over network isolation beyond the system defaults to meet their threat model for executing untrusted code at scale.
In this post, we show how Benchling built a defense-in-depth security architecture to run AI agent-generated scientific code across thousands of life sciences tenants. Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. Benchling uses AgentCore Code Interpreter, a capability of Amazon Bedrock AgentCore, in Amazon Virtual Private Cloud (VPC) mode. This approach combines account-level isolation, Amazon Route 53 Resolver DNS Firewall, and VPC endpoint policies to help prevent data exfiltration while enforcing per-job data access controls.
Benchling’s AI application generates scientific code that runs on behalf of researchers across thousands of tenants. The primary use case is AI agent-generated scientific code, though Code Interpreter is also used for simpler calculations and as a code-generation sandbox. The security requirements are strict. Each session must access only that tenant’s data, with no cross-tenant visibility. Code can’t establish unauthorized network connections or exfiltrate data through any vector. Every execution session must be fully isolated, and the solution cannot require one AWS Identity and Access Management (IAM) role per tenant, as that would create unsustainable role sprawl at this scale.
During their security review, the Benchling team evaluated the network isolation properties of each Code Interpreter network mode against their threat model. While Sandbox mode restricts outbound access to Amazon Simple Storage Service (Amazon S3) operations, Benchling’s security posture requires customer-controlled network isolation. They needed to define exactly which domains can resolve and which endpoints are reachable. They also needed to continuously validate those controls through their own integration test suite. For an application handling sensitive scientific data across thousands of regulated life sciences tenants, relying solely on application-managed network restrictions wasn’t sufficient. They needed a solution where Benchling owned the security controls end to end. It had to block unauthorized network vectors, including DNS, without managing per-tenant IAM role sprawl or exposing their main production account to untrusted execution environments.
Figure 1 shows the complete solution architecture. On the left, the Production Account contains the Benchling Stack, IAM Roles, AWS STS, and Customer Data in Amazon S3. Tasks are dispatched to the Untrusted Code Account on the right, a separate AWS account containing the ACCI VPC. This VPC has no internet gateway and no NAT gateway. The Code Interpreter runs inside a dedicated Security Group restricted to port 443, with no outbound path to the public internet.
DNS queries from the Code Interpreter are evaluated by Route 53 Resolver DNS Firewall, which applies a three-priority resolver policy. Priority 10 blocks known malicious domains, Priority 100 allows only explicitly listed endpoints, and Priority 200 blocks the remaining queries. Below the Security Group, VPC Endpoints provide the only permitted network paths. An S3 Gateway endpoint and an Interface endpoint handle authorized S3 access, while NACLs and Prefix List routing restrict traffic to only these endpoints. Per-job credentials are injected into each session through AWS STS from the Production Account, scoping data access dynamically. A Continuous Validation suite runs integration tests that simulate exfiltration attempts against this configuration.
Benchling’s solution uses a dedicated AWS account for untrusted code execution, separate from their main production account. AI-generated code runs in this isolated “Untrusted Code Account,” providing scope containment. If something goes wrong, the main Benchling production account, with its customer data and access roles, is not directly exposed.
This untrusted code account hosts AgentCore Code Interpreter (ACCI) alongside Benchling’s existing container-based execution environment, which uses gVisor (a container sandbox runtime that intercepts application system calls to provide kernel-level isolation) for per-job isolation. The gVisor environment is Benchling’s pre-existing compute isolation layer and isn’t part of the pattern prescribed in this post. Both execution environments have their own IAM roles with scoped permissions, making sure that neither can escalate access beyond its intended boundary.
When a task is dispatched from the production account to the untrusted account, data access is scoped per job. Only the specific data needed for that job is made accessible. Production account credentials and the broader customer data store are not directly exposed to untrusted code.
Maintaining one IAM role per tenant would create unsustainable role sprawl across thousands of tenants. Instead, Benchling injects credentials into each ACCI session on a per-job basis through AWS Security Token Service (AWS STS), scoping access dynamically without accumulating static roles.
The ACCI VPC is designed with a “nothing unless explicitly allowed” philosophy. There’s no internet gateway and no NAT gateway. Code running in this VPC can’t reach the internet directly. The centerpiece of the DNS exfiltration defense is Amazon Route 53 Resolver DNS Firewall. It uses a three-priority resolver policy following a denylist, allowlist, deny all pattern:
The first rule evaluated, at highest priority, blocks resolution of known unintended domains. This catches obvious threats before they hit any allow logic. For example, if Benchling identifies domains associated with known data exfiltration toolkits or command and control infrastructure, those domains are blocked at this layer regardless of any other configuration. This rule exists as a fast path for threat intelligence. Rather than relying solely on the absence of a domain from the allow list, Benchling can proactively enumerate hostile endpoints and make sure they are rejected immediately. This rule also provides observability. Queries that hit the explicit deny list generate DNS Firewall logs, signaling potential malicious activity and giving the security team an early warning that code in the sandbox is attempting suspicious resolution.
The second tier is an allow list that permits DNS resolution only for explicitly listed domains. In practice, this is limited to the S3 endpoints needed for data access and usually nothing else. The allow list is deliberately minimal because every permitted domain represents a potential exfiltration vector. Benchling scopes resolution to only the specific S3 bucket endpoints required for job execution. Even if malicious code attempts to contact a legitimate AWS service endpoint for unintended purposes, it cannot resolve that endpoint unless Benchling has explicitly approved it. This gives Benchling full ownership of the network boundary. Unlike relying on system defaults that may change between service versions, the allow list is a customer-controlled artifact that Benchling can audit, version, and update on their own schedule.
The final rule is a catch-all that returns NODATA for any DNS query not explicitly allowed by P100. This is what makes DNS exfiltration impossible. In a typical DNS tunneling attack, malicious code encodes stolen data as subdomain labels in a DNS query (for example, base64payload.example.com) and relies on recursive resolution to deliver that query to a bad actor-controlled authoritative nameserver. With this catch-all in place, every domain not on the strict allow list receives a NODATA response. There is no resolution path for encoded exfiltration queries to traverse. The DNS recursion chain is broken at the very first hop. This final rule is what transforms the VPC from “restricted” to “sealed.” Without it, any new domain or overlooked endpoint would default to permitted resolution. With it, the security posture is inverted: nothing resolves unless Benchling has made a deliberate decision to allow it.
Benchling’s Product Security team first proved this approach effective in a proof-of-concept VPC. They tested each layer of the defense individually. DNS tunneling attempts confirmed that the Route 53 Resolver DNS Firewall returned NODATA for any domain not on the explicit allow list. Direct IP connection attempts confirmed that prefix list routing and NACLs restricted traffic to port 443 and ephemeral ports only, with no path to arbitrary external hosts. API call attempts confirmed that VPC endpoint policies rejected requests targeting any S3 bucket outside the scoped set. The absence of an internet gateway, NAT gateway, and default security group meant there was simply no outbound path for traffic that bypassed these controls.
After the proof of concept validated the architecture, Benchling’s Infrastructure team incorporated these exfiltration simulations into their continuous integration test suite. The tests exercise the same vectors a real bad actor would use. These include DNS tunneling through encoded subdomain queries, direct connections to unauthorized endpoints, and attempts to reach S3 buckets outside the VPCE policy scope. If any test resolves a domain it shouldn’t, reaches an external endpoint, or moves data outside the approved buckets, the pipeline fails and blocks the release.
This approach matters because security configurations are not static. VPC settings change as infrastructure evolves, new endpoints get added to support feature development, and IAM policies are updated as teams onboard new services. Without continuous validation, a configuration that was secure at deployment time could silently degrade as the environment around it changes. By treating exfiltration resistance as a testable property rather than a one-time setup, Benchling makes sure that any future infrastructure change that inadvertently weakens the security boundary is caught before it reaches production.
With no internet gateway or NAT gateway in the VPC, AWS service access must flow through VPC endpoints. Benchling deploys a Gateway endpoint for in-region Amazon S3 access and an Interface endpoint for cross-region S3 access. Each endpoint has an attached policy that explicitly lists which S3 buckets it is permitted to reach. Any request targeting a bucket not in that policy is rejected at the network layer before it reaches S3.
This creates a defense independent of IAM. Even if untrusted code obtains valid credentials for a bucket it should not access, the endpoint policy blocks the request. Credentials restrict what a session is authorized to do, and endpoint policies restrict what the network is physically capable of delivering. Rather than granting the ACCI role broad access to all tenant buckets, Benchling injects scoped credentials into each session on a per-job basis through AWS STS. A compromised session can only reach the one tenant it was dispatched to serve.
Traffic is further constrained by prefix list routing and NACLs that restrict communication to port 443 and ephemeral return ports only. The Code Interpreter runs in a dedicated security group with no default fallback rules. There’s no port, no protocol, and no network path available for data to leave the environment except through the explicitly scoped VPC endpoints.
Benchling configures Gateway VPC endpoints for in-region S3 access and Interface VPC endpoints for cross-region S3 access. Each endpoint has an attached VPCE policy that explicitly lists only the specific S3 buckets authorized for a given execution context. Any API call targeting a bucket not in that policy is rejected at the network layer before it reaches the S3 service. This means that even if untrusted code somehow obtained valid credentials for another tenant’s bucket, the request would still fail. The network itself refuses to carry the traffic. This creates a defense independent of IAM, so credential theft alone is not sufficient to access unauthorized data.
Rather than pre-provisioning IAM roles for each of thousands of tenants, Benchling injects scoped credentials into each ACCI session through AWS Security Token Service (AWS STS). Each job receives only the permissions needed for its specific tenant’s data. The production account determines what data a job can access, generates appropriately scoped temporary credentials, and injects them into the session at dispatch time. The Code Interpreter Execution Role has S3 access restricted to the main stack bucket. At launch time, a session policy is passed into each Code Interpreter execution that restricts S3 access to the specific tenant’s path prefix within the authorized bucket. This makes sure that code running inside the sandbox can only reach data belonging to the tenant it was dispatched to serve. This is the “per-job data export scoping” shown in the architecture. Benchling evaluated the alternative of granting the ACCI role broad access to all tenant buckets and rejected it because a single compromised session would then have a path to any tenant’s data.
Beyond DNS Firewall and VPC endpoint policies, NACLs restrict traffic to port 443 and ephemeral return ports only, prefix list routing makes sure traffic can only reach VPC endpoints, and the Code Interpreter runs in a dedicated security group with no default fallback rules. The attack surface is reduced to the Code Interpreter and its scoped VPC endpoints alone.
Before adopting AgentCore, Benchling’s team evaluated building their own sandboxing solution. The requirements were clear. They needed ephemeral execution sessions, per-job isolation, no persistent state, and the ability to run inside a VPC where they could apply their own network security controls. Building this in-house would have meant designing custom container orchestration, implementing sandbox lifecycle management, and building network isolation primitives from scratch. They would also need to continuously patch security vulnerabilities while keeping pace with evolving threat vectors. This represents significant ongoing engineering investment diverted from Benchling’s core product, with no differentiation for their customers.
AgentCore Code Interpreter in VPC mode bypassed that entire workstream. Each session is isolated and short-lived, with no persistent state between jobs. AWS handles the sandbox lifecycle, including patching, scaling, and hardening the execution environment. Running Code Interpreter inside Benchling’s own locked-down VPC meant they could layer existing AWS security primitives such as DNS Firewall, VPC endpoint policies, and NACLs on top without building custom networking. This freed Benchling’s infrastructure team to focus on product security controls rather than sandbox maintenance.
“We were able to buy instead of build a secure solution with AgentCore Code Interpreter.”
— Jeremy Stashewsky, Application Security Engineer, Benchling
Since deploying AgentCore Code Interpreter in VPC mode in early April 2026, Benchling has scaled to more than 600 code execution sessions per day, serving AI agent-generated scientific workloads across more than 250 distinct tenants per week. This demonstrates broad adoption across their customer base without compromise to their security posture. Since deployment, Benchling has reported zero security incidents and zero cross-tenant data leakage.
“Giving AI agents a code interpreter is non-negotiable for the scientific accuracy our customers demand, but our threat model assumes any agent- or user-written code could be unintended. We needed a true sandbox with zero network access except for S3. AgentCore Code Interpreter in VPC mode, combined with Route 53 DNS Firewall and VPC endpoints, let us close every exfiltration vector we tested (including DNS) without building it ourselves.”
— Benchling
Running AI-generated code in a multi-tenant environment introduces exfiltration vectors that traditional sandboxing does not fully address. DNS resolution, in particular, is often overlooked because standard network controls focus on HTTP, egress ports, and direct connections. The pattern Benchling implemented provides a blueprint for closing this gap without building custom sandboxing infrastructure.
The architecture starts with account-level isolation, separating untrusted code execution from production systems entirely. Amazon Bedrock AgentCore Code Interpreter in VPC mode provides managed, ephemeral execution within that isolated account. Route 53 Resolver DNS Firewall seals the DNS exfiltration vector with a deny list, allow list, deny all policy. VPC endpoint policies restrict service access to only the specific S3 buckets each job requires. Per-job credential scoping through AWS STS makes sure that even a fully compromised session cannot reach beyond a single tenant’s data.
No single control in this architecture is sufficient on its own. It’s the combination of all these layers, validated continuously through automated testing, that bypasses entire classes of exfiltration vectors. Each layer catches what the others might miss, and the continuous validation makes sure the posture holds as infrastructure evolves.
To get started with Amazon Bedrock AgentCore Code Interpreter in VPC mode, see the Code Interpreter documentation and the VPC configuration guide. You can deploy a locked-down VPC with Route 53 DNS Firewall and VPC endpoint policies following the patterns described in this post. If you are already running untrusted code in a sandboxed environment, consider whether your current architecture accounts for DNS as an exfiltration channel, and whether you have continuous validation proving that it does.
Benchling is the AI platform for biotech R&D, unifying scientific data and automating workflows to accelerate discovery and development. Trusted by more than 1,300 companies worldwide, from pioneering startups to global leaders like Merck, Moderna, and Sanofi, Benchling gives scientists a single place to capture, connect, and act on data across the entire R&D lifecycle. With Benchling AI, agents and models work directly inside scientific workflows, grounded in structured data. The result is faster teams, better molecules, and breakthroughs that reach the world sooner.
Jeremy is an Application Security Engineer at Benchling.
Meghana is a Solutions Architect at AWS, where she partners with ISV customers to design secure, scalable multi-tenant architectures spanning data platforms and AI workloads. She is a co-author of this post and led the technical engagement with Benchling.
Anil is a Sr Solutions Architect at AWS focusing on AI/ML and agentic architectures for ISV customers. He works with partners to design and deploy secure, scalable agent solutions using Amazon Bedrock and AgentCore.
Insurance claims adjusters spend over 100 minutes per case manually reviewing medical records. The EXL AI-powered Medical intelligent document processing (IDP) solution, built on AWS, transforms this process. It combines IDP with domain-specific large language models (LLMs) to extract, summarize, and query medical information at enterprise scale.
In insurance claims adjudication and life underwriting, medical records are the foundation of every decision. Claim adjusters and underwriters must review these records, often several hundred pages long, to assess validity, determine payouts, or make underwriting decisions.
The challenge isn’t simply volume. Medical records are unstructured, filled with specialized clinical terminology, and require the reviewer to connect disparate data points about a patient’s condition and its evolution over time. The documents themselves span dozens of types: chiropractic care notes, diagnostic tests, emergency room visits, operative reports, physician consultations, prescription drug reports, psychiatric evaluations, lab results, independent medical examination (IME) reports, and peer reviews, among others.
This review demands deep medical domain expertise, sustained concentration, and interpretive judgment. Given this complexity, the process is slow, manual, and prone to inconsistencies. Different professionals interpret the same medical data in different ways. The consequences are real: delayed claim settlements, accuracy issues in evaluations, increased indemnity costs, adverse customer experience, and heightened regulatory scrutiny.
EXL is a data analytics, AI, and digital solutions provider serving Fortune 500 organizations for over 25 years. With over 50,000 professionals globally, EXL brings deep expertise in insurance, healthcare, banking, capital markets, retail, media and communications, and energy to reimagine business models, deliver measurable outcomes, and accelerate innovation.
EXL addressed this challenge by combining two complementary AI applications into a single end-to-end solution, hosted on AWS:
Together, these applications form an automated, scalable pipeline that transforms raw medical documents into actionable intelligence for claims adjusters, underwriters, and care coordinators.
EXL built the solution on AWS to keep model development and production inference under one roof with consistent security controls. Amazon SageMaker AI provides the managed training and inference environment for the domain-specific EXL Insurance LLM: multi-GPU fine-tuning, isolated experimentation separated from production, and real-time inference endpoints that scale with claim volume. Amazon Bedrock complements this with on-demand access to general-purpose foundation models through a single API. With this access, EXL can apply the right model to each task: the fine-tuned Insurance LLM for domain reasoning and general-purpose models for broader language tasks, without managing additional infrastructure. Both services operate within access controls scoped by AWS Identity and Access Management (IAM), which is essential for a workflow handling protected health information.
The solution runs entirely within an AWS Region, with upstream and downstream client applications connecting through secure APIs. The architecture follows an 11-step flow, from ingestion through output delivery, with a separate model development environment for continuous improvement.
The pipeline is built on the following AWS services:
A critical differentiator of this solution is the EXL Insurance LLM: a domain-specific large language model fine-tuned specifically for insurance claims workflows involving medical records. Rather than relying on general-purpose LLMs that lack specialized insurance and medical domain knowledge, EXL built a purpose-trained model on Amazon SageMaker AI, benchmarked against general-purpose models on insurance-specific NLP tasks.
General-purpose models like GPT-4 or Claude possess broad language understanding but lack the specialized vocabulary, reasoning patterns, and workflow awareness needed for insurance claims adjudication. Insurance claims involve multiple distinct tag types for medical record annotation, domain-specific summarization formats (economic and non-economic damages), and negotiation guidance generation. These tasks require deep domain adaptation that prompting alone cannot achieve consistently at scale.
EXL curated training data from nine years of insurance claims operations, comprising over 13,500 records spanning both structured database records and unstructured medical documents. The data preparation pipeline on AWS included:
EXL used Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) on Amazon SageMaker AI. This approach adapts the model efficiently without modifying all parameters of the base model, reducing compute costs while maintaining performance. The training used:
In internal benchmarking by EXL, the fine-tuned EXL Insurance LLM showed strong performance across key claim-workflow tasks (tagging, summarization, question-answering, and reasoning), assessed using automated metrics (BLEU, ROUGE, BERTScore, METEOR) and blind review by three insurance subject-matter experts.
For methodology and detailed results, see EXL white paper on the Insurance LLM.
With this pipeline, EXL reduced medical record review time from days to hours, with human-in-the-loop validation at critical stages helping maintain quality while reducing turnaround time.
The pipeline moves each document through seven stages, from ingestion to structured output delivery. The following sections walk through each stage.
The pipeline begins when upstream applications submit extraction requests through the Ingestion API, built on Amazon API Gateway. Documents arrive through multiple channels and in multiple formats. AWS Lambda functions handle initial file processing using Apache Tika for parsing and post-OCR normalization, storing raw documents in Amazon S3.
After authentication through Amazon Cognito, the orchestration engine (AWS Step Functions) takes over, creating sub-requests based on the input and routing content to appropriate processing modules.
Xtrakto.AI’s classification engine is template agnostic and inference based. Rather than relying on document layout or predefined templates, it uses few-shot and transfer learning methods to classify content based on meaning and context. As a result, the system can classify new document types with limited training samples. Bundled files (email messages with multiple attachments) are split into individual sub-documents, each routed to the appropriate downstream extraction module.
This is the core of the pipeline, where Xtrakto.AI’s extraction capabilities come together across preprocessing, computer vision, and domain-specific extraction.
Documents undergo machine readability checks, OCR with Amazon Textract, file type conversion, and text embedding generation. These steps run as Lambda functions coordinated by Step Functions. Computer vision models (CNNs, RCNNs) deployed on Amazon SageMaker AI process the pixel-level image data to address quality issues common in scanned medical records: low resolution, noise from wrinkles or stains, skewness, and mixed handwritten and printed content. These models identify duplicate pages, detect bounded and unbounded tables, identify extraction zones, detect signatures, and interpret barcodes and QR codes.
Approximately 25–30 percent of documents contain handwritten content, ranging from structured form fills (low complexity, approximately 50–60 percent of handwritten volume) to semi-structured annotations (approximately 15–20 percent) to fully free-form physician notes (approximately 15–20 percent). Each type requires specialized processing.
Xtrakto.AI doesn’t configure input templates to look for information at specific locations. Extraction is context based:
Amazon SageMaker AI hosts the family of inference models used for extraction. Traditional approaches (SVM, gradient boosting) handle structured classification tasks, and transformer architectures (BERT, GPT, BART variants) enriched with domain-specific medical and insurance data handle contextual extraction.
Every extracted field receives a confidence score of 0-100. Fields below a configurable threshold are routed to human validators through the EXL Xtrakto.AI validation screen for verification. This feedback continuously improves model accuracy over time.
Extracted data is augmented using internal and external reference databases stored in Amazon DynamoDB and Amazon RDS. This enrichment step validates extracted codes against ICD-10, CPT, and Healthcare Common Procedure Coding System (HCPCS) libraries, normalizes dates and terminology, and resolves cross-field consistency issues. Enriched data is stored back in the Amazon S3 data lake for downstream consumption.
This is where the EXL Insurance LLM takes over, served from inference endpoints on Amazon SageMaker AI. Complex medical records spanning hundreds of pages are condensed into structured summaries tailored to the user’s needs.
The summarization engine offers flexibility: users choose between short, medium, or long summaries depending on their workflow. The Insurance LLM extracts, labels, summarizes, and presents the most relevant clinical information while preserving the original context and narrative flow of the document.
Through a feedback mechanism, users can rate and correct summaries. These corrections feed into model fine-tuning on Amazon SageMaker AI, continuously improving summarization quality.
Beyond summaries, users need to ask specific questions about a medical record and get precise, sourced answers. The querying capability, powered by the Insurance LLM on Amazon SageMaker AI, supports three modes:
The Insurance LLM understands the intent behind each query and provides accurate answers grounded in the underlying document data. It handles multiple queries simultaneously, making it practical for high-volume operational use.
For complex cases requiring clinical judgment support, the solution provides deep reasoning capabilities with full traceability:
This traceability is essential in regulated environments where decisions must be auditable and defensible.
Because this workflow handles protected health information and produces AI-generated clinical and claims insights, responsible-AI controls are built into the deployment rather than added on. Generative outputs pass through content-filtering and grounding checks before they reach a reviewer, so summaries and answers stay anchored to the source record and within policy. Source-level traceability makes every output auditable back to the originating document, human-in-the-loop validation gates low-confidence results, and de-identification procedures that align with HIPAA requirements protect patient data throughout the pipeline. Together these controls help the solution ship safely in a regulated healthcare context.
The final stage generates output through AWS Lambda functions and delivers results through the Results API (Amazon API Gateway). Downstream applications retrieve processed data in the format they need:
A large healthcare payer faced significant operational challenges in clinical case management. Like the claims adjusters described earlier, the payer’s nurses and care coordinators were also spending over 100 minutes per case, here on manual data retrieval, validation, and preparation of clinical summaries. Information was fragmented across multiple systems: electronic health records, care management systems, claims systems, and scanned medical documentation.
The organization implemented the EXL Medical IDP solution to replace these fragmented manual workflows with a unified, intelligent pipeline.
What was deployed:
Results:
Medical records sit at the center of critical insurance and healthcare decisions, yet the process of extracting intelligence from them has remained largely manual for decades. The EXL Medical IDP solution demonstrates that this no longer needs to be the case.
By combining Xtrakto.AI’s template-agnostic document processing with the domain intelligence of the EXL Insurance LLM, EXL created a solution that handles the full lifecycle, from raw document ingestion through intelligent extraction, summarization, querying, and structured output delivery. The pipeline runs on AWS services including Amazon API Gateway, Amazon Cognito, AWS Step Functions, Amazon Textract, Amazon SageMaker AI, Amazon Bedrock, Amazon S3, Amazon CloudWatch, and Amazon DynamoDB.
The key principle underlying this solution is augmentation, not replacement. Human expertise remains central through confidence-based routing, human-in-the-loop validation, and continuous feedback loops. AI handles the volume and the repetitive pattern recognition. Humans handle the judgment and the exceptions.
Learn more about healthcare and insurance solutions from EXL and explore AWS solutions for healthcare and insurance. To go deeper, see the Amazon Bedrock documentation and Amazon SageMaker AI fine-tuning documentation. You can also explore various AWS samples on GitHub.
For a related customer story, read AI-Powered Collections: How EXL Uses AI on AWS for Debt Recovery at Scale.
Anutosh is a Solutions Architect at AWS India. He partners with enterprise customers to architect secure, resilient, and data-driven cloud environments that accelerate business outcomes. He specializes in designing scalable architectures for cloud migration and modernization, while integrating data analytics, cybersecurity, and machine learning to solve complex technical challenges.
Ashish is VP, Digital and AI Solutions at EXL, where he leads solutioning, go-to-market, and development of AI solutions, including Xtrakto.AI for US Insurance (Life & Annuities, P&C) and domain-fine-tuned LLMs. He has experience across client and operations transformation, with a background in Decision Analytics and Financial Planning & Analysis. Ashish holds FLMI and CPCU designations, AWS certifications in Generative AI, Cloud Architecture, and Machine Learning, a Six Sigma Master Black Belt, and 4 USPTO AI patents.
Soumita is AVP at EXL, where she leads solutioning and productization of AI-powered intelligent document processing (IDP) across the healthcare value chain. She serves as the Functional Design Expert for the Medical value pool, driving large-scale healthcare automation programs for US payer and provider organizations, covering prior authorization, clinical case summarization, provider contract intelligence, HEDIS abstraction, medical claims review, and more. She holds a management degree from the Indian Institute of Management, Bangalore.
Chaitanya is VP, Digital R&D at EXL, and product owner of EXL’s multi-patented Enterprise Content Intelligence platform, Xtrakto.AI. He has led the development of AI solutions spanning Referral Management, Contract Intelligence, Case Summarization, and New Business Submissions across Insurance, Healthcare, and Banking. Chaitanya brings a niche combination of AI, Cloud, Product Design, and Change Management expertise. He holds a B.E. from IIT Guwahati, a product management certification from the Indian School of Business, and 3 USPTO AI patents.
Weather-sensitive industries increasingly have access to observations that offer an earlier, more local view of changing conditions. Energy companies collect measurements across wind and solar assets, emergency management teams rely on radar and local sensors, and satellite providers continuously observe the Earth. This data helps organizations understand and manage physical risk across sectors such as capital markets, insurance, agriculture, and logistics.
With the AI data assimilation tools in NVIDIA Earth-2, you can process these observations more efficiently. By incorporating proprietary or third-party data, you can use these tools to issue forecasts more frequently, keep estimates aligned with real-time conditions, and tailor your forecasting pipeline to specific regions and applications.
This tutorial covers two techniques:
For this tutorial, you will need:
You might run a regional weather forecasting pipeline for managing energy production and demand, drawing observations from wind and solar parks, transmission corridors, or densely populated areas. With AI data assimilation, you can use these observations to constrain your forecast where local accuracy matters most, helping you improve operational decisions. The same techniques can support other sectors using observations from production sites, event venues, logistics networks, or other assets where local conditions directly drive decisions.
You can use Score-Based Data Assimilation (SDA) to incorporate observations into diffusion-based AI downscaling and forecasting models such as CorrDiff and StormCast. SDA guides the model toward predictions that are consistent with your observations without requiring to retrain the model.
Figure 1, below, shows how the process works under the hood. Diffusion models generate high-resolution predictions through a sequence of denoising steps. At each step, SDA compares the intermediate prediction with your observations and nudges the model in the right direction. The output of SDA is probabilistic, with less uncertainty near observation locations and a wider spread further away, where predictions are increasingly governed by the other model inputs and the underlying AI simulations.
To nudge the model, you define an observation operator, which maps the model output to the quantity you would expect to observe at each measurement location. This is particularly straightforward for in situ measurements of physical quantities such as temperature or wind speed. In this case, the simplest form of an operator interpolates nearby grid values to each observation location. It is also possible to create operators for proxy measurements or observed impacts. For example, the power output of a wind turbine can act as a proxy measurement of wind speed.
SDA unlocks two major capabilities:
The effectiveness of SDA depends on several factors: the number, spatial distribution, and accuracy of your observations; the characteristic length scales of the field you are predicting; and the quality and well-posedness of the observation operator.
CorrDiff is a technique for AI-based downscaling. Earth2Studio provides a CorrDiff model pretrained over Europe that turns 0.25° weather fields into 2.2-km predictions. Using AI data assimilation, you can improve these predictions with observations where local accuracy matters. With the refined outputs, you can then initialize a regional forecast or create a reanalysis dataset for calibrating downstream models.
Start by loading the pretrained model. We limit the domain to a part of the Netherlands and northwestern Germany and choose to assimilate 10-meter wind speeds.
from datetime import datetime
from earth2studio.data import GHCNHourly
from earth2studio.models.da import CorrDiffCosmoEra5SDA
domain = dict(lat_min=50.2, lat_max=53.8, lon_min=4.6, lon_max=10.4)
sda = CorrDiffCosmoEra5SDA.load_model(
CorrDiffCosmoEra5SDA.load_default_package(),
assimilate_variables=("u10m", "v10m"),
resolution="rea2",
domain=domain,
number_of_samples=1,
sampler_steps=12,
amp=True,
).to("cuda")
Fetch the ERA5 data for low-resolution conditioning and GHCN wind observations over the domain.
# Fetch and regrid ERA5 inputs onto the high resolution regional grid # Follow the link to the example below for the full implementation init_time = datetime(2024, 1, 26) x = fetch_and_regrid_era5(init_time, domain) # Fetch GHCN hourly 10-m wind observations over the model domain lat, lon = sda.model.lat_output_numpy, sda.model.lon_output_numpy bbox = lat.min(), lon.min(), lat.max(), lon.max() ghcn = GHCNHourly(stations=GHCNHourly.get_stations_bbox(bbox)) obs = ghcn(init_time, ["u10m", "v10m"]).dropna(subset=["observation"])
Lastly, run the model with the input data. We perform two runs to measure how the additional observations affect the results.
prior = sda(x) # free downscaling, no observations analysis = sda(x, obs) # guide the diffusion toward the observations
For a complete implementation, see the example in Earth2Studio, from which the code above was adapted.
StormCast is a technique similar to CorrDiff but designed for high-resolution, regional forecasting. Earth2Studio includes a StormCast model pretrained over the contiguous U.S. (CONUS) that is initialized with HRRR and makes predictions at a 3-km resolution. The computation and dissemination of a new HRRR analysis takes some time, but you can use SDA to combine the currently available analysis with the latest observations to update your forecast.
Start by loading the pretrained model. We limit the domain to the central U.S.
import numpy as np
from earth2studio.data import GHCNHourly
from earth2studio.models.px import StormCastCONUS
# Limit the domain to the central U.S.
hrrr_lat_lim, hrrr_lon_lim = (305, 785), (595, 1203)
model = StormCastCONUS.load_model(
StormCastCONUS.load_default_package(),
hrrr_lat_lim=hrrr_lat_lim, # comment out for full CONUS domain
hrrr_lon_lim=hrrr_lon_lim, # comment out for full CONUS domain
num_diffusion_steps=18,
num_sda_diffusion_steps=96, # more steps for SDA for better stability
sda_std_obs=0.15,
sda_gamma=1e-3,
).to("cuda")
Next, fetch the HRRR analysis for model initialization and define the observation data source over the model domain.
# Fetch HRRR initial conditions
# Follow the link to the example below for the full implementation
init_time = datetime(2026, 4, 17, 18)
x, coords = fetch_hrrr(init_time)
# Define GHCN hourly data source for the model domain
lat, lon = model.lat, model.lon
bbox = lat.min(), lon.min(), lat.max(), lon.max()
ghcn = GHCNHourly(
stations=GHCNHourly.get_stations_bbox(bbox),
time_tolerance=timedelta(minutes=15),
)
We can now run the model using observations during the initial rollout steps before transitioning to forecasting without additional observations. Similar to the illustration in Figure 2, above, this approach uses observations to bridge the gap between the latest analysis and current conditions, after which the forecast proceeds independently.
For a pipeline initialized with HRRR, only one SDA-informed step is typically relevant before a new analysis arrives. When using a global analysis for initialization, multiple rollout steps can benefit from SDA.
# Initialize generator and get the first output (analysis passthrough)
gen = model.create_generator(x.clone(), coords.copy())
x, coords = next(gen)
# Run the first part of the rollout with SDA
for step in range(nsteps_sda):
valid_time = np.array(
[coords["time"][0] + coords["lead_time"][0] + np.timedelta64(1, "h")]
)
obs = ghcn(valid_time, ["u10m", "v10m", "t2m"])
x, coords = gen.send(obs) # advance one step with observations
# Run the remaining rollout without SDA
for step in range(nsteps_non_sda):
x, coords = next(gen) # advance one step without observations
You can find a full implementation of the example in the Earth2Studio example library.
You can assimilate observations with a custom model by extending its Earth2Studio model wrapper. To do this, use the diffusion utilities in PhysicsNeMo. We start with x0_predictor, a pre-trained denoising diffusion model that takes a noisy sample and its noise level as inputs and predicts a noise-free sample. Without SDA, the diffusion sampling for the model would be implemented like this:
from physicsnemo.diffusion.noise_schedulers import EDMNoiseScheduler from physicsnemo.diffusion.samplers import sample # Construct diffusion scheduler sigma_min, sigma_max = 0.01, 100 scheduler = EDMNoiseScheduler(sigma_min=sigma_min, sigma_max=sigma_max) # Get denoiser from scheduler denoiser = scheduler.get_denoiser(x0_predictor=x0_predictor) # Generate sample latents = sigma_max * torch.randn(shape) sample(denoiser, latents, noise_scheduler=scheduler, num_steps=num_steps)
To use SDA, we transform the x0_predictor into a score-predicting model with SDA guidance. We use DataConsistencyDPSGuidance, which associates each masked pixel with a corresponding observed value. You can use it to assimilate observations from weather stations, proprietary sensors, or similar point-based sources.
from physicsnemo.diffusion.guidance import (
DataConsistencyDPSGuidance,
DPSScorePredictor,
)
# Setup SDA guidance
guidance = DataConsistencyDPSGuidance(
mask=mask, # binary mask that identifies pixels with observations
y=y_obs, # gridded observations
std_y=sda_std_obs, # the remaining parameters are SDA settings
norm=sda_dps_norm,
gamma=sda_gamma,
sigma_fn=scheduler.sigma,
alpha_fn=scheduler.alpha,
)
# Convert x0_predictor to score predictor
score_predictor = DPSScorePredictor(
x0_predictor=x0_predictor,
x0_to_score_fn=scheduler.x0_to_score,
guidances=guidance,
)
denoiser = scheduler.get_denoiser(score_predictor=score_predictor)
# Generate sample (identical to non-SDA example)
latents = sigma_max * torch.randn(shape)
sample(denoiser, latents, noise_scheduler=scheduler, num_steps=num_steps)
For more advanced SDA pipelines, use ModelConsistencyDPSGuidance to derive simulated observations from multiple grid points. This approach requires you to provide a PyTorch model that maps each sample to the corresponding simulated observations. With a custom PyTorch model, you can also assimilate observed impacts. For example, you can use a wind power model to assimilate turbine output measurements.
For a complete implementation, have a look at the StormCast CONUS wrapper in Earth2Studio, on which the example above is based.
Most global weather forecasting pipelines are initialized with an estimate of the current weather derived through numerical data assimilation. Numerical data assimilation is computationally demanding, which reduces the timeliness and refresh rate of forecasts and makes it harder to integrate custom observations.
With an AI-based technique called HealDA, you can estimate the state of the global atmosphere in a matter of seconds. This allows you to issue forecasts closer to current conditions or compute a custom reanalysis.
HealDA maps remote-sensing and in situ observations within a time window to a global gridded atmospheric state. It consists of two main components: an observation encoder and a vision transformer (ViT) backbone. The encoder ingests heterogeneous observations as point clouds, embedding each scalar value into a token together with metadata such as geolocation and time. These tokens are then aggregated onto the target grid and processed by the ViT backbone.
You can use a pretrained global data assimilation model as a starting point. If you have custom conventional observations, you can typically incorporate them without modifying the model.
For proprietary satellite data, you can adapt the encoder to support your data sources. This flexibility lets you tailor the data assimilation system to your region or application. You can use the same technique to train a regional instead of a global system. To get started, see the HealDA training pipeline in the open-source Python library PhysicsNeMo.
Earth2Studio provides a pretrained global data assimilation model for research purposes. It integrates data from microwave sounders, radio occultation, surface stations, aircraft, buoys, and other sources onto a 1° HEALPix grid (HPX64).
First, load the model.
from datetime import timedelta
import numpy as np
from earth2studio.data import UFSObsConv, UFSObsSat, fetch_dataframe
from earth2studio.models.da import HealDA
model = HealDA.load_model(
HealDA.load_default_package(),
lat_lon=True, # regrid from HEALPix to regular lat/lon
).to("cuda")
Next, fetch the input observations from the NOAA UFS replay repository. We use conventional and satellite observations.
# HealDA was trained on the UFS replay window: 21h before to 3h after analysis time
time_tolerance = (timedelta(hours=-21), timedelta(hours=3))
analysis_time = np.array([np.datetime64("2024-01-01T00:00")])
# input_coords() returns the schemas the two observation DataFrames must satisfy
conv_schema, sat_schema = model.input_coords()
# fetch_dataframe attaches the request_time metadata the model needs
conv_df = fetch_dataframe(
UFSObsConv(time_tolerance=time_tolerance),
time=analysis_time,
variable=np.array(conv_schema["variable"]),
fields=np.array(list(conv_schema.keys())),
)
sat_df = fetch_dataframe(
UFSObsSat(time_tolerance=time_tolerance),
time=analysis_time,
variable=np.array(sat_schema["variable"]),
fields=np.array(list(sat_schema.keys())),
)
Then call the model with the observation data frames.
# stateless model - call it directly for a one-shot analysis, or use # create_generator for cycled assimilation analysis = model(conv_obs=conv_df, sat_obs=sat_df)
You can find an extended example for running HealDA in the Earth2Studio example library.
Earth2Studio gives you access to a broad range of data sources for developing, initializing, and validating weather models, including observations from different platforms and sensor types.
Among these are gridded data from geostationary satellites (GOES, Himawari, Meteosat) and radar networks (MRMS, OPERA), which you can use directly to train and rapidly update regional, high-resolution forecasting models such as StormScope. These sources are especially useful when you want to forecast quantities that depend on insolation or precipitation, like solar power production, cooling processes, and reservoir inflows.
For developing and benchmarking a data assimilation system, Earth2Studio also lets you access archives of conventional observations like GHCN/ISD, NNJA, and UFS, as well as operational observations from GDAS and ASOS. These sources provide variables such as temperature and wind speed as data frames. Observations from polar-orbiting satellite systems, including MetOp and JPSS, are also available.
Earth2Studio provides a unified interface across all data sources. You instantiate a data source object and call it with a list of timesteps and variable names. Forecast data sources also accept a list of lead times. This consistent interface makes it easy to combine multiple data sources within the same workflow or connect your own observations to a pipeline.
era5 = NCAR_ERA5() da_era5 = era5(datetime(2025, 7, 15), ["t2m", "z500"]) print(da_era5.shape) # (1, 2, 721, 1440) ifs = IFS_FX() da_ifs = ifs(datetime(2026, 7, 15), timedelta(hours=48), ["t2m"]) print(da_ifs.shape) # (1, 1, 1, 721, 1440) goes = GOES(satellite="goes19", scan_mode="C") da_goes = goes(datetime(2026, 7, 15), ["abi01c", "abi02c", "abi03c"]) print(da_goes.shape) # (1, 3, 1500, 2500) ghcn = GHCNHourly(stations=["USW00013301"]) df_ghcn = ghcn(datetime(2026, 6, 15), ["t2m", "ws10m"]) print(df_ghcn.shape) # (10, 7)
For the full list of supported data sources, see the API reference in the user guide.
Explore end-to-end AI data assimilation examples in the Earth2Studio example library. To connect your own observations to a pipeline, follow the custom data source example.
AI data assimilation lets you issue more accurate, timely forecasts by incorporating the observations that matter to your region or organization.
Visit the Earth2Studio user guide to get started with AI data assimilation and explore the broader capabilities of AI weather models.
Generative AI inference is uniquely hard: models are tens to hundreds of gigabytes, latency requirements are measured in tokens per second, cold starts can span multiple minutes as containers and weights transfer, GPU capacity is constrained, and traditional monitoring tools expose none of the token-level signals that matter in production.
Amazon SageMaker AI offers customers the ability to deploy AI models and consume them by the instance (instead of by the token), using two paths: managed endpoints for teams that want AWS to handle infrastructure and operations, and Amazon SageMaker HyperPod Inference for teams that need Kubernetes-native control over dedicated GPU clusters. Year-to-date in 2026, SageMaker AI delivered 13 new capabilities across these two paths and this post walks through these capabilities and benefits to enterprises, startups and public sector.
The table below compares the two deployment paths across seven dimensions.
| Dimension | Endpoints | HyperPod |
| Infrastructure | Fully managed by AWS | Managed Kubernetes stack |
| Deploy target | Console, SDK, CLI | kubectl, Terraform, Console, CLI, SDK |
| Scaling | Managed auto scaling with Amazon CloudWatch | Auto scaling with Karpenter, KEDA, CloudWatch |
| Customization and Control | Customizable at the container and model layers | More customizability with Node level access, frameworks and AMI. |
| API protocol | OpenAI compatible with SageMaker endpoint | HTTP, gRPC and custom load balancer capability |
| Best for | Fast and fully managed deployment with minimal ops overhead | Kubernetes-based, train-to-serve multi-cloud/hybrid-cloud deployments |
| Corresponding Launches: | ||
| Launches | Inference recommendations, Capacity Aware Inference, OpenAI API, Container Caching, Observability, Async Inference Inline Payloads, Prefix-Aware Routing | Simplified Operator, Tiered KV Cache, Data Capture, Performance Features, Disaggregated Prefill and Decode for HyperPod Inference, Model Caching |
Managed SageMaker Inference endpoints are the faster path for teams that want AWS to handle GPU provisioning, scaling, and operational monitoring. You bring the model and define the performance target. SageMaker handles the rest. The seven launches year-to-date in 2026 below address deployment, capacity, integration, scaling, observability, and async simplification.
Blog: Amazon SageMaker AI now supports optimized generative AI inference recommendations
Choosing the right instance type, serving container, and optimization settings for a generative AI model typically takes two to three weeks of manual benchmarking against 1000+ combinations, requiring expertise most teams do not have in-house. Inference recommendations automate this end-to-end.
Customers specify a model and performance goal (cost, latency, or throughput). SageMaker then runs a three-step process:
The output is a SageMaker Model Package with deployment-ready configurations and validated metrics: time to first token (TTFT), inter-token latency (ITL), P50/P90/P99 latency percentiles, throughput, and cost projection. In a demonstrated example, throughput optimization on GPT-OSS-20B delivered 2x tokens per second at the same request latency. There is no additional cost for generating recommendations. Customers with ML Reservations can benchmark on reserved capacity at no extra charge, and inference recommender can also be used to evaluate alternative instance types.
Blog: Capacity-aware inference: automatic instance fallback for SageMaker AI endpoints
When a SageMaker endpoint required a single instance type, a capacity shortage meant the endpoint failed before serving a single request. Instance pools address that single point of failure.
Customers define a prioritized list of up to five instance types. SageMaker automatically works through the list at endpoint creation, during scale-out, and during scale-in. At creation, SageMaker tries the first-choice type and falls back immediately if capacity is unavailable. During scale-out, the next available type in the priority list absorbs demand. During scale-in, fallback instances are removed first, so the fleet trends back toward preferred hardware as capacity opens up.
Per-instance-type CloudWatch metric dimensions enable weighted scaling policies for heterogeneous fleets. Each pool entry can reference a separate optimized model configuration (tensor parallelism on high-memory instances, speculative decoding on mid-tier, quantization on smaller fallbacks), and inference recommendations can generate these per-hardware configurations automatically. Supported for single-model, inference component, and async endpoints in all commercial AWS Regions.
Blog: Announcing OpenAI-compatible API support for Amazon SageMaker AI endpoints
Applications built on the OpenAI SDK, LangChain, or Strands Agents previously required custom client adapters and authentication rewrites to work with SageMaker-hosted models. That migration cost was a real barrier.
SageMaker endpoints now expose an /openai/v1 path supporting Chat Completions with streaming. Migration requires changing only the endpoint URL. SDK calls, streaming logic, and prompt formatting remain identical. Authentication uses bearer tokens generated from existing AWS credentials, valid for up to 12 hours, removing SigV4 signing complexity.
Multi-model endpoints allow hosting multiple models, each callable through the same OpenAI SDK with independent resource allocation. For agentic workloads, AI agents can run entirely on customer-owned GPU infrastructure using the same OpenAI-compatible interface they were built on. Available in 14 AWS Regions, with support for vLLM and SGLang AWS Deep Learning Containers and custom containers implementing the /v1/chat/completions path.
Blog: Introducing container caching in Amazon SageMaker AI for faster model scaling
During inference auto scaling events, new instances responding to traffic spikes previously had to pull the full container image from Amazon Elastic Container Registry (Amazon ECR) before serving requests. For large serving containers exceeding 10 GB, that pull alone added several minutes of dead time to every scale-out event.
Container caching pre-pulls images automatically, so new instances launch with the container already available locally. Zero configuration, no code changes, no container modifications. It activates automatically on supported accelerator instance types. With Qwen3-8B on ml.g6.2xlarge using the LMI container (17.7 GB compressed), end-to-end startup latency dropped from 525 seconds to 258 seconds, a 51% reduction. Model download time also improved, from 168 seconds to 77 seconds, because the image is no longer competing for network bandwidth. Early access customers observed improvements ranging from 38% to 65%.
Container caching is the third layer in a three-part scaling optimization suite:
| Layer | Optimization | Impact |
| Detection | Sub-minute CloudWatch metrics | Triggers scale-up 6x faster than standard 1-minute metrics |
| Existing instances | Instance-store data caching | Removes image pull and model download for instances already running |
| New instances | Container image caching | Avoids image pull time; 51% startup latency reduction demonstrated |
Token-level latency, KV cache pressure, GPU memory trends, and inference component placement across Availability Zones are signals that scattered CloudWatch metrics could not surface together, forcing teams to correlate problems manually after users had already been affected.
SageMaker now emits 100+ detailed inference metrics via native OpenTelemetry, paired with a pre-built Insights dashboard in Amazon CloudWatch. Zero instrumentation required. New endpoints have observability enabled by default, with metrics flowing within two minutes of reaching InService status. The dashboard covers three areas:
A PromQL-compatible endpoint lets teams query SageMaker metrics directly from Amazon Managed Grafana or a PromQL-compatible tool via SigV4 authentication.
Blog: Amazon SageMaker AI async inference now supports inline request payloads
Async inference previously required uploading every input payload to Amazon Simple Storage Service (Amazon S3) before invoking the endpoint, even for a simple JSON prompt of a few hundred bytes, adding architecture complexity and latency on every request.
The InvokeEndpointAsync API now accepts a Body parameter with payloads up to 128,000 bytes directly in the request, removing the S3 pre-staging step for the vast majority of async workloads. Key benefits: one fewer network round-trip per request, no input bucket provisioning or IAM s3:PutObject grants, immediate size and parameter validation, and avoidance of the S3 PUT charge per invocation. Fully backward compatible. Existing InputLocation workflows continue unchanged. Available in 31 AWS Regions.
Resource: Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference
A new routing strategy that reduces LLM latency by directing requests with shared prompt prefixes to the same instance. In many LLM applications, a large portion of the prompt (system instructions, retrieved documents, conversation history) is repeated across requests. Normally, each instance recomputes these shared tokens from scratch, wasting GPU resources.
Prefix-aware routing solves this by using the beginning of each request as a fingerprint to consistently route similar prompts to the same instance, maximizing KV cache reuse. It includes built-in safeguards for overload protection and stable behavior during scaling events.
Benchmarks on Llama 3.1 70B across 7 instances showed significant gains: for long-context workloads (8,000-token prefixes), P90 TTFT dropped by 33–37%, P50 TTFT by 71–77%, and KV cache hit rates jumped from ~25% to 82%. Short-context workloads also improved, with P90 TTFT reduced by 24–37%. The routing overhead is minimal, adding only 1.3–1.9 milliseconds per request.
SageMaker now offers three routing strategies: RANDOM (default), LEAST_OUTSTANDING_REQUESTS, and the new PREFIX_AWARE. The feature is ideal for RAG applications, multi-turn conversations, templated bots, and code completion scenarios.
Enabling it requires only setting RoutingStrategy, PrefixLength, and ConcurrencyThreshold in the endpoint configuration. No changes to model containers or serving frameworks are needed. It also supports multi-tenant prefix isolation, inference components, and dynamic LoRA adapters. The feature is available today on SageMaker real-time inference endpoints.
HyperPod Inference extends HyperPod’s cluster resilience into the serving layer for teams who need Kubernetes-native control. It is built for practitioners who want to own their GPU infrastructure while still getting AWS-managed reliability on top. The six launches year-to-date in 2026 below address deployment, latency, compliance, and compute specialization.
Blogs: Unlock efficient model deployment: Simplified Inference Operator setup on Amazon SageMaker HyperPod | Best practices to run inference on Amazon SageMaker HyperPod
Deploying an LLM on Kubernetes typically requires writing and maintaining Deployments, Services, ConfigMaps, HorizontalPodAutoscaler configs, and health check wiring for each model. For teams managing dozens of models, that handcrafted infrastructure becomes an engineering burden in itself.
The Simplified Inference Operator is a native EKS add-on that installs in a single step through the AWS console, CLI, SDK, kubectl, or Terraform. Once installed, teams deploy models by submitting a single custom resource definition instead of a stack of low-level Kubernetes objects. Key capabilities include:
Blog: Managed tiered KV cache and intelligent routing for Amazon SageMaker HyperPod
For long-context and multi-turn workloads, LLMs recompute key-value attention values for shared prefixes on every request. Without caching, that redundant computation accumulates directly as latency and GPU cost.
HyperPod Inference manages a two-tier KV cache. The L1 tier lives in CPU memory on each node for low-latency local reuse. The L2 tier uses Redis for cross-node sharing, so a cached prefix computed by one model pod can be reused by other pods in the fleet. Intelligent routing keeps the cache effective by directing requests to the right instances:
Together, tiered caching and intelligent routing deliver up to 40% latency reduction for long-context and multi-turn workloads compared to a non-cached baseline.
Resources: Amazon SageMaker HyperPod data capture for inference workloads (What’s New) | Enhancing enterprise inference on HyperPod with data capture, Hugging Face, NVMe, and Route 53 integration
Regulated enterprises need tamper-evident logs of inference activity for compliance, drift monitoring, and offline evaluation dataset construction. Building that logging infrastructure across multiple request paths from scratch is non-trivial.
HyperPod Inference data capture provides three capture points enabled via the custom resource definition (CRD): the SageMaker endpoint (full request and response at the application boundary), the ALB (load balancer traffic for routing visibility and latency measurement), and the model pod (request and response at the container boundary for model-level debugging). Captured data flows to Amazon S3 with no custom sidecar containers or application instrumentation required. Teams can enable capture selectively at any of the three points to keep storage costs proportional to actual needs.
Resources: Disaggregated prefill and decode for LLM inference on SageMaker HyperPod | Amazon SageMaker HyperPod now supports disaggregated prefill and decode (What’s New)
When prefill and decode share the same GPU pool, a long prefill for a complex prompt blocks token generation for every concurrent user in the queue. Under mixed traffic, this makes per-token latency unpredictable in proportion to request complexity.
Disaggregated Prefill and Decode (DPD), shipped in Inference Operator v3.2, separates these phases onto distinct GPU pools. Prefill GPUs handle prompt processing. Once the KV cache for a request is ready, it transfers to the decode pool over EFA using GPU-Direct RDMA, a direct memory transfer that bypasses the CPU entirely. Decode GPUs then generate output tokens without interference from incoming prefill work. Each pool scales independently: if prefill throughput is the bottleneck, more prefill GPUs can be added without touching the decode fleet.
Validated on Llama 3.3 70B under mixed traffic, DPD produced measurably more consistent TTFT and ITL distributions compared to colocated prefill and decode. Operators specify separate instance pools for prefill and decode nodes in the custom resource definition. The Inference Operator manages EFA configuration and KV cache transfer automatically.
Amazon SageMaker HyperPod introduces new capabilities that enhance deployment flexibility, performance, and security for enterprise generative AI inference. Hugging Face Hub Integration lets you deploy models directly without pre-staging weights to S3, with support for gated models, revision pinning, and token isolation across vLLM, TGI, and SGLang runtimes. Local NVMe Model Loading reduces cold-start latency by reading weights from node-local storage instead of pulling over the network—ideal for autoscaling and scale-from-zero scenarios. When NVMe isn’t available, automatic fallback to cloud storage facilitates reliability. Amazon Route 53 DNS Management automatically creates, updates, and cleans up DNS records for custom inference domains through simple CRD configuration. Custom Service Accounts with IRSA provide pod-level IAM permissions, giving infrastructure teams fine-grained control over security boundaries. Together, these features help teams deploy AI applications faster without compromising governance or operational visibility.
Resources: Reduce inference cold starts on Amazon SageMaker HyperPod with model caching
When deploying large language models on Amazon SageMaker HyperPod, cold starts create significant delays as pods must download model weights from remote storage and pull container images from Amazon ECR before serving requests. This problem compounds during scale-out events when multiple pods start simultaneously.
SageMaker HyperPod now offers model caching, which addresses this through two complementary mechanisms. The weights cache pre-downloads model weights to local NVMe storage on each node, enabling reads at approximately 7 GB/s instead of waiting for remote downloads. The image cache pre-pulls inference container images onto nodes via a DaemonSet, saving 5 to 7 minutes per pod start. Both caches use preferred (not required) scheduling, so pods can still start on uncached nodes with a graceful fallback.
The feature is managed through two Custom Resource Definitions (CRDs): ModelDataCacheConfig for weights and ModelImageCache for container images. The operator handles the full lifecycle automatically, including cache invalidation when model sources change.
Enabling caching requires adding a modelCacheConfig section to your existing InferenceEndpointConfig or JumpStartModel resource, with toggles for weights and image caching independently. It supports most model sources including Amazon S3, Amazon FSx for Lustre, and Hugging Face Hub.
Benchmarks show around 60% faster scale-out for models ranging from 57 GB to 145 GB. Key limitations include per-node storage (each node maintains its own copy), NVMe capacity constraints, and the fact that source updates at the same path are not auto-detected. Cleanup is automatic when you delete the parent resource. The feature is now generally available in all supported HyperPod regions.
Our feature launches focus on reducing time-to-market, letting customers use state-of-the-art capabilities out of the box with strong price-performance. Each of these launches addresses a distinct friction point across the inference lifecycle, from first deployment decision to production operations:
From deployment to scaling to operations, these launches cover every layer of the inference stack, across both managed endpoints and Kubernetes-native clusters. Competitive advantage in AI inference increasingly comes not from choosing the best model, but from operating the most efficient inference stack. Using the capabilities described in this post does not require a team of AI infrastructure experts or researchers. The AWS Experience-Based Acceleration program brings these capabilities to enterprises and startups to help them configure and optimize instance-based AI inference. It works by understanding your inference workloads, data modalities, SLAs, and cost targets, running benchmark evaluations, and configuring your inference stack to run AI inference at scale.
Continued at the source.
You’re deploying a model on a system. It starts up, prompts are getting responses. Now the hard question: Is this fast?
Your instincts might lead you to send curl commands, hand-roll an asyncio script, or vibe code yet another one-off load generator. All of these paths have the same problem: single-process performance limits, Python’s GIL capping concurrency, or numbers measured against a reference you built yourself. Either way, you end up with results you can’t fully trust, attached to tooling you’ll have to rewrite the moment requirements change.
What you need is a load client that can saturate a real server without becoming the bottleneck, produce output you can act on, and take five minutes to configure, not five hours. That’s NVIDIA AIPerf.
AIPerf is the designated successor to GenAI-Perf and is a ground-up rewrite. The design choices reflect hard lessons from running LLM benchmarks at scale:
For this walkthrough we’ll use Qwen3-0.6B served through vLLM. The model choice is deliberate; it’s small enough to run on a single GPU and fast enough to iterate on without waiting. The point isn’t to benchmark Qwen3-0.6B specifically; it’s to establish the measurement loop. Once you have that, swapping in a different model or endpoint is a one-flag change.
Start the Server
Pull and start vLLM with the reasoning parser enabled:
docker pull vllm/vllm-openai:latest docker run --gpus all -p 8000:8000 -e HF_TOKEN vllm/vllm-openai:latest \ --model Qwen/Qwen3-0.6B \ --reasoning-parser qwen3 \ --host 0.0.0.0 --port 8000
We can use uv to install a central copy:
uv tool install aiperf
Or for a virtual environment:
uv venv venv source venv/bin/activate uv pip install aiperf
One platform note: on aarch64, the crick dependency ships as source-only and requires a C toolchain (build-essential on Debian/Ubuntu, Development Tools on RHEL). If the install stalls on that package, that’s why.
With the server up and AIPerf installed, we can now run our first profile:
aiperf profile \ --model Qwen/Qwen3-0.6B \ --endpoint-type chat \ --streaming \ --url localhost:8000 \ --synthetic-input-tokens-mean 128 \ --synthetic-input-tokens-stddev 0 \ --output-tokens-mean 128 \ --output-tokens-stddev 0 \ --extra-inputs min_tokens:128 \ --extra-inputs ignore_eos:true
A few flags here are doing more work than they look like:
--synthetic-input-tokens-stddev 0 and --output-tokens-stddev 0 pin the workload to exactly 128 input and 128 output tokens per request. This reproduces a commonly used static benchmark that holds request and output lengths constant.
--extra-inputs min_tokens:128 and --extra-inputs ignore_eos:true tell the model to actually emit 128 tokens rather than stopping early. Without these, the output token count is a suggestion. The model stops whenever it naturally finishes, which can be well short of your target OSL. Throughput numbers end up lower than they should be, and they’re not reproducible across runs.
--streaming is not optional if you want to measure TTFT and ITL. Without streaming, the server batches the full response before sending it, and there are no first- or decode-token events to measure.
We’ll walk through how to read these numbers in the next section. For now, notice the shape of the output in Figure 2, below: latency broken down by percentile, throughput in tokens per second, and request-level statistics all in one place. That’s the baseline you’ll be comparing everything else against.
Once a run completes, AIPerf prints a metrics table to the console and writes the full results to CSV and JSON. Here’s what you’re looking at.
The core four:
For full definitions of these and every other metric AIPerf reports, see the Metrics Reference.
Getting the full picture. Each of the above is reported in percentile breakdowns (p25, p50, p75, p90, p95, p99) alongside their minimums, maximums, averages, and standard deviations. These breakdowns matter because they can highlight long tail distributions; a server with a healthy mean TTFT and an outlier p99 looks fine in aggregate and fails in production.
Beyond the core four. With DCGM or pynvml available, AIPerf also pulls GPU power draw, utilization, and memory consumption into the same run output. Correlating a latency spike with a memory pressure event doesn’t require a separate profiling session, the telemetry is already there.
Now that our feet are wet with a static benchmark, we can start exploring something more dynamic. The section above provided an extremely fixed traffic pattern, but real inference traffic doesn’t follow a static pattern. To benchmark with a scenario that’s less rigid, we can use some of AIPerf’s synthetic workload knobs to introduce variability to our requests.
aiperf profile \ --model Qwen/Qwen3-0.6B \ --endpoint-type chat \ --streaming \ --url localhost:8000 \ --request-rate 10 \ --arrival-pattern poisson \ --synthetic-input-tokens-mean 512 \ --synthetic-input-tokens-stddev 128 \ --output-tokens-mean 128 \ --output-tokens-stddev 32 \ --random-seed 42 \ --request-count 200
A few things changed from the static benchmark above.
--arrival-pattern poisson with --request-rate 10 means requests arrive at an average of 10 per second, with inter-arrival times drawn from an exponential distribution. The server now experiences bursts and gaps rather than a single user stream, which is what queuing actually looks like under real traffic.
--synthetic-input-tokens-stddev 128 introduces variance around the 512-token mean, producing a mix of short and long prompts. The server has to handle variable prompt lengths during prefill rather than identical ones.
--output-tokens-stddev 32 adds variance on the output side. Notice that min_tokens and ignore_eos are gone from this command. In the static benchmark those flags pinned outputs to exactly 128 tokens to keep the baseline clean; we’re deliberately releasing that constraint so the output distribution can vary.
--random-seed 42 makes the Poisson timing and synthetic length draws reproducible. Rerunning this command produces the same sequence of requests.
--streaming is not optional. Without streaming, the server batches the full response before sending it, and there’s no first- or decode-token events to measure.
Looking at the LLM metrics from this run, the distributions are noticeably wider than the static baseline — which is expected when more requests are simultaneously competing for GPU access and prefill lengths vary per request.
Looking at the graphs in Figure 4, below, you can see that the Poisson command line introduced a request rate centered, but not exactly matching, around 10 requests/second. This arrival rate emulates jitter around when requests arrive compared to the constant mode which guarantees a fixed 10 requests/second.
You can see in Figure 5, below, that there is a variation in the request length centered around the mean of 512 tokens, with input sequence lengths ranging 154 to 818 tokens.
Comparing TTFT between the two runs, you can see that the Poisson run shows a much wider spread. More requests are simultaneously competing for GPU access, prefill lengths vary, and prefill and decode operations overlap. The single-concurrency case is an idealized scenario which runs one request at a time presenting the lowest possible TTFT, at the cost of throughput.
In Figure 6, above, you can see that the single user run experiences less TTFT variability than the much more varied workload in the Poisson experiment.
The tutorials in the AIPerf repo are the fastest way in. The AIPerf repo and docs are the canonical reference for new features and contributions.
AIPerf is a collaborative effort between NVIDIA and external contributors. Thank you to the following: Loki Ravi, Dan Ferguson, and Sheng Moua (AWS) for the continual collaboration, cross-company validation, and efforts to standardize on AIPerf; Aaron Batilo (Coreweave) for the Weights & Biases exporter, acceptance-length spec-decode datasets, and hardening sweep/credit-dispatch reliability under concurrency; Shounak Ray (Baseten) for faithful Baseten trace replay support; Michael Feil (Baseten) for faster trace loading, and session affinity headers. Cristian Lopez (Pinterest) for his close collaboration on the DAG benchmarking methodology. We’re grateful to Ben Hamm for his product guidance while we designed, planned, and implemented AIPerf.
Open-weight models are changing the economics of building and deploying AI at scale. Rapid gains in intelligence and efficiency mean companies can match each workload with the right balance of capability, speed, and cost. AWS is building for a future in which organizations can adopt open-weight innovation with the reliability and security required for production.
Today, Kimi K3 from Moonshot AI is available on Amazon Bedrock, giving you a powerful new option for coding and knowledge work. According to Moonshot AI, Kimi K3 is its most capable model and the first open model to reach 2.8 trillion parameters. It combines native vision capabilities with a 1-million-token context window and delivers an approximate 2.5x improvement in scaling efficiency over Kimi K2. These advances make Kimi K3 well suited to long-running coding and knowledge workflows that require sustained context across large repositories, documents, and images. Kimi K3 is the first open-weight model on Amazon Bedrock to support explicit prompt caching, helping you reduce latency and input costs when reusing context across model calls.
The launch of Kimi K3 reflects the sustained investment by AWS in open-weight models on Amazon Bedrock. Since 2025, Bedrock has added dozens of open-weight models from providers including DeepSeek, Google, MiniMax, Mistral AI, Moonshot AI, NVIDIA, OpenAI, and Qwen. Supporting this expanding selection is continued advancement of the inference technology that serves these models at scale. In 2026, Bedrock added support for tool calling, structured output, reasoning, response streaming, and the Responses and Chat Completions APIs. Because these are platform capabilities rather than per-model integrations, new open-weight models can benefit from them as they become available on Amazon Bedrock.
As with all open-weight models on Amazon Bedrock, you can adopt Kimi K3 without changing your security posture. Your data is processed within the AWS data boundary, is not shared with the model provider, and is not used to train the underlying model. Zero data retention is always enabled for inference requests, while zero operator access prevents even AWS operators from accessing your prompts and completions during inference. Together, these protections let you use open-weight models with confidence while maintaining control of your data.
To try Kimi K3, open the Amazon Bedrock console, go to Test > Playground, and select Kimi K3 as the model. From there, you can test your first prompt.
Programmatically, you can call the model using the bedrock-runtime endpoint, which supports the OpenAI-compatible Responses and Chat Completions APIs, and the Amazon Bedrock Invoke and Converse API APIs.
You can invoke Kimi K3 through a cross-Region inference profile. For workloads without regional restrictions we recommend using the global profile, global.moonshotai.kimi-k3, which routes each request to any supported commercial AWS Region worldwide. Global cross-Region inference costs approximately 10% less than a geographic profile. The US geographic profile, us.moonshotai.kimi-k3, keeps processing within the US geography for data residency requirements.
bedrock:InvokeModel, bedrock:InvokeModelWithResponseStream, and bedrock:CallWithBearerToken.Here is a quick example that uses the OpenAI SDK and the aws-bedrock-token-generator library for Python to generate short-term bearer tokens for authentication to Amazon Bedrock.
from aws_bedrock_token_generator import provide_token
from openai import OpenAI
region = "us-west-2"
oai_client = OpenAI(
api_key=provide_token(region=region),
base_url=f"https://bedrock-runtime.{region}.amazonaws.com/openai/v1",
)
resp = oai_client.responses.create(
input="What is Byte-Pair Encoding, in AI?",
model="global.moonshotai.kimi-k3",
)
print(resp.output_text)Long-running coding and knowledge workflows often resend stable context, such as repository instructions, tool definitions, or reference documents. With explicit prompt caching, you identify reusable prompt prefixes so later requests can use cached content. When a request matches a cached prefix, Amazon Bedrock can reduce response latency and input token costs.
Caching for Kimi K3 on Amazon Bedrock:
prompt_cache_breakpoint to a supported input content.With the OpenAI Python API, explicit caching can be configured as shown in the following example:
resp = oai_client.responses.create(
model="global.moonshotai.kimi-k3",
# Enable explicit caching mode:
extra_body={"prompt_cache_options": {"mode": "explicit"}},
input=[
{
"type": "message",
"role": "system",
"content": [
{
"type": "input_text",
"text": SYSTEM_PROMPT,
# A long, static system prompt is a great target for caching:
"prompt_cache_breakpoint": {"mode": "explicit"},
},
]
},
{
"type": "message",
"role": "user",
"content": [
{
"type": "input_text",
"text": USER_INPUT,
# Multiple breakpoints can also be defined, for layered cache:
"prompt_cache_breakpoint": {"mode": "explicit"},
},
],
},
],
)
if resp.usage.input_tokens_details.cached_tokens:
print("Hit cache!")You can explore the Moonshot AI on AWS samples repository for more examples.
In addition to using the APIs directly, you can use Kimi K3 through the wide range of coding assistants, personal agents, and agentic frameworks that support Amazon Bedrock specifically, or OpenAI-compatible model providers in general.
There are several popular coding agents available to builders today, so consider OpenCode as an example. OpenCode is open source, model agnostic, and has a native amazon-bedrock model provider, which uses the Converse API.
To get started, you can configure the amazon-bedrock provider either in your user-level or project-level opencode.json configuration files as shown in the OpenCode documentation. With the provider configured, OpenCode will automatically detect available Amazon Bedrock models which you can select from using the /models command. For example, a minimal ~/.config/opencode.json file could look like:
{
"$schema": "https://opencode.ai/config.json",
"model": "amazon-bedrock/global.moonshotai.kimi-k3",
"provider": {
"amazon-bedrock": {
"options": {
"region": "us-west-2",
"profile": "PLACEHOLDER-YOUR-AWS-PROFILE-NAME"
}
}
}
}Once the Amazon Bedrock provider is set up, you can use the /models command to switch models to global.moonshotai.kimi-k3 and start building.
Kimi K3 can build substantial features and work over long-horizon tasks. In the following video, we try it out building a single-file browser-based game to get started:
Figure 1: Building a browser-based game with Kimi K3 in OpenCode
Beyond coding, Hermes Agent is one example of an open source assistant for general productivity. It can be used through a desktop app or popular messaging apps as well as the terminal, and supports use cases like deep research and task automation where Kimi K3 can also perform well.
As detailed in their documentation, Hermes natively supports models on Amazon Bedrock. To get started:
hermes model from your terminal.global.moonshotai.kimi-k3 as a custom model name.If you use named profiles to manage multiple AWS credentials in your environment, then at the time of writing you need to set the AWS_PROFILE environment variable or use your default profile for Hermes. Alternatively, you can switch to an API key. Follow the open issue here for updates on support for setting AWS profile via the Hermes configuration file.
Once the Amazon Bedrock provider is set up and the model configured, you can start using Kimi K3 for your agentic workflows in Hermes. For example, see the following short video in which we ask the agent to build out a personalized study plan:
Figure 2: Building a personalized study plan with Kimi K3 in Hermes Agent
Kimi K3 is available today on Amazon Bedrock through the US Geo (us.) and Global (global.) cross-Region inference profiles. See Bedrock documentation for the full list of supported Regions. For pricing information, see Amazon Bedrock pricing.
Give Kimi K3 a try in the Amazon Bedrock console, or explore the Moonshot AI on AWS samples repository on GitHub.
Interested in how Amazon Bedrock can support your team? Connect with us to start the conversation.
Alex is an AI Specialist Solutions Architect at AWS, based in Singapore. He focuses on how open source technologies and open weight models can help customers around the world to build innovative AI solutions and tackle AI governance challenges.
Saurabh is a Senior Product Manager for Amazon Bedrock and Amazon SageMaker Inference. He is passionate about working with customers and partners, motivated by the goal of democratizing AI. He focuses on core challenges related to deploying complex AI applications, inference with multi-tenant models, cost optimizations, and making the deployment of generative AI models more accessible. In his spare time, Saurabh enjoys hiking, learning about innovative technologies, following TechCrunch, and spending time with his family.
William is Principal Product Manager for Amazon Bedrock.
Tanvi is a Product Marketing Manager for Amazon Bedrock at Amazon Web Services (AWS), where she helps customers adopt and scale AI applications and agents with Amazon Bedrock.
Sofian is a technology leader with over 12 years of experience building AI solutions, and leading high-performing teams to maximize customer outcomes. He is passionate about empowering diverse talents to drive global impact and achieve their career aspirations.
Organizations building multi-model agentic AI applications face growing infrastructure complexity. Managing container orchestration, scaling policies, identity, and observability for multiple model types adds operational overhead. Teams often spend more time on infrastructure than on agent logic development.
Developers running agentic frameworks on self-managed infrastructure such as Amazon Elastic Container Service (Amazon ECS) with AWS Fargate have full control over their deployment configuration. As agentic workloads evolve and scale, teams might choose to adopt managed runtimes that provide built-in session management, identity, and observability.
Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. AgentCore runtime, its managed deployment capability, handles container lifecycle, scaling, identity, and observability, so you can focus on your agent code.
In a previous post, Agentic AI with multi-model framework using Hugging Face smolagents on AWS, we showed how to build a healthcare AI agent with multi-model orchestration on self-managed infrastructure. In this post, we show you how to migrate that multi-model agent to Amazon Bedrock AgentCore runtime. The migration reduces infrastructure management while preserving agent capabilities, including triple-model orchestration and vector-enhanced knowledge retrieval.
This solution migrates a multi-model healthcare AI agent to Amazon Bedrock AgentCore runtime while preserving the existing agent logic. The agent processes medical queries across three model backends with vector-enhanced knowledge retrieval, all running inside a single AgentCore-managed container. You can direct each query to the model backend suited to the task. A domain-specific model such as BioM-ELECTRA-Large-SQuAD2 on Amazon SageMaker AI handles specialized biomedical queries, and a foundation model (FM) such as Llama 3.1 70B Instruct by Meta on Amazon Bedrock handles broader medical reasoning. This approach helps healthcare teams address a range of query types while reducing the operational overhead of managing the underlying infrastructure.
The standalone version from the previous post deployed on Amazon ECS with AWS Fargate includes container orchestration, scaling, identity, and observability configured by the user. The AgentCore version wraps the same agent logic with the AgentCore runtime decorator pattern, and AgentCore runtime handles these operational concerns automatically.
Hugging Face smolagents is an open source Python library designed to build and run agents using a few lines of code. This solution uses Hugging Face smolagents framework as a reference implementation, demonstrating that AgentCore runtime supports any agentic framework. With the bring-your-own (BYO) agent approach, you can deploy existing agent code to AgentCore runtime without rewriting or adapting to a specific framework.
Note: This solution is a sample implementation for demonstration purposes. Production deployments handling medical or other sensitive queries use Amazon Bedrock Guardrails for content filtering and grounding validation as a standard control.
The solution consists of the following services and features:
Note: The previous post (standalone version) uses Claude 3.5 Sonnet V2 by Anthropic. This post uses Llama 3.1 70B Instruct by Meta, demonstrating that AgentCore runtime is model-agnostic. The model choice is an implementation decision, not a requirement.
The following diagram illustrates the solution architecture and how the agent orchestrates across three model backends.
A client web interface connects to Amazon Bedrock AgentCore runtime, which hosts the healthcare agent container. The container uses the Hugging Face smolagents framework with the AgentCore runtime decorator. AgentCore runtime provides built-in identity and observability. The agent orchestrates across three model backends: Amazon SageMaker AI with BioM-ELECTRA, Amazon Bedrock with Llama 3.1 70B Instruct by Meta, and a containerized model server with BioM-ELECTRA. The solution includes Amazon OpenSearch Service for vector-enhanced knowledge retrieval.
This solution supports deployment options with each backend optimized for different scenarios:
The three backends implement Hugging Face Messages API compatibility, providing consistent request and response formats regardless of the selected model service.
The complete implementation is available in the sample-healthcare-agent-with-agentcore-on-aws GitHub repository.
This section walks through migrating the existing healthcare AI agent to Amazon Bedrock AgentCore runtime using the AgentCore CLI.
Before you deploy the solution, you need the following:
Amazon Bedrock AgentCore runtime uses a decorator pattern to wrap your agent logic. The key components are:
@app.entrypoint – decorates the function that AgentCore runtime calls when a request arrives.app.run() – starts the AgentCore runtime server.The following code shows the AgentCore integration pattern:
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
@app.entrypoint
def healthcare_agent_entrypoint(payload):
user_input = payload.get("prompt", "")
model_type = payload.get("model_type", "sagemaker")
# Your existing agent logic here
agent = TripleHealthcareAgent(vector_store=vector_store)
response = agent.run(user_input, model_type=model_type)
return str(response)
if __name__ == "__main__":
app.run()The agent code between the decorator and return statement remains unchanged from the standalone version. AgentCore runtime handles container lifecycle, scaling, identity, and observability automatically.
Create an AgentCore project and add your existing agent using the AgentCore CLI.
Install the AgentCore CLI:
npm install -g @aws/agentcoreCreate a new AgentCore project:
agentcore create --project-name healthcareagent --no-agent --build Container --language Python --protocol HTTP --model-provider Bedrock --memory noneAdd your existing agent as a bring-your-own (BYO) agent:
agentcore add agent --name healthcare_agentcore --type byo --build Container --language Python --protocol HTTP --network-mode PUBLIC --code-location ./agent-code --entrypoint healthcare_agentcore.py --framework Strands --model-provider BedrockNote: The --framework flag specifies the CLI template. The actual agent code uses Hugging Face smolagents, which is compatible with AgentCore runtime regardless of the template selection.
pyproject.toml in your agent code directory to define dependencies:
[project]
name = "healthcare-agentcore"
version = "1.0.0"
requires-python = ">=3.10"
dependencies = [
"smolagents>=1.24.0",
"transformers>=4.55.0",
"boto3>=1.37.0",
"opensearch-py>=3.1.0",
"requests-aws4auth>=1.3.1",
"bedrock-agentcore>=0.1.0",
"numpy>=1.26.0",
"requests>=2.32.0",
"docker>=7.1.0",
]Dockerfile:
FROM public.ecr.aws/docker/library/python:3.12-slim
RUN pip install --no-cache-dir uv
WORKDIR /app
COPY pyproject.toml ./
RUN uv pip install --system -r pyproject.toml
COPY . .
EXPOSE 8080
CMD ["python", "healthcare_agentcore.py"].dockerignore to keep the image size within the 2 GB limit:
venv/
.venv/
__pycache__/
.git/
*.pycWith the project configured, you can deploy the agent using a single CLI command.
Deploy the agent:
agentcore deploy -yThe CLI builds the container, pushes it to Amazon Elastic Container Registry (Amazon ECR), and creates the AgentCore runtime agent. Deployment takes approximately 10–15 minutes.
You can test the deployed agent in two ways: using the AgentCore CLI or programmatically with boto3.
Invoke the agent using the AgentCore CLI:
agentcore invoke --prompt '{"prompt": "What are the side effects of metformin?", "model_type": "llama"}'Or, invoke programmatically using boto3:
This path invokes the same deployed agent as the CLI, using the boto3 SDK directly. The agentRuntimeArn identifies your deployed agent, contentType specifies the request format, and payload carries the prompt and model selection.
import boto3, json
client = boto3.client('bedrock-agentcore', region_name='us-west-2')
payload = json.dumps({
"prompt": "What are the side effects of metformin?",
"model_type": "llama"
})
response = client.invoke_agent_runtime(
agentRuntimeArn='<your-agent-runtime-arn>',
contentType='application/json',
accept='application/json',
payload=payload.encode('utf-8')
)
result = response['response'].read().decode('utf-8')
print(result)The standalone version and the AgentCore runtime version deploy the same agent in different ways. The following sections describe what each path provides.
The standalone version runs on Amazon ECS with AWS Fargate. You define ECS task definitions and service configuration, set auto scaling policies, configure IAM roles per service, and set up observability through Amazon CloudWatch. Deployment uses a Docker build, an Amazon ECR push, and an ECS service update. This path gives you full control over container configuration, networking, and scaling behavior. The agent code lives in healthcare_agentcore.py, integrates with Amazon Bedrock, Amazon SageMaker AI, and the containerized backend, and uses Amazon OpenSearch Service for vector search.
The AgentCore runtime version runs the same healthcare_agentcore.py agent code with the AgentCore decorator pattern. AgentCore runtime provides container orchestration, session-based scaling, identity management through IAM integration, and observability through built-in tracing and logging. Deployment uses a single command (agentcore deploy). The model integration (Amazon Bedrock, Amazon SageMaker AI, containerized backend) and vector search (Amazon OpenSearch Service) remain the same as the standalone version.
Both deployment approaches have distinct advantages. Amazon ECS with AWS Fargate provides full control over container configuration, networking, and scaling policies, suitable for teams with existing container operations expertise or specific infrastructure requirements. Amazon Bedrock AgentCore runtime is suited for teams that prefer managed infrastructure and want to focus primarily on agent logic development.
Regardless of the deployment path, the following elements remain unchanged when migrating from the standalone version to AgentCore runtime:
BedrockAgentCoreApp decorator + existing code).To avoid incurring future charges, delete the resources you created when you no longer need them. If you plan to continue using the deployed agent, no action is required.
agentcore remove allThen deploy again to tear down the AWS resources:
agentcore deployaws sagemaker delete-endpoint --endpoint-name healthcare-agentcore-endpoint-1 --region us-west-2aws opensearch delete-domain --domain-name healthcare-vector-store --region us-west-2In this post, we showed how to migrate a multi-model healthcare AI agent from self-managed Amazon ECS with AWS Fargate infrastructure to Amazon Bedrock AgentCore runtime. The migration required no changes to the core agent logic. The same healthcare_agentcore.py file orchestrates across Amazon Bedrock, Amazon SageMaker AI, and a containerized model server. It runs on AgentCore runtime with the addition of the AgentCore decorator pattern (BedrockAgentCoreApp, @app.entrypoint, and app.run()). For healthcare teams, this pattern directs specialized biomedical queries to a domain-specific model such as BioM-ELECTRA-Large-SQuAD2 on Amazon SageMaker AI. It routes broader medical reasoning to a foundation model such as Llama 3.1 70B Instruct by Meta on Amazon Bedrock. Together, these backends support a range of query types.
For teams that choose managed infrastructure, AgentCore runtime handles container orchestration, scaling, identity management, and observability. You can focus on agent logic development instead. The framework-agnostic design supports a wide combination of models and agentic frameworks, making this migration pattern applicable across industries including healthcare, financial services, and manufacturing.
To get started, clone the sample-healthcare-agent-with-agentcore-on-aws GitHub repository and follow the deployment steps in this post. To understand the standalone implementation that this post migrates from, see Agentic AI with multi-model framework using Hugging Face smolagents on AWS. If there are questions about getting started with Amazon Bedrock AgentCore, speak with an AWS generative AI Specialist.
Sanhita Sarkar, PhD, drives global AI/ML and generative AI partner solutions at AWS. She brings extensive leadership experience across edge, cloud, and data center environments, holds several patents, has published research papers, and serves as chair for technical conferences.
Agents are no longer experiments. They process claims, write and review code, coordinate across systems, and run for hours without supervision. As agents take on more complex, longer-running work, the infrastructure underneath them must evolve just as fast.
We built Amazon Bedrock AgentCore to help developers build, connect, and optimize agents securely at scale. AgentCore runtime, a capability of Amazon Bedrock AgentCore, is the managed compute layer that gives developers a fully managed environment to deploy and run agents without building or maintaining infrastructure.
Since launch, thousands of teams have used it to run production agents. Every conversation with those teams teaches us something about what agents need next: faster responsiveness as workloads scale, finer control over resource allocation, and economics that track actual usage precisely.
Today, we are announcing the new AgentCore runtime, purpose-built for the speed, flexibility, and cost efficiency that production agents demand.
It brings better memory management, reclaiming memory as a session releases it instead of holding it at the peak. It also delivers consistent cold start times regardless of container size or agent concurrency. You get the serverless model you already liked, now more elastic. Memory is released back the instant a session ends, startup times stay consistent regardless of size or concurrency, and the bill tracks the work your agent does.
Many agents started as chat bots: you asked, it answered, and the exchange ended in seconds. Then came coding agents that work for minutes to hours, holding context across many steps, running while you watch or step away. Now agents are becoming ambient, always on, triggered by events, running unattended, surfacing only when a job finishes or hits a decision that needs a person. And there are far more of them: no longer novelties but running everywhere. They are embedded in products, behind everyday features, and increasingly launched by other agents.
The first version of AgentCore runtime built a strong foundation for this spectrum of agents: serverless, session isolation, scale to zero, and pay only for what you use. Today’s launch of the new runtime extends that foundation across the full spectrum, staying fast and consistent for interactive agents, and durable and affordable for long-running, more autonomous agents.
With AgentCore runtime, you can focus on the agent instead of worrying about the scalable infrastructure needed underneath it. Two things make that possible, and they’re the reasons customers reach for it:
Together they make it cheap to keep many agents idle most of the time and even cheap to run one that stays busy. The consumption model bends to the workload instead of forcing the workload to bend to it.
As agents move from short question-and-answer sessions to ambient, always-on work, that same model runs into two challenges.
Memory is expensive, and today you pay the peak. A session holds on to memory from the moment it allocates it until the session ends, because nothing reclaims it along the way. This works when the allocated memory is used to serve subsequent resources without incurring the latency to fetch it again. However, a long-running or bursty agent keeps paying for its high point the whole time it runs, well after it has stopped using that memory. For an agent that spikes now and then but sits idle most of the day, that is the gap between paying for the peak around the clock and paying for the real usage.
Startup times vary. Every new session has to start before it can do any work, so fast, predictable startup is central to a good experience. It matters most when a person is waiting on an agent that paused for input and needs to resume. The catch is the hardware-enforced isolation these sessions depend on: a session that lands on an already-initialized environment starts in under 100 milliseconds, but keeping environments hot enough to guarantee that means holding compute in reserve. So most sessions begin with a cold start: booting a fresh environment, pulling the image, and initializing the agent before the first request runs. That latency penalty grows with image size and concurrency, and it’s worst under bursty traffic, exactly when most sessions arrive and the fewest ready environments remain. That inconsistency is what a waiting user feels.
The workarounds are heavy. To cover both challenges, customers often build the machinery themselves: holding spare environments ready so requests avoid a cold start, optimizing memory allocation, and tearing it all down again to keep the bill in check. Keeping capacity ready ahead of demand is costly and complex for anyone to run. It reserves scarce compute whether or not that compute is working, and it still gives way when a burst outruns what was set aside. This is undifferentiated work, and none of it is the agent itself.
The enhanced AgentCore runtime takes care of both challenges for you, starting with lower memory consumption tracked to what you use. The new runtime now starts each session from a small, efficient memory profile rather than a full provisioned footprint. Additional memory is allocated and paged in on demand as the workload needs it. Based on an analysis of allocation patterns across billions of sessions, we tuned the new runtime to reclaim memory when it goes cold and is unlikely to be accessed again. It no longer holds that memory until the session ends. With the original runtime, allocated memory remained held even if it wasn’t used by subsequent requests, so the usage tracked the high watermark. With the new runtime, memory that is released or goes cold is reclaimed, and the bill tracks those changes over the lifetime of the session.
Faster, more consistent cold starts come as a direct benefit of smaller profiles at startup. The enhanced runtime prepares the environment once, snapshots it, and restores that snapshot for each new instance. Because the snapshot stays small and consistent, so do the starts, no matter the image size or how much concurrency you run. Rather than repeating the boot-and-initialize work on every cold start, the platform restores an environment that is already up. The runtime now delivers consistent starts in a tight, predictable range.
What we measured. To isolate what the platform itself adds to a cold start, we tested an empty echo agent that returns its input and calls no model and no tools. The timing reflects the runtime’s start path rather than any application work. A Python client on an Amazon Elastic Compute Cloud (Amazon EC2) instance in us-west-2 called agents in us-east-1 over the public internet with no virtual private cloud (VPC) peering, using the boto3 SDK. These are client-side numbers, so each one includes the round trip between the two AWS Regions on top of the platform’s own start time. We sent 5,000 cold invocations per agent across both versions and five image sizes, within default account quotas.
Measured this way, the new runtime delivers a P75 cold start latency of about 2 seconds from a 200 MB image all the way to 2 GB, because image size has no effect on it. The original runtime’s latency, by contrast, rises with image size, from roughly 5.4 seconds to nearly 30 seconds.
To put this latency in perspective, it helps to separate cold start latency from what a user waits on. Start time is how long it takes to get a ready environment before your agent code handles its first request. It is not the time the agent spends working. In a production agent, most of the wall-clock time a user experiences comes from the agent loop and its model calls, often several seconds each. In our echo test, the agent’s own code ran in about 34 milliseconds at P75, so nearly everything here is platform start time. The new runtime makes the platform’s portion of the start time fast and predictable, which matters most when a person is waiting on an interactive agent.
A practical tip for interactive agents. You can hide the start time almost entirely by beginning the session as soon as the user engages, for example when they open a chat, even before they type in the input box, rather than waiting for them to submit. The session warms while they are greeted and while they type their first request, so by the time they send that message, the environment is ready.
The next generation of the runtime reworks how sessions use memory, how agents load, and what you pay for.
Beyond what we shipped today, several capabilities are on the way to give you more choice over pricing, compute, compatibility, and control.
Committed baseline discounts. Today’s consumption-based pricing stays and works well for spiky and scale-to-zero workloads. Alongside it, the new runtime will add a baseline pricing option: you reserve a memory floor for a session and burst above it on demand. Baseline pricing suits steady, always-active agent sessions that want predictable cost, while consumption pricing continues to provide greater elasticity.
Larger compute and storage. Expand your agent’s environment with more RAM, vCPU, and session storage.
x86 support. Run the agent, tool, or environment you already have with x86 microVMs. Teams whose code or dependencies target x86 can move an agent, a tool, or an execution environment to AgentCore as-is.
Greater lifecycle control. Suspend and resume sessions with memory snapshotting. Attach to runtime hooks to serialize state before an active session terminates, so sessions can resume indefinitely.
Scoped identity for unattended agents. Unattended agents raise a question a chat turn never did: what is this agent allowed to do when no one is watching it act? Session context keys will give each session its own scoped identity, so an unattended agent, tool, or environment acts with exactly the permissions defined for it and nothing more.
To get started with the new runtime, set the platformVersion parameter to V2 when you create or update a runtime. See the AgentCore Developer Guide for more details on using the runtime.
You can find samples in the AgentCore GitHub samples repo. An accompanying load test example shows the new runtime’s consistent cold start latency in your own AWS account.
Evandro is a Sr. Data Scientist working on Amazon Web Services. He is part of the Global GTM team that helps AWS customers overcome business challenges related to AI/ML on top of AWS, mainly on Amazon Bedrock AgentCore and Strands Agents. He has more than 18 years of experience working with technology, from software development, infrastructure, serverless, to machine learning. In his free time, Evandro enjoys playing with his son, mainly building some funny Lego bricks.
Mark is a Principal AI Architect for AWS, helping customers design and build agentic AI solutions. Mark’s work covers a wide range of use cases, with a primary interest in AI agents at enterprise scale. He is a worldwide tech lead for Agentic AI, including Bedrock AgentCore. Mark has helped companies in insurance, financial services, media and entertainment, healthcare, utilities, and manufacturing. Prior to joining AWS, Mark was an architect, developer, and technology leader for over 25 years, including 19 years in financial services.
Shishir is a Principal Engineer in AWS, currently building Amazon Bedrock AgentCore Runtime. His experience spans the full agentic stack, drawing on deep work across AI systems, from developing conversational agents in Alexa and LLM post-training and customization to recommender systems in Prime Video. He now focuses on making the infrastructure that powers production agentic systems more reliable, efficient, and scalable.
Abhishek is a Senior Software Development Engineer at AWS on the Bedrock AgentCore team. He is the tech lead for AgentCore Runtime and has led the design and development of multiple AgentCore services from the ground up, including Runtime, Code Interpreter, and Browser. He has 12 years of experience building distributed systems, previously on Bedrock and SageMaker. Outside of work, he likes playing soccer and tennis, and spending quality time with family.
Aniketh is a Software Development Engineer at AWS on the Amazon Bedrock AgentCore team, working on AgentCore Runtime with a focus on the performance and efficiency of agent execution at scale. He has over five years of experience building large-scale distributed systems at Amazon, previously on Amazon SageMaker, and now works on making the infrastructure behind production agentic systems faster and more reliable as it scales to meet growing demand. Outside of work, he enjoys hiking, watching movies, and playing cricket.
Rahul is a Software Development Engineer at AWS, where he builds AgentCore Runtime systems that enable AI agents to run reliably at scale. He is passionate about building distributed systems and optimizing infrastructure to simplify the lifecycle of AI agents. Outside of work, he plays semi professional cricket and enjoys exploring the outdoors.
Deploying a Hugging Face model to production means making a dozen decisions: choosing the right serving container for the model’s architecture, confirming the current image tag for your AWS Region, and matching an instance type to the model’s memory footprint. Beyond infrastructure, you must wire autoscaling so you don’t burn GPU hours on an idle endpoint. You also set Amazon CloudWatch alarms that catch silent failures before your users do. After you’ve made those decisions, Amazon SageMaker AI collapses that work into hours.
This kind of structured, repeatable work is exactly what coding agents, like Kiro and Claude Code, are built for. It’s tempting to describe a model to deploy in a coding agent, walk away, and come back to a working endpoint. In practice, an unguided coding agent might make wrong decisions, producing endpoints that are fragile, costly, or quietly wrong. The problem gets worse for newer models, since their training data might not include the latest deployment knowledge.
In this post, you learn how to deploy production-ready Hugging Face models on SageMaker AI using agent skills. You install six skills from Hugging Face Skills, point a coding agent at a Hugging Face model, and get back a real-time endpoint with autoscaling, Amazon CloudWatch alarms, the correct serving container from the AWS Deep Learning Containers (DLC) catalog, and a verified teardown path. Real-time endpoint is the default, but the skills also support real-time with scale-to-zero, serverless inference, asynchronous inference, batch transform, and Amazon Bedrock Custom Model Import. The skills are open source, use only Python and the AWS Command Line Interface (AWS CLI), and work unchanged on macOS, Linux, and Windows.
To show what the skills actually prevent, it helps to watch what a capable agent does without them. We tested both Kiro (with Auto or Claude Fable 5) and Claude Code (with Opus 4.8) for the request:
deploy the small [Qwen/Qwen3-0.6B] (https://huggingface.co/Qwen/Qwen3-0.6B) model to a real-time endpoint, write the plan to a file first, and keep a log of every action.Both coding agents initially chose Text Generation Inference (TGI) as the serving container to deploy, an understandable choice given that TGI was the default for years and model training data is full of tutorials that reach it. But the TGI build available in the Region predated Qwen3’s architecture and couldn’t load the model. The endpoint failed its health check. The agent bumped the TGI version, redeployed, failed again, and pivoted to vLLM. This resulted in multiple deployment failures, each of which billed GPU time as it started and then crashed.
The second request failed more quietly. We asked the same agent to deploy a multimodal mixture-of-experts (MoE) diffusion model released only weeks before the test. The coding agents confirmed it existed, and again wrote a script built on TGI, a text-generation server with no backend for a discrete-diffusion image-text model. Nothing failed loudly. You would find out only when the endpoint refused to come up.
The two runs share the same root cause: missing deployment facts, not reasoning failure. The agent planned and debugged well. What it lacked was current, specific knowledge. Recent Qwen models need vLLM. Python 3.13 has no working wheels for much of the machine learning (ML) stack. Container images should be resolved from the published AWS Deep Learning Containers catalog. This knowledge changes faster than model weights get updated. So we make it into editable skill files rather than rely on the latest release of a model to absorb it.
Table 1 compares the model deployment made by the unguided agent against the agent with skills installed.
| Deployment concerns | Unguided agent | With skills |
| Serving container | TGI first → health-check failure → vLLM | vLLM, chosen before any resource was created |
| Image URI | Discovered by trial and error | Resolved from the AWS DLC catalog, with fallback when the registry query was denied |
| Autoscaling | None | Target tracking, 1–2 instances |
| Monitoring | None | Three CloudWatch alarms (latency, errors, overhead) |
| Documentation | README recommended TGI, the SageMaker SDK, and Python 3.13 | Plan and scripts matched what actually ran |
| Region, role, environment | Correct natively | Correct by rule |
| Teardown | A script you could run | Run, then verified the resources were gone |
Table 1: The same request, run by the agent without and with the skills installed
The rest of this post shows how we deploy Hugging Face models on SageMaker AI endpoints (the right-hand column of Table 1) using agent skills.
Six skills from the Hugging Face Skills GitHub repo cover the end-to-end deployment workflow. The planner skill orchestrates the other five, as shown in the following diagram.
hf-cloud-sagemaker-deployment-planner (orchestrate, ask only what's needed)
│
├── hf-cloud-aws-context-discovery (discover local AWS context)
├── hf-cloud-python-env-setup (set up an isolated Python environment)
├── hf-cloud-sagemaker-iam-preflight (verify a usable execution role)
├── hf-cloud-serving-image-selection (select the right container family and image URI)
└── hf-cloud-sagemaker-production-defaults (deploy with autoscaling, alarms, and tags)An agent skill is an open standard package consisting of a folder with a required SKILL.md file. This file includes metadata (name and description, at minimum) and instructions that tell an agent how to perform a specific task. Skills load through progressive disclosure. An agent reads a skill on demand when the current task matches its description. The following is a trimmed version of the hf-cloud-serving-image-selection skill.
---
name: hf-cloud-serving-image-selection
description: Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen --- including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible --- never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs
---
Serving Image Selection
The serving container is the single thing most likely to break a deployment
that "looked correct on paper". Wrong container, stale tag, or wrong AMI all
produce the same opaque `Failed to pass health check` error.The skills drive five AWS services. Amazon SageMaker AI hosts the endpoint, AWS Identity and Access Management (IAM) provides the execution role. Amazon Elastic Container Registry (Amazon ECR) and AWS Deep Learning Containers supply the serving image, while Amazon CloudWatch powers the alarms. All helper scripts in skills call these services through Boto3 and the AWS Command Line Interface (AWS CLI), which retains full control over what gets created. The SageMaker Python SDK works too, but the skills default to Boto3.
The deployment follows six phases:
boto3.To follow along, you need the following:
This post deploys Qwen/Qwen3-0.6B to a single ml.g5.xlarge real-time inference instance in US East (N. Virginia) Region (us-east-1). Confirm your account has available quota for this instance type before you start.
Note that a real-time endpoint bills continuously whether it serves traffic, so delete the endpoint when you’re done or follow the teardown steps at the end of this post.
Kiro supports two skill scopes: workspace and global. The workspace skills reside in your project under .kiro/skills/ and apply only to project-specific workflows. The global skills reside under ~/.kiro/skills/ and are available across all workspaces.
To install the six skills from the Hugging Face Skills GitHub repo in the current workspace, enter the following request in a Kiro default agent chat session:
Install six agent skills from the huggingface/skills repo, pinned to commit
f3186efbbc322121eb5d0f31e8a1d669ee961159, into this workspace.
Source: https://github.com/huggingface/skills.git
Commit: f3186efbbc322121eb5d0f31e8a1d669ee961159
Skills live under the repo's skills/ directory:
- hf-cloud-sagemaker-deployment-planner
- hf-cloud-aws-context-discovery
- hf-cloud-python-env-setup
- hf-cloud-sagemaker-iam-preflight
- hf-cloud-serving-image-selection
- hf-cloud-sagemaker-production-defaultsKiro summarizes the installed files as shown in Figure 1. Note that this post tested and used the repo with a specific SHA: f3186efbbc322121eb5d0f31e8a1d669ee961159.
To confirm all six skill directories are present, enter / in the Kiro chat session to see available skills as slash commands, as shown in Figure 2.
With the skills installed, you describe the model to the agent in plain language, and the planner skill takes over. You don’t specify which container family to use, how to find the execution role, or which production defaults to attach, because those decisions live in the skills.
Enter the following request in the Kiro chat session:
I need to deploy a model on AWS SageMaker, and I don't want to deal with all the console selecting and boto3 myself. The model is Qwen3 0.6B, pinned to commit `c1899de289a04d12100db370d81485cdf75e47ca`, called from an internal app. Figure out the best way to deploy it and walk me through it. Write the plan to a file first, and keep a log of every action you take.To deploy the model, complete the following steps:
Review the plan. The agent writes a deployment plan to a file and waits for your approval before creating any billable resources.
AWS context discovery and container selection. The agent discovers the AWS context (profile, Region, account). The hf-cloud-serving-image-selection skill selects vLLM for Qwen3 and resolves the image URI from the AWS DLC catalog.
Approve the deployment when the agent asks. The hf-cloud-sagemaker-production-defaults skill creates the model, endpoint configuration, and endpoint as a unit, then attaches autoscaling and CloudWatch alarms.
Verify. Review the smoke-test result the agent reports after the endpoint reaches InService.
The following lines come from the deployment log the agent kept during the run:
## Step 5: Serving Image Selection
| Value | Resolved to |
| Image URI | `763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-vllm:0.28.0-transformers5.15.0-gpu-py312-cu130-ubuntu24.04` |
| InferenceAmiVersion | `al2-ami-sagemaker-inference-gpu-3-1` |
| `SM_VLLM_MODEL` | `Qwen/Qwen3-0.6B` |
| `SM_VLLM_HOST` | `0.0.0.0` (else vLLM binds localhost, ping fails, container dies) |
| `SM_VLLM_TRUST_REMOTE_CODE` | `false` |
| `SM_VLLM_MAX_MODEL_LEN` | `8192` |For a gated model, add a HUGGING_FACE_HUB_TOKEN environment variable.
Deployments often stop at the execution role when calling iam:CreateRole fails on a corporate account whose AWS IAM Identity Center session has no IAM write access. The hf-cloud-sagemaker-iam-preflight skill reverses the order: find first, create only as a last resort.
Its check_role.py script searches the account for existing roles that match patterns such as AmazonSageMaker-ExecutionRole-* and *SageMaker*Execution*, and ranks them by last-used date. It also validates the trust policy and returns the Amazon Resource Name (ARN). It creates a role only when none exists and the caller has iam:CreateRole permission. Note that the created role carries AmazonSageMakerFullAccess. We recommend updating the role to grant only the permissions it needs, following the principle of least privilege.
The hf-cloud-sagemaker-production-defaults skill turns an endpoint from a demo into a production deployment. It applies the defaults in Table 2 to every endpoint, establishing an operational baseline. For production deployment, you need to add user-specific configurations, such as Amazon Virtual Private Cloud and AWS Key Management Service configurations.
| Resource | Name | Billing |
| Model | qwen3-06b-internal |
none |
| Endpoint config | qwen3-06b-internal-20260904-1913-config |
none |
| Endpoint | qwen3-06b-internal-20260904-1913 |
$1.408/hr per instance |
| Autoscaling target + policy | endpoint/.../variant/AllTraffic, min 1 max 4 |
none |
| CloudWatch alarms x3 | <endpoint>-Invocation5XXErrors, -ModelLatencyP99, -OverheadLatencyP99 |
negligible |
Table 2: Production defaults the skill applies to every endpoint
Agent skills define the workflow, but the coding agent still makes judgment calls within it. In one case, when the agent queried Amazon ECR for the newest image tag, the call was denied because the IAM Identity Center role lacked ecr-public:DescribeImages permission. Rather than fail the deployment, the agent fell back to the known-good tag the skill ships as a safety net and recorded the reason in the log. In another case, after the smoke test returned HTTP 200, the agent noticed that the actual answer was never emitted. This is because the model’s reply was truncated at max_tokens while still inside the Qwen3 reasoning block. The agent flagged this in the log as a configuration issue: the calling application should raise the token limit rather than treat the test as a pass.
A real-time endpoint bills for its instance the entire time it exists. Delete the resources you created to avoid ongoing charges. The sagemaker-production-defaults skill includes a teardown.py script that removes the deployment resources and then confirms they’re gone.
To clean up, complete the following steps:
teardown.py directly with the endpoint name and Region.Six reusable agent skills turn an unguided coding agent into one that deploys Hugging Face models on SageMaker AI endpoints with production-ready features. Each deployment includes the proper container, autoscaling, CloudWatch alarms, and a verified teardown path. Without these skills, agents reach for outdated containers, skip production safeguards, and leave misleading documentation. This post walks through each skill: AWS context discovery, Python environment setup, IAM role resolution, container selection from the AWS DLC catalog, and deployment with production defaults.
To get started, install the skills from the Hugging Face Skills GitHub repo and deploy your first model. The skills are open source and contributions are welcome.
Teams can also use Amazon SageMaker JumpStart to deploy a set of popular Hugging Face models directly from the console, and Inference Recommendations to automatically benchmark and select the optimal instance type for their workload.
Amazon SageMaker AI Developer Guide – Real-time inference
AWS Deep Learning Containers documentation
Dario holds a master’s degree in Artificial Intelligence. He is an engineer at Hugging Face, where he works on cloud partnerships, including with AWS, and developer tooling. His work sits at the intersection of ML infrastructure and systems, focused on making it easier to deploy and train machine learning models at scale.
Álvaro Bartolomé is a Technical Lead at Hugging Face, where he focuses on building and optimizing scalable machine learning infrastructure across cloud platforms. Álvaro is passionate about productionizing generative AI models, high-performance inference, and making state-of-the-art ML accessible through open source and open science.
Qiong (Jo) Zhang, PhD, is a Senior Solutions Architect at AWS, specializing in Data and AI. Her current areas of interest include distributed training and AI-Driven Software Development. She holds 30+ patents and has co-authored 100+ journal and conference papers. She is also the recipient of the Best Paper Award at IEEE NetSoft 2016, IEEE ICC 2011, ONDM 2010, and IEEE GLOBECOM 2005.
Sanhita Sarkar, PhD, drives global AI/ML and generative AI partner solutions at AWS. She brings extensive leadership experience across edge, cloud, and data center environments, holds several patents, has published research papers, and serves as chair for technical conferences.
Eliminate GPU waste. Reduce first-token latency by up to 82%. Install one Kubernetes-native addon with zero application changes.
Running large language models (LLMs) at scale on GPU clusters is expensive. The default Kubernetes load balancers are making it worse. Round-robin and least-connections algorithms have no visibility into what’s happening inside your GPUs: which pods have saturated KV caches, which are mid-way through long-context generations, or which already have the LoRA adapter your request needs loaded in memory.
The result? Round-robin routing causes requests to pile up behind busy pods while idle capacity remains unused. First-token latency spikes to 4+ seconds during traffic bursts. GPU utilization becomes uneven and unpredictable. You over-provision to compensate. This burns money on GPUs that aren’t doing useful work.
Today, we’re excited to announce Amazon SageMaker HyperPod Inference Gateway. It is a Kubernetes-native, GPU-aware routing system that deploys as a single EKS managed addon on your existing HyperPod infrastructure. It uses real-time GPU signals to place every inference request on the best-suited pod, delivering lower latency with no changes to your model servers or client applications.
“A chatbot user waiting 4.4 seconds for the first token now sees it in under 800 ms.”
The Inference Gateway uses a two-tier design built entirely on Kubernetes-native primitives.
The first tier installs directly on each HyperPod/EKS cluster as the amazon-sagemaker-hyperpod-inference addon. It consists of three core components, all built on the open-source Gateway API Inference Extension:
High-performance L7 proxy that terminates incoming HTTPS traffic and exposes a single private endpoint per cluster.
Inspects each incoming OpenAI-compatible request body, extracts the model field, and routes to the correct model pool. Supports multi-model routing: one gateway, many models.
The intelligence layer. EPP consumes real-time Prometheus metrics from every model-serving pod and uses a weighted scoring algorithm to select the best-suited backend:
Each scorer carries a configurable weight, so you can tune routing behavior for your specific workload (latency-sensitive chat compared to throughput-optimized batch).
The second tier adds fleet-wide coordination across multiple clusters and regions, with cross-cluster failover, global rate limiting, and cost-aware traffic shaping. Tier 2 builds on top of Tier 1. Each cluster’s per-cluster gateway continues to handle local intelligent routing.
The Inference Gateway deploys with a single addon install and a declarative InferenceGatewayConfig custom resource. No sidecars, no service mesh, no application code changes.
aws eks create-addon \
--cluster-name my-hyperpod-cluster \
--addon-name amazon-sagemaker-hyperpod-inference \
--addon-version v2.0.0-eksbuild.1 \
--configuration-values '{"inferenceGateway": {"enabled": true}, "inferenceOperator": {"enabled": true}}'Add a label to your existing model server deployments so the gateway can discover them:
spec:
template:
metadata:
labels:
app: vllm-llama # The gateway matches on thisCreate a single InferenceGatewayConfig resource that defines your models and routing behavior:
apiVersion: inference.sagemaker.aws.amazon.com/v1alpha1
kind: InferenceGatewayConfig
metadata:
name: my-gateway
spec:
tls: {}
bbr:
enabled: true
schedulers:
- name: llama-70b
modelName: "llama-3.1-70b"
modelSelector:
matchLabels:
app: vllm-llama
targetPort: 8000
scheduler: llm-dThe gateway exposes a standard OpenAI-compatible endpoint. Your existing client code works unchanged:
curl -X POST "http://<gateway-endpoint>/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"model": "llama-3.1-70b", "messages": [{"role": "user", "content": "What is Kubernetes?"}], "max_tokens": 100}'That’s it. No SDK changes. No SigV4 signing for inference traffic. Standard HTTP with an OpenAI-compatible schema.
Running multiple models on the same cluster? The Body-Based Router handles it natively. Define multiple schedulers in your config, and the gateway automatically routes each request to the correct model pool based on the model field in the request body.
One gateway. Multiple models. Zero routing logic in your application.
Serving fine-tuned LoRA adapters on a shared base model? The Inference Gateway routes adapter requests to pods that already have the adapter loaded in GPU memory. This eliminates costly adapter swap latency.
The EPP’s LoRA Affinity Scorer identifies which pods have the requested adapter resident and routes accordingly. If no pod has it loaded, the request goes to the pod with the most available capacity to load it quickly.
The gateway degrades gracefully at every level:
| Failure Scope | Behavior | Recovery |
| Pod failure | EPP excludes pods with stale metrics. Routes to healthy pods | Automatic when metrics resume |
| Pool exhaustion | Returns HTTP 429 with Retry-After header | Autoscaling adds capacity |
| Cluster failure | GIR detects stale heartbeat, redirects traffic within 35s | Gradual ramp-up on reintroduction |
| Regional failure | Cross-region routing activates automatically | Higher latency, no availability impact |
The gateway emits metrics at every layer, all surfaced through your existing monitoring stack:
How much performance are you leaving on the table with default Kubernetes routing? To find out, we benchmarked four models ranging from 8B to 235B parameters, deployed on p5.48xlarge (H100) and g5 (A10G) instances. All traffic was routed through internal Application Load Balancers, matching the exact path a production request travels. A dedicated client node group generated controlled load while model servers ran in isolation on a separate server node group, ensuring zero resource contention under high concurrency. Every result that follows uses the gateway’s default routing configuration, with no tuning required.
GPU-aware routing delivers the biggest gains exactly where round-robin struggles most. We tested three real-world scenarios across four models (8B to 235B parameters).
In production, GPU fleets are rarely uniform. When a smaller-memory instance saturates under traffic that its larger peers handle comfortably, round-robin keeps sending requests to overloaded pods. The Inference Gateway detects this imbalance in real time.
Bursty demand is the norm for most LLM workloads. A single replica cycles between overloaded and idle, creating latency spikes that round-robin cannot smooth out. The gateway absorbs these bursts by steering requests toward pods with available capacity.
Workloads like multi-turn conversations and document Q&A share a common prompt prefix across requests. The gateway’s Prefix Cache Hit Rate scorer routes these requests to pods that already have the prefix cached, avoiding redundant computation.
The pattern across all three scenarios is consistent: the more your fleet diverges from uniform, the more you gain. On a fully uniform fleet under steady traffic, replicas hold near-identical utilization, and the gateway performs on par with round-robin. This makes intelligent routing most valuable for the conditions production traffic actually creates: mixed hardware, bursty demand, and shared prompt prefixes. You can turn it on without hand-tuning anything first.
All figures are measured against a Kubernetes round-robin baseline on the same model replicas, using the gateway’s default routing configuration.
| Workload Condition | TTFT P95 | TTFT P99 | Throughput |
| Mixed GPU generations (Llama-3.1-8B) | –97% | –97% | +8% |
| Mixed GPU generations (Qwen3-32B) | –98% | –97% | +50% |
| Bursty traffic (Llama-3.1-70B) | –94% | –98% | +12% |
| Bursty traffic (Qwen3-235B) | Comparable | –89% | Comparable |
| Shared prompt prefix (Llama-3.1-8B) | –26% | –43% | Comparable |
| Uniform fleet, steady traffic (Qwen3-235B) | Comparable | Comparable | Comparable |
“Comparable” means the difference fell within run-to-run variance.
The Inference Gateway is not a separate platform you deploy alongside Kubernetes. It is Kubernetes:
SageMaker HyperPod Inference Gateway (Tier 1, per-cluster routing) is available today in regions where inference add-on is available.
Ready to stop wasting GPU capacity on naive routing? Install the Inference Gateway addon and start seeing first-token latency improvements in minutes rather than weeks.
Vinay is a Worldwide Leader for Specialist Solution Architect, Generative AI at AWS, where he collaborates with customers in designing cutting-edge AI solutions, leveraging AWS technologies. Prior to AWS, Vinay has over two decades of experience in finance—including roles at banks and hedge funds—he has built risk models, trading systems, and market data platforms. Vinay holds a master’s degree in computer science and business management.
Piyush is a Senior Software Engineer at AWS, working on Amazon SageMaker with a focus on building performant, scalable inference systems for large language models. His technical interests span AI/ML, databases, and search technologies, where he specializes in developing production-ready solutions that enable efficient inference at scale. His work involves optimizing system performance, implementing intelligent routing mechanisms, and designing architectures that support both research and production workloads, with a passion for solving complex distributed systems challenges and making advanced AI capabilities more accessible to developers and organizations. Outside of work, he enjoys traveling, hiking, and spending time with family.
Shreya is a Software Development Engineer at AWS, working on Amazon SageMaker with a focus on building scalable inference systems for large-scale AI workloads. She is passionate about developing reliable, high-performance solutions and delivering quality products that accelerate AI adoption through robust infrastructure. Outside of work, she enjoys traveling and cherishing moments with family.
Hiring at scale in industries such as retail, logistics, hospitality, and others has its fair share of challenges. Recruiting teams are expected to fill hundreds of roles within tight timelines, often with limited capacity and with tools that weren’t designed to seamlessly work together. As a result of this, applications pile up, phone screens get delayed, and strong candidates move on before anyone has a chance to reach out to them. The cost of this isn’t just operational. Every unfilled role slows production, impacts customer experience, and affects revenue. Most organizations are stitching together an Applicant Tracking System (ATS), a scheduling tool, spreadsheets, and disconnected feedback loops to manage a process that demands speed and coordination across every step. The hiring workflow itself becomes the bottleneck.
Today, we’re launching Amazon Connect Talent, an AI hiring solution built for talent acquisition leaders managing scaled hiring. It delivers AI-led interviews, data-driven assessments, and consistent evaluation, helping recruiters identify strong candidates more efficiently while providing applicants with a flexible interview experience. Informed by decades of Amazon’s hiring science, Amazon Connect Talent provides transparency for every assessment, interview, and candidate score, enabling recruiters to stay in control of final hiring decisions.
Recruiters configure the evaluation criteria, assessments, and interview questions based on job requirements. AI agents then conduct interviews and assessments at scale, processing thousands of candidates across the hiring pipeline. For candidates, the experience is flexible. AI agents are available day or night, so a candidate can complete their interview at 9 PM after putting the kids to bed, from any device, without scheduling delays. For recruiters, the next morning brings a dashboard of scored candidates, complete transcripts, and clear evaluation reasoning for every score, ready for review and final decision. This is human-AI collaboration applied to hiring. AI handles the screening and evaluation, and recruiters focus on what matters most, making informed hiring decisions with the full picture in front of them.
Amazon Connect Talent lets recruiters configure AI-led interviews and candidate assessments in minutes, tailored to each role.
Candidates experience a consistent, structured AI-led interview — available whenever they’re ready, day or night.
Amazon is one of the world’s largest employers, and Amazon Connect Talent puts decades of Amazon’s hiring science to work for organizations, with a highly configurable solution that adapts to specific hiring requirements. With consistent, evidence-based assessments applied to every candidate, organizations can interview more people than ever before, surface qualified candidates that might have been missed, and make better hiring decisions at scale.
Connect Talent’s AI focuses exclusively on measuring a candidate’s job-related competencies, testing abilities including problem-solving, logic, listening, and role-specific capabilities. All candidate data is anonymized during AI evaluation, removing factors that can introduce unconscious human preconceptions. The assessments are designed around competencies associated with success in the role, not subjective impressions like confidence level or speaking style.
Connect Talent communicates clearly to both recruiters and candidates about what data is collected, how it is used, and what is not collected. Candidates are informed of what to expect before they proceed to take the evaluation. Each competency is scored against a rubric that defines what a strong response and a weak response look like, and every score is tied to specific evidence from the interview so the result can be traced back to what the candidate said. Traditionally, hiring teams can only evaluate a fraction of applicants, but Connect Talent changes that. With consistent, evidence-backed assessments applied to every candidate, whether evaluating the first or the thousandth, organizations get the same standard of evaluation across the entire pipeline.
Recruiters maintain final decision authority over every hire. Connect Talent gives them scored candidate summaries with competency breakdowns, complete interview transcripts, comparative analytics, and clear reasoning behind every score, all in a single view instead of pieced together from different places. Recruiters can pull up full transcripts and notes at any time, review how the AI scored a candidate, and make the final hiring decision with confidence that speed hasn’t come at the cost of quality. A retail organization ramping up 500 warehouse roles for peak season, for example, can have AI agents interview candidates around the clock while recruiters walk in each morning to a dashboard of structured, evidence-based evaluations ready for their review and final call.
Amazon Connect Talent lets recruiters configure AI-led interviews and candidate assessments in minutes, tailored to each role.
Amazon Connect Talent lets recruiters review candidate responses and evaluation scores with details and full transparency
Hiring data is sensitive. Candidate information deserves the same level of protection you’d expect for financial records or health information. Amazon Connect Talent delivers enterprise-grade security built on AWS infrastructure: the same foundation trusted by banks, hospitals, and government agencies worldwide.
Built-in security controls help protect candidate data, with rigorous measures to meet your requirements. Access is controlled, auditable, and configurable. When your candidates share their information, they can trust it’s protected.
Amazon Connect Talent improves business outcomes by getting recruiters out of the workflow management cycle and empowering them with faster decision-making. Candidates get a faster and more flexible hiring experience. Organizations fill roles before lost revenue, rising costs, and competitive disadvantage set in. AI leads the interviewing while recruiters make the final decisions backed by data. This is human-AI collaboration where it matters.
Your business moves fast and now your hiring can too.
Learn more about Amazon Connect Talent and discover how AI-powered hiring can transform your talent acquisition strategy.
Ayesha is a Principal Solutions Architect, Applied AI at AWS. With over a decade of experience at the intersection of human and AI collaboration specializing in customer experience. She simplifies the complexity of AI, helping organizations stay focused on the outcomes that matter most. Outside of work, Ayesha enjoys long walks, exploring new travel destinations, and writing poetry.
Kate is the Principal Product Manager for Amazon Connect Talent, AWS’s AI-powered hiring service. She brings 20 years of experience building global businesses, including more than 15 years at Amazon and AWS across product, business, and operations. At Amazon, she founded The Drop, Amazon’s made-on-demand fashion brand, and led sustainability for Amazon Private Brands. That path shapes how she approaches Connect Talent today, pairing product innovation with the operational rigor and ethical judgment that hiring decisions demand.
Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in OpenUSD, add physics properties, render preflight views, and validate the result against simulation-ready (SimReady) requirements. This workflow follows that process from a scene in Blender to a simulation-ready OpenUSD handoff for NVIDIA Isaac Sim or NVIDIA Isaac Lab.
In practice, however, if you’re building agents for robotics, the workflow can often get stuck. It’s tempting to blame the hard part on the policy, the model, or the training loop, but the bottleneck often happens earlier: the robot does not have a simulation-ready world to train in. The extra work required to get a 3D scene into that state is laborious, time consuming, and frequently out of scope for the robotics simulation engineer.
This post walks through an agent workflow for preparing a Blender scene for robotics simulation using NVIDIA Omniverse Libraries. Codex, powered by OpenAI GPT-6 Astra, coordinates the overall task, interprets results, and guides iteration. Specialized subagents built with the Hermes agent harness and deployed through NVIDIA NemoClaw use Omniverse Libraries to inspect the scene, author simulation metadata, configure physics, and render visual preflight views.
Together, these components connect reasoning, tool execution, and validation into a repeatable process for delivering a simulation-ready OpenUSD world.
The scene exists—the assets are there, created by a 3D artist in Blender—but is it usable for simulation? Has all of the following prep work been done?
This prep work is tedious, repetitive, and easy to get wrong. It is also exactly the kind of work agentic systems should help with, if they have the right tools. The point of using NVIDIA Omniverse Libraries in an agent workflow is to integrate the tools agents need to build SimReady worlds.
A general-purpose agent such as Codex by ChatGPT or Claude Cowork by Anthropic can look at a Blender scene and recognize that it needs to be made simulation-ready. Recognition is useful, but it’s not enough. To help a robotics developer, the agent must be able to act on the scene:
A broad request to make a 3D scene ready for simulation becomes a multi-agent engineering workflow. Codex or Claude serves as the main agent, coordinating the overall task of preparing the scene for simulation. NVIDIA NemoClaw provides a reference architecture for building the specialized subagents that perform each job. These subagents can use open source agent harnesses such as Hermes, OpenClaw, or LangChain, configured with different NVIDIA Nemotron models for vision, reasoning, and tool use.
In a configuration using Astra and Hermes, Codex uses Astra to translate the developer’s objective into tasks, identify dependencies, and review results from specialized Hermes subagents deployed through NemoClaw. For example, making an object grabbable requires coordinated updates to its semantic label, rigid-body configuration, and collision geometry. Astra helps connect those requirements across subagents and determine which checks are needed before the workflow proceeds.
NVIDIA Omniverse Libraries provide the tools the subagents call to act on the scene. OpenUSD operations establish the shared scene structure, ovphysx authors and checks physics properties, ovrtx renders visual preflight views, and SimReady validation evaluates the resulting assets against a target simulation profile.
Each subagent owns a specific job and its acceptance criteria. Safe, mechanical issues can be fixed automatically. Decisions that depend on developer intent, such as an uncertain semantic label or physical behavior, are escalated to a human with the relevant context and a proposed next step.
The pattern is:
Together, these layers turn the prompt, “Make this scene simulation-ready” into a tool-driven workflow with specialized jobs, a persistent scene state, validation gates, and human review where judgment matters.
Begin by specifying the main objective for the orchestration agent, including the input, desired output, destination, and validation criteria. This gives Codex or Claude enough structure to coordinate the overall task, route work across specialized NemoClaw subagents, and decide when the job is actually complete.
Input: Blender scene Goal: Prepare it for robotics simulation Output: USD-based simulation-ready world Destination: Isaac Sim or Isaac Lab Validation: Visual preflight + SimReady validation
By specifying the main objective, the task changes from simply “make this scene better” to a coordinated agent workflow. Codex or Claude manages the overall request, NemoClaw subagents reason through specialized jobs, and Omniverse Libraries provide the tools that modify, render, validate, and prepare the world.
After setting your goal, follow the steps below.
The first subagent connects to Blender through a Model Context Protocol (MCP) server and uses it to inventory the scene. MCP gives the agent a controlled tool interface into Blender: instead of guessing from screenshots or relying on manual exports, the agent can call tools to inspect objects, collections, transforms, materials, cameras, lights, and scene metadata. That scene inventory becomes the shared context the rest of the subagents use.
This subagent should answer:
The output should be structured as follows:
{
"objects": 142,
"materials": 37,
"missing": [
"semantic_labels",
"collision_meshes",
"camera_sensors",
"physics_materials"
]
}
This provides a shared starting point for the other subagents.
The Hermes inspection subagent returns its structured findings to Codex. Astra uses this inventory and the developer’s robotics objective to identify missing information and plan the next tasks. For example, identifying the robot’s target objects helps determine which assets need movable-body properties and which sensor viewpoints require review. Codex then delegates those tasks to the relevant Hermes subagents deployed through NemoClaw, with clear acceptance criteria and unresolved assumptions flagged for developer input.
The workflow in this post uses The Junk Shop by Alex Trevino (original concept by Anais Maamar) for the demo (Figure 2). Codex coordinates NemoClaw to orchestrate which subagent is needed for the requested task. In this instance, NemoClaw is working through Blender MCP to run The Junk Shop scene and inventory the objects, materials, scene structure, and more.
Blender is the authoring environment. USD is the simulation handoff because it gives agents and downstream tools a shared, structured representation of the world. Once the scene is authored into USD, subagents can inspect prims, add metadata, validate requirements, and pass the same world forward to Isaac Sim or Isaac Lab without relying on fragile one-off exports.
The USD authoring agent uses Omniverse Libraries to preserve hierarchy, transforms, materials, labels, physics metadata, and sensor definitions.
A useful rule for agent builders is: if another agent or simulator needs to rely on it later, author it into USD.
USD is built for layered, nondestructive scene composition, so agents can add labels, physics metadata, sensor definitions, materials, and validation data without flattening the original creative work. That keeps the workflow from becoming a collection of temporary edits trapped inside one tool and gives every downstream step a shared, inspectable source of truth.
Robots do not just need geometry. They need meaning. The semantic-labeling agent turns anonymous meshes into task-aware objects: shelves, bins, floors, obstacles, grabbable items, and robot targets. By authoring those labels into USD, the workflow gives downstream agents and robotics tools a shared vocabulary for perception, validation, synthetic data, and training setup.
The semantic-labeling agent tags prims with task-relevant classes:
shelfbinboxfloorobstaclegrabbable_objectrobot_targetno_go_zoneThe agent can infer labels from object names, hierarchy, shape, and context. But it should also flag uncertainty:
Tagged 118 prims. 9 labels need review.
Figure 4 shows Codex orchestrating NVIDIA NemoClaw. NemoClaw coordinates with subagents through Blender MCP. NVIDIA Omniverse Libraries are the tools the subagents act with to do the task. In this instance, the ovrtx agent inspects the Blender scene through this workflow and applies the semantic segmentation and labels required for the scene to become robotics simulation-ready.
This is important because labels become the bridge between scene content and robotics workflows: perception, task setup, synthetic data, and validation.
A material that looks fine in Blender may still be incomplete for simulation. In a viewport, it may be enough for a shelf to look metallic or a bin to look plastic. In a robotics workflow, those surfaces need material attributes that downstream systems can use for rendering, sensing, physics, domain randomization, and validation. The material agent turns visual appearance into simulation-useful metadata.
The material agent should inspect visual materials and author simulation-relevant material metadata. In a warehouse scene, that could mean identifying metal shelving, cardboard boxes, plastic bins, concrete floors, rubber wheels, or glass panels.
The goal is not prettier materials. The goal is more useful information for simulation and validation.
If the robot will need to perceive the world, sensors should not be an afterthought in the training environment. Camera and lidar configuration shapes what the robot can observe, what data gets generated, and whether the training scene reflects the real task. Authoring sensors early lets agents validate placement, field of view, range, polling rate, occlusion, and target visibility before the scene reaches Isaac Sim or Isaac Lab.
A sensor subagent can author camera and lidar sensors into the scene with:
This approach allows the workflow to surface useful questions before training starts:
Figure 6 shows NemoClaw orchestrating a subagent to call the tool needed for the job: ovrtx. ovrtx loads a scene containing a configured lidar, warms up the sensor pipeline, renders one point-cloud frame, reads valid point data using the count channel, prints summary statistics, and visualizes the points with intensity-based colors.
At this point, the scene is no longer only visual. It becomes a physically aware digital twin world. The objects are no longer just meshes with materials; they have collision shapes, mass, friction, rigid body behavior, and rules for how they interact. That means a box can be picked up, a shelf can block motion, and a robot can test actions against a world that behaves physically instead of just looking correct.
The ovphysx subagent adds or validates the following:
Common failures are exactly the tedious issues that are difficult to address later:
46 objects missing collision meshes. 12 grabbable objects marked static. 7 collision meshes too complex. 3 props floating above the floor.
The ovphysx agent owns this job. It turns those findings into a repair plan, applies safe fixes automatically, and routes ambiguous cases to a human.
For example, the agent can generate simple collision meshes for static props, mark floors and shelves as fixed colliders, assign rigid-body properties to grabbable objects, and flag anything where the physical behavior depends on task intent. The output is not just a cleaner scene; it is a physics-readiness report that downstream agents and validation tools can use.
Figure 7 shows NemoClaw orchestrating a subagent to call the appropriate tool needed to make the 3D scene physics enabled: ovphysx. ovphysx is used to add the rigid body properties, colliders, mass, and friction properties to ensure that the scene is simulation-ready when it is transferred to Isaac Sim or Isaac Lab.
Why render before simulation? Because validation can tell an agent that the scene is structurally acceptable, but rendering shows whether the scene is usable. The ovrtx agent can generate robot-camera and review views, check for hidden targets, bad lighting, clipped sensors, broken materials, or unreadable objects, and route issues back to the right fix agent before training time is wasted.
The ovrtx agent renders review and robot viewpoints so the workflow can check key points:
At this point, ovrtx is providing visual QA for the agent pipeline.
The Hermes rendering subagent, running within the NemoClaw environment, calls ovrtx to generate review images and returns them with the relevant scene metadata to Codex. Astra can use this evidence to investigate discrepancies and coordinate targeted follow-up tasks. If a labeled target is absent from a robot-camera view, Codex can ask the sensor and scene-inspection subagents to check camera orientation, clipping settings, and possible occluders. After a correction, the rendering subagent produces another view so the result can be checked. This connects visual review to an actionable repair loop.
Finally, the validation agent runs SimReady validation against the target profile. This is the acceptance gate for the agent workflow. SimReady Foundation defines standards and validation profiles for simulation-ready USD content, and the validation agent uses those profiles to check whether the scene is actually ready to move forward. If validation fails, the report becomes a task list for the fix agents. If it passes, the scene is ready for Isaac Sim or Isaac Lab handoff.
The report should be actionable:
Validation failed: 14 issues - 10 auto-fixable - 4 require review
Fix agents can repair safe issues. Ambiguous failures are reported to a human. For example, “I fixed 10 validation issues automatically. Four require review: two uncertain semantic labels, one grabbable object with conflicting physics settings, and one object that may be either an obstacle or a target.” The human approves the intended behavior, then the agents apply the fix and rerun validation.
The Hermes validation subagent returns the SimReady report to Codex, where Astra helps determine which repairs and follow-up checks are needed. Codex delegates those tasks to the appropriate Hermes subagents deployed through NemoClaw. Moving a sensor may require another ovrtx visibility check, while changing an object’s role may require updates to both semantic labels and physics properties. The subagents apply approved changes and rerun the relevant checks, and Codex summarizes the changes, validation evidence, and unresolved decisions for the developer.
The goal is a scene that meets the simulation contract, not simply a file export.
Figure 9 shows NemoClaw orchestrating the subagent needed to call the SimReady Blender addon, which is used to validate the scene on the target SimReady profiles. If the validation reports any failures, the agent flags for human review before proceeding.
Robot training does not start when the policy runs. It starts when the world is ready, which takes more than a scene that looks good. It requires a USD world with semantic labels, simulation-aware materials, sensors, physics properties, visual preflight, and validation against a target profile. That is too much tedious glue work to leave entirely to humans, and too concrete to solve with prompts alone.
The useful pattern is a set of subagents with real tools:
Blender MCP inspects the scene. Omniverse Libraries author the world. USD carries the contract. Semantic labels add meaning. Sensors define perception. ovphysx makes it physical. ovrtx makes it visually testable. SimReady validation makes it acceptable. Isaac Sim / Isaac Lab makes it trainable.
The path forward is agentic engineering with real tools: Codex or Claude to orchestrate, NemoClaw to coordinate the subagents, and NVIDIA Omniverse Libraries to allow those agents to act on the scene.
Recommended systems include:
Ready to get started? Select one scene-prep bottleneck, give it to a subagent, connect it to an Omniverse tool, and add a validation gate.
To learn more, check out these resources:
Join us on September 30 at 11:00 am Pacific time for OpenUSD Insider Livestream: Developing a Physical AI Simulation Live with GPT-6 Astra and NVIDIA Omniverse Libraries.
AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through a sequence of steps. It selects tools, evaluates their results, and continues reasoning within an increasingly long conversation.
This workflow places new demands on edge inference. The model must generate tokens quickly, process long shared histories efficiently, and produce valid tool calls within a limited power and memory envelope.
In the MLPerf Inference v6.1 Edge Agentic benchmark, NVIDIA TensorRT Edge-LLM ran Qwen3.6-27B on a single NVIDIA Jetson AGX Thor Developer Kit. The system achieved 52.33 tokens per second and completed all 1,007 turns of the performance workload in 24 minutes and 36 seconds, 6.4x faster than the llama.cpp reference submission of 2 hours and 37 minutes. The result uses NVFP4 quantization, tree-based multi-token prediction (MTP), and KV cache reuse.
MLPerf Edge Agentic measures an OpenAI-compatible model endpoint in two phases: performance and accuracy.
The performance phase replays recorded software-engineering agent trajectories. The model receives a user request, generates a tool call, observes the tool result, and continues the same conversation. The workload contains 20 conversations and 1,007 generated turns. Input length grows across turns and reaches approximately 23.5K tokens, making long-context processing an important part of the measurement. Intersection of Union (IoU) based inline accuracy is also measured to ensure the performance phase is running the agent correctly.
The accuracy phase uses Berkeley Function Calling Leaderboard (BFCL) v4 prompts with single-turn only and reasoning off to balance accuracy and evaluation time on edge devices. It evaluates whether the model selects the correct function, generates valid arguments, and avoids calling a tool when no tool is required.
The TensorRT Edge-LLM submission used Qwen3.6-27B in SingleStream mode on one Jetson AGX Thor Developer Kit with 128 GB of unified memory at the MAXN power mode..
| Metric | Result |
|---|---|
| Output throughput | 52.33 tokens per second |
| Median time to first token | 247.12 ms |
| Median time per output token | 14.68 ms |
| BFCL overall accuracy | 87.94% |
Table 1. MLPerf v6.1 NVIDIA Edge Agentic submission result summary
The MLCommons Edge Agentic example also publishes a llama.cpp reference run on Jetson AGX Thor, which uses Qwen3.6-27B with Q4_K_M quantization and completes in 2 hours and 37 minutes. The TensorRT Edge-LLM submission completes the workload in 24 minutes and 36 seconds, or 6.4x less time.
Low-batch LLM decoding on edge platforms is known to be heavily bounded by DRAM bandwidth. Reducing the size of both weights and activations reduces the memory footprint of the kernels and therefore boosts decoding performance.
The submitted Qwen3.6-27B model uses NVFP4 for weights and activations, including the language-model head, and FP8 for the KV cache. NVFP4 is a 4-bit floating-point format supported by the NVIDIA Blackwell GPU in Jetson AGX Thor. TensorRT Edge-LLM uses optimized kernels to accelerate the quantized model while maintaining the accuracy required by MLPerf.
The smaller model representation also leaves more of the 128 GB unified memory available for the long context, speculative decoding state, and application workloads. Developers can start from a published, calibrated Qwen3.6-27B NVFP4 checkpoint. A deployment team can use a published quantized checkpoint or perform post-training quantization once on any development system before deploying the model.
Each new request in an agent trajectory contains most of the preceding conversation plus a new model response or tool result. Without reuse, the model must prefill the shared history again on every turn. The cost increases as the conversation grows.
TensorRT Edge-LLM identifies reusable prompt prefixes and restores their cached attention KV pages. Qwen3.6 uses a hybrid model architecture, so the runtime also restores the recurrent state and partial KV-page state required to continue execution correctly. It then prefills only the new suffix of the conversation.
KV cache and recurrent-state reuse reduce repeated long-context computation across the complete agent trajectory. This optimization complements tree-based MTP: cache reuse reduces the cost before generation begins, while MTP reduces the number of target-model steps during generation.
On this workload, ~96% of the prompt tokens are served with hot cache. The runtime only prefills ~0.5M of the total 13.6M prompt tokens across the turns.
Standard autoregressive decoding generates one token for each model invocation. Multi-token prediction uses a draft model to predict several future tokens, which the target model then verifies together. Recent models typically have official MTP weights trained and shipped together with the main model.
In addition to traditional linear MTP, TensorRT Edge-LLM implements a tree-based MTP implementation. Instead of keeping only one predicted continuation, the runtime organizes high-probability candidates into a tree. The target model verifies the candidates in one forward pass, and the runtime accepts the matching path. If multiple candidates are accepted, the system advances generation by several tokens.
You can adjust drafting parameters when launching the server. The MLPerf server configuration uses 8 draft steps, the top-2 candidates at each drafting depth, and a 16-node verification tree. Tree-based verification is useful for function calling because tool names, JSON syntax, and common argument structures are often predictable, while multiple branches can preserve likely alternatives for individual argument values. Compared with a linear MTP with 3 draft steps, tree-based MTP could achieve an additional ~40% decoding performance gain for this workload.
The implementation used for this submission is available on the TensorRT Edge-LLM release/0.9.1-mlpinf branch. The branch includes the model export settings, TensorRT engine build commands, server configuration, and MLPerf client configuration
1. Clone TensorRT Edge-LLM and initialize its submodules.
git clone --branch release/0.9.1-mlpinf \ https://github.com/NVIDIA/TensorRT-Edge-LLM.git cd TensorRT-Edge-LLM git submodule update --init --recursive
2. Download the calibrated NVFP4 checkpoint.
huggingface-cli download \ centml/Qwen3.6-27B-NVFP4-W4A4-mlpinf \ --local-dir "$WORK/Qwen3.6-27B-NVFP4-W4A4-mlpinf"
3. Follow mlperf/README.md to build TensorRT Edge-LLM, export the checkpoint with the tree-MTP interface, and build the base and draft TensorRT engines.
$VENV/bin/python -m tensorrt_edgellm.scripts.export \ "$WORK/Qwen3.6-27B-NVFP4-W4A4-mlpinf" \ "$WORK/onnx" \ --mtp-tree-base --skip-visual
4. Launch the OpenAI-compatible TensorRT Edge-LLM server.
export REPO="$PWD" export VENV=/path/to/venv-edgellm-export export WORK=/path/to/mlperf-artifacts bash mlperf/serve_edgellm.sh
5. Clone the MLCommons endpoint harness, install its BFCL dependencies, update the model and tokenizer paths in mlperf/config.yaml, and run the benchmark.
git clone https://github.com/mlcommons/endpoints.git cd endpoints python3.12 -m venv .venv source .venv/bin/activate pip install -e ".[dev,bfcl]" inference-endpoint benchmark from-config \ --config "$REPO/mlperf/config.yaml"
The supplied configuration runs the performance and accuracy phases with temperature 0, seed 42, reasoning disabled, and concurrency 1. Use the --accuracy-only option to run only the BFCL accuracy phase. For more information about the dataset and client configuration, see the MLCommons Edge Agentic example.
For full MLPerf Inference v6.1 results across all submissions, see the MLCommons announcement. For server-scale Vera Rubin performance, see NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut.
Acknowledgments
This work represents contributions from the TensorRT Edge-LLM, ModelOpt, Jetson, and MLPerf teams at NVIDIA, especially Zihao Kong, Xiang Guo, Yoco Xiao, Qikai Li and Ashwin Nanjappa. Thanks to the MLCommons community for developing the Edge Agentic benchmark and endpoint harness.
cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to tile-based GPU kernels, it splits mutable outputs into disjoint pieces and preserves the host-side ownership contract across kernel launches. It also allows programmers to opt out locally when they need lower-level control, enabling direct execution of Tile IR operations.
The TileGym CUDA tile kernel library has accumulated a large library of production kernels written in CUDA Tile Python (cuTile Python) and Triton-TileIR (nvtriton). To make all of these kernels available in Rust as well, our team built an AI agent skill that translates cuTile Python and Triton-TileIR kernels into cuTile Rust.
Using this skill, we ported all 24 public TileGym operators to cuTile Rust and reached 99.5% of cuTile Python performance on average. They contain roughly 40 GPU kernels in total, ranging from element-wise operations to flash-attention decode, Multi-head Latent Attention (MLA), and mixture-of-experts (MoE) models. Note that some operators need multiple kernel variants.
Each conversion starts from whichever reference implementation the operator has (cuTile Python or Triton-TileIR) and runs through a bounded multi-agent pipeline covering analysis, the device kernel, host and FFI code, and benchmarking. Every stage ends in a machine-checkable verdict, with validator scripts and Tile IR diffs deciding whether a conversion moves forward. The main challenge is that cuTile Python JIT compilation specializes each kernel implicitly at call time, whereas Rust requires that you declare every specialization in the kernel’s signature.
This post explains how we developed a multi-agent workflow to translate cuTile Python and Triton-TileIR kernels into cuTile Rust, with checks for correctness and performance at each stage. It covers what the gap looks like in a real kernel, how the skill is structured so that no stage has to be taken on trust, and how the resulting kernels perform against their references. The skill ships in the TileGym repo, so you can apply it to your own kernels.
cuTile Python, Triton-TileIR, and cuTile Rust are three front ends over the same IR: CUDA Tile IR, the cuda_tile dialect. All three feed the same tileiras compiler, which performs the tile-level optimizations and emits the GPU binary. This shared foundation makes translating across the CUDA Tile family practical and, just as important, verifiable.
cuTile Python ─┐ Triton-TileIR ─┼─► CUDA Tile IR (cuda_tile dialect) ─► tileiras ─► cubin cuTile Rust ─┘
The TileGym production tile kernels are written against the first two front ends. Because all three meet at the same IR, porting a kernel to cuTile Rust is not a re-optimization problem. It is re-expressing the same tile program in a safer host language, with the same compiler and the same performance model underneath. The shared IR makes translation checkable.
A faithful port should reproduce the reference kernel’s IR structure: the same memory-op families, same tile shapes, and same reductions. Because all three front ends emit the same dialect, this can be directly verified by dumping the reference kernel Tile IR and the translated kernel Tile IR and “diffing” them before a single test is executed.
This enables checking the agent’s output structurally, not just functionally. A wrong-but-plausible translation (a TMA load with wrong cost hint or a dropped divisibility attribute, for example) can pass tests yet still be incorrect outside of test coverage and may bring performance regressions. These issues can be easily checked and fixed by comparing with the reference IR. The IR diff stage is central to the pipeline described in this post.
Two additional aspects of the Rust front end are important to note for this discussion. First, the Rust source is compiled ahead of time. Tile shapes and element types are checked by rustc. The crate embeds the kernel AST, and at first launch the runtime specializes it with the concrete const-generic values and compiles a cubin (cached thereafter). The GPU binary itself is still JIT-compiled, but the implicitness is gone: nothing is specialized unless the kernel signature declares it. Second, in TileGym, cuTile Rust is simply another backend. tilegym.set_backend("cutile-rs") routes the same operator API to the Rust kernels.
The two front ends differ in where specialization happens. cuTile Python JIT specializes on whatever it sees at call time. cuTile Rust specializes only on what the kernel signature declares. Most of the translation work comes from spelling out what the Python source leaves implicit. The main cases are summarized in the following table.
| cuTile Python (implicit JIT) | cuTile Rust (AOT Rust source) | Consequence for translation |
|---|---|---|
Untaken if ct.Constant branches are dropped before compilation | Both branches must type-check | One Python kernel becomes multiple structural Rust entries (for example, layer_norm splits into 2-D nchw and 1-D w1 entries because the branch changes tile rank) |
Any dtype combination compiles on demand | The FFI dispatches over a fixed symbol/dtype table | Supporting a dtype is an explicit ABI extension; the shared table spans f32/f16/bf16/i32/i64/f8e5m2/f8e4m3fn |
| The JIT type system is the input validation | Past the C ABI there is no safety net, so a wrong stride is a silent corruption, not an exception | Two defensive layers: semantic checks in the Python wrapper, ABI checks (null/dtype/device) behind the FFI with named return codes |
The following section illustrates these differences using a real kernel example.
This example kernel is intentionally simple so you can compare the two versions line by line. First, in cuTile Python:
@ct.kernel
def softmax_kernel(output, input, TILE_SIZE: Constant[int]):
row_idx = ct.bid(0) # one CTA per row
row = ct.load(input, index=(row_idx, 0), shape=(1, TILE_SIZE),
padding_mode=ct.PaddingMode.NEG_INF)
row = ct.astype(row, ct.float32)
row_max = ct.max(row, axis=1, keepdims=True)
numerator = ct.exp(row - row_max)
denominator = ct.sum(numerator, axis=1, keepdims=True)
out = numerator / denominator
out = ct.astype(out, input.dtype)
ct.store(output, index=(row_idx, 0), tile=out)
And the same kernel in cuTile Rust:
#[cutile::module]
pub mod softmax_module {
use cutile::core::*;
#[cutile::entry()]
pub fn softmax_kernel<E: ElementType, const TILE_SIZE: i32>(
output: &mut Tensor<E, { [1, TILE_SIZE] }>, // one row per CTA
input: &Tensor<E, { [-1, -1] }>,
) {
let row_idx = get_tile_block_id().0; // ct.bid(0)
// ct.load(..., padding_mode=NEG_INF): a safe partition view whose ragged
// columns pad with -inf, then a load of this CTA's row.
let token: Token = get_tensor_token(input);
let row_view: Partition<E, { [1, TILE_SIZE] }> = make_partition_view(
input, const_shape![1, TILE_SIZE], padding::NegInf, dim_map::Identity, token);
let row: Tile<E, { [1, TILE_SIZE] }> = row_view.load([row_idx, 0i32]);
let row: Tile<f32, { [1, TILE_SIZE] }> = convert_tile(row); // ct.astype(f32)
let row_max: Tile<f32, { [1] }> = reduce_max(row, 1i32);
let shifted = row - row_max.reshape(const_shape![1, 1])
.broadcast(const_shape![1, TILE_SIZE]);
let numerator: Tile<f32, { [1, TILE_SIZE] }> = exp(shifted);
let denominator: Tile<f32, { [1] }> = reduce_sum(numerator, 1i32);
let out = numerator / denominator.reshape(const_shape![1, 1])
.broadcast(const_shape![1, TILE_SIZE]);
let out: Tile<E, { [1, TILE_SIZE] }> = convert_tile(out); // ct.astype(dtype)
output.store(out); // ct.store
}
}
You can read the correspondences directly. They’re this clean because both front ends are thin surfaces over the same Tile IR ops:
Constant[int] parameters become const generics (const TILE_SIZE: i32), instantiated per launch shape by the host through the same Tile IR JIT.ct.load(..., padding_mode=NEG_INF) becomes two explicit steps. First build a make_partition_view(..., padding::NegInf, ...), then a Partition::load—the same TMA-backed view load that the reference IR contains, with the ragged tail padded to -inf.ct.bid(0) maps to get_tile_block_id().Tile<f32, {[1, TILE_SIZE]}>, and a keepdims=True reduction becomes a reduce_* followed by an explicit reshape and broadcast.The IR diff then confirms that Rust compiles to the same op inventory as the Python original: one view load, reduce_max/reduce_sum on the right axis, one view store, and TMA on both ends. Note that not all of the shipped kernels in TileGym use this fully safe style yet. Each port has to reproduce the reference kernel Tile IR exactly, so where only an unchecked API reproduces it, the port uses that API. We’re still migrating those kernels onto the safe surface shown in this post.
The example kernel.rs is already a complete, first-class cuTile Rust kernel. A Rust application can depend on the cutile crate, include the kernel module, and launch its entry directly through the crate typed API (ownership checks, tile types, and all) with no FFI involved.
The C-ABI layer serves a narrower purpose: plugging those kernels into the TileGym Python dispatch and test framework (and, by the same mechanism, any non-Rust host).
Each operator exports one C symbol from the aggregated cdylib (one libcutile_kernels.so for the whole library). Tensors cross as a plain descriptor struct (ptr, ndim, shape[], strides[]) mirrored between Rust and Python:
#[unsafe(no_mangle)]
pub unsafe extern "C" fn cutile_softmax(
out: *const TensorDesc, inp: *const TensorDesc,
n_rows: i32, tile_size: i32, device_id: i32, raw_stream: u64,
) -> i32 {
let out_d = unsafe { &*out };
let inp_d = unsafe { &*inp };
let device = Device::new(device_id as usize).expect("device");
let stream = unsafe { Stream::borrow_raw(raw_stream as *mut c_void, &device) };
let mut y = unsafe { borrow_f32(out_d, device_id as usize) };
let x = unsafe { borrow_f32(inp_d, device_id as usize) };
let y_part = (&mut *y).partition([1, tile_size as usize]);
match softmax_kernel(y_part, &*x).sync_on(&stream) {
Ok(_) => 0,
Err(_) => -1,
}
}
On the Python side, cffi binds that symbol from a cdef string that is the single source of truth for the signature. The wrapper is a thin layer with validation checks:
_FFI_CDEF = """
int32_t cutile_softmax(
const TensorDesc* out, const TensorDesc* inp,
int32_t n_rows, int32_t tile_size,
int32_t device_id, uint64_t raw_stream);
"""
def softmax(x):
x = x.contiguous(); m, n = x.shape
y = torch.empty_like(x)
rc = lib.cutile_softmax(_desc(y), _desc(x), m, next_pow2(n),
x.device.index or 0,
torch.cuda.current_stream().cuda_stream)
assert rc == 0
return y
Note that the launcher never copies, never allocates, and never takes ownership. borrow_f32 wraps the PyTorch device pointer in a ManuallyDrop<Tensor>, so Rust can hand the kernel its tensors without ever freeing memory it does not own, and the kernel launches asynchronously on the caller CUDA stream. From a PyTorch perspective, this looks like any other extension op.
This is also friction-free within TileGym, because cuTile Rust compiles lazily. The backend tracks source freshness, so editing any kernel.rs (or the crate manifest) makes the next call automatically rebuild the shared library before dispatch, with no explicit cargo build in the develop-test cycle. Iterating on a tile kernel in Rust is as easy as it is in Python: change the kernel, run the test, and the new binary is already in place.
The tilegym-converting-python-to-rust agent skill, shipped in the NVIDIA/TileGym GitHub repo, is built around one design decision: the agent that loads it does no engineering work at all. Reading SKILL.md turns the top-level agent into a pure orchestrator whose only authority is routing; the work happens in specialized subagents it spawns, each loading only the reference documents its stage needs. We’ll walk through each subagent type and its role in the conversion.
The analyzer solves the “JIT hides the spec” problem. In a reference kernel, constants are baked in once the DSL lowers to the cuda_tile dialect, untaken branches vanish, and launch parameters live in host code. The analyzer also selects the baseline: an operator often has both a cuTile Python and a Triton-TileIR implementation, so the analyzer benchmarks each, compares them, and selects the faster one per structural variant as the reference the port must match.
Before any Rust exists, it dumps the Tile IR of that reference for each variant (the ground truth for the kernel writer) and writes analysis.json, a machine-readable spec of variants, constants, dtypes, tolerances, launch grids, autotune space, and the chosen baseline. Everything downstream routes from this file.
The kernel writer produces kernel.rs and nothing else. Barred from host code, its failures stay attributable. Its hard problem is the translation gap itself, distilled into the skill’s 49 coding rules. It proves its work twice. First functionally, with an in-Rust pipeline test that runs the kernel with no FFI and no Python, so a numerics bug can’t hide behind host plumbing. Second structurally, by clearing an IR self-check against the analyzer’s reference dump.
The host/FFI builder makes validated kernels callable from TileGym (the C-ABI launcher plus the Python wrapper) and owns the correctness checks, runs the operator’s real TileGym test suite across all dtypes and shapes, and only its ALL_PASS verdict unlocks benchmarking. This is the first point where the full stack (kernel, launcher, and wrapper) runs end-to-end.
The performance validator runs the CUPTI benchmark protocol (device-time measurement, per-config pairing against the reference on the same GPU) and requires the geometric mean to land within 5% of the reference. Its job is not to optimize but to measure honestly.
Two specialists join only on failure. Neither edits code; both diagnose by reading IR. The IR-diff analyst is spawned when a correctness test fails or a benchmark looks off. It diffs the reference Tile IR against the generated IR variant by variant and classifies each divergence. Crucially, this separates a mistranslation (route back to the kernel writer with a specific fix) from an upstream compiler bug that no kernel change can fix.
The residual-performance investigator takes a correct kernel that is slow on some input shapes and root-causes the gap on both sides of the boundary: the device side (memory-op family, codegen) and the host side (launch configuration, autotune, and wrapper logic). It emits a report that the kernel writer acts on.
Two key reasons motivate this design. First, a full conversion runs on the order of millions of tokens. Second, the split isolates blame. Because the kernel is proven in isolation before any host code exists, a later failure has a tractable owner.
Three choices make this split work. Subagents communicate only through artifacts with fixed schemas, never through conversation. Each stage ends with a machine-checkable verdict the orchestrator routes on without reading prose. And the shared cuda_tile dialect makes IR diff the backbone of verification—used both as the kernel writer’s self-check before tests run and as the IR-diff analyst’s deep comparison when something fails—rejecting structurally wrong translations (a reduction on the wrong axis, a lost mask) that would otherwise pass as plausible.
A conversion run is a small state machine, and the orchestrator’s own instructions fit in a lean SKILL.md. The steps are detailed in Figure 1 and following.
scripts/preflight.sh verifies env vars and toolchain paths. A non-zero exit stops the run: the environment is unusable, and no amount of agent effort fixes a missing compiler.<VALIDATOR_OUTPUT> block and one VERDICT: line. The orchestrator checks the exit codes inside the block, then routes purely on the verdict; it never infers a fix from prose. A malformed return earns exactly one same-agent repair respawn, never an escalation.host → the builder respawns itself; kernel → the IR-diff analyst assigns the owner; env → stop), and a failed perf benchmark routes once through the residual-perf investigator. A missing owner tag is itself a failure. The orchestrator stops rather than guessing because a host fault misrouted to the kernel stage wastes an entire retry.validate_kernel.sh recheck the full 17-file output contract across all stages: reports, IR dumps, correctness, and performance logs.On disk, the skill packages each agent’s role, the shared knowledge, and the validators separately, so every subagent loads only what it needs:
skills/tilegym-converting-python-to-rust/
├── SKILL.md # entry point + orchestration contract
├── agents/*.md # one instruction file per stage
├── references/
│ ├── coding-rules.md # numbered rules (each from a real failure)
│ ├── op-mapping.md # ct.* -> cutile-rs API table
│ ├── ir-diff-checklist.md # what counts as a critical IR divergence
│ ├── pipeline.md # in-Rust pipeline test harness
│ └── performance-checklist.md # benchmark protocol
├── concepts/ # tensor-vs-pointer, FFI bridge, transpose
├── scripts/ # diff_ir.sh + validate_*.sh per agent
└── examples/{softmax,bmm}/ # two fully worked conversions
The coding rules are the distilled failure history. Each one exists because an early conversion produced a kernel that compiled but was wrong without it. They range from the narrow to the structural: assume_div_by applies only to pointers and never to Tensor entries, a broadcast must be preceded by a reshape, and reduction-axis bookkeeping must be exact for every tile rank.
Three layers turn the markdown into a running system: activation, contracts, and the outer driver.
Activation: The runtime activates the skill by matching a task against its description (“convert, port, or translate Triton-TileIR or cuTile Python GPU kernels to cuTile Rust”). On match, the top-level agent loads SKILL.md only, a lean file that turns it into the orchestrator. It never reads the subagent files; those load inside the subagents themselves, alongside just the reference documents their stage needs.
Contracts: Between layers, everything is a file or a fixed-format string. Spawn prompts are minimal pointers, stage outputs are artifacts with schemas, and returns are a validator block plus a verdict line. The entire authority of the orchestrator is routing, and the entire authority of the validator scripts is exit codes. Nothing in the loop depends on one LLM interpreting the prose of another LLM. This is what makes 24 unattended conversions repeatable rather than lucky.
The outer driver: In production, a one-shot driver wraps the skill to make each conversion a hands-off batch job. It creates a fresh checkout on a per-operator branch, hides any pre-existing implementation of the target operator (so the agent must translate), launches the agent in a container detached from the operator’s terminal, and polls progress from outside. When the run ends, the driver applies the captured repo diff and runs the acceptance check: TileGym correctness is green, proof that the cuTile Rust backend actually executed, and CUPTI geomean speedup ≥ 0.95 against the cuTile Python baseline. Only a green outcome autocommits. A thin batch driver runs the operator list with at most two attempts each and pushes the branches that pass; failed conversions land as a diagnosis trail.
With the tilegym-converting-python-to-rust skill, kernel conversion becomes far more efficient. Token cost drops to about half on average, every operator is validated for numerical correctness, and each one hits a geomean speedup of ≥0.95 versus cuTile Python. The final performance numbers come from the CI benchmark pipeline itself: CUPTI device time on NVIDIA DGX B200 (one exclusive GPU per backend, 347 paired configurations across the 24 operators). For each configuration, the best measurement is taken across four CI runs.
Overall geomean is 0.995, parity with cuTile Python. The shared-IR architecture primarily explains these results. Both front ends feed the same tile program into a shared optimizer, and a faithful translation inherits the reference’s performance by construction. All 24 operators clear the 0.95 check, and about a third come out ahead of the reference, with the largest wins on element-wise and normalization kernels. Each conversion lands as a standard six-file changeset, so review stays mechanical.
Figure 2 reports CUPTI device time, which isolates the kernel itself. Wall-clock time and device time answer different questions on submicrosecond kernels. Wall-clock time includes launch and scheduling cost and reflects what the user experiences, while CUPTI device time compares the kernels in isolation. We measure wall clock and report device time to keep the operator-to-operator comparison about the kernels.
cuTile Rust can also emit Tile IR directly. The DSL exposes the Tile IR instruction set as part of its unsafe API surface. In principle, you could write a kernel to match the Tile IR emitted by other front ends exactly. However, such kernels become uninterpretable, so the skill is biased to generate idiomatic code. Because the experiments capture only device time, we expect that matching the emitted Tile IR exactly would match performance exactly across front ends.
The agent skill that translates cuTile Python and Triton-TileIR kernels into cuTile Rust and all converted operators ships with TileGym. Access the skill through skills/tilegym-converting-python-to-rust/. It includes per-stage agent instructions, a coding rulebook, concept guides, validator scripts, and worked softmax and bmm examples. Access the kernels through src/tilegym/ops/cutile_rs/, including one <op>_kernel/ per operator plus the aggregated cutile_kernels crate. Requirements: CUDA 13.1+, a Blackwell GPU for the perf check, Rust 1.89+, and the tileiras compiler.
To get started, point any agent at the repo and ask it to “add a cutile-rs backend for <op>“. The pipeline handles analysis, kernel, FFI, correctness, and benchmarking. For more details refer to the TileGym README on GitHub.
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Quan Nguyen-Tri, Mukul Ranjan, Zhiqiang Shen
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision processes without prior knowledge of the transition model. Each team member makes decisions based on local private information and delayed common information shared across the team. Using only this available information, each member learns an approximate low-rank Markov decision process and applies least-squares value iteration to compute its policy. This yields a fully decentralized learning and planning algorithm that requires neither a centralized coordinator nor centralized training. We show that the resulting member-side solutions approximate the centralized team solution: despite partial observability, unknown dynamics, and delayed common information, each member recovers the corresponding component of an approximate team-optimal policy. We further establish finite-sample performance guarantees and derive a corresponding sample-complexity bound for the proposed algorithm.
Xiaoxing Ren, Thomas Parisini, Andreas A. Malikopoulos
A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to allocate tasks and coordinate workers. To address this limitation, we introduce Agensh, a scalable self-organized multi-agent harness without a central orchestrator: concurrent workers execute a multi-agent cooperation loop, continuously gathering context, claiming and self-assigning sub-tasks, taking action and sharing findings, verifying results, and merging progress in an asynchronous manner. The loop is supported by the agentic organization infrastructure comprising three components: a shared workspace holds proposed, ongoing, and completed work; a message interface lets workers communicate; and shared context retains reusable findings and work intentions. To test the scalability of Agensh, we evaluate it on the five hardest ProgramBench tasks with GPT-5.6-sol (high). Scaling from 1 to 128 agents raises the mean final test-pass rate from 19.31% to 28.78%, an approximately 49% relative improvement. Larger organizations reach comparable test-pass rates earlier. On pandoc, scaling from 1 to 1,024 agents raises the final test-pass rate from 33.89% to 55.06%. Worker trajectories further show that different forms of self-organized cooperation gradually emerges and standardizes as the organization grows. These results reveal the number of agents as a new scaling dimension for multi-agent organizations to expand the frontier of general intelligence, offering a practical solution for complex tasks under hard latency constraints or time budgets.
Zhihao Zhan, Ting Song, Li Dong, Shaohan Huang, Jianxun Lian, Yan Xia et al.
Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what, whom each statement concerns, how individuals perceive one another, what information is shared by the group, and how states change over time. Recent studies on multi-party dialogue benchmarks show that existing general-purpose LLM memory systems tend to lose person and group relations or struggle to integrate clues distributed across members, groups, and time. Together, these issues reveal two core bottlenecks: message attribution and relational understanding in multi-party dialogue, and state reconstruction from interleaved histories. To address both, we propose $\textbf{SpeakerMem-R1}$: its dual-track memory stores speaker-labeled verbatim messages and derived states organized into person-level and group-level views, then combines evidence from both tracks by entity, event, and time at query time. To reduce attribution and update errors during structured memory construction while enabling local deployment, we train Writer-R1 with SpeakerLevenshtein and speaker-conditioned GRPO. On GroupMemBench, SocialMemBench, and EverMemBench, SpeakerMem-R1 achieves binary accuracies of 47.9%, 69.2%, and 61.9%, respectively. On the publicly reported EverMemBench leaderboard from EverMind-AI, we achieves 62.33%, the best reported result among the latest state-of-the-art frameworks. It also achieves 70.85% on all 1,986 LoCoMo questions, which we use as a two-person long-term conversation boundary test. In a controlled evaluation of 305 questions, RL raises the SFT Writer's mean accuracy from 57.38% to 68.20%. We report both binary accuracy and token-F1, and ablations show that the verbatim and structured tracks, as well as person-level and group-level views, are complementary under the standardized evaluation interface.
Haobo Zheng, Tan Tang, Yan Chen, Weijie Wang, Yingcai Wu
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Trang Nguyen, Eulrang Cho, Bingqing Chen, Tim Dettmers
We introduce SWE-Serve, a benchmark for evaluating agents on production inference engineering tasks. Implementing an inference feature can require coordinating multiple changes across the serving stack, including model support, runtime execution, and public APIs. Existing benchmarks provide limited coverage of production inference engineering: repository-level software engineering benchmarks do not target inference, while general terminal-agent benchmarks include only a few inference tasks. Dedicated inference benchmarks, meanwhile, focus primarily on isolated kernel generation or performance optimization rather than repository-scale production feature implementation. SWE-Serve provides 53 repository-grounded tasks derived from recent production changes to SGLang, spanning six inference engineering families. Each task executes on either CPU or a single GPU (H100) and is evaluated with hidden functional and regression tests, including, where applicable, end-to-end (E2E) serving tests and calibrated performance gates. Executable no-op and oracle controls, adversarial verifier review, and closed-book execution support task validity and evaluation integrity. Across 11 models and 31 model-effort configurations, the best-performing configuration achieves 75% mean pass@1. SWE-Serve exposes a substantial gap between completing tasks locally and achieving production correctness. On 19 tasks with end-to-end coverage, model-serving E2E tests reject roughly one-third of patches that pass every other test (45.9% under the verifier versus 69.4% with E2E tests excluded from scoring), with pass rate increasing for each model's best-performing configuration. By making the production correctness gap directly measurable, SWE-Serve enables the field to track whether future agents move beyond completing tasks locally to achieving production correctness.
Jennifer Williams, Dave Farris, Jeff Farris, Jiantao Jiao
Agents using the Model Context Protocol (MCP) rely on semantic matching to select tools from third-party servers, exposing a semantic supply-chain risk through attacker-controlled metadata and outputs. We introduce A2M (Attraction-to-Manipulation), a two-stage black-box framework for hijacking MCP agents. The Attraction phase optimizes tool metadata to increase invocation probability; the Manipulation phase uses execution traces to refine adversarial tool returns that steer agents toward attacker-desired outcomes. On LiveMCPBench, direct attacks optimized and evaluated on GLM-4.6 achieve a macro-average malicious tool invocation rate of 93.6% across four scenarios, increase weighted token costs to 32.4$\times$ the benign baseline under Cognitive Denial of Service, and attain a mean attack success rate of 74.4% across Information Exfiltration, Environment Integrity Compromise, and Reasoning Derailment. Transfer to four other models without re-optimization yields corresponding macro-averages of 63.6%, 2.7$\times$, and 24.5%. These findings motivate stronger tool vetting and runtime isolation in MCP ecosystems. Code is publicly available at https://github.com/Lilaizhen/A2M.
Laizhen Li, Xuan Wang, Peicheng Zhao, Juanjuan Zhao, Kejiang Ye, Cheng-zhong Xu et al.
Large language model (LLM) agents often handle streams of related tasks, yet standard harnesses repeatedly ask the model to reconstruct the same control decisions inside each task's context. We study whether task feedback can instead turn recurring control into reusable executable code, while reserving LLM calls for task-specific semantic reasoning. We introduce Growing Harness, a failure-guided training paradigm that learns the agent harness itself from a strategy-free scaffold that exposes fixed model and tool interfaces but encodes no task-solving controller. Function-level execution traces localize each failure to a bounded code surface, an optimizer repairs a window of failures jointly, and a success-first held-out gate rolls back repair sequences that harm prior capability. Accepted edits accumulate in one shared harness, allowing its control structure to emerge from task feedback. Across BrowseComp-Plus and WebArena-Verified with three deployment models from 4B to 120B parameters, Growing Harness achieves the highest mean success in five of six benchmark-model settings and trails the best mean by 0.7 pp. in the sixth. Relative to a Tool-Calling agent, it reduces LLM calls by 76.0-91.8% and deployed-agent inference cost by 74.4-98.6%. On WebArena-Verified, its success remains 44.7-45.3% across model scales, whereas Tool-Calling falls to 6.7% with the 4B model. Ablations show that trace-local edits, joint repair, and gate-based rollback each improve final success. These results show that persistent program growth can move recurring control out of model context and into low-cost code, yielding reusable specialist agents that remain effective with smaller deployment models.
Laizhen Li, Jiarui Li, Juanjuan Zhao, Kejiang Ye, Ye Li, Cheng-zhong Xu et al.
Typed decision models are built for settings where model outputs are consumed directly by software. Instead of generating free-form text, they return a decision over a predefined set of options. By construction, every output conforms to the required schema. Yet this guarantee does not tell us whether the model interprets the options as intended. We study Jev and two Jev-like models with open weights by changing how option names are assigned to rubrics. Each option consists of an option name and a textual rubric that defines what the option means. We change only which option name is assigned to each rubric; the question, state, rubric wording, and set of option names remain exactly the same. On 1200 workflow decisions with task-specific rubrics, renaming the two options from 0/1 to no/yes changes 70.4 more answers per hundred (95% CI: [67.6, 73.1]) and shifts AUC from .94 to .23, revealing a systematic reversal in the decision ranking rather than simple uncertainty. The same operation has little effect with neutral option names. This pattern holds across all 4 predicates, where the effect is at least 7.4x larger than under the neutral control, and becomes stronger as the number of options increases. The effect also depends on the read-out geometry: a second model family that mean-pools over the full option span flips 4.1x less often. The hosted model exhibits the same behavior: the swap changes AUC from .8146 to .5806 and produces 24x as many answer flips as its test-retest floor. In contrast, replacing the option names with random character strings returns all model families to the neutral-control regime without reducing accuracy. The failure therefore depends on the semantic polarity of the option names rather than on the renaming operation itself. Across all conditions, the type-error rate remains 0%, even when decision accuracy degrades substantially.
Yu Sun, Junhao Xu
X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray collapses 3D anatomy into a 2D projection, causing structures to overlap and anatomical boundaries to be ambiguous, even to experts. As a result, labeling X-ray databases for training general-purpose segmentation systems is impractical, leaving morphometric and functional X-ray analysis confined to narrow anatomical regions and applications. To this end, we present FleXray, a generalist model for anatomical segmentation across the entire body in clinical X-rays. Instead of curating large, manually annotated X-ray datasets, we build a scalable, physics-based generative X-ray data engine. Using existing 3D whole-body CT segmentation datasets and generative image-editing models, we simulate fully-annotated 2D X-rays with diverse appearances, physiological properties, and imaging geometries. Trained on these simulations, FleXray accurately segments 60 anatomical structures across unseen research datasets and in-the-wild X-rays. We further show that FleXray makes X-rays directly amenable to quantitative analysis, enabling automated measurements for disease grading, robust navigation during X-ray-guided interventions, and data-efficient learning of pathological targets. We release the model, code, a full-body X-ray segmentation dataset, and a local, easy-to-use browser-based tool at https://flexray.csail.mit.edu .
Victor Ion Butoi, Vivek Gopalakrishnan, John V. Guttag, Adrian V. Dalca, Neel Dey
Large language models are increasingly used to generate SystemVerilog Assertions from natural-language specifica- tions and register-transfer-level designs. Existing datasets and benchmarks support important goals such as large- scale training, formal evaluation, specification-to-assertion generation, and mutation-based testing. A complemen- tary need is to study whether a generated assertion cap- tures externally observable behavior or depends on inci- dental details of one RTL implementation. We present EquivSVA, a formally verified dataset organized around behavior families. Each family contains four structurally distinct RTL implementations of the same externally ob- servable behavior, shared interface-level gold properties, three controlled mutants, and formal-validation evidence. EquivSVA contains 120 behavior families across 12 cat- egories, 480 reference RTL implementations, 914 gold properties, and 360 mutants. Every final family passes a fixed 17-job validation suite covering RTL equivalence, gold-property proofs, property reachability, mutant dis- tinguishability, and gold-property checks on mutants. We also provide fixed family-safe train, development, and test splits. As a small demonstration of the analyses en- abled by the dataset, we evaluate the publicly released, Apache-2.0-licensed Qwen2.5-Coder-7B-Instruct model on the held-out test split. Of 293 interface-only generated properties, 93 are formally sound, and the number of sound properties varies across equivalent implementations for 14 of 24 test families. These results illustrate how behavior-family organization can support controlled stud- ies of assertion-generation robustness without requiring changes in intended functionality. The dataset, generators, validation scripts, and case-study artifacts are publicly released at https://github.com/aditigupta96/EquivSVA.
FNU Aditi
Large language models (LLMs) are increasingly applied to the automated repair of C/C++ security vulnerabilities, and compile rate is a commonly reported proxy for progress: whether the generated patch compiles. We argue that compile rate is a scientifically unreliable metric for single-function vulnerability repair, and we support this with five controlled experiments over 203 vulnerable functions from Big-Vul, three open-source code LLMs (350M to 6.7B parameters), and three prompting strategies. Compile rate (i) barely responds to an intervention that substantially improves the generated code; (ii) is dominated by evaluation-harness and dataset artifacts rather than model quality, with about 64% of compile failures not attributable to the model, a share that is nearly invariant across models; (iii) shifts by 1.8 to 2.7 times on identical patches under a single compiler-standard flag, with zero regressions; (iv) ranks the three models in the opposite order to reference-similarity metrics; and (v) rewards non-repairs when used as an optimization target, since a compiler-feedback loop raises compile rate while similarity to the human fix falls, with manual inspection finding deletion- and placeholder-style non-repairs among the newly compiling outputs. The natural fallback, whole-function CodeBLEU, also fails: an unchanged copy of the vulnerable input outscores every model. We also examine diff_F1, a change-aware screen that scores only the edited region. It gives exactly zero credit to a no-op and near-zero credit to some, though not all, of the deletion-based gaming patches we observed, while still crediting genuine partial edits, so it may serve as a cheap screen before deeper, execution-based analysis. It is not a repair-quality metric, and we report where it falls short. Our findings argue for change-aware, execution-grounded evaluation of LLM-based vulnerability repair.
Om Nepal, Sushant Aryal, Oluseyi Olukola, Nick Rahimi
Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters or neighborhood size. Conversely, we propose automatic depth-based local center clustering (A-DLCC), a fully data-driven method that eliminates numerical parameter tuning. A-DLCC uses the $β$-integrated local depth to identify stable exemplars, points consistently central across multiple locality levels, termed local centers, which are ranked by their representativeness. Each local center induces a group of similar points, with group-level similarity measured by a proposed nonparametric metric called group-level local similarity. To guide merging, we incorporate the bottleneck path idea from graph theory, which forms the basis of our adaptive merging criterion. Based on this criterion, we design a single agglomeration rule in which a group is either absorbed by a neighbor it reaches better than itself or bonded to a neighbor that both sides find more reachable than their own background, every merge being additionally required to be carried by a contact stronger than a configuration-model null expects. The rule automatically estimates the number of clusters and decides when to stop merging. Experiments on synthetic and real data show that A-DLCC produces interpretable clustering results without parameter tuning.
Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
Detection of overlapping communities is essential for modelling networks in which nodes participate simultaneously in multiple structural or functional groups. Existing graph neural network approaches commonly rely on local message passing, which can obscure community boundaries through smoothing and limit the representation of structurally relevant long-range dependencies. We introduce Diffusion-Induced Spatial Attention Community Detection (DISCO), a deep-learning framework that combines a structural prior derived from influence spreading dynamics, sparse multi-head attention, and non-negative community-affiliation learning. The prior identifies candidate interactions beyond immediate graph neighbours and biases attention according to their structural proximity, while a Bernoulli-Poisson edge-reconstruction objective enables overlapping community inference from node attributes and structural profiles, or both. Benchmark experiments show that DISCO performs competitively against established graph convolutional and graph attention approaches across different input configurations. To demonstrate its practical applicability, we present a proof-of-concept cybersecurity use case in which changes between community assignments inferred from consecutive communication-network snapshots provide an interpretable anomaly signal. Temporal community similarity identifies structural deviations, while node-level contributions help locate the devices associated with them. DISCO therefore provides both a flexible method for overlapping community detection and a foundation for analysing structural change in dynamic networks.
Kosti Koistinen, Vesa Kuikka, Joni Herttuainen, Matthew Hendren, Brian Holt, Kimmo K. Kaski
AI tools for digital product design now offer prompt-to-design capabilities, allowing designers and their non-designer colleagues to create prototypes through conversational workflows with large language models (LLMs). While these tools promise time savings, experimental evidence in product design remains limited compared with evidence from software engineering. We conducted a randomized controlled trial with 50 product designers and 50 product managers to evaluate prospective time savings from leveraging Figma Make in design work. Participants attempted three standardized design tasks with or without access to Figma Make. Among participants who completed the study tasks, access to Figma Make was associated with approximately 20% shorter completion times, with larger gains among product managers. Our findings suggest that prompt-to-design tools may enable product managers to further contribute to design work, while the benefits for professional designers may be task dependent.
Remy Stewart, Olabode Anise, Andrew Hogan, Augustus Griffin
Long-context LLMs focus on retrieving distant evidence from extensive context, yet existing work has largely focused on overcoming distance alone. In this work, we identify the Proximity Trap, insufficient attention to distant evidence often arises less from distance itself than from cumulative competition with abundant, task-irrelevant proximal background. To address the Proximity Trap, we introduce LYRA (Long-context heavY-tailed Relevance Alignment), a t-distributed directional matching mechanism that reshapes the context retrieval distribution, directing more attention mass toward task-relevant evidence, while preserving the relative positional information encoded. Extensive experiments on LongBench-v2, RULER, and LongBench demonstrate consistent improvements across context lengths and task categories. We further introduce ProxBench, a multi-level fine-grained benchmark for evaluating distant evidence utilization under increasing proximal background interference. Project page: https://xiaoyuyoung.github.io/LYRA/
Xiaoyu Yang, Jie Lu, Wei Duan, En Yu
Software vulnerabilities are often discovered long after they are introduced, making it difficult to identify the vulnerability-inducing commit (VIC) responsible for introducing the underlying vulnerable condition. Existing VIC identification techniques largely rely on git blame to trace vulnerable code through revision history and use positional heuristics, such as selecting its earliest or most recent modification. However, the true VIC may occur anywhere within this history, and vulnerable behavior may depend on code that evolves across multiple revisions. We therefore argue that VIC identification requires reasoning about how vulnerability-relevant code evolves, rather than simply where a candidate commit appears in the revision history. We present TraceVIC, a temporal graph-based approach for identifying and ranking VICs by reasoning over code evolution. TraceVIC first localizes likely root-cause lines and traces their histories across revisions, constructing graph representations that capture program structure within each revision and the evolution of vulnerability-relevant code across the history. It reasons over the resulting revision history, using temporal edges to preserve correspondences between program elements across consecutive revisions, and directly ranks candidate commits according to their contribution to the vulnerable condition. Ablation results show that modeling the full revision history improves F2 from 0.637 to 0.814. TraceVIC improves F2 by up to 28.7% over state-of-the-art methods and identifies a valid VIC for 78 of 79 vulnerabilities across four unseen C/C++ projects.
Fnu Tanish, Samiha Shimmi, Samikshya Chapagain, Hamed Okhravi, Mona Rahimi, Lei Zhang
Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathematical and code reasoning substantially impaired. Long generations often degenerate into repetitive loops, exhausting the decoding budget without completing a solution. We trace this gap to quantization-amplified exposure bias: QAD trains on fixed corpus prefixes, while quantization-induced deviations compound along the model's own autoregressive trajectories. To address this mismatch, we introduce an on-policy distillation (OPD) stage that places teacher supervision where the quantized model actually goes. Starting from a QAD checkpoint, the student generates through the quantized forward path used at deployment and receives feedback from a frozen full-precision teacher on its own prefixes, combining dense token-level guidance with task-verifier rewards. Across four models at 2.79 and 1.88 effective bits, OPD raises average BF16 performance retention from 35% to 70% on MATH-500 and from 66% to 91% on HumanEval while preserving short-form performance, with reasoning gains substantially exceeding those of continued teacher-forced QAD in matched-budget comparisons. By coupling QAD's stable low-bit initialization with OPD's on-policy reasoning recovery, our framework provides a comprehensive sub-3-bit solution that preserves broad capabilities while restoring long-form reasoning.
Yuanteng Chen, Zhilei Liu, Peisong Wang, Yuantian Shao, Chuangyi Li, Weining Wang et al.
Offline reinforcement learning and off-policy evaluation evaluates dynamic treatment rules based on retrospectively collected data prior to deployment. In recent AI applications, state and reward information is recorded as complex text or image, which recent AI advancements such as LLM-as-a-judge can label with unknown bias. Expert annotation may be available but at a higher cost. For example, safety classification via cheap but imperfect classifiers vs. expensive expert review. We show how a limited budget for ground-truth data-annotation can be used via doubly-robust OPE with missing rewards, and we optimize variance-optimal annotation probabilities for sequential off-policy evaluation, where the target policy value is estimated from annotated data. We characterize the optimal annotation probabilities for sequential forward-monotone annotation protocols, and provide a feasible batch-adaptive implementation. Our work is motivated by a collaboration with a homelessness services nonprofit that writes casenotes for individuals over time. Our method can be used to unlock trustworthy inference from casenote data and answer new inferential questions such as: how does expanding outreach effort over time affect progress towards a housing application and improvement in housing placement? In simulations and on two real datasets - casenotes from the nonprofit and human-preference votes from LMArena - we see reductions in RMSE of 34-65% for housing placement and 17-68% for progress towards a housing application at budgets of 40% of full annotation and above, and by 55-62% at every budget on LMArena.
Woojin Chae, Ezinne Nwankwo, Haitong Qin, Angela Zhou
A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to explore this quantum-classical boundary, as they capture both the classical field behavior and the intrinsic quantum nature of light. Here, we address this problem from a learning-theoretic perspective by asking: When are bosonic Gaussian states classical to learn? That is, under what conditions (if any) can an n-mode bosonic Gaussian state be learned with as few samples, and with operations as simple, as are needed to learn a classical 2n-variate Gaussian distribution? We establish a smooth crossover in learnability governed by the state's thermal fluctuations: - Cold Gaussian states are non-classical to learn: When the covariance matrix satisfies $Σ\le(\frac12+O(\frac1n))I$, i.e. close to the vacuum covariance, tomography under single-copy (i.e., non-entangled) measurements fundamentally requires $Ω(n^3)$ copies, strictly exceeding the sample complexity $Θ(n^2)$ of learning classical Gaussian distributions. We show that this hardness persists even when few-copy entangled measurements are allowed. - Warm Gaussian states are classical to learn: When thermal fluctuations exceed the vacuum noise, parameterized by $Σ\ge(\frac12+ν)I$ for any parameter $ν>0$, we prove that single-copy tomography requires $N=Θ\left(n^2\min(n,1+ν^{-1})\right)$ copies. This bound is tight and is achieved by simple, non-adaptive, unentangled heterodyne measurements. Crucially, for $ν=Ω(1)$, the sample complexity drops to $Θ(n^2)$, matching the classical case. Our results tightly characterize a quantum-to-classical crossover in the learnability of bosonic Gaussian states, reveal a novel connection between fundamental physics and statistical learning theory, and have implications for real-world sensing experiments.
Senrui Chen, Antonio Anna Mele, Francesco Anna Mele, John Preskill
Large language models increasingly tackle hard reasoning problems by spending more test-time compute, yet the dominant strategy remains naive repeated sampling: draw many independent solutions and hope one is correct. Because such sampling explores only through local decoding noise, it tends to produce many near duplicate attempts rather than genuinely different ideas. We ask whether exploration can instead be steered at a semantic level, by first sampling problem specific concepts, hints, or strategies and then conditioning answer generation on them. We refine this into a simple, more exploratory procedure that emits many diverse concepts in a single trajectory, and evaluate it on hard problems where repeated sampling struggles. We then go a step further and make concept generation trainable: a small concept generator is optimized with reinforcement learning so that its concepts maximize the downstream success of a larger, frozen answer generator. On hard mathematical reasoning problems, the trained concept generator substantially improves the answer generator's pass@k over naive repeated sampling at the same answer generation allocation, surpasses concepts drawn from much larger untuned models, and transfers to answer generators it was never trained against, including a model from a different family. A small model can thus be trained into an effective, reusable search policy for a much larger one.
Ismail Labiad, Matthieu Kowalski, Marc Schoenauer, Rémi Munos, Julia Kempe
A coding agent must emit a valid tool call--a parseable invocation of a tool in the provided schema--before the harness can execute its chosen action. We study how local serving stacks affect this protocol step and show that measured outcomes can depend on the serving layer rather than model behavior alone. In Ollama, the default tools= request is gated per model by a static template flag: some models are accepted and return calls as text, some return native tool_calls, while Phi-3 and Gemma-3 are rejected before inference. In our harness, rejection and retry exhaustion are not preserved as structured failure metadata, so downstream analysis can misclassify them as model non-calls and naively report 0% fidelity. Adding a text tool list while retaining the native channel recovers much of the measured fidelity for accepted models, whereas a uniform text protocol reduces fidelity for Llama-3.2, which has native tool-call support. Cross-stack probes on Ollama, llama.cpp, vLLM, and SGLang show different handling of the same request. Constrained decoding removes parse failures but can induce non-termination, and turn-pooled versus per-instance estimates differ by up to about 55 points. We conclude with a checklist for treating serving behavior as part of the evaluation protocol.
Lijuan Tang, Yuemeng Zheng
Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discriminative capability of interpretable stylometric features extracted solely from submitted text. Using data from 90 participants who wrote both independently and with ChatGPT assistance, we evaluate eight machine learning classifiers while keeping data from the same participant together during validation. On the held-out test set, Random Forest achieved an ROC-AUC of 0.87 and an F1-score of 0.84, with False Positive and False Negative rates of 22.2% and 11.1%, respectively. SHAP analysis shows that lexical and grammatical characteristics drive the resulting predictions. The findings suggest that transparent, text-intrinsic features provide measurable signal for detecting GPT-assisted writing.
Rajesh Kumar, Nabeel Siddiqui, Alexander Fuchsberger
Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably assessed without probabilistic forecasts. We present PROSWIN, a probabilistic machine learning model that forecasts the hourly solar wind speed (SWS) at Earth with a four-day lead time. The approach combines solar images and magnetograms using a deep neural network coupled to a distributional regression algorithm. Because standard error metrics underweight the relevance of HSS peaks, we further introduce the prediction score, a model-selection metric that jointly rewards timeline and HSS peak accuracy. On 14 years of data, our forecast achieves very well-calibrated uncertainties (<1% average deviation). Using the continuous ranked probability score (CRPS), a metric that assesses distributional accuracy, we obtain a timeline CRPS of 41.0 km/s, an HSS peak CRPS of 45.3 km/s, and a prediction score of 42.3 km/s. We find that the 171 Å channel is an important complement to the typically used 193 Å and 211 Å channels and that the prediction score for model selection improves the applicability of the model. Compared to selected models from the literature, ours is the only one that is accurate for both timeline and HSS peak values, rather than trading one off against the other. These results support the advantages of probabilistic over single-value solar wind models. The introduced methods are also transferable to other forecasting problems.
Daniel Collin, Yuri Shprits, Luca Chiarabini, Stefan J. Hofmeister, Nadja Klein, Guillermo Gallego
Generative AI (GenAI) applications have flourished enabling users to chat with large language models, and to create agents to act on their behalf for a variety of tasks. The pace of development of capabilities in this field is incredibly fast with security and safety taking a back seat. Unfortunately, the slower pace at which security and safety mechanisms have evolved has led to real incidents. Policy enables the definition of desirable behavior of applications, and for that reason, it is a cornerstone of making systems secure and compliant. Policy however means different things to different practitioners creating confusion and siloed solutions that are not adequate for compliance. This paper takes a tour of the good, the bad and the ugly when it comes to policy enforcement in GenAI applications. We propose a methodology to systematically analyze and dissect existing approaches to define and enforce policy found in the wild. Based on this principled analysis, we provide recommendations and call for action for the community to address. This paper is a companion extension of USENIX Security 2026 Enigma talk titled "From Alignment to Access Control: A Unified View of GenAI Policy Enforcement" by the author Nathalie Baracaldo.
Nathalie Baracaldo
In grokking an early fit to the training data separates from a much later improvement in generalization. During this delay, training can move from a fixed neural tangent kernel (NTK) regime to one in which task-relevant kernel eigendirections continue to evolve. We provide a quantitative theory for how this transition from lazy to rich learning can produce delayed generalization. For homogeneous networks trained with squared loss and $L_2$ weight decay, we show that a finite residual remains after memorization, with larger residual fractions in target components associated with smaller NTK eigenvalues. These residuals feed back into the dynamics of the NTK itself, and projecting the resulting dynamics onto task-relevant spectral directions yields a reduced system in which residual-driven kernel growth competes with weight decay. This system predicts that the grokking timescale is controlled by the product of learning rate and weight decay, that feature learning slows logarithmically near a critical decay above which task-aligned NTK structure can no longer support generalization, and that stronger decay can prevent fitting altogether. We test these predictions in modular addition. In a homogeneous MLP, task-aligned Fourier structure continues to emerge in the NTK after training accuracy has saturated, and an 84$\times$90-grid of trained networks across varying learning rate and weight decay recovers the predicted phase geometry and inverse-product scaling of the generalization time with learning rate and weight decay. A one-block Transformer shows similar macroscopic phase structure in a 42$\times$45-grid, as well as the same transition-time scaling despite violating exact homogeneity. Together, these results provide a mechanistic derivation connecting post-fit feature learning to both the onset of generalization and its phase structure in the learning rate and weight decay plane.
Lenz Pracher, Pascal de Jong, Oskar Lieshaus, Alan Jeffares, Steffen Rulands
Collaboration topology shapes both the performance and execution cost of LLM-based multi-agent systems. Because tasks differ in complexity and required capabilities, recent approaches generate task-specific collaboration graphs that specify agent participation and information flow. However, representative topology generators use either individual agents or predefined groups throughout an organization, overlooking differing collaboration needs across subtasks. Our key insight is to select granularity locally for each functional role, combining fine-grained control with reusable collaboration patterns within one organization. Learning such organizations requires exploring a combinatorial construction space with limited intermediate feedback from final-answer rewards. Therefore, we propose MAGIC, a dense-reward reinforcement learning framework for mixed-granularity graph generation. Specifically, MAGIC constructs a mixed-granularity agent graph by sequentially selecting a functional role, instantiating it as a single agent or reusable group, and connecting it to existing units. We directly optimize the construction policy using returns from trajectories sampled under the current policy and use potential-based reward shaping to provide intermediate feedback from probe-based utility and structural signals while preserving the cumulative task reward. MAGIC outperforms state-of-the-art baselines across eight benchmarks and demonstrates strong inference efficiency in our efficiency study.
Kairui Yang, Ziheng Yi, Xunkai Li, Minghao An, Zhanke Liu, Zekai Chen et al.
Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit attributes needed to be joined. We study Discovery-Driven Integration, where the relevant sources and their missing relational structure must be discovered before integration. In this setting, unstructured text provides the evidence that connects otherwise disjoint tables. The fundamental challenge is to discover the relationships at a fine-grained level that connect individual rows from different tables through specific sentences. We formalize this task as Text-Mediated Join Path Discovery and propose a horizontal bidirectional cross-attention architecture called LOKI Latent-space Optimization for Knowledge Integration) that learns contextualized representations of table rows and sentences. Through a global table-text contrastive objective, fine-grained row-sentence associations emerge without explicit local supervision. Existing multi-modal discovery methods largely retrieve coarse-grained column-text associations, whereas integration systems assume supplied row-text links, schemas, or queries. LOKI instead transforms these implicit associations into explicit, interpretable join paths, organizes them into relation-consistent groups, and materializes them as typed integrated tables with sentence-level provenance. Comprehensive evaluations on real-world benchmarks demonstrate that LOKI consistently outperforms state-of-the-art multi-modal data discovery approaches, and materializes typed integrated tables with 0.982 macro typed-pair precision while being up to 40 times cheaper in LLM API cost than direct prompting.
Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal
We study statistical rates in entropic optimal transport in the semi-discrete regime where one measure has finite support and the other is subGaussian. Our main result establishes parametric convergence rates for the empirical dual potentials to their population counterparts, with no dimension dependence in the leading term. Our result relies on tailored strong concavity analysis of the semi-dual objective, coupled with specialized bounds for the semi-discrete potentials. As a consequence, we obtain fast rates for downstream quantities derived from the optimal coupling. Chiefly, the empirical barycentric projection achieves a squared-error rate $n^{-1}$, matching the fully compact case and improving over the less favorable $n^{-1/2}$ rate known for fully subGaussian settings. Altogether, these results may indicate a lower complexity adaptation phenomenon whereby the statistical complexity of the barycentric projection is governed by the discrete measure. As an application, we analyze Sinkhorn-EM, an EM-type algorithm in which the E-step is replaced by an entropic optimal transport problem. In a well-specified and balanced two-component Gaussian mixture model, we prove $\sqrt{n}$-consistency of the empirical iterates to their population counterparts for any fixed number of iterations, matching classical EM rates up to a $\sqrt{\log n}$ factor. Simulations support the theory.
Tomas Gonzalez, Gonzalo Mena
Successful agent execution need not identify which future product improvement its user would value. We present a decision-specific audit that maps a declared observation channel and product-value contrast to compatible intervals and witness populations. Its foundations are established identification and decision theory; the contribution is an executable measurement workflow and a controlled study of its limits. A frozen experiment makes 4,800 requests to two pinned model snapshots on shared synthetic tasks. All 36 conservative primary intervals remain unresolved despite different execution accuracy. An exploratory 2,400-call follow-up records supplied preferences and resolves three of nine comparisons per model. A deterministic extractor resolves seven of nine without model calls or calibration observations, exposing unnecessary uncertainty introduced by model-generated reports. A further 14,400 controlled multinomial simulations distinguish structural ambiguity from weak identification and finite calibration precision. We propose a source-labeled decision receipt and provide an offline viewer for inspecting the audit. These results motivate preserving decision-relevant structured input and diagnosing why a decision is unresolved before collecting more telemetry. The study contains no human participants or real customer outcomes. Full proofs, raw model provenance, controlled experiments, and reproducible analyses accompany the report.
Shivam Gupta
ggml-meta: resolve multi buffer views (#29266)
ggml-meta: resolve multi buffer views
add TODO to revisit if graph allocator gets refactored
Website:
Attestations:
macOS/iOS:
Linux:
Android:
Windows:
openEuler:
UI:
cuda: top-k MoE should always fire (#28432)
Website:
Attestations:
macOS/iOS:
Linux:
Android:
Windows:
openEuler:
UI:
sycl : support new UT case for mul_mat_hadamard fp16 (#29218)
Website:
Attestations:
macOS/iOS:
Linux:
Android:
Windows:
openEuler:
UI:
Signed-off-by: Andreas Karatzas akaratza@amd.com
Co-authored-by: OpenAI Codex noreply@openai.com
Full Changelog: rust-v0.156.0...rust-v0.156.1
Full Changelog: v0.34.3...v0.34.4-rc0
Changes since langchain-openai==1.6.3
release(openai): 1.6.4 (#40775)
chore(model-profiles): refresh openai model profile data (#40774)
Changes since langchain-anthropic==1.7.2
release(anthropic): 1.7.3 (#40773)
chore(model-profiles): refresh anthropic model profile data (#40772)
fix(anthropic): auto-route with_structured_output to method="json_schema" for fable and opus 5.5 (#40766)
chore(anthropic): update docs for Opus 5.5 (#40765)
feat(anthropic): send mid-conversation SystemMessages in place (#40622)
chore(deps): bump anyio from 4.11.0 to 4.14.2 in /libs/partners/anthropic (#40643)
GET /api/show now advertises each model's thinking controls and default:
Available in the CLI with:
ollama show gemma4
thinking
levels false, true
default true
Available in the API with:
curl http://localhost:11434/api/show -d '{"model": "glm-5.3-flash:cloud"}'{
"thinking": {
"values": ["low", "high", "max"],
"default": "max"
}
}Also available on ollama.com directly for cloud models.
Full Changelog: v0.34.2...v0.34.3
This release contains additional security hardening for next/og. For more information, check out https://nextjs.org/blog/nextjs-security-update-september-22-2026
This release contains a security fix for GHSA-vcvr-r3jv-pc5j: Remote Code Execution in next/og ImageResponse
claude-opus-5-5), now the default Opus model — 1M context, $4/$20 per Mtok with $0.20/Mtok cache reads/skills list, and a skill's state options in /plugin can be clickedCLAUDE_CODE_MAX_MCP_DESCRIPTION_LENGTH to change the 2,048-character cap on MCP tool descriptions and server instructions for every MCP server in the sessionhook_execution_complete OpenTelemetry eventacceptEdits, allow rules and auto mode no longer approve one landing outsidepath, file_text, file_content or a stray description instead of file_path and content/model, /effort, /config, /status, /usage, /plugin, /sandbox, /permissions, /artifacts, /mobile, /login, /upgrade, /usage-credits, /install-github-app, /setup-bedrock, /setup-vertex) quitting Claude Code instead of closing the dialogn closing dialogs and a stray y confirming them; Enter and Esc accept and cancel (bind y/n to confirm:yes/confirm:no in keybindings.json to restore)installed_plugins.json keeping the install-time commit after updating a plugin from a GitHub repository or git URL that tracks a branch or tag~/.claude/skills/ being moved to ~/.claude/skills/.trash/ when a manifest.json in that folder listed their names/workflows briefly showing a one-row list before opening the only run/model and /permissions) in fullscreen mode/plugin and /skills; off now shows a dim ◯/plugin, /skills and /mcp losing its right border in fullscreen mode/mcp showing △ in the server list but ⚠ in the detail view for the same server; the list, detail views and /plugin now all show ⚠/config settings list and in selection lists such as /model, /memory and permission prompts/skills menu wrapping past the first or last skill instead of stopping there/config list; it now does nothing there/compact when its saved history held a malformed notice about MCP tools that could not be loaded/config crashing and some on/off preferences being misread when a preference that has moved to settings.json still holds a value like null or "false" in ~/.claude.jsonclaude --bg) being unable to run git, hooks, plugins and other helper programs when an environment variable handed to the session contained a NUL character! shell-mode prompt stashed with Ctrl+S coming back as a plain prompt when restored, and / listing file paths right after stashing oneclaude agents showing a blank, unresponsive screen instead of an error when the temp directory is full, not writable or owned by another userclaude mcp remove still showing as needing authentication instead of reconnectingclaude plugin update clearing a plugin's recorded commit and moving it to version "unknown" when the official marketplace's snapshot file is a link or too large/ultrareview reporting a stopped cloud review as completed or as an error to retry, and waiting out the full timeout when its session was deleted or the signed-in account changed--configure-git~/.claude/session-env, image-cache or another cleaned-up folder--retire-at release losing its finished signal; the runner now briefly waits for the turn to be reported before stopping the sessionctrl+l / cmd+k in fullscreen mode clearing the transcript view (added in 2.1.260); they redraw the screen again/permissions: focus returns to the rule list after viewing, adding or deleting a rule, and the delete-rule and remove-directory confirmations now default to No/permissions tab navigation: ←/→ and Tab pressed in a rule list now switch tabs without moving focus to the tab bar/cost cache-miss causes to name thinking mode and thinking display changes/install-github-app: the GitHub CLI check and repository selection steps now show "Esc to cancel"/artifacts and /workflows lists: a scrollbar at the right edge shows how much of a long list is hidden and where you are in it/plugin's Add Marketplace form in fullscreen: it no longer draws a box inside the pane, and its text and key hints line up with the rest of /plugin/workflows detail view in fullscreen: it no longer draws a second horizontal rule under the pane's divider/btw asked while a tool is still running: the side question now knows that call is in progress instead of reading it as a failed one@ file suggestions: a file whose name contains the query now ranks above one that only matches across its folder names/ultrareview uploads: renamed copies of key files, such as id_rsa copy or kubeconfig (1).yaml, now also stay on your machine--debug-file writes a debug log to a path you choose/effort became per-model to no longer apply to newly released models such as Opus 5.5; they start at their default until you pick a level/effort in -p or the Agent SDK, a project, managed or --settings effortLevel, or a per-model level/autocompact's footer hint to name ←/→, the keys that adjust other ordered values/fast's footer to name Space as the toggle keygit:// remotes there now need GIT_ALLOW_PROTOCOLPermissionRequest hooks: an agent-type hook no longer runs there, since its answer could never allow or deny the request; it now shows an error pointing to command or http hooks/status, showing the session's version, account, model and server details/sandbox/chrome/export, to copy or save the conversation as plain text/skills that opens it/plan that switches to plan mode, sends a first planning prompt, or shows the session's plangh and GitHub API calls inside a cloud session on a GitHub Enterprise Server repository failing after about eight hours; the token now renews automaticallyWith the release of Grok 4.7 https://x.ai/news/grok-4-7 I would like to
add Grok 4.7 to the xAI provider and the supergrok provider
Manually verified all thinking efforts
Release Notes:
This release features 762 commits from 315 contributors (104 new)!
--load-format ipc_cache instead of reloading from disk (#54921), now covering FP4 checkpoints (#55465) and multi-node TP (#55468).HiSparseConnector (#53781), with Prometheus counters (#56061), a host cache shared across TP ranks (#56629), and the attention config inferred from the connector (#57041).--return-sampling-mask compacted on GPU, fixing an about 2x RL step-time regression (#54901).--engram-config (#54371), and torch.compile removed from the NVIDIA implementation so FP8 fits on a single GB300 (#55272).quantization_config.targets (#51285) and on partially pre-quantized checkpoints from any quant method (#51392), W4A16 DSA with the nvfp4_fp8_ds_mla KV cache (#51724), FlashInfer CuTeDSL NVFP4 W4A16 default over Marlin on SM100/103 (#53014), NVFP4 in the torch linear backend (#53319), per-quantization linear backend overrides (#51204), AutoRound 2/3/5/6/7-bit on CUDA (#52890), and DeepSelect top-k for the DSA sparse indexer (#56464).vllm serve via --enable-scale-out, replacing VLLM_ENABLE_SCALE_OUT_ENDPOINTS (#54579, #55176); GPTQ activation ordering (g_idx) removed (#54809); items deprecated for 0.29 removed, including the VLLM_PREFIX_CACHE_RETENTION_INTERVAL and VLLM_MM_HASHER_ALGORITHM env vars (#55353); the all Mamba cache mode deprecated (#55041); python -m vllm.entrypoints.grpc_server deprecated in favor of vllm serve --grpc (#56746); YaRN aligned with Transformers so vendor YaRN aliases no longer re-scale max_model_len (#56446).| Platform | Install |
|---|---|
| PyPI (CUDA 13.0) | pip install vllm |
| PyPI (CUDA 13.0, uv) | uv pip install vllm --torch-backend=auto |
| ROCm | pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/0.30.0/rocm723 |
| XPU | uv pip install vllm --extra-index-url https://wheels.vllm.ai/0.30.0/xpu --extra-index-url https://download.pytorch.org/whl/xpu --index-strategy unsafe-best-match |
| Platform | Docker Image |
|---|---|
| CUDA 13.0 (Default) | docker pull vllm/vllm-openai:v0.30.0 |
| CUDA 12.9 | docker pull vllm/vllm-openai:v0.30.0-cu129 |
| CUDA 13.0 + Ubuntu 24.04 | docker pull vllm/vllm-openai:v0.30.0-ubuntu2404 |
| CUDA 12.9 + Ubuntu 24.04 | docker pull vllm/vllm-openai:v0.30.0-cu129-ubuntu2404 |
| ROCm | docker pull vllm/vllm-openai-rocm:v0.30.0 |
| CPU | docker pull vllm/vllm-openai-cpu:v0.30.0 |
| XPU | docker pull vllm/vllm-openai-xpu:v0.30.0 |
Pre-built release artifacts are available in the Assets section at the bottom of this page, including:
Continued at the source.
Changes since langchain-fireworks==1.6.1
fix(fireworks): use current completions model in LLM tests (#40740)
hotfix(fireworks): use available model in LLM tests (#40737)
release(fireworks): 1.6.2 (#40735)
chore(model-profiles): refresh model profile data (#40665)
chore(deps): bump anyio from 4.11.0 to 4.14.2 in /libs/partners/fireworks (#40639)
chore(deps): bump urllib3 from 2.7.0 to 2.8.0 in /libs/partners/fireworks (#40587)
chore(deps): bump langsmith from 0.12.1 to 0.12.6 in /libs/partners/fireworks (#40586)
chore(deps): bump pygments from 2.20.0 to 2.21.0 in /libs/partners/fireworks (#40585)
chore(deps): bump idna from 3.19 to 3.20 in /libs/partners/fireworks (#40584)
chore(model-profiles): refresh model profile data (#40541)
chore(model-profiles): refresh model profile data (#40500)
chore(model-profiles): refresh model profile data (#40452)
chore(model-profiles): refresh model profile data (#40416)
chore(model-profiles): refresh model profile data (#40399)
chore(model-profiles): refresh model profile data (#40277)
chore(model-profiles): refresh model profile data (#40217)
chore(model-profiles): refresh model profile data (#40198)
chore(model-profiles): refresh model profile data (#40171)
chore(model-profiles): refresh model profile data (#40009)
chore(deps): bump orjson from 3.11.6 to 3.12.0 in /libs/partners/fireworks (#40129)
chore(deps): bump langsmith from 0.10.16 to 0.12.1 in /libs/partners/fireworks (#40130)
Changes since langchain-deepseek==1.1.0
fix(deepseek,infra): resolve compatible minimum OpenAI dependencies, bump min ver (#40738)
release(deepseek): 1.1.1 (#40734)
chore(deps): bump anyio from 4.11.0 to 4.14.2 in /libs/partners/deepseek (#40641)
chore(model-profiles): refresh model profile data (#40399)
fix(deepseek): route strict mode to the beta endpoint (#40249)
chore(model-profiles): refresh model profile data (#39906)
chore(model-profiles): refresh model profile data (#39844)
fix(deepseek): map prompt_cache_hit_tokens to cache_read (#39668)
chore(model-profiles): refresh model profile data (#39625)
chore(model-profiles): refresh model profile data (#39166)
chore(deps): refresh lockfiles (#38746)
chore: bump vcrpy from 8.1.1 to 8.2.1 in /libs/partners/deepseek (#38318)
chore: bump langsmith from 0.8.3 to 0.8.18 in /libs/partners/deepseek (#38320)
docs: refresh README installation and resources (#38119)
release(core): 1.4.7 (#38111)
fix(core,partners): rename package version trace metadata (#38110)
release(core): 1.4.6 (#38061)
feat(core,partners): add package version tracking to tracing metadata (#35295)
chore(infra): bump mypy to 2.1 and unify type-check config across the monorepo (#36470)
feat(standard-tests): validate tool call chunks during streaming (#34707)
chore(partners): bump locks (#38052)
hotfix(openai): min core dep (#37990)
test(langchain,partners): disable pytest-benchmark under xdist to silence PytestBenchmarkWarning (#37901)
Changes since langchain-openrouter==0.2.8
release(openrouter): 0.2.9 (#40736)
chore(model-profiles): refresh model profile data (#40705)
chore(model-profiles): refresh model profile data (#40685)
chore(model-profiles): refresh model profile data (#40665)
chore(deps): bump anyio from 4.13.0 to 4.14.2 in /libs/partners/openrouter (#40627)
chore(model-profiles): refresh model profile data (#40600)
chore(model-profiles): refresh model profile data (#40541)
chore(model-profiles): refresh model profile data (#40500)
chore(model-profiles): refresh model profile data (#40452)
chore(model-profiles): refresh model profile data (#40436)
chore(model-profiles): refresh model profile data (#40416)
chore(model-profiles): refresh model profile data (#40399)
chore(model-profiles): refresh model profile data (#40358)
chore(model-profiles): refresh model profile data (#40317)
chore(model-profiles): refresh model profile data (#40277)
chore(model-profiles): refresh model profile data (#40258)
chore(model-profiles): refresh model profile data (#40217)
chore(model-profiles): refresh model profile data (#40198)
chore(model-profiles): refresh model profile data (#40171)
chore(model-profiles): refresh model profile data (#40009)
chore(model-profiles): refresh model profile data (#39954)
chore(model-profiles): refresh model profile data (#39928)
chore(model-profiles): refresh model profile data (#39906)
chore(model-profiles): refresh model profile data (#39875)
chore(model-profiles): refresh model profile data (#39844)
chore(model-profiles): refresh model profile data (#39824)
chore(model-profiles): refresh model profile data (#39789)
chore(model-profiles): refresh model profile data (#39751)
chore(model-profiles): refresh model profile data (#39710)
chore(model-profiles): refresh model profile data (#39692)
chore(model-profiles): refresh model profile data (#39670)
Changes since langchain-openai==1.6.2
release(openai): 1.6.3 (#40719)
fix(openai): expose inferred Responses API routing at initialization (#40715)
chore(deps): bump anyio from 4.11.0 to 4.14.2 in /libs/partners/openai (#40629)
fix(openai): support GPT-6 request constraints (#40443)
Changes since langchain-core==1.6.3
release(core): 1.6.4 (#40718)
chore(core): deprecate chat message history (#40711)
chore(deps): bump anyio from 4.12.0 to 4.14.2 in /libs/core (#40634)
chore(deps): bump soupsieve from 2.8.4 to 2.9 in /libs/core (#40574)
Initial release
release(typesafe): 0.0.1a3 (#40693)
feat(typesafe): make classifier questions invocation-scoped (#40659)
release(typesafe): bump to 0.0.1a2 (#40576)
feat(typesafe): experimental AutoModeMiddleware (#40545)
feat(typesafe): experimental ModelRouterMiddleware (#40543)
fix(typesafe): trace usage metadata (#40570)
feat(typesafe): TypeSafeClassifier (#40542)
CLAUDE_CODE_AUTO_MODE_SERVER=0 opts out on Bedrock, Vertex, Foundry and gateways); warns on billed fallback. See https://code.claude.com/docs/en/auto-mode-classifier-billingAuto mode server row to /status showing whether this session's auto mode classifier runs on the server/config (not yet on Bedrock, Vertex or Foundry)CLAUDE_GATEWAY_PROXY_IS_EGRESS_BOUNDARY=1 for Claude apps gateways whose only egress is a forward proxy: every outbound request hands the proxy the hostname instead of resolving it locallyheaders: map on Claude apps gateway upstreams, to send static headers to a proxy you run in front of a provider/tasks is openclaude -p and Agent SDK sessions that could hang with no result after an internal error; they now report the error and exit with code 1--resumeANTHROPIC_API_KEY users when ~/.claude.json holds a malformed customApiKeyResponses valueclaude update hanging when a minimum or maximum version is set, if a proxy returns an invalid version; a malformed minimumVersion is now ignoredclaude update on winget- or apk-managed installs reporting "up to date" when the version lookup failedclaude plugin install sometimes failing and breaking the installed copy when reinstalling a plugin version that a session or another program was using; an unchanged copy is now left aloneuXXXX text as a \uXXXX escape, which could make an edit of a non-ASCII character rewrite an escaped backslash sequence instead\u0000 written as an escape sequence; escaped control characters now stay as literal textclaude --bg) exiting when a plugin's LSP server exited or closed its stdin/mcp or /plugin manage with a malformed claudeAiMcpEverConnected value in ~/.claude.json~/.claude.json holds a malformed theme value/clear (restart, --continue, --resume) missing part of their first message when a SessionStart hook printed output, causing a full prompt-cache miss/resume picker and other panels that cover the prompt area$TMPDIR expanding empty in Bash commands that run outside the sandbox while sandboxing is enabledNO_PROXY when a proxy is setstrictKnownMarketplaces or blockedMarketplaces entry silently disabling the whole enterprise marketplace policy~/.cache/claude/staging/plugin not stripping terminal control characters from messages on the Installed tab, such as the error of a failed plugin update/plugin → Installed and /skills crashing when a skill or legacy command is named like a built-in Object property such as constructor or toString/plugin closing with no message when every install in a multi-select failed/plugin Installed, and Remove not clearing such a rowinstalled_plugins.json, and installed_plugins.json keeping the old commit after updating a pinned-commit plugin--plugin-url archive a reload falls back to when its download fails~/.claude.json holds a malformed placeholder record/loginclaude agents dispatch input during key repeat or very fast inputkeybindings.json rebinds Enter in the Chat context, for example to chat:queueSubmitclaude -p --resume, the SDK, a VS Code extension window reload) starting the session's cost and usage totals at zero; headless sessions now save their totals at exit--worktree sessions when .claude/skills is untrackedsandbox.excludedCommands glob exempting an entire compound Bash command from the sandbox when only one part matched; every part must now match-p) use: the first turn no longer waits on the per-directory CLAUDE.md lookupCLAUDE_GATEWAY_ALLOW_LOOPBACK/plugin Installed: an MCP server listed apart from its plugin now shows which plugin it belongs toclaude plugin install on an already-installed plugin: it now says when the marketplace offers a newer version and names the claude plugin update command/ultrareview when there's nothing to review: messages say which case you're in, offer a command that reviews your latest commit, and a new repository's first commit is reviewed in full${VAR:?} guard, so headless runs can recover/model on the Anthropic API; it is greyed out only when your organization's settings disable it/ultrareview in non-interactive sessions to refuse when the repository has no base branch or shared historyagent() prompts on Bedrock, Vertex and Foundry to reach the subagent framed as script-authored text, so the safety classifier does not read them as the userclaude -p runs launched outside an SDK or IDEtaskOutputMaxChars setting and TASK_MAX_OUTPUT_LENGTH no longer have any effect/logout in the typed command menu/tasks that opens it/copy/config usage text instead of opening settings, and made typed /mcp, /hooks, /memory, /rewind and similar commands open their dialogs/effort/fast not saving fast mode as the default, so it was lost when the extension relaunched Claude CodeChanges since langchain==1.4.1
release(langchain): 1.4.2 (#40621)
fix(langchain): preserve model-generated tool calls in HITL tool call edits and add notice to ToolMessage (#40463)
400 … Input tag 'advisor_20260301' when ANTHROPIC_BASE_URL points at a proxy or gateway (2.1.275 regression)ollama, with options to sign in or continue locally. Setup completion is shared with the desktop app on macOS and Windows.ollama://apps to open the desktop app’s Apps page directly on macOS and Windows.Full Changelog: v0.34.1...v0.34.2
/status shows itotelHeadersHelper fails, so sessions that silently export no telemetry are noticedsyncClaudeAiSkills: false or syncClaudeAiPlugins: false/plugin install <plugin> --marketplace <source>, which offers to add the marketplace before installing the plugin--forward-subagent-text stream-json and SDK output dropping the messages of subagents spawned by a context: fork skill, and of forked skills invoked by a subagent or another forked skillfileSuggestion command or typing @./@./claude plugin marketplace update deleting a GitHub marketplace's local copy when the fetch failed and the marketplace was named after its repositoryclaude plugin marketplace list showing a password or token stored in a git, ssh or marketplace URL</ccmemory>-style closing tag occasionally appearing in responsesAPI Error: 400 on every turn for users behind a network gateway that rewrites API error responses when a beta request header is rejected--resume, the resume picker preview, resumed background agents and the transcript view failing on a session whose saved history contains a malformed task-reminder or @-file attachment entry/rewind in a forked or background session restoring a zero-filled or truncated file when the session's file-history backups could not be fully copied~/.claude.json holds a malformed mcpNeedsAuthNoticed value--resume and --continue dropping a conversation's earlier thinking when a built-in tool it started with has since been switched off by a server-side flagclaude --resume session picker never reaching the clipboard--plugin-dir or --plugin-url archive--drain-wait-sec losing the final result of a turn that finished during a SIGTERM drain; the runner now waits briefly for the turn to be reportedSubagentStop hooks with a specific matcher firing for every stopping subagent whose agent type was emptyhooks/ or config//update-config writing Write(path) permission rules, which file permission checks don't match, instead of Edit(path) rules--system-prompt that contains a __SYSTEM_PROMPT_DYNAMIC_BOUNDARY__ line: the text above it is now cached globally, as the SDK's array form already is/desktop error when Claude Desktop does not open: it now says why and what to do nextListPlugins tool description so Claude knows it lists plugins enabled on your claude.ai account, not plugins installed locally with /plugin/logout for Claude apps gateway sign-ins to also end the session on gateways that advertise token revocationbrowser_batch "Permission denied" after a redirectnpm pack --ignore-scripts and integrity-verified, so a package's install scripts no longer runCLAUDE_CONFIG_DIR entry in the environmentVariables setting making Claude Code keep its files in the workspace/remote-control being ignored while Remote Control is still connecting: running it again now turns Remote Control off immediatelyclaudeCode.scrollToBottomOnSend setting to turn off the jump on sendInitial release
release(typesafe): bump to 0.0.1a2 (#40576)
feat(typesafe): experimental AutoModeMiddleware (#40545)
feat(typesafe): experimental ModelRouterMiddleware (#40543)
fix(typesafe): trace usage metadata (#40570)
feat(typesafe): TypeSafeClassifier (#40542)
Signed-off-by: jiahanc 173873397+jiahanc@users.noreply.github.com
Co-authored-by: OpenAI Codex codex@openai.com
This PR bumps the version of the HTML extension to v0.3.2.
Release Notes:
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
Co-authored-by: Finn Evers finn@zed.dev
This PR bumps the version of the GLSL extension to v0.2.5.
Release Notes:
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
Co-authored-by: Finn Evers finn@zed.dev
This PR bumps the version of the Proto extension to v0.3.4.
Release Notes:
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
Co-authored-by: Finn Evers finn@zed.dev
CLAUDE_CODE_MCP_STARTUP_WAIT_MS to bound how long the first non-interactive turn waits for connecting MCP servers (0 = don't wait)effort attribute to the claude_code.llm_request OpenTelemetry trace span, matching the api_request eventclaude_code.managed_settings_resolved OTel event: managed-settings sources and policy helper state; redacted settings and digests with OTEL_LOG_MANAGED_SETTINGS=1store.connect_timeout_seconds to the Claude apps gateway config to lengthen the Postgres connect timeout (default 5 seconds), and improved the boot error when the database is unreachable to point to store.postgres_url and the configured timeoutenduser.sub, the IdP subject, to the telemetry Claude Desktop and Cowork send through a Claude apps gateway/rewind hint) ends the loophttp that only speak legacy HTTP+SSE failing to connect when they answer the first request with 422 or another 4xx errortimeout was setlistChanged/mcp re-authentication/goal) ending with "Prompt is too long" instead of compacting when the context overflowed again after a reactive compaction/goal being lost when resuming (--continue / --resume) a session that had compactedclaude agents losing --model, --effort, --permission-mode, --allow-dangerously-skip-permissions and --agent after an auto-update relaunchmodel: "opus" on Bedrock, Vertex or Foundry leaving the session's model when its id has no recognizable model family (unless ANTHROPIC_DEFAULT_OPUS_MODEL is set)file:// URIclaude -p --resume started with CLAUDE_CODE_RESUME_INTERRUPTED_TURN not reporting background tasks the previous process left unfinished/usage-credits instead of CLI flags that cannot be used there/schedule saving a routine's prompt without its message role when Claude writes the routine in the shape that listing routines returns/status not showing the apiKeyHelper failure that its own error banner told you to check/fast on in non-interactive sessions reporting on and then turning off under an organization's managed fast mode policy; it now says the organization has disabled it~/.claude--strict-mcp-config with an empty --mcp-config holding the first non-interactive turn for up to MCP_TIMEOUT on incidental MCP serversCLAUDE_GATEWAY_DRAIN_TIMEOUT_MS)installed_plugins.json being rewritten on nearly every start-up when plugin policy comes from remote managed settings, which made Claude Desktop reload every open session's pluginsbin/ directories changed$schema in hooks/hooks.json showing an "unknown key" notice${VAR} placeholders in MCP configs.zip being served from a stale extraction after several overlapping reloads--input-format stream-json sessions: the first turn no longer waits up to 2s for still-connecting MCP servers whose tools tool search defers; they arrive on a later turnOTEL_LOG_RAW_API_BODIES=file:<dir> output: a new index.jsonl and request_body_id / message.id event attributes link each response to its request file and transcript message/login now explains the refusal, and the gateway log says which limit was hit and which setting to changeMCP_SDK_GENERATION=v1 or MCP_PROTOCOL_NEGOTIATION=legacy)/code-review to use leaner inline review prompts for every model that has no tuned settings of its own, instead of spawning many review subagents"type": "sdk" MCP entries in .mcp.json, settings, plugins and agent files to be skipped with a warning: only an SDK host application can register in-process serversgit lfs pull in the checkout fetches them/status GitHub line to read "Cloud sessions", and /web-setup, /ultrareview, and teleport messages to say "cloud session" instead of "Claude Code on the web"claudeCode.lockEditorGroups setting to stop Claude from locking the editor groups it opens in/btw side question asked in a new conversation's first seconds occasionally showing another session's side-question historyCLAUDE_CONFIG_DIR changed in the Environment Variables setting~/.claude/settings.json unparseable or dropping a setting$XDG_CONFIG_HOME/git/ignore when XDG_CONFIG_HOME is an absolute pathmailto: prefix; they now show as the plain, clickable addressThis week's release includes an optional cursor movement animation, a setting to open Markdown files directly in the rendered preview, Emmet wrap with abbreviation support, and configurable window title formatting.
on_new_window setting to choose whether new windows show the Launchpad (launchpad, the default) or an empty untitled buffer (empty_tab). (#63522; thanks albertbogusz)ctrl-tab jumping to random documents when the mouse moved during a quick tab switch. (#52671; thanks OmChillure)editor: rotate selections forward and editor: rotate selections backward when using cursors on nonconsecutive lines. (#63937; thanks timvermeulen)Learn about the Zed Guild.
Date column. (#62679; thanks dem1tris)editor: wrap with abbreviation). (#63383)constexpr keyword. (#63833; thanks Jesse-Cooper)window_title_format and window_title_separator settings. (#54379; thanks jknlsn)
${projectName}, ${fileName}, ${filePath}, ${relativePath}, ${branch}, ${remoteName}, ${remoteHost}, ${appName}, and ${separator}.window.title and window.titleSeparator are set.markdown_preview.open_markdown_files_in_preview setting to open Markdown files directly in the rendered preview. (#63462; thanks joshkent94)cursor_animation.enabled to true. (#63195; thanks tiny-paris)project_panel.title_tooltip_delay setting.dev: Debug Filesystem Watching to inspect local watcher events, watch roots, and scan exclusions, and export diagnostic captures as JSON. (#64186)command_palette.use_command_history setting to disable history-based command palette ranking without erasing history. (#64181)shift-backspace or the remove button. (#64181)EMFILE (Too many open files) errors in large workspaces. (#64188)settings.json. (#62949; thanks interkelstar)cmd-alt-f (macOS) and ctrl-alt-f (Linux/Windows), being shadowed by the file finder. (#63376; thanks dcdeniz)agent: manage skills appearing in the command palette when AI was disabled. (#63598; thanks kai-xlr)git_gutter_width was set to a small custom pixel value. (#63434; thanks somtri)ask_user options being truncated instead of wrapping in the Agent Panel. (#63656; thanks cmdr-chara)preview_tabs.enable_preview_from_project_panel setting being ignored when opening files from the Project Panel with the keyboard. (#63758; thanks cmdr-chara)o not continuing the comment prefix inside C-style multiline comments. (#63751; thanks IbrahimKhan12)mailto: URI handling when using Help → Email Us.... (#64090)cmd-c (macOS) and ctrl-c (Linux/Windows) instead of Markdown. Copying as Markdown moved to the context menu, and the markdown::CopyAsMarkdown action can still be bound. (#63884)markdown_preview key as font_size, font_family and code_font_family. Existing settings are migrated automatically. (#63462; thanks joshkent94)Every public example of Jev — TypeSafe AI's System One decision model — indexed by the decision it makes, not by the blog that mentioned it.
Searchable site · 中文 · Patterns · Compatibility · Vetting
Filter by clicking a bar. Two more views: primitives · compatibility. Every filter and entry is a shareable URL.
⚠️ Not the product, not an SDK, not affiliated with TypeSafe AI, and not a recommendation. A row means the link resolved and a person read it — nothing more. See what is verified.
Three primitives. Every pattern below is built out of them, and the asymmetry in the last row is the single most common source of bugs.
Input is text only — string, JSON object, or array of text. Context is 64k tokens per request, 32k for the state plus the longest question. Output tokens are free. There are no published weights, so it cannot be run locally. Full cross-platform differences: docs/compatibility.md.
Six things in reading order. Hand-picked, because "most starred" is not the same as "read this first".
Quickstart The canonical first call: one support ticket, one Choice, one Score and one Noul in a single request, in Python, JS and cURL.
Jev 1.13 known limitations The most useful page in the docs and the least linked. It explains, among other things, that a Choice over options and one Noul per option answer different questions.
Example: three primitives in one request Written from the official API reference and checked field by field against it, but not executed against the live API.
fast-jev-compaction Exactly two nouls per tool call: does knowing this call happened still matter, and is the full output still needed verbatim. Despite the word "scored" in its own description, no score primitive is used.
ai-cookbook: Jev track The best structured tutorial found. It states plainly that typed output does not guarantee a correct decision, lists the documented weaknesses, and qualifies its own cost illustration rather than selling it.
Hermes Agent: Jev compaction evaluation The single most credible row in this catalog. Recall came out below their existing summariser, and at a matched context budget it tied plain recency ordering. Cost was genuinely far lower. Publishing a negative result on a hyped model is rare.
Every decision pattern, sized by how many examples exist. This doubles as the index — the names link to the sections below. A zero is a research gap, not a rendering bug.
Two patterns have no examples yet. Both are plausible fits nobody appears to have published — see docs/status.md.
Almost every performance number circulating about this model is the vendor's own, produced with reference answers derived from other models' judgements rather than human ground truth. These are the independent measurements in the catalog — several are negative results, which is exactly why they are worth reading first.
Hermes Agent: Jev compaction evaluation — Ported the Jev compaction approach, measured it against their shipping summariser, and published the conclusion not to adopt it.
Benchmark · ★248,249 · Py · noul
The single most credible row in this catalog. Recall came out below their existing summariser, and at a matched context budget it tied plain recency ordering. Cost was genuinely far lower. Publishing a negative result on a hyped model is rare.
worldmonitor: news threat classification — Two Choice questions over threat level and category, held in shadow mode after a blind evaluation found Jev merely tied the incumbent model.
Benchmark · ★87,261 · TS · choice · ⚠ shadow mode
Wired in but deliberately inert: by their own statement nothing Jev returns reaches a label, a cache row or an alert. Ships a golden fixture. A model to copy for how to trial a new model without betting production on it.
no-mistakes: review context selection — One Score per candidate file to pick review context, with a measured outcome: materially more billed input for essentially no wall-clock gain.
Benchmark · ★8,611 · Go · score
Their own recommendation was to keep the feature opt-in, off by default, and ship no savings claim. That is what an honest measurement looks like.
hippo-memory — Biologically-inspired memory for AI agents. Decay, retrieval strengthening, consolidation. Zero runtime deps, SQLite, MCP. Benchmarked retrieval with an opt-in TypeSafe Jev reranker.
Benchmark · ★756 · kitfunso · TS
Probing Jev's behaviour with repeated API calls — Independent Korean-language notes reporting that reversing the order of options shifted a probability enough to flip a 0.9 threshold.
Benchmark · ★190 · Py · ⚠ no licence unverified claims
The most actionable engineering caveat found anywhere: if option order alone can move a probability past your threshold, your threshold is not as stable as it looks. Independent and unreplicated, so treat the magnitude as indicative.
jevbench — JevBench v1 - a benchmark for Jev-class typed decision models: smart, cheap, fast, reliable, open.
Benchmark · ★94 · fstandhartinger · Py
windtunnel — A WebMCP benchmark, measures WebMCP against other browser-agent interfaces.
Benchmark · ★79 · nekuda-ai · TS
typesafe-ai-benchmark — A gateway that mimics the structured-output shape, used to benchmark against it.
Benchmark · ★38 · iammrduncan · TS
smartmoney-cub — Read-only trading journal and review harness: Jev typed judgments, agent integration, and a reproducible finance benchmark. No orders, no advice.
Benchmark · ★26 · myc0576 · Py
jev-capability-atlas — Independent, evidence-based map of when TypeSafe's Jev actually holds up vs. breaks down — real API-call receipts, not a leaderboard. 中文為主的雙語 repo。
Benchmark · ★25 · zaious · Py
jev-benchmarks — Probability-aware evaluation for typed decision models: calibration, selective risk, latency, and reproducible benchmarks.
Benchmark · ★17 · abdelstark · Py
jev-rag-benchmark — Reproducible benchmark for measuring Jev reranking quality, latency, and cost in RAG
Benchmark · ★14 · erendikmenn · Py
jev-rerank-bench — An independent head-to-head against dedicated rerankers across fourteen datasets.
Benchmark · ★7 · anessbelbati · Py
An independent measurement rather than a vendor figure, and a direct comparison against purpose-built rerankers — the comparison that matters for the search-ranking pattern.
jev-benchmark — Benchmarks and a playground for TypeSafe's Jev (System One) model: chess, and who-is-the-player-talking-to for speech-to-text game NPCs
Benchmark · ★6 · wondertwins · Py
jev-korean-benchmark — Reproducible early-access evaluation of Jev on Korean understanding and medical text, with runtime and cost evidence
Benchmark · ★6 · mahlernim · Py · ⚠ no licence
jev-ood-calibration — Independent calibration test of TypeSafe's Jev on a task it cannot have seen: 900 rule-generated support tickets (choice / score / boolean) plus 3 public benchmarks via Vercel AI Gateway. Raw responses, ECE with noise floor, temperature refit, per-type sign of miscalibration. Reproducible for ~
Benchmark · ★6 · scienthoon · Py
jev-little-airways — A show-and-tell capability study for Jev, TypeSafe's System One decision model.
Benchmark · ★5 · lbotinelly · TS
jev-phishing-bench — Jev (TypeSafe) vs Claude Haiku 4.5 on 2 000 phishing emails: accuracy, calibration, latency, cost. Reproducible benchmark.
Benchmark · ★5 · anisselbd · Py · ⚠ no licence
legalforecastbench — LegalForecast-MTD benchmark alpha and official evaluation workflows
Benchmark · ★5 · johnhughes3 · Py
sysone-bench — First independent head-to-head benchmark of System One decision models (Laya vs Jev) on byte-identical inputs
Benchmark · ★4 · instax-dutta · Py
ego-jev-ultrafast — Jev drives your Ego Lite browser: one typed-choice request per step. Single-file, zero-dependency port of browser-use/jev-ultrafast with multi-model benchmarks and extra guardrails. Unofficial.
Benchmark · ★3 · shikaizhong-design · JS
jev-dspy-lab — Reproducible calibration and selective-risk benchmarks for Jev/TypeSafe decisions in DSPy workflows
Benchmark · ★3 · jmanhype · Py
jev-exploration — Jev (TypeSafe) exploratory thread: claim audit, live demos, and runnable code
Benchmark · ★3 · samuelsacco · Py · ⚠ no licence
origin-civilization — AI life-and-civilization simulation: TypeSafe Jev makes every decision (typed, probabilistic, auditable); LLMs plan — OpenAI-compatible APIs, local models (Ollama, LM Studio), Claude Code, Codex.
Benchmark · ★3 · jacquesgariepy · TS
jev-agent-failure-benchmark — Benchmarking Jev (Typesafe.ai) against a strong LLM on the Who&When Pro agent-failure-attribution benchmark (text subset).
Benchmark · ★2 · tokentrim · Py
jev-play-ping-pong — Jev plays browser table tennis in real time: structured telemetry, typed decisions, ordinary Chrome inputs, and auditable evidence.
Benchmark · ★2 · icohen007 · JS
jev-routing-experiment — Benchmarking TypeSafe's Jev decision model as a cost-efficient LLM router on RouterArena
Benchmark · ★2 · tokentrim · Py
zerosweep — Autonomous System-One Triage Engine & Benchmark powered by TypeSafe AI (Jev). 75ms inference, $0 output tokens, and RLCD epistemic safety gates.
Benchmark · ★2 · sysadarsh · TS · ⚠ no licence
antigravity-mcp-semantic-search-with-typesafeai — Fast semantic code search & diff sanity auditor for AI coding assistants (Antigravity, Cursor, Claude Code) powered by TypeSafe System One.
Benchmark · ★1 · greenyamao · Py · ⚠ no licence
dsh-jev-verify — Jev (TypeSafe System One) decision tools + live verification benchmark for DeepSeek Harness: jev_decision (choice/score/noul) and jev_verify, honest by design.
Benchmark · ★1 · xienda · JS
jev-eval — Benchmark TypeSafe Jev against any OpenRouter model on your own labelled classification data: accuracy, calibration, latency, cost
Benchmark · ★1 · 4esv · Py · ⚠ no licence
jev-secret-detection — Measures how well TypeSafe's RLCD-Jev model spots real secret credentials in file snippets
Benchmark · ★1 · teyhouse · Py · ⚠ no licence
jev-sim — Jev-compatible /v1/systemone server reading typed decisions from LLM logits, benchmarked against TypeSafe's Jev on the same items via JevBench
Benchmark · ★1 · dashbi1 · Py
jevsbistro — 3D restaurant service simulator for benchmarking low-latency decision models
Benchmark · ★1 · andrewsilber · TS
padflow-jev-evals — Typed-decision benchmark from PadFlow (land development SaaS): schemas, anonymized labeled rows, and a runner for confidence-calibrated models like TypeSafe Jev.
Benchmark · ★1 · zsavage8 · Py
agent-handoff-gate — An experimental protocol for evidence-aware agent handoffs, bounded worker continuation, and TypeSafe/Jev-assisted review, with reproducible evaluation.
Benchmark · ★0 · zsoxi · Py
jev-calibration-audit — Independent API-only calibration audit of TypeSafe AI's Jev decision model
Benchmark · ★0 · jujumilk3 · Py
jev-certify — Finite-sample guarantees for Jev (TypeSafe's System One). Conformal risk control turns calibrated probabilities into certified routing thresholds; prediction-powered inference audits them. 2,412 decisions on CLINC150 for $0.23 — including the shift and prevalence cases where the guarantee break
Benchmark · ★0 · nikkoxgonzales · Py
jev-enterprise-decision-fabric — Architecture for running many semantic decisions through one validated path, with a labelled 111-case benchmark comparing TypeSafe Jev against a Claude baseline, and a dashboard for inspecting any single decision. Experimental, not production.
Benchmark · ★0 · ghubnab99 · C#
jev-llm-router-benchmark — Benchmark-driven Jev router and judge for cost-aware, reliable LLM coding workflows
Benchmark · ★0 · erendikmenn · Py
jev-orderby-bench — Does ORDER BY over a Jev probability put rows in a defensible order? Independent ranking, calibration and invariant measurements of TypeSafe AI's Jev: passes six pre-registered gates on 360 labeled rows, fails four of six on graded product relevance.
Benchmark · ★0 · yodablocks · Py
jev-trace-classifier — Application of TypeSafe Jev (noul judgment primitive) on the collusion.wiki corpus: agent vs human page authorship, head-to-head vs local Qwen3.8-Flash-Next
Benchmark · ★0 · sypherin · Py
smoking-extraction-benchmark — Synthetic smoking-history extraction benchmark comparing TypeSafe Jev and OpenAI structured outputs, with reproducible accuracy, cost, and latency results.
Benchmark · ★0 · vclic · Py · ⚠ no licence
An early-access test of TypeSafe's Jev: calibrated judgments for half a cent — The best independent test found: 24 Norwegian documents on one pinned model version, opening with a case the model got wrong while correctly reporting low confidence.
Benchmark · Lindfors
Methodology is stated cleanly and scoped honestly as a single-day snapshot. Leading with a failure case is what makes it a real calibration test rather than a testimonial.
Testing TypeSafe Jev, Mistral and Gemini for local event validation — The only three-way head-to-head found, with each model's prompt tuned separately and the scope limited to one task rather than a general ranking.
Benchmark · Near Here
Self-limits correctly: a use-case study, not a model leaderboard. That restraint is rarer than the numbers.
The primary index. Each heading is a decision an agent has to make; the rows are examples of making it. Caveats appear as short tags — the full note for each row is in catalog.json and on the site.
Which tool or action the agent should call next.
Continued at the source.
Different models, the same brief - a collection of demos built from shared prompts, with source, screenshots, and notes.
projects/
<project>/
PROMPT.md # Shared prompt
README.md # Results and links
models/
<model>/
README.md # Model, harness, assistance, and run instructions
app/ # Implementation
screenshots/ # Optional captures
templates/
project/
model/
Copy the project template into projects/<project>/ and write a concise shared prompt. Each model follows the repository rules, keeps its implementation in models/<model>/app/, and uses the model record template for its README. Link the results from the project README.
Issues and PRs are welcome, including prompt ideas and model implementations. Read the contribution guidelines for reproducibility and comparison requirements.
微信（Windows 4.x）旁挂的回复辅助：本地 OCR 读屏上的对话 → Jev 判断意图/情绪 → 给出 3 条候选回复 → 一键填入微信输入框。发送永远手动，程序不替你按发送。
判断内核来自安卓版 Finderchangchang/jev-chat-JARVIS， 这里把采集换成了 Windows 端的窗口截图 + 离线 OCR。
普通使用直接下载，不用装 Python、不用碰源码。 后面的「源码运行」是给开发者的。
👉 下载最新版
jev-chat-windows-vX.Y.Z.zip（约 146 MB）jev-chat-windows.exe要求：Windows 10 1903+ / 11，微信 Windows 4.x，两个 API key（判断一个、起草一个，见下）。
首次启动会弹设置页填这两个 key。key 写进 Windows 用户环境变量（注册表 HKCU\Environment）——
全程只有 JEV_API_KEY 和 LLM_API_KEY 这两个，不落任何文件；其余设置写在 exe 旁边的
config.json，整个文件夹拷走设置也跟着走。
exe 没签名，SmartScreen 会拦一下：「更多信息」→「仍要运行」。介意就往下看「自己打包」，自己打的更踏实。
第一次启动
设置页的「模型」卡片分两节，各填一把 key：
两把 key 各管一节，互不相干；同一节里换来源要重填一次 key（只存这一把）。
为什么起草默认 DeepSeek 官网直连
起草是两次网络调用里重的那次。OpenRouter 在国外，从国内过去要等好几秒、还时不时抽风；
DeepSeek 官方 API（api.deepseek.com）国内直连，起草基本就是一次 HTTP 请求的时间，体感差好几倍。
所以起草默认就是它，不用改。
判断那一步比起草轻得多，慢一点无所谓，默认走 OpenRouter 即可；嫌慢就把它也换成 TypeSafe 直连。 两把 key 都只进注册表，不落文件。
日常怎么用
花多少钱
只有对方来新消息才调一次模型：一次起草（DeepSeek Flash）+ 一次 Jev 判断，十分钟没人说话就是十分钟零调用。 思考模式默认关，别开——起草三句话用不上，慢好几倍还贵。
| 回复建议：「当前会话」跟随微信、群聊多一行「回复对象」，3 条候选带 Jev 概率百分比，推荐那条置顶 | 设置：关系背景、说话风格、参考上下文条数、群聊指定回复对象（往下还有「模型」卡片） | 采集暂停：不再读微信，已有候选照样能填入、能复制 |
这是个人自用工具，下面几条是硬约束，代码里就是这么写的：
app/、core/）
里没有 .save()。probe/、tools/ 下的开发脚本（人工排查、预览界面用的）会把图存成文件，但这些
脚本不在发布包里，普通用户拿到的 exe 不含它们。JEV_API_KEY（判断）和 LLM_API_KEY（起草），
不管来源选哪家都是这两个槽。都写进注册表 HKCU\Environment（跟 setx 同一个地方），任何文件里
都不出现 key，也绝不进日志（报错文本一律脱敏）。老版本按来源分开存的 OPENROUTER_API_KEY /
DEEPSEEK_API_KEY 仍然能读到，保存一次就迁到新名字上。什么会出网：判断（JEV_API_KEY）去你选的 OpenRouter 或 TypeSafe 直连；起草（LLM_API_KEY）发给你
在设置里选的那家接口（DeepSeek 官网、OpenRouter、OpenAI、Moonshot、智谱、通义、硅基流动、
Anthropic、Gemini，或者自填的 OpenAI 兼容 / Anthropic 兼容地址），加上启动时（可关）一次到
GitHub 查版本号。本项目没有任何自建服务器，聊天内容只在触发分析的那一刻，发给你自己在设置里
配置的那个接口，本项目不收集、不落盘、不进日志。发出去的内容固定是：最近 N 条对话文本（N =
设置里的「参考上下文」，默认 10；群聊带发言人名）、关系设置、你自己最近 12 条 60 字以内的短
消息（当口吻样本，链接和长段不送）、你填的说话风格，群聊指定了回复对象的话再加一个对象名。
除此之外没有别的。OCR 全程离线。GitHub 版本查询只带 UA 和当前版本号，不夹带任何聊天内容。
会不会因此被微信封号？ 本项目不 hook、不注入、不读微信的数据库或进程内存、不调用微信的任何 私有接口或账号体系——只截自己这一个窗口的画面做 OCR，跟读屏软件、录屏软件是同一类操作。
WGC 截微信窗口（GPU 合成窗口也能截，被遮挡也能截）
→ 像素锚点定位消息区（认底色和分隔线，不写死坐标，深浅主题通用）
→ OCR 面板头部的会话名当 key（头部像素没变就不重跑），记录、上下文、候选都按会话分开存
→ RapidOCR 只认消息区那一块
→ 按气泡颜色分 me / her，灰字（引用块、时间戳、群里的发言人名、链接卡片）过滤掉，
发言人名摘出来挂到它下面那条消息上
→ 跟上一帧比，滚动翻出来的旧消息不重复上报
→ 冒出新的 her 消息才调 core.engine.analyze()：三段式
① Jev 判断（7 道题）→ ② 把判断当小抄喂给起草，写 3 条候选 → ③ Jev 只排序
→ 悬浮窗给判断摘要 + 3 条候选 → 点「填入微信」
截图和 OCR 跑在独立子进程里（一帧 OCR 250~800ms，放 Qt 主线程界面会僵），父进程只管界面和网络调用。
判断 + 排序（key：JEV_API_KEY）
| 来源 | 地址 | 默认模型 |
|---|---|---|
| OpenRouter（默认） | openrouter.ai/api/alpha/decisions |
typesafe/jev-1.13 |
| TypeSafe 直连 | api.typesafe.ai（官方 typesafe-sdk） |
jev-latest |
起草 3 条候选（key：LLM_API_KEY）
| 来源 | 接口协议 | 地址 | 默认模型 |
|---|---|---|---|
| DeepSeek 官网（默认） | OpenAI | api.deepseek.com |
deepseek-flash |
| OpenRouter | OpenAI | openrouter.ai/api/v1 |
deepseek/deepseek-v4.1-flash |
| OpenAI | OpenAI | api.openai.com/v1 |
自己选 |
| Moonshot (Kimi) | OpenAI | api.moonshot.cn/v1 |
自己选 |
| 智谱 GLM | OpenAI | open.bigmodel.cn/api/paas/v4 |
自己选 |
| 通义千问 | OpenAI | dashscope.aliyuncs.com/compatible-mode/v1 |
自己选 |
| 硅基流动 | OpenAI | api.siliconflow.cn/v1 |
自己选 |
| Anthropic | Anthropic | api.anthropic.com |
自己选 |
| Google Gemini | Gemini | SDK 自带 | 自己选 |
| 自定义 · OpenAI 兼容 | OpenAI | 自己填 | 自己选 |
| 自定义 · Anthropic 兼容 | Anthropic | 自己填 | 自己选 |
没有默认模型的来源，在设置页点「获取模型」拉一次列表自己挑（也能直接手打模型 id）。
三种协议各走自家官方 SDK（openai / anthropic / google-genai），不自己拼 HTTP；
判断那条 OpenRouter 的路是唯一的例外——typesafe-sdk 把路径写死成 /v1/systemone，
打不到 OpenRouter 的 /api/alpha/decisions。
两节各一把 key，都必填。链路是三段式（issue #4）：先让 Jev 答 7 道判断题，把
「对方意图 / 对方需要 / 建议动作 / 紧张度」折成一小段中文小抄喂给起草，三条候选都顺着这个判断写；
最后再问 Jev 一次「哪条候选最合适」，概率就是卡片上的百分比。一次分析两次 Jev 调用——
以前是盲起草 + 判断和排序一次问完，起草读错意图时三条会一起跑偏，Jev 只能矮子里拔将军。
判断那次要是挂了（限流、超时），自动退回老路：盲起草 + 一次合问，行为跟以前一样；
排序那次挂了就按第一条推荐，判断照样显示。
温度 1.2，max_tokens 400；思考模式默认关，开了会带上各家自己的思考开关、max_tokens 提到 4000
（思考过程也算进去，400 会把答案截断）。思考开关只有 DeepSeek / OpenRouter / Anthropic / Gemini 认。模型只给出 1~2 条时会带着它的回答追问一次补齐，还不够就按实际
条数走（少于 2 条就不排序）。
微信 Windows 4.x（进程 Weixin.exe，窗口类 Qt51514QWindowIcon）界面自绘在一块 GPU 合成画布上
（MMUIRenderSubWindowHW）。UIA 树只有 2 个节点、没有控件树——probe/probe_win.py、
probe/probe_win2.py 实测证伪。
所以唯一干净的非侵入采集路 = 截自己的微信窗口 + 本地 OCR。离线、零 token。
？！～ 留着，那是语气）。下载 exe 的只看前三条；Python 只有源码运行 / 自己打包才需要。
Weixin.exe）JEV_API_KEY，默认来源 OpenRouter（或
TypeSafe 直连）；起草用 LLM_API_KEY，默认
DeepSeek 官网。详见下面「使用说明」Win10 上 WGC 会在微信窗口外画一圈黄框，系统不给关；Win11 才能关掉。 嫌碍眼就把标题栏的采集开关拨到「已暂停」，黄框立刻消失。
普通使用请直接用上面的下载即用。想改代码、调 prompt、自己打包才需要这一节。
git clone https://github.com/jev-chat/jev-chat-windows.git cd jev-chat-windows python -m venv .venv .venv\Scripts\activate pip install -r requirements.txt python main.py
PyCharm / VS Code 里直接 Run main.py 也行。
首次启动会自动弹出设置页：填两把 key（判断 JEV_API_KEY、起草 LLM_API_KEY，见上面「使用说明」），
选你们的关系（恋人 / 朋友 / 同事 / 家人 / 自定义）。key 写进注册表 HKCU\Environment，重启后依然有效，
不落任何文件；其余设置写进项目根的 config.json（已在 .gitignore 里）。
双击 build.bat（没有 .venv 会自己建一个，装依赖、调 PyInstaller，一路到底），或者手动：
pip install -r requirements.txt pyinstaller pyinstaller --noconfirm --clean jev.spec
出来的是 dist\jev-chat-windows\，整个文件夹就是成品（onedir：onefile 有 150MB 要每次启动解压）。
推一个 v* tag，.github/workflows/release.yml 会在 windows-latest 上打好、压成 zip 挂到 Release 上；
手动触发（workflow_dispatch）只出 artifact，方便试打包。
改完点「保存设置」，下一次生成立即生效，不用重启。
| 控件 | 作用 | 存在哪 |
|---|---|---|
| 你们的关系 | 恋人/朋友/同事/家人/自定义，起草和判断都按它把握称呼和分寸 | config.json → relationship（默认 romantic partners） |
| 说话风格（可选） | 一句话描述自己的口吻，只喂给起草；留空就只靠最近消息模仿 | config.json → style |
| 参考上下文 | 起草和判断各看最近多少条消息，3~30 | config.json → context（默认 10） |
| 群聊指定回复对象 | 开了群聊里才有「回复对象」那一行，候选针对 TA 写 | config.json → reply_target（默认关） |
| 启动时检查更新 | 开了才在启动时查一次 GitHub 最新版本号，有新版本就在标题栏下面提示 | config.json → check_update（默认开） |
| 调试视图 | 另开一个窗口实时显示截到的画面和识别框，看识别在哪一步认错。拨一下立刻生效，不用点保存；关掉那个窗口等于关掉开关 | config.json → debug_view（默认关） |
| 判断 · 来源 | OpenRouter 还是 TypeSafe 直连 | config.json → jev_provider（默认 openrouter） |
| 判断 · 密钥 | 上面选哪家就填哪家的 key。已配置时留空 = 保留 | 注册表 HKCU\Environment → JEV_API_KEY |
| 判断 · 模型 | 可手打，也可点「获取模型」拉列表挑 | config.json → jev_model（空 = 该来源默认） |
| 起草 · 来源 | 上面那张表里的任意一家 | config.json → draft_provider（默认 deepseek） |
| 起草 · Base URL | 只有两个「自定义」来源才出现这一行 | config.json → draft_base_url |
| 起草 · 密钥 | 上面选哪家就填哪家的 key。已配置时留空 = 保留 | 注册表 HKCU\Environment → LLM_API_KEY |
| 起草 · 模型 | 可手打，也可点「获取模型」拉列表挑 | config.json → draft_model（空 = 该来源默认） |
| 起草时开启思考模式 | 开了模型先想再写，慢好几倍、贵一些 | config.json → thinking（默认关） |
主界面上那几个（标题栏的采集开关、「当前会话」和「回复对象」下拉、「填入时带 @」勾选框）只在内存里， 不落盘，重启回默认。
几个注意：
main.py 入口：父进程只管界面，子进程采集，队列传消息（IDE 直接 Run）
app/ UI + 采集层
capture.py 找微信窗口 + WGC 盯帧 + 像素锚点定位消息区；帧全程内存
ocr.py RapidOCR 读消息区 → 按颜色分 me/her/灰字 → 滚动去重；另读头部的会话名
worker.py 采集子进程主循环（截图 → 定位 → OCR → 去重 → 丢队列）
fill.py 填入不发送：写剪贴板 → 点输入框 → Ctrl+V，到此为止
overlay.py 置顶悬浮窗：会话/回复对象、判断摘要、3 条候选、聊天记录、设置页（PySide6 + Fluent）
debugwin.py 调试视图：另一个窗口画当前帧 + 每个识别框的分类；只在内存里画，不存图
settings.py 两把 key 只进注册表，其余设置落 config.json
core/ Jev 判断内核，平台无关，跟安卓原版同一套口径
engine.py 唯一入口 analyze(messages, relationship) → 候选 + 排序 + 判断
providers.py 两张来源表（判断 / 起草）：协议、地址、默认模型；纯数据，不认 key
llm.py 三种协议的薄适配层，一律走官方 SDK：openai / anthropic / google-genai
jev_client.py Jev 判断客户端：OpenRouter（urllib）/ TypeSafe 直连（typesafe-sdk）；脱敏、退避
questions.py 7 道判断题 + build_state() + build_rank_question() + 判断小抄 guidance_text() / 中文标签 CHOICE_LABELS
draft.py 起草 3 条候选：拼提示词、解析、过滤、不足时追问补齐；调用走 llm.py
tools/
demo.py 端到端冒烟：拿一段写死的对话跑完整链（需 key + 联网）
preview_ui.py 用合成数据预览界面（含 --state debug 的调试视图），不采集不联网不碰微信；可 --screenshot 出图
make_icon.py 生成 docs/icon.ico（打包图标），图标已提交，换颜色才用重跑
probe/ 一次性探针，结论已写进本文，留着是为了可复现
probe_win.py UIA 能不能读微信聊天文字 → 证伪（树是空的）
probe_win2.py UIA 证伪 v2：分清「树是空的」和「有树没文字」，顺带试 LegacyIAccessible
probe_notify.py 微信来消息走不走 Windows 通知平台（能监听到就零 OCR）
probe_ocr.py OCR 读不读得准中文气泡、左右说话人分不分得开
probe_ocr_speed.py RapidOCR 一帧多久、裁小能快多少（结论：det_limit_type 必须 'max'）
probe_ocr_live.py WGC 持续盯窗口 + 变了就 OCR，新文字实时打控制台
probe_printwindow.py 试 PrintWindow + PW_RENDERFULLCONTENT 能不能绕开 Win10 黄框（未验证）
probe_laya.py Laya（开源本地决策模型）能不能替 Jev：英文题跑 multilingual / typed-decisions → 都接近随机
probe_laya_cn.py 同上，中文题问 multilingual → 更差
probe_laya_en.py 把对话人工译成英文再喂 typed-decisions → 好一点，但生气那段仍判成闲聊
jev.spec PyInstaller 打包定义（onedir），build.bat 和 CI 共用这一份
build.bat 本地一键打包（双击就行）
.github/workflows/release.yml 推 v* tag → windows-latest 上打包 → zip 挂到 Release
requirements.txt 依赖（纯 ASCII 注释：中文 Windows 上 pip 按 GBK 读会炸）
NOTICE 出处、第三方组件许可证与商用约束
docs/KICKOFF.md 最初的需求和硬约束说明
docs/icon.ico 程序图标，tools/make_icon.py 生成
docs/ui_*.png README 里那三张截图，tools/preview_ui.py --screenshot 出的
config.json 你自己的设置，不进仓库（在 .gitignore 里）
tools/ 和 probe/ 里的脚本都按「项目根在 PYTHONPATH 里」写（PyCharm 默认会把内容根加进去）。
命令行跑 tools/demo.py 得自己带上：set PYTHONPATH=. && python tools/demo.py。
代码里没有 sys.path 补丁。
probe/probe_printwindow.py 是
PrintWindow + PW_RENDERFULLCONTENT 的替代方案探针，还没在微信 4.x 上验证过，能出图就能换掉 WGC。@名字 也只是纯文本，
微信不会把它变成真正的 @ 提醒——真 @ 得走微信自己的选人面板，本工具不模拟那套按键。fill 靠点击输入框坐标：算的是消息区底线下方 40px、左边界右侧 60px，微信改布局就得跟着调。未发版
analyze() 改成三段式 —— Jev 先答 7 道判断题，判断折成中文小抄喂进
起草提示词，最后 Jev 只做排序；一次分析两次 Jev 调用。判断那次失败自动退回老路（盲起草 + 判断和
排序一次问完），排序失败就按第一条推荐openai / anthropic / google-genai），可点「获取模型」拉
接口的真实列表；key 收敛成两把 JEV_API_KEY / LLM_API_KEY，换来源复用同一个槽，老的
OPENROUTER_API_KEY / DEEPSEEK_API_KEY 仍能读到，保存一次自动迁移settings.save 部分保存（某项传 None）会把 config.json 里那几项清空——写文件前没先把要保留
的值读出来NOTICE、LICENSE 加上游版权行，README 加「版权与许可」「免责声明」和封号问答，重写
「什么会出网」；发布 zip 带上 LICENSE/NOTICEv0.1.8
app/version.pyv0.1.7
restype/argtypes，修句柄被截断导致的崩溃（来自 PR #2）；剪贴板被占
重试、AttachThreadInput 抢前台、粘贴前 Ctrl+End 追加再填requirements.txt 钉 rapidocr 1.4.x（1.2.x 构造参数会 KeyError）v0.1.6
jev-chat-windowsbuild.bat / jev.spec）、CI release 产物名同步改名v0.1.5
v0.1.4
v0.1.3
max_tokens 400），设置里另给一个「起草时开启思考模式」
开关，开了提到 4000["…"]、逗号连着的多个数组、带编号的 JSON 行都能剥干净v0.1.2
v0.1.1
v0.1.0
HKCU\Environmentjev.spec + build.bat）+ 推 v* tag 自动出 ReleaseCopyright © 2026 rezoch340 与 jev-chat 贡献者。代码以 MIT 协议开源，另见 NOTICE。
基于 jev-chat-windows（https://github.com/jev-chat/jev-chat-windows）二次开发。第三方组件与商用：本项目自己的代码是 MIT，但 Windows 发布包（PyInstaller 打的 zip）里打进了 PySide6-Fluent-Widgets，该组件是 GPLv3 协议，非商用免费，商用需要 向作者购买商业授权。因此发布包整体受 GPLv3 约束：想商用的人请自己去买那份商业授权，或者自己把这个 组件换掉，本项目不代为处理。其余依赖（PySide6、RapidOCR、windows-capture、openai / anthropic / google-genai / typesafe-sdk 等）的许可证见 NOTICE。
免责声明：本项目只处理你自己设备上、你自己有权查看的聊天。请在自己设备上自用；装到别人机器上 读别人的聊天记录是另一回事，本项目不为那种用法背书。请遵守微信软件许可协议与当地法律法规，微信 改版可能导致本项目的界面识别失效。使用本项目造成的后果由使用者自行承担，作者不负责。
微信弹出一条消息 → 悬浮窗立刻告诉你这句话的真实意图、风险几级、该怎么回。
纯只读、零封号风险——不注入、不 hook、不解密数据库，只是「看屏幕 + 本地模型判断」。
先自查：常见问题解答（FAQ）——配置文件、日志、模型路径、安装报错、旧版空白面板速查。 交流群、公众号等联系方式见文末「交流反馈」；数据流向与隐私见 PRIVACY.md。
JEV_BOXES=1 启动即开、菜单栏可切）：OCR 命中的消息实时框在微信窗口上，对方/我分色 + 置信度面板使用 macOS 原生浅色磨砂材质：顶部是当前聊天、分析状态、正在处理的消息与上下文；中间依次显示意图、识别率、风险等级和行动建议；底部按话术分组展示候选回复。当前风险圆点会轻微呼吸提示。每条候选左侧是本地排序概率，右侧仍只有「复制」和「填入」；候选行会随完整文字自动增高，不截断内容，发送始终由用户在微信里手动完成。
面板默认开三组话术：高情商话术、贴吧老哥 v1.0、阴阳怪气，每个下拉可换成其余语气或「不用」；「不用」的话术槽只保留一行下拉选择，不生成也不占候选行；开启或关闭话术只改变面板高度，不改变判断与轮询流程。黄色窗口按钮收起到聊天名与状态，红色窗口按钮退出。
聊天标题支持单字和较短的联系人名称；识别到聊天标题变化时，即使最后一条消息相同，也会重新分析。
仅支持 Apple Silicon（M 系列）Mac，macOS 13+。不支持 Intel Mac，也不要通过 Rosetta 运行（本地判断模型依赖的 torch 没有 Intel 版本，#19）；启动时会检测并以中文提示原因。
只想用：Releases 下载 .app，解压拖进「应用程序」，第一次右键 → 打开（没做公证，双击会被 Gatekeeper 拦）。
弹窗若显示「已损坏，无法打开，你应该将它移到废纸篓」（浏览器下载的 zip 常见，右键打开也绕不过），别删——在终端清掉隔离属性即可：
sudo xattr -r -d com.apple.quarantine /Applications/jev-jarvis.app
.app 若改过名（如「jev-jarvis 2.app」），把命令里的目录名换成实际路径。
首次启动按提示授予「屏幕录制」权限（系统设置 › 隐私与安全性 › 录屏与系统录音，给 jev-jarvis 打开），退出重开生效；「填入」另需「辅助功能」权限，第一次点会弹系统授权框。v0.3.1 及更早的旧版本还需把 python3.12 那条一并打开。
缺少可用的 uv 时，两种启动入口都先完整下载并执行官方安装脚本（下载含超时和重试），失败后尝试已有的 Homebrew。失败提示区分网络、证书、磁盘和安装器错误，详细输出见 ~/Library/Logs/jev-jarvis.log。官方脚本安装到 ~/.local/bin，不修改 shell 配置。
从源码跑（微信在运行、终端已授予屏幕录制）：./start.command。分层自测：
uv run python src/perception.py # 感知层：识别到的消息 + 耗时 uv run python src/judge.py "这个需求你今天跟一下" # 单条消息出判断 uv run python src/judge_zh_test.py # 22 条中文意图回归 uv run python src/generate.py --check # 生成层凭据解析 uv run python -B -m unittest discover -s tests # 发出消息/异步结果回归（合成 OCR，不读屏） uv run python probe/bootstrap_regression.py # 两种启动入口的离线回归；不联网、不实际安装
两层、两个 key、都可以不填：判断层不填时首次启动会引导选择——配置 key 在线判断，或下载离线模型（约 3.8 GB）；也可以稍后再说，面板会持续提示。生成层打包版内置共享 key，不配也能出候选，数据流向见 PRIVACY.md。全部配置在一个 env 文件（不提供第二种格式）：
点击悬浮窗右上角 齿轮图标（模型设置），或菜单栏 J → 模型设置…，可编辑 Jev、OpenAI 兼容、Anthropic 兼容三组密钥、服务地址与模型。 设置窗口显示在悬浮窗上方，不会被面板遮挡。各页统一使用底部「保存配置」：模型配置保存后必须退出并重新打开应用；「会话记录与背景」页的修改保存后立即生效。
$XDG_CONFIG_HOME/jev-jarvis/env（未设置时为 ~/.config/jev-jarvis/env），显示具体路径。只修改所编辑服务的字段，保留其他配置、注释和未识别行，文件权限设为 600。文件被其他程序修改时拒绝覆盖，需重新打开窗口。/models 接口动态获取，再下拉选择；不内置模型清单。Jev 按官方 models[].name 读取（当前列表为别名，未列出的版本号仍可手填）；OpenAI/Anthropic 按 data[].id 读取。接口不支持、失败或返回空列表时明确提示，仍可手填，不自动换模型或服务。空下拉显示「暂无」（仅作提示，不作为模型保存或调用），仍可手填；底部动态提示以蓝色显示进行状态、绿色显示成功、红色显示错误。列表可见不代表一定有生成权限，选定后再测试。--check 的配置解析成功代替连接成功。.env 或内置共享密钥复制进用户文件。各配置页顶部突出显示本次启动正在使用自己的密钥、内置共享密钥或本地判断，以及实际来源；生成页同时标明当前启用的服务，优先级保留在窗口下方。.env；生成层 OpenAI 组优先于 Anthropic 组，均未配置才使用内置共享密钥。清空当前文件的密钥不会禁用其他来源中的密钥。由终端或启动器导出的值也显示为「环境变量」。OPENAI_* 使用 OpenAI 格式，ANTHROPIC_* 使用 Anthropic 格式；自定义地址不需要包含服务名称。Ollama 可填 http://localhost:11434/v1、密钥 ollama，模型从本地服务获取或手填。Jev 地址带不带末尾 /v1 都行，与手动配置共用同一条拼接规则。$(security find-generic-password …) 等 shell 表达式提供密钥，窗口不执行表达式、不展示其内容，未输入新密钥时保留原行；仍由已有启动器执行。要在窗口测试该服务，需明确输入密钥；保存将用输入值替换原表达式。外部注入的密钥继续遵循环境变量优先级。JEV_BOXES、JEV_TONES、OPENAI_EXTRA_BODY 暂仍通过 env 配置，保存窗口不会改动它们。OpenAI 连接测试沿用当前启动的 OPENAI_EXTRA_BODY；完整话术管理等留待后续扩展。也可继续手动编辑：
mkdir -p ~/.config/jev-jarvis cat > ~/.config/jev-jarvis/env <<'ENV' # 判断层（可选）：TypeSafe Jev，不填用本地 decider-2b export TYPESAFE_API_KEY="" # 生成层：任意 OpenAI 兼容端点 export OPENAI_API_KEY="sk-你的key" export OPENAI_BASE_URL="https://api.deepseek.com" export OPENAI_MODEL="deepseek-chat" # 端点的思考模式要靠额外字段关时填（Qwen3 这类不关会慢几十倍） # export OPENAI_EXTRA_BODY='{"enable_thinking":false}' ENV chmod 600 ~/.config/jev-jarvis/env
deepseek-chat（最快）；智谱 glm-4-flash（换 ANTHROPIC_API_KEY/ANTHROPIC_BASE_URL/ANTHROPIC_MODEL，两组都填 OpenAI 组优先）；本地 Ollama qwen2.5:7b（完全不出网）TYPESAFE_BASE_URL 三种填法等价可用——只到主机（https://api.typesafe.ai）、带版本段（…/v1，自动补动作段，不会出现 /v1/v1/…）、或填完整动作路径（填到动作段为止，原样使用、不再拼接）。第三方 TypeSafe 兼容网关填网关地址 + 网关 key，模型名按网关填写（如 Vercel AI Gateway 填 https://ai-gateway.vercel.sh/v1/evaluate、模型 typesafe-ai/jev；OpenRouter 填 https://openrouter.ai/api/alpha/decisions、模型 typesafe/jev-1.13，key 用 OpenRouter 的 sk-or-…，响应同为 systemone 形状）JUDGE_BACKEND）：首次启动（未配判断层 key 且离线模型未下载）会弹一次选择，结果写进 env：cloud=在线判断（不下载、不加载本地模型）、local=离线判断（预热时下载，选过就不再问）、skip=稍后再说（不再弹，消息时面板提示）。不写此键时：模型已在本地就照常使用，未下载则不会自动下载，面板提示引导。模型设置的「判断 · Jev」页可删除离线模型（显示实际占用）或启用离线判断max_tokens，候选 0 条，面板只报「候选生成失败」——DeepSeek 认准 deepseek-chatJEV_TONES（| 分隔、每条「名字=说明」，同名覆盖内置，重启生效），如 摸鱼大师=像资深摸鱼选手，把活推得漂亮又不失礼。有质量门槛：说明至少 10 字，写清「什么语气 + 别变成什么」；太短的（如「夸我」）不会加载，启动日志会写明原因——说明太空模型就没得发挥，候选只会平庸uv run python src/generate.py --check、uv run python src/judge_jev.py在设置的「会话记录与背景」页开启 记录聊天历史（默认关闭）。应用只累计实际读到的已发送消息，包含本人和其他成员；每个聊天固定保留最近 100 条，不自动翻页、不读取微信数据库。重复读屏按消息序列衔接，相同文字的不同消息可以分别保留；无法可靠衔接时会少记；后续连续读屏确认的新消息作为独立片段保存，模型不会跨未知断层拼接上下文。
「聊天背景」可写相关背景信息，例如「AAA 是群主，BBB 是公司老板」。「保存配置」统一保存这些背景、记录开关及条数。保存后，仍有效的当前目标会重新分析。背景不占消息条数，清空编辑框后保存即可删除。
JEV_HISTORY=0（关闭）或 1（开启），JEV_CONTEXT_MESSAGES=20。非法条数不接受；启动时无效值回退为 20。通过本页保存的值重启后仍可编辑；真正由启动环境变量传入的值优先，对应选项不可编辑，需修改环境变量后重启。历史和背景保存在 ~/Library/Application Support/jev-jarvis/conversations.json，源码运行与打包 .app 使用同一位置，不随启动时的工作目录变化。
历史和背景仅保存在本机，但推理时会发给当前选择的模型服务及配置的中转；服务地址可在对应模型设置页配置，修改后需重启生效，服务方的留存政策由其决定。应用不增加历史云同步或项目后台存档，详见 隐私说明。
| 内容 | 位置 | 大小 | 清理 |
|---|---|---|---|
判断层本地模型 decider-2b（首次启动引导选择后才下载，判断+排序共用） |
~/.cache/huggingface/hub/models--Mapika--decider-2b |
~3.8 GB | 模型设置 →「判断 · Jev」页「删除模型…」；或 rm -rf ~/.cache/huggingface/hub/models--Mapika--decider-2b；之后走本地判断会重新下载 |
| 会话历史与背景 | ~/Library/Application Support/jev-jarvis/conversations.json（权限 600） |
每会话最多 100 条，背景独立 | 设置 → 会话记录与背景 → 管理记录；背景清空后保存。删除应用不会自动删除此数据文件 |
| Python 运行环境（venv） | ~/Library/Application Support/jev-jarvis/venv |
~0.7 GB | 删除 .app 不会连带删它，需手动删 |
生成层配 Ollama 的话模型在 Ollama 自己的目录（~/.ollama），非本项目下载。
src/perception.py 顶部常量需重新校准）；多窗口优先识别主窗口「微信 / WeChat / Weixin」，且只认这三个应用名——名字里含「微信」的兄弟应用（微信读书、微信输入法、企业微信）不再参与窗口选择tail -40 ~/Library/Logs/jev-jarvis.logTYPESAFE_API_KEY 走云端判断——这是为了防止 #37 那种加载把系统推入内存高压、进程被系统直接终止的情况菜单栏「YOLO 检测框」同时显示消息框和输入目标，约每秒刷新：蓝色实线表示辅助功能接口定位到的输入控件，橙色虚线表示从截图边界推测的输入区；无法定位时显示原因。虚线不代表已取得可写控件，也不修复 #17 的聊天区域固定比例问题。
聊天识别会在同一张截图上检测输入区边界，并将输入区排除在消息、上下文和画面变化判断之外；视觉边界不可用时尝试辅助功能定位，仍无法确认则暂停分析。输入草稿不会作为待回复消息。左侧列表的固定比例限制仍未解决。
悬浮窗跟随 macOS「降低透明度」设置：关闭时保留原版磨砂；开启时使用浅灰绿底、白色卡片与灰绿边线，增强区域区分。切换设置后自动更新，无需重启。
顶部消息区将昵称与正文分开，正文默认显示两行；长消息可点击「展开」查看，超长内容在区域内滚动。「收起」恢复两行，展开操作不会重新请求 AI。
「填入」优先通过辅助功能接口写入并读回确认。部分微信版本不提供输入控件时，显式点击「填入」会尝试视觉兼容路径：复核窗口、输入区和标题，激活微信、点击输入区、输入文字，再用 OCR 核对。该路径需要屏幕录制及辅助功能权限，会移动鼠标；填入期间请勿操作键鼠或切换聊天。不会自动按发送键，也不使用剪贴板或 Cmd+V；换行和制表符转换为空格。
兼容路径读到已有草稿时停止，提示使用「复制」手动插入；辅助功能路径仍追加原有文字。窗口、焦点或会话变化时停止，画面无法确认时提示检查草稿，不自动重试。视觉边界及 OCR 都可能误判，标题检查也不是会话 ID，不能消除用户同时操作时的竞争；深色主题、多显示器与其他微信版本仍需更多验证。
uv run python -B -m unittest discover -s tests；macOS 原生窗口与按钮流程：uv run python -B probe/settings_smoke.py（临时配置 + 本地测试服务，不使用个人密钥）。./packaging/build_app.sh；发版 ./packaging/release.sh --publish（干净 worktree 构建 + 解压回验 + gh release）。版本号只有 pyproject.toml 一处；有开发者证书可加 --sign "Developer ID Application: ..."Copyright © 2026 eatmoreduck 与 jev-chat 贡献者。代码以 MIT 协议开源，另见 NOTICE。
LICENSE 与 NOTICE，并在产品「关于」页、说明文档或发布页写明来源。推荐写法：基于 Jev 聊天助手（https://github.com/jev-chat/jev-chat-jarvis-mac）二次开发。用着有问题、想提需求、想一起改，扫码进群（1 群已满，从 2 群开始扫，满了顺序换下一个）；有新版本发布会在群里和公众号通知，建议关注；群二维码 7 天失效，过期了在 issue 说一声：
2 群 |
3 群 |
4 群 |
5 群 |
都满了或者不想进群，直接找我：加个人微信（备注来意），或关注公众号后台私信（新版本发布同样在公众号通知）：
个人微信 |
公众号 |
如果你觉得我写的这玩意儿对你有点帮助，欢迎请我喝杯咖啡。咖啡因一到位，脑子就开始冒泡，源源不断地驱动我往前跑；哪天我更新得特别勤，说明这杯续上了 😄
隐私与数据流向详见 PRIVACY.md：聊天内容只发给模型服务商——推荐自配 API key 或本地 Ollama；内置免费通道经作者中转，承诺与提醒见该页。
点击折叠
| 感谢 博查 赞助了本项目！博查是一个给 AI 用的搜索引擎，让你的 AI 应用连接世界知识，获得干净、准确、高质量的搜索结果。提供 Web Search API、Bocha Jev API 等多种联网搜索和模型服务。open.bocha.cn | |
| 感谢 小优店铺 赞助了本项目！小优店铺是一家数字商品与账号服务店铺，为本项目的用户提供选购渠道。点此前往。 | |
| 感谢 速创猫 Vytal 赞助了本项目！速创猫 Vytal 是专业的 AI 视频工作流平台，提供可批量复用的视频工作流，降低内容制作门槛，服务内容创作者、培训机构及中小团队。点此前往。 |
悬浮窗：危险等级、对方真实意图、排好序的 3 条候选回复 |
设置页：判断 / 回复 / 视觉三路接口分别可配 |
| 平台 | 状态 | 采集方式 | 备注 |
|---|---|---|---|
| 微信 Android | ⏸ 已停止支持 | — | 微信 8.0.52+ 对普通无障碍服务隐藏了消息文字，且近期对部分账号/设备的聊天界面开启防截屏（FLAG_SECURE），两条读取路径都不通，没有干净的读取方式；1.4 起不再读取微信 |
| QQ Android | ✅ 全链路 | 无障碍读节点 | 9.3.50 实测（群聊）；1v1 按同结构推断 |
| X / Twitter 私信 | ✅ 全链路 | 解析 Compose 节点的 content-desc | 12.25 实测，中文界面；英文界面未验 |
| 飞书 / Lark | ✅ OCR 兜底（真机验证） | 无障碍读气泡矩形 + ML Kit 离线 OCR 识别正文 | 正文自绘不在无障碍树里，1.3 起对每个气泡矩形做 OCR；我/对方按已读状态判 |
| 任意其它 App | ✅ 手动 | 悬浮窗菜单「截屏识别一次」整屏 OCR | 不自动、不分我/对方（全部当作对方所说并在面板标注） |
| 桌面端 / 网页 | ⏳ 规划 | 截图 + OCR / 视觉 | 同一内核，换采集方式 |
本项目只读你自己设备上、你自己有权查看的聊天，不针对任何单一平台。
1. 装包。 仓库里有签好名的 release 包：apk/jev-assistant-v1.4-release.apk（Android 11+）。各版本安装包也在 Releases。
adb install -r apk/jev-assistant-v1.4-release.apk
2. 填密钥。 打开 App → 设置 →「接口」分三张卡：判断接口 / 回复接口 / 视觉接口。最简单只填「判断接口」一栏的 OpenRouter API Key，其余两栏留空会自动继承这把密钥就能用。想换回复模型（默认 deepseek/deepseek-chat-v3.1，国内 Gemini / OpenAI 会被区域限制）就在「回复接口」选预设（OpenRouter / DeepSeek 官方 / 通义兼容）或自填地址，每张卡都有独立的一键连通测试。
3. 开权限。 按主页向导开三项：
装过 debug 包的要先卸载再装 release（签名不同），卸载会清掉密钥和设置。小米 / HyperOS 重装后悬浮窗权限会被重置，装完按向导再开一次。
ACTION_SET_TEXT，失败自动退到剪贴板粘贴，任何情况下都不发送。在设置 → 分析 →「知识库与联系人」。
https://jev.bocha.cn 与模型 bocha-jev-v1（协议与 TypeSafe 一致），页面上会显示官方地址并支持一键复制，当前限时免费。全新安装默认使用博查 Jev；已经配置过判断接口的老用户不受影响，provider 和密钥都不会被改动。不会。程序只把选中的回复填进输入框，发送键永远由你自己点。转账、红包、收款一律不碰。
需要 root 或 Xposed 吗？会不会封号？不需要 root，也不用装任何模块。它不修改聊天软件的安装包、不注入进程、不调用对方 App 的接口或账号体系，只读系统无障碍服务暴露出来的界面内容，和读屏软件的工作方式一样。
我的聊天记录会被上传吗？聊天内容只在你触发分析的那一刻，发给你自己在设置里配置的模型接口。项目没有任何自建服务器，不收集、不落盘、不进日志。历史记录默认关闭，开启后也只存在手机的 App 私有目录里。
悬浮球不见了，或者读不到消息怎么办？多半是国产 ROM 把后台进程冻结了。先确认无障碍、悬浮窗、自启动、省电无限制四项都开着，小米 / HyperOS 尤其要开后两项。重装后悬浮窗权限会被重置，按主页向导再开一次。在聊天界面里随便点一下通常能自愈。
飞书里读不到正文？其它 App 能用吗？飞书的消息正文是自绘控件，无障碍树里没有文字，1.3 起改为对每个气泡矩形做离线 OCR。没有专门适配的 App，可以在悬浮窗菜单里点「截屏识别一次」，整屏 OCR 后同样能分析，只是不区分我方和对方。
要花钱吗？软件本身免费开源。模型调用走你自己的 API Key，按用量在对应服务商那边结算，项目不经手任何费用。
升级之后没反应？把系统设置里的无障碍开关关掉再打开一次。1.3 起新增了截屏能力，服务需要重新绑定才会生效。
QQ / X / 飞书 ──(无障碍读节点)──▶ 采集最近消息
│
┌───────────────────┴───────────────────┐
▼ ▼
Jev 判断（一次 7 道题） 生成模型起草 3 条候选
意图 / 危险 / 需求 / 动作 / 该不该回 │
└───────────────────┬───────────────────┘
▼
Jev 给 3 条候选排序
▼
半透明悬浮窗展示 → 复制 / 填入（不发送）
background（关系 + 联系人备注 + 命中笔记）和 history（历史消息）。ACTION_SET_TEXT，失败则剪贴板 + ACTION_PASTE，不发送。capture/ChatAppAdapter.kt 实现 ChatAppAdapter：pkg 是包名，extract(root, res) 从无障碍树取出标题和消息列表（Msg(side, text)，side 为 me / other），不在聊天窗时返回 null。capture/ChatCaptureService.kt 的 adapters 加一行。先用 adb shell uiautomator dump 看目标 App 暴露了什么，已有四个适配器覆盖了四种情况：
| App | 树的情况 | 适配器怎么做 |
|---|---|---|
| 节点开放，有 id | 正文 id/mjn、标题 id/371，按气泡贴哪侧头像判谁说的 |
|
| 微信 | 已隐藏消息文字，部分设备还开启了防截屏 | 已停用：1.4 起不再接入采集分发，适配器代码保留在仓库中，以便日后微信策略变化时恢复 |
| X | Compose，无 id，text 为空 | 解析 content-desc 发件人：正文。时间。Read，发件人是「你」即我方 |
| 飞书 | 正文自绘，树里没有文字 | 树上拿 bubble_content_container 矩形与已读状态，OCR 每个矩形的正文 |
适配器返回 null 表示不在聊天窗，返回空消息列表表示在聊天窗但树里没正文——只有后者会触发 OCR 兜底。
QQ、X 全程只有一个 Activity，判「是不是聊天窗」要看树里有没有该有的节点（如输入框），不能看 Activity 名。
构建与目录结构JDK 17 + Android SDK（platform 35 / build-tools 35）。
./gradlew assembleDebug # app/build/outputs/apk/debug/app-debug.apk ./gradlew assembleRelease # 需要仓库外的签名 properties，路径由 JEV_KEYSTORE_PROPS 指定
app/ — Android 应用（Kotlin，传统 View）
capture/ 无障碍采集：ChatAppAdapter.kt 各 App 适配器、ChatCaptureService.kt 分发服务、前台保活、ocr/ 截屏与离线识别jev/ Jev 客户端与题目集 · overlay/ 悬浮窗 · core/ 配置与数据模型（含 core/kb/ 知识库存储与上下文构建）KnowledgeActivity 知识库管理页（笔记 / 联系人）tools/jev/ — Jev 题目集与校准脚手架（Python）docs/ — 设计与验收文档apk/ — 签好名的 release 包：、上午 / 下午、Read 是中文界面实测；英文界面只做了兜底，未验。tools/jev/）。FLAG_SECURE）截不到。如需联系，请公众号私信。 合作、赞助、反馈、进群失败、二维码过期，都走公众号私信，其它渠道不一定看得到。
想听真实需求：你在哪个聊天 App 上最想要这个副驾？希望它判断什么、怎么提示、什么绝对不能碰？扫码进群直接说。1 至 7 群已满，不要再扫；8、9 群任选一个，请勿重复加入。
8 群 |
9 群 |
以下七群已满，请勿再扫：
1 群 |
2 群 |
3 群 |
4 群 |
5 群 |
6 群 |
7 群 |
群二维码 7 天有效（本批到 2026-09-29），过期了公众号私信要新码。
同在 jev-chat 组织下：
隐私政策见 PRIVACY.md（说明读取了什么、发给谁、存在哪里、怎么删除）。
Copyright © 2026 Finderchangchang 与 jev-chat 贡献者。代码以 MIT 协议开源，另见 NOTICE。
基于 Jev 聊天助手（https://github.com/jev-chat/jev-chat-jarvis）二次开发。免责声明：本项目只处理你自己设备上、你自己有权查看的聊天。请遵守微信、QQ、X、飞书等各软件的许可协议与当地法律法规，作者不对使用后果负责。
如果你觉得我写的这玩意儿对你有点帮助，欢迎请我喝杯咖啡。咖啡因一到位，脑子就开始冒泡，源源不断地驱动我往前跑；哪天我更新得特别勤，说明这杯续上了 😄
随手支持，不用有压力；不支持也没关系，点个 Star 或提条建议同样能让我开心很久。
Jiamu Zhang1 Tianze Yang1 Yucheng Shi2 Liang Wu1
1 Nokia, Sunnyvale, CA 2 Tencent Hunyuan
Qwen3-8B on a real BANKING77 item. Every number is a model output.
Tip
🆕 L2 has landed. A closed-form head per question, solved on 100–300 labels in seconds, served from one prompt stopped at two thirds of the model's depth. It follows its question across rewordings without new labels. Jump to it ↓
Ask any open LLM a typed question and get back a decision with a probability you can threshold, read from one prefill of its next-token distribution. No generation, no parsing, no fine-tuning. Raw logits change their answer when you reorder the options, and their confidence cannot be trusted; AnyJev fixes the first with zero labels and the second with a few hundred.
| raw logits | AnyJev L0 | AnyJev L1 | |
|---|---|---|---|
| Labels required | none | none | 100–500 |
| Answer flips when options are reversed | 0.230 | 0.073 | 0.077 |
| Accuracy | 0.747 | 0.803 | 0.807 |
| Calibration error (ECE) | 0.240 | 0.184 | 0.095 |
| Auto-decidable at ≤5% error | 7.7% | 46.3% | 52.0% |
Qwen3-8B, BANKING77 20-way, 300 test items. Full table incl. every ablation: docs/results_bench.md
The last row is the point. Accuracy moves by 6 points, but the share of traffic you can safely automate goes from 7.7% to 52.0%, a 6.8× difference on this task (a point estimate at n=300; the interval is wide, see Limitations). With raw logits a "0.9" is not trustworthy enough to act on, so everything goes to a human. Once the probability means what it says, you can set a threshold.
📦 1. Install
pip install "anyjev[hf]"💬 2. Ask typed questions. L0 is on by default and needs no labels.
from anyjev import Decider, Question from anyjev.backends.hf import HFBackend d = Decider(HFBackend("Qwen/Qwen3-8B")) route = Question.choice("Which team should handle this?", ["billing", "technical", "sales", "other"], name="route") risky = Question.noul("Is this tool call destructive or irreversible?", name="risky") done = Question.score("How complete is the task?", bins=5, name="done") r = d.decide({"conversation": [...], "tool_call": {...}}, [route, risky, done]) r["route"].distribution # {"billing": 0.81, "technical": 0.07, ...} r["risky"].p_true # 0.12 r["done"].value # 0.35 r.level # "L0"
🎯 3. Add labels when you have them. A temperature is L1; a closed-form head is L2, the accurate one.
d.calibrate(risky, states, labels) # 100–500 labels → L1 (a temperature) d.fit_head(route, states, labels) # 100–300 labels → L2, one forward + a closed-form solve, seconds d.save_artifacts("qwen3-8b.json") # d.load_artifacts(...) next time; ~100 KB per head r = d.decide(state, [route], level="auto") # L2 where a head routes, else L1, else L0 r["route"].level # "L2"
🔁 4. Or let the loop feed it. d.observe(route, state, label) stores labels as they arrive and solves the head by itself at 30, re-solving at 60, 120, …
⚡ Serving. The transformers backend (anyjev.backends.hf) serves every level today; serving through vLLM / SGLang is on the roadmap, not in this release. For many states and one question, d.decide_batch(states, question).
🎬 Try it in one command. python -m demo.jev_mode --backend fake runs the whole thing on a synthetic model in under a second, no download. --lifecycle plays the deployment loop; drop --backend fake to run a real Qwen3 with the shipped heads (demo).
| Level | Needs | Does | Does not |
|---|---|---|---|
raw |
nothing | restricted softmax over label tokens (what the clones do) | anything about bias or calibration |
L0 |
nothing | averages position bias out over the K rotations and divides out the label prior | make the model's uncertainty calibrated |
L1 |
100–500 labels per question | temperature scaling on top of L0 | change the ranking |
L2 |
100–300 labels per question, a local model | a closed-form head (shrunk LDA / ridge) on the hidden state at ~⅔ depth, one prompt per state | transfer to another question or model |
Every Decision carries its level, so downstream code can refuse to act on the wrong one. L0 costs K prefills for a K-option choice (about 0.25 s per decision at batch 32 on one H100, K = 20); L2 costs less than one plain forward — one prompt, stopped early: 0.68× on Qwen3-8B.
L2 is not a training run. Labels buy a head in one closed-form solve (seconds on a CPU, no gradients, the model's weights untouched). After that only the head's feature mean and scale move, re-estimated from unlabelled traffic — so the head follows its question across rewordings and option orders by itself, and new labels are needed only for a new question.
Reworded, the Qwen3-8B head as is drops from 0.77 to 0.65–0.70; 30 unlabelled requests of the new wording bring it back to 0.74–0.75, against 0.77 for a fully relabelled refit (JSON).
One decision at serving time. A stored head answers from one truncated forward. Without one, the same call falls back to L1 or L0 exactly as before; the routing is in docs/method_v3.md.
Deployment lifecycle: day 0 at L0, labels from the loop, heads in seconds
flowchart LR
D0["day 0: define the questions,<br/>serve with level auto;<br/>every answer is L0, zero labels"] --> C["collect labels from the loop:<br/>review queue, outcomes, or the LLM<br/>being replaced; dec.observe fits at 30"]
C --> F["fit_head per question;<br/>export_artifacts to one JSON per model"]
F --> S["serve: L2 where a head routes,<br/>L1 or L0 elsewhere"]
S --> W{"what changed?"}
W -->|"wording or option order"| S
W -->|"new question or option set"| C
W -->|"new base model"| R["re-solve every head from<br/>the stored labelled states"]
R --> S
classDef shipped fill:#dcfce7,stroke:#0f9d76,color:#0f172a
classDef decision fill:#fef3c7,stroke:#d97706,color:#0f172a
class D0,C,F,S,R shipped
class W decision
A shift in the states (not the wording) is invisible to the recentring, so a periodic spot check on a labelled slice stays in the recipe. Full method: docs/method_v3.md.
|
🔁 Order flips cut every model × task row, at L0, zero labels 3 models × 3 tasks → |
🧩 Typed-decisions accuracy Qwen3-32B and 30B-A3B at L2, 300 labels per question; Jev 0.727 as published, fine-tuned Laya 0.768 5 models → |
⚡ Cost of one decision of a single plain forward, Qwen3-8B at L2: one prompt, stopped at block 24 of 36 latency → |
Jev mode, on LocalLLaMA/typed-decisions (20 questions, 300 labels each, 2,000 held-out decisions):
| model | L0, zero labels | L2 | block | cost vs one forward |
|---|---|---|---|---|
| Qwen3-1.7B | 0.494 | 0.730 | 18 / 28 | 0.70× |
| Qwen3-4B | 0.564 | 0.786 | 24 / 36 | 0.69× |
| Qwen3-8B | 0.647 | 0.771 | 24 / 36 | 0.68× |
| Qwen3-30B-A3B | 0.630 | 0.799 | 40 / 48 | not measured |
| Qwen3-32B | 0.700 | 0.798 | 52 / 64 | 0.84× |
Pooled ECE at L2 is 0.03–0.05. Jev 0.727 and fine-tuned Laya 0.768 on the same set, as published by their authors. Every cell: docs/results_exit.md
A 1.7B at 64% of its depth reaches the number Jev publishes; a 4B ties the fine-tuned 421M Laya. 100 labels already put the 8B head at 0.740 (20 labels: 0.654, 300: 0.772).
More: Jev mode in full · 2048 and Minesweeper · NanoJev maze · when L0 helps · small models · the research log, negative results included
Heads you can load today, and what a head costsanyjev-heads/<model>.json ships 23 heads per model (the 20 typed-decisions questions and three bench tasks) for Qwen3-1.7B / 4B / 8B / 30B-A3B / 32B, built and validated through the same fit_head → decide_batch path a user runs (scripts/build_heads.py). A head is a [hidden, K] matrix plus a bias, a standardisation vector and a temperature: ~100 KB, solved in 2–8 s on the 1.7B–8B.
The big model's heads also distil into a small one without gradients: the 32B's heads labelling 1,200 generated cases per workflow lift the 1.7B from 0.730 to 0.760 (the 4B and 8B do not move). docs/jev_mode.md
All models and tasks in one figureEvery number is regenerated from committed JSON (bash scripts/regen_docs.sh); a second run from a clean checkout reproduced every zero-label number bit for bit. Not affiliated with TypeSafe AI or Jev; rows published by their authors were not rerun here.
choice, noul and score from one prefill, nothing generatedrequire=level="auto", observepython -m demo.jev_mode)Dated plan and help-wanted files: ROADMAP.md.
Also: at most 26 options in the letter readout (a span readout is on the roadmap, not in the code); coverage at 5% risk is a high-variance estimate at n = 300; the headline tables are Qwen models; every decision here is scored in isolation, not inside an agent loop.
Backends and bench providers are one file each; several are help wanted (ROADMAP.md, CONTRIBUTING.md). Changes: CHANGELOG.md. Credits: CREDITS.md.
@software{anyjev2026, title = {AnyJev: Turn any LLM into a Jev-style decision model}, author = {Zhang, Jiamu and Yang, Tianze and Shi, Yucheng and Wu, Liang}, year = {2026}, url = {https://github.com/nokia-applied-research/AnyJev} }
Apache-2.0, see LICENSE. Datasets keep their own licenses, see THIRD_PARTY.md.
AI video prompt cheat sheet & Claude Skill for Veo 3, Google Flow, Kling, Sora, Runway, Hailuo, Luma, Midjourney — camera angles, camera movement, cinematic lighting, composition, color grading, mood, and a ready-to-use prompt formula.
Bộ từ điển prompt điện ảnh cho AI tạo video/ảnh — 700+ thuật ngữ về góc máy, chuyển động camera, ánh sáng, bố cục, màu sắc, cảm xúc, có giải thích tiếng Việt và công thức ghép prompt dùng được ngay.
A Claude Skill (works with Claude Code, Claude Cowork and Claude.ai) that teaches the AI professional cinematography vocabulary so your text-to-video and text-to-image prompts stop being vague ("a nice cinematic scene") and start being precise ("medium close-up, low angle, slow dolly in, rim lighting, teal and orange grading").
It is model-agnostic: the vocabulary works for Veo 3 / Google Flow, Kling, Sora, Runway Gen-4, Hailuo, Luma Dream Machine, Pika, Midjourney, Stable Diffusion, Flux, Nano Banana, and any future model that reads English prompts.
| File | Content |
|---|---|
SKILL.md |
The skill itself: workflow, prompt formula, condensed keyword tables, ready-made combos by video type, mood → combo lookup, pre-flight checklist |
references/01-camera-angles-and-movement.md |
70+ camera angles & shot sizes, 60+ camera movements (dolly, arc, crane, FPV drone, dolly zoom, bullet time…) with "when to use" |
references/02-lighting.md |
80+ lighting terms (golden hour, rim light, Rembrandt, chiaroscuro, volumetric, practical, neon…) |
references/03-composition.md |
80+ composition rules (rule of thirds, leading lines, S-curve, negative space, repoussoir…) |
references/04-lens-technical-film.md |
Lenses, aperture, shutter, bokeh, flare, filters, film stocks, aspect ratios, color grading tech |
references/05-style-color-mood.md |
100+ art styles, 80+ color palettes/grading, 100+ mood/emotion terms |
references/06-materials-weather-pose.md |
Materials & textures, weather & atmosphere, character poses |
examples/ |
Worked prompt examples for common video types |
[Shot size + Angle], [Subject + appearance], [Specific action], [Setting + weather],
[Lighting], [Camera movement], [Style + color], [Mood], [Technical]
Example:
Medium close-up, low angle, a young woman in a red áo dài walks slowly through a rainy
Saigon alley at night, neon signs reflecting on wet asphalt, rim lighting from city lights,
slow dolly in, cinematic, teal and orange grading, melancholic mood, shallow depth of field,
35mm film grain
Rules baked into the skill: one camera movement per clip, one main action per clip, lighting must match weather/time of day, style ↔ color ↔ mood must point the same way, max ~8 technical keywords, keep character description identical across clips of the same story.
Claude Code
git clone https://github.com/Rylaispirit/cinematic-video-prompt-skill.git ~/.claude/skills/cinematic-video-promptOr per-project: clone into .claude/skills/cinematic-video-prompt inside your repo. Then just ask: "write a Veo 3 prompt for a rainy night street scene" — the skill loads automatically.
Claude.ai / Cowork
Zip the folder (SKILL.md must be at the root of the zip) and upload it under Settings → Capabilities → Skills.
Any other AI tool (ChatGPT, Gemini, local LLM)
Paste SKILL.md as a system prompt / custom instruction. The reference files can be pasted on demand when you need deeper vocabulary.
> Write a Kling prompt: a monk meditating on a mountain at dawn, epic feeling
> Make this prompt more cinematic: "a cat sitting on a window"
> Give me 3 clips for a product video of a ceramic mug, consistent style
> Which lighting should I use for a horror scene in an old house?
> Explain what "dolly zoom" does and when to use it
The skill answers with an English prompt in a code block, plus a one-line explanation of the choices (in the user's language).
A raw list of 700 terms is hard to use. The skill adds the layer that matters: which term to pick for which feeling, how to order them, what not to combine, and ready combos for storytelling videos, product videos, food, talking-head training videos, night street scenes, travel, action and vertical Reels/TikTok.
PRs welcome — especially new camera-movement "golden prompts" that you have verified work well on a specific model (please name the model).
MIT — free to use, modify, and share.
Một Claude Skill (dùng được với Claude Code, Claude Cowork, Claude.ai) giúp AI hiểu đúng ngôn ngữ quay phim chuyên nghiệp. Thay vì prompt mơ hồ kiểu "một cảnh đẹp điện ảnh", bạn sẽ có prompt chính xác kiểu "medium close-up, low angle, slow dolly in, rim lighting, teal and orange grading".
Dùng được cho mọi model tạo video/ảnh đọc prompt tiếng Anh: Veo 3 / Google Flow, Kling, Sora, Runway, Hailuo, Luma, Pika, Midjourney, Stable Diffusion, Flux, Nano Banana…
SKILL.md — phần AI đọc: quy trình làm việc, công thức ghép prompt, bảng từ khóa rút gọn theo 11 nhóm, combo sẵn theo loại video (video kể truyện, kinh dị, cổ trang/tu tiên, sản phẩm, ẩm thực, đào tạo talking head, đường phố đêm, du lịch, hành động, Reels dọc), bảng cảm xúc → combo, checklist trước khi đưa prompt.references/ — bộ tham chiếu đầy đủ 700+ thuật ngữ, mỗi thuật ngữ có giải thích tiếng Việt dễ hiểu và gợi ý khi nào dùng: góc máy & chuyển động camera, ánh sáng, bố cục, ống kính & chất phim, phong cách & màu & cảm xúc, chất liệu & thời tiết & tư thế.examples/ — ví dụ prompt hoàn chỉnh cho các loại video hay gặp.Claude Code — chạy lệnh:
git clone https://github.com/Rylaispirit/cinematic-video-prompt-skill.git ~/.claude/skills/cinematic-video-prompt(hoặc clone vào .claude/skills/cinematic-video-prompt trong thư mục dự án). Sau đó chỉ cần nói: "viết prompt Veo 3 cảnh phố đêm mưa" — skill tự bật.
Claude.ai / Cowork — nén thư mục thành file zip (file SKILL.md phải nằm ngay gốc zip), vào Settings → Capabilities → Skills và tải lên.
ChatGPT / Gemini / tool khác — dán nội dung SKILL.md vào system prompt hoặc custom instruction. Khi cần tra sâu thì dán thêm file trong references/.
> Viết prompt Kling: nhà sư thiền trên núi lúc bình minh, cảm giác hùng vĩ
> Làm prompt này điện ảnh hơn: "a cat sitting on a window"
> Cho tôi 3 clip video sản phẩm ly gốm, giữ nhất quán style
> Cảnh kinh dị trong nhà cổ nên dùng ánh sáng gì?
> Dolly zoom là gì, dùng khi nào?
Skill trả về prompt tiếng Anh trong code block (copy được ngay) kèm một dòng giải thích tiếng Việt vì sao chọn góc máy / ánh sáng / chuyển động đó.
Danh sách 700 thuật ngữ rất khó tra khi đang làm việc. Skill bổ sung phần quan trọng nhất: chọn từ nào cho cảm giác nào, xếp thứ tự ra sao, không được ghép gì với gì (ví dụ "golden hour" + "heavy downpour"), và combo có sẵn cho từng loại video để bạn chỉ cần thay chủ thể.
Hoan nghênh pull request — đặc biệt là các "golden prompt" chuyển động camera bạn đã thử và thấy chạy tốt trên một model cụ thể (ghi rõ model).
MIT — dùng, sửa, chia sẻ tự do.
Keywords: AI video prompt, cinematic prompt, Veo 3 prompt, Kling prompt, Sora prompt, Runway prompt, text to video prompt guide, camera movement prompts, lighting prompts, prompt engineering for video, Claude skill, prompt tạo video AI, prompt Veo 3, prompt Kling, từ điển prompt điện ảnh, hướng dẫn prompt video AI.
Specialised AI models for logo design.
A logo is not a picture. It is a constructed object with rules: a mark that holds at sixteen pixels and on the side of a building, letterforms spaced by eye rather than by metric, clear space derived from the mark's own geometry, and lockups that still read when one of them is all you have room for.
General image models do not work this way. They produce something logo-shaped — a plausible arrangement of marks with no construction behind it, no reasoning about the business, and nothing you can hand to a printer or a sign maker.
Inkloom is building models that construct a mark the way a studio does, as a sequence of decisions that can each be explained:
| Stage | What it produces |
|---|---|
| Brand analysis | Turns a description of a business — sector, audience, tone, competitors — into concrete constraints: stroke weight, width, geometry, counter shape, which symbol families fit |
| Typography | Selects and fits letterforms against those constraints, then does the work that makes a wordmark: optical spacing, kerning at display size, a custom ligature where the name needs one |
| Symbol construction | Composes geometric primitives under construction rules — shared radii, tangent junctions, consistent terminals — so the result is built rather than sampled |
| Composition | Optical alignment rather than mathematical centring, clear-space ratios taken from the mark itself, and the lockup variants a brand actually needs |
The output is meant to be a specification, not a bitmap: a mark you can describe, defend and reproduce.
Early access is open at inkloom.art. Create an account, redeem a code, and credits are reserved against your account.
Generation is not live yet. We would rather say that plainly than imply otherwise: every page in the product says so, credits are described as reserved rather than spendable, and the feature flags that would switch generation on default to off and are not togglable from the console — because enabling a flag whose feature does not exist exposes a broken surface rather than a feature.
What runs today is the platform the models will ship on. Accounts and authentication, the credit ledger, the access-code system, the operations console, and the machinery around them: backups that are restore-tested rather than merely taken, an alerting pair where each half watches what the other cannot see, and a deployment path that refuses to migrate a database whose identity has not been confirmed.
Generation itself, then the things that only make sense once it exists: export in the formats a designer and a printer each need, brand kits, revision history on a mark, and paid plans. None of it is claimed as present until it is.
This source is published so the engineering can be read and audited — in particular the security and data-handling claims we make. It is not a distribution: see LICENCE.
Found a security issue? SECURITY.md says where to send it and what to expect. Please do not open a public issue.
Operational documentation — deployment, incident response, environment and runbooks — is kept internal. Source comments occasionally point at it by filename; that is a reference for the people who run the service, not a broken link. It describes how the service is operated, which is of no use to a reader and of some use to an attacker.
Built on Cloudflare Workers, Postgres and React Router, with the application and its API served from one origin — which is what makes the session cookie first-party and removes cross-origin handling entirely.
The test suite runs against a real database, a real browser and a real mail server rather than mocks of any of them, because the guarantees that matter here are transaction guarantees and a mock cannot have one. The test that matters most fires twenty-five simultaneous redemptions of a single code at a real database and asserts that exactly one redemption, one ledger entry and one balance exist afterwards.
| General | support@inkloom.art |
| Security | security@inkloom.art — please read SECURITY.md first |
Copyright © Inkloom. All rights reserved.
A catalog of API tools an agent can call — 660+ generative-media models available through muapi out of the box, plus a growing set of third-party tools (SEO, enrichment, social, scraping, and more) that anyone can add with a single PR.
This is a reference catalog, not a live proxy. Every entry is documentation —
what a tool does, what it costs, how to call it — not something this repo calls
for you. models/ entries run through your own muapi key; providers/ entries
run through the contributor's own account with that provider.
Agents: read llms.txt — one fetch teaches you how to browse and
use this whole catalog, no install or auth required.
The tools worth calling from an agent are scattered across dozens of vendors, each with its own docs, auth quirks, and pricing page — and most of the useful ones sit behind a subscription nobody buys for a single call (Semrush $139/mo, Moz $99/mo, Crunchbase $99/mo), or behind docs vague enough that you don't know what a call actually costs or returns until you've already signed up. This catalog puts the facts that matter — auth shape, real pricing, a captured example response — in one consistent shape, so an agent (or a person) can scan it and know exactly what a tool needs before ever opening its docs.
models/*.yaml |
providers/*.yaml |
|
|---|---|---|
| What it is | One of muapi's own hosted generative-media models | A third-party API a contributor already uses |
| Called with | Your muapi API key | The contributor's/your own key for that provider |
| Who adds it | Auto-synced from muapi's live catalog | Anyone, via PR |
| Editable by PR? | No — see "muapi-hosted models" below | Yes — this is the open contribution path |
ls providers/ models/ # browse what's catalogued cat capabilities.yaml # browse by category instead — media.*, seo.*, people.*, ... cat providers/<provider>.yaml # base_url, auth, endpoints, pricing for a third-party tool cat models/<model>.yaml # what a muapi-hosted model does, its cost, its docs page
providers/_TEMPLATE.yaml to providers/<your-provider>.yaml.CONTRIBUTING.md for the
full checklist, including the one non-negotiable step: get a real key and
confirm at least one endpoint actually works before opening the PR. A schema
that was never called against the real API is not accepted.python3 scripts/catalog_validate.py providers/<your-provider>.yaml
status to verified.See CONTRIBUTING.md for the full guide, including selection
heuristics (what gets accepted vs. rejected) and common gotchas per auth style.
These entries are generated directly from muapi's own live catalog — not hand-written,
and not open to arbitrary edits, since they describe what muapi itself already runs.
Each one deliberately omits how muapi actually serves the model (no base_url, no
auth details, no vendor name) — only the model itself, its cost, and a link to its
docs page. Missing a model, or see one that's wrong? Open an issue rather than a PR;
the catalog is refreshed from the source of truth periodically.
draft (providers only) — submitted, not yet independently verified by a maintainer.verified (providers only) — a maintainer confirmed the entry against a real key
and a real call; examples/<id>.json holds a real captured response.live (models only) — currently available through muapi.Treat draft entries as a starting point, not a guarantee — verify before relying
on one yourself.
MIT — see LICENSE.
Find code by what it does. JevGrep helps coding agents find relevant code when they do not know the file name or symbol to search for.
Ask a question such as “Where is session expiry handled?” and JevGrep scans the authorized repository, asks Jev to score all eligible fragments, then returns the original source excerpts with their paths and line numbers. The calling agent can read those files in detail and continue its work with less exploratory context.
Use the CLI or connect a coding agent through the local MCP server.
JevGrep is useful when a coding agent needs to:
It complements exact tools such as rg. If you already know the symbol or literal,
ordinary text search is usually faster.
JevGrep searches every valid UTF-8 text file, regardless of repository language or extension.
Install the public package from npm:
npm install -g @nassim-arifette/jevgrep jevgrep --version
The unscoped package name jevgrep belongs to a different project. Use the complete
scoped name above when installing. The installed command is still jevgrep.
Package: @nassim-arifette/jevgrep
To install a development checkout instead:
git clone https://github.com/nassim-arifette/jevgrep.git
cd jevgrep
npm ci
npm run build
npm link
jevgrep --versionnpm link makes the jevgrep command available from any directory on the computer.
Configure the provider and key once for the computer:
jevgrep init --global
TypeSafe AI is proposed first. To use Vercel AI Gateway instead:
jevgrep init --global --provider vercel
To use OpenRouter:
jevgrep init --global --provider openrouter
The command stores the credential in the user's JevGrep configuration directory, not
in a repository. TYPESAFE_API_KEY, AI_GATEWAY_API_KEY and OPENROUTER_API_KEY
environment variables take priority over the corresponding stored value.
Run init once from the repository root:
cd path/to/my-project
jevgrep initThe default root is the current directory. You can also provide it explicitly:
jevgrep init --root path/to/my-project
Provider credentials are global, but repository authorization is not. Each repository
must be authorized separately. Its trusted profile is stored outside the repository.
Interactive init asks before enabling remote evaluation for this repository:
Allow sending eligible source excerpts from this repository to Vercel AI Gateway? [y/N]
Answer y to search immediately. Enter or n keeps remote evaluation disabled.
Non-interactive initialization also leaves new profiles disabled. Optional scan caps are disabled by default;
configure them if you want to limit usage.
init also creates a commented .jevgrepignore in the repository when one does not
already exist. Existing exclusions are preserved; .gitignore is already respected.
jevgrep doctor jevgrep inspect
doctor checks the selected provider, credential state, authorized root, limits and
cache without making a network request.
inspect shows which files and fragments are eligible, what was excluded and how much
work a search would perform. It also stays offline.
If you did not enable remote evaluation during init, review the scope and limits,
then edit the profile path printed by init and set
remote_evaluation_enabled to true to allow source disclosure to the selected provider.
jevgrep search --query "Where is session expiry handled?"Useful options:
# Search only selected directories jevgrep search --query "How are permissions checked?" --scope src --scope tests # Return the canonical JSON response jevgrep search --query "Where is the cache invalidated?" --json # Read a multiline question from a file jevgrep search --query-file question.txt # Allow a deterministic partial scan when an enabled scan cap is exceeded jevgrep search --query "How does synchronization work?" --allow-partial
JevGrep automatically finds the authorized project for the current directory, including
when the command runs from a subdirectory. --config <path> remains available as an
explicit override.
| Provider | Setup | Model |
|---|---|---|
| TypeSafe AI | jevgrep init --global --provider typesafe |
jev-1.13.0 (pinned) |
| Vercel AI Gateway | jevgrep init --global --provider vercel |
typesafe-ai/jev |
| OpenRouter | jevgrep init --global --provider openrouter |
typesafe/jev-1.13 |
The TypeSafe transport follows the documented System One HTTP contract and is covered with simulated responses. It has not been tested against a real account in this project. Vercel AI Gateway has been checked on a small authentication example, including a repeat search served entirely from the score cache.
OpenRouter uses its alpha Decisions endpoint, POST https://openrouter.ai/api/alpha/decisions,
with Bearer authentication and structured Noul questions. The adapter supplies both
true and false criteria, reads answers[id].noul, usage.input_tokens,
usage.output_tokens and the response id, and disables provider fallback.
Its request and response handling were reviewed against the
official OpenRouter OpenAPI specification
(DecisionsRequest, DecisionsNoulQuestion, DecisionsResponse) on 2026-09-20.
No live OpenRouter request or automated test was run for this integration.
The alpha API may change. See the Jev model page
and OpenRouter configuration example.
To switch an existing global and project profile to Vercel:
jevgrep init --global --provider vercel jevgrep init --provider vercel
Use --provider openrouter in both commands to switch to OpenRouter.
JevGrep exposes the same search engine through a stdio MCP server:
jevgrep mcp
The server exposes one tool, semantic_search_code. Starting it does not scan files or
contact a provider. A tool call performs a search using the authorization associated
with the current directory.
Configure and authorize the repository first. One server process serves one repository.
Use the absolute profile path printed by jevgrep init so the server does not depend
on the client's working directory. Replace the example paths below.
After installing JevGrep:
claude mcp add --transport stdio jevgrep -- jevgrep mcp --config "/absolute/path/to/config.json"Check claude mcp get jevgrep and /mcp in Claude Code. See the
Claude Code MCP documentation.
Add an entry to your Codex config.toml:
[mcp_servers.jevgrep] command = "jevgrep" args = ["mcp", "--config", "/absolute/path/to/config.json"] tool_timeout_sec = 360
The suggested client timeout leaves a margin over JevGrep's default 300-second search deadline. Adjust both for your workload. See the Codex MCP documentation.
For clients that accept mcpServers configuration:
{
"mcpServers": {
"jevgrep": {
"command": "jevgrep",
"args": ["mcp", "--config", "/absolute/path/to/config.json"]
}
}
}Credentials saved by init --global are available to clients running as the same OS
user. Environment keys must be available to the client process. Do not commit keys
in MCP configuration.
If the client cannot find jevgrep or launch an npm shim on Windows, use absolute
paths to node and the installed dist/cli.js. See the
installation guide.
These examples have not yet been qualified with real Codex and Claude Code sessions.
Confirm that your client lists semantic_search_code and completes a search.
Search evaluation is remote. When you run jevgrep search, eligible source fragments
are sent to the configured provider together with:
JevGrep excludes common credential files, .env files, dependencies, build output,
generated files, minified files and files that match credential patterns. Links and
junctions are not followed. Run jevgrep inspect to review the eligible scope before
the first live search.
Credential filters cannot detect every secret; add repository-specific exclusions in
.jevgrepignore where needed.
The credential is never placed in the search payload, result or cache. Redirects are not followed by either transport. Provider retention and privacy policies still apply to anything sent remotely.
Human-readable output is the default. Pass --json for the validated response contract.
The result includes coverage information, exclusions, stop reasons and exact excerpts,
so an empty or partial result is not presented as proof that code does not exist.
| Code | Meaning |
|---|---|
0 |
complete result |
2 |
invalid request, configuration problem or rejected preflight |
3 |
partial result |
4 |
fatal runtime failure |
130 |
interrupted |
Results go to stdout. Diagnostics and measurements go to stderr.
JevGrep caches provider scores outside the repository, independently for each question and fragment. Changing another fragment does not invalidate an unchanged score. Provider, endpoint, model, query, source, location, criterion and layout remain part of the identity. Only misses are grouped into requests.
New TypeSafe direct profiles pin jev-1.13.0 and use the configured cache TTL (seven
days by default). Vercel's typesafe-ai/jev and OpenRouter's typesafe/jev-1.13
use the conservative rolling policy: scores can be reused for up to 15 minutes.
OpenRouter may resolve the requested model to a dated revision in its response;
the version alias is not treated as an immutable cache identity. Existing direct profiles
using jev-latest or jev-preview use the same short-lived policy.
Rolling reuse can briefly serve a score from an earlier model revision. doctor
shows this policy and its effective TTL. Set cache.rolling_ttl_seconds to 0 to
disable it, or to an integer from 1 to 900 to shorten it. cache.enabled: false
disables all score reuse. Existing profiles do not need to be recreated.
Clear the cache for the current project with:
jevgrep cache clear
Cached entries contain scores and identities, not source text, questions or credentials.
Fragments remain small enough to return precise excerpts. Requests pack fragments by the estimated tokens in the complete serialized payload, including the query, criteria and metadata.
| Transport | Aggregate ceiling used | Target with tokenizer headroom |
|---|---|---|
| TypeSafe direct | 64,000 tokens | 44,800 reference tokens |
| Vercel AI Gateway | 32,000 tokens (conservative local policy) | 22,400 reference tokens |
| OpenRouter | 32,000 tokens (conservative local policy) | 22,400 reference tokens |
TypeSafe documents 64k total and 32k for shared state plus one question. Gateway and OpenRouter advertise a 32k context; using it as an aggregate ceiling is conservative, not a claim that they document the same total-question limit. All three paths keep 30% headroom because the provider tokenizer is not public, and locally limit each request to 64 questions and 256 KiB. These last two limits are application safeguards. See TypeSafe model limits and the Gateway model catalog and OpenRouter Jev model page.
inspect and search planning use the same serializer and token estimator; inspect
uses a sample query, so its estimate can differ from an actual search. Estimates are
not provider billing. File preparation still runs on every search: there is no
persistent repository index.
npm ci
npm run typecheck
npm test
npm run build
npm run smokeRun the complete local verification gate with:
npm run verify
The test suite is offline and does not use provider credentials. Run npm run bench
for the local performance baseline, or npm run bench:retrieval -- --validate-only
to check the annotated retrieval pilot without network access. Real retrieval runs
use an explicit provider configuration. See benchmark commands and interpretation.
CI verifies benchmark correctness without enforcing machine-dependent timing limits.
MIT © 2026 Nassim Arifette.
A list of things built with Jev, TypeSafe's model for typed decisions, with the numbers behind them: who posted each demo, how many followers they have, how many likes it got, and what the limits of the model are. This list is open source (CC0), free to copy and reuse, and sponsored by AY Automate. It is unofficial and is not affiliated with TypeSafe.
Every entry links to the original post or repository. Ideas that nobody has shipped are in their own section and marked as ideas.
The 30 most-liked demos, ranked. Click a card to open the original post. Each card shows the demo's rank, area, author, likes, reposts, and reach (likes divided by the author's followers). Preview frames are low-resolution stills from the builders' own videos and belong to them. If an author wants one removed, open an issue. Full metrics for every demo are in docs.
Short answers to the questions people ask most, each with a source.
What is Jev? Jev is a model from TypeSafe AI that answers typed questions instead of writing text. Each question is a Choice, a Score or a Noul (yes or no with a probability), and the answer comes back as a number or a pick with a confidence. It does not generate text. See What Jev is.
Is Jev the same as the "Jev" that searches show for Jevons paradox or Deltarune? No. The word has other meanings. Search for "TypeSafe Jev" or "Jev AI model".
How do I call the Jev API?
Send a POST to https://api.typesafe.ai/v1/systemone with a bearer key. A full curl example is in docs/api-quickstart.md.
How much does Jev cost? TypeSafe lists $42 per billion input tokens, and output tokens are free. Vercel says Jev is free on AI Gateway until Sept 25. See Reported cost and latency.
What can I build with it? Routers, classifiers, judges, guardrails, triage and game agents. The Top 30 demos and Browse by area show real examples.
What are the limits of Jev? It reads literally, is weak at math, counting and dates, and accuracy drops with irrelevant state. See Limits of Jev 1.13.
Is Jev better than an LLM? It is a different tool. Use Jev for fast typed decisions and an LLM for writing. Many demos pair them.
Which open-source Jev projects exist? More than 150 repositories. See Open source and Long tail.
Is this list official? No. It is unofficial, open source under CC0, and sponsored by AY Automate.
Short on time? Read in this order.
Every tracked demo, grouped by what it does and ranked by likes inside each group.
| Area | Demos | Top demo | Likes |
|---|---|---|---|
| Content and growth | 18 | Real-time slop detector as you scroll | 7,180 |
| Apps and tools | 17 | Instant compaction for Claude | 10,435 |
| Agents and computer use | 14 | Flight search with Browser Use | 8,723 |
| Triage and routing | 9 | 500 emails for 3.5 cents | 3,853 |
| Games and real time | 7 | Jev plays Doom | 4,890 |
| Research and data | 7 | jevlike | 2,018 |
| Trading and markets | 2 | jev-trader | 4,913 |
Continued at the source.
A curated awesome list of public projects and practices built on Jev, TypeSafe AI's System One model for typed decisions.
This README is the homepage aggregate of the current category files, so the latest accepted entries are visible here without drilling into subpages.
A curated list of public projects and developer patterns built on Jev, TypeSafe AI's System One model for typed decisions.
What is Jev? Jev is not a chat model. It does not write text or hold conversations.
Instead, it takes unstructured state alongside a typed question and returns a typed decision—such as a choice, a score, or a boolean—accompanied by a confidence rating.By eliminating token-by token decoding, Jev acts as a fast, low-latency decision layer directly inside software.
Developers use it to handle classification, infrastructure routing, rubric scoring, verification gates, and autonomous agent guardrails.Goal of this ListMost discussions about Jev are scattered across launch threads, social media, and one-off prototypes.
This repository centralizes those pieces to answer two practical questions for developers:
Most Jev discussion is scattered across launch threads, model-gateway listings, and one-off prototypes. This list answers two practical questions quickly:
We do not include:
Inclusion means one thing: the entry satisfies the inclusion rules above. It is not a quality review, a security audit, or a recommendation. We do not verify that a project compiles, that its tests pass, that its published numbers reproduce, or that its license permits your use.
This matters most for projects that arrive in bulk. When one author releases several repositories on the same day, they commonly share a single scaffold — the same AGENTS.md, CLAUDE.md, STATE.md, and CHANGELOG.md — land in one or two commits each, and may ship considerably more prose than code. Such projects can be entirely legitimate; they are simply unproven. Treat them as leads, not as validated tools.
Before adopting an entry, check it yourself:
| Check | Why it matters |
|---|---|
| Does the code actually call the Jev API? | An entry can read well on a README alone. Look for a real request carrying typed questions, and a parsed answer coming back. |
| Is there a runnable check? | A test, an example with expected output, or a public demo. No check means no evidence that it works. |
| Do the numbers have a source? | Any accuracy, latency, cost, or volume figure should be traceable to the linked page. We strip claims we cannot verify, but the project page itself may still carry them. |
| How much of the repository is code? | Some projects are mostly prompt documents. That can be legitimate — just know which one you are getting. |
| Is there a license? | A few entries have none, which limits reuse and redistribution. |
Found something wrong? Open an issue or a pull request — removal is as valid a contribution as addition. Rules for AI-assisted work, project depth, and submission rate live in CONTRIBUTING.md.
Each entry lives in exactly one category. When a project could fit multiple categories, we choose the one closest to its direct application domain.
Source file: categories/classification-routing.md
NOTRA_JEV_CLASSIFIERS flag routes brand-visibility classifiers off an LLM and onto Jev Boolean decisions at a 0.5 threshold, targeting 300 ms p50.Choice (invoice or general) and routes each inbound email to the matching handler./v1/systemone endpoint to new-api so typed decisions sit behind the same gateway as chat models.Choice and Noul questions, sending low-confidence answers to human review.Source file: categories/verification-guardrails.md
Noul checks about source and build files, escalates suspicious chunks for a second pass, and returns implicated files and lines before execution.typesafe_permission_reviewer builtin so the agent's permission decisions run through Jev rather than an LLM call.Boolean questions per paragraph (stacked hedges, restating closers, not-X-but-Y turns, naked cost figures) at a 0.7 threshold; CLI, pre-commit hook, GitHub Action and Claude Code skill; measured 182 ms median and 1 of 54 clean paragraphs flagged against 37 for Haiku 4.5.jev-pref.json rules that Jev checks against each diff hunk, staged file set, or pull request, returning fix_now or advisory findings to the coding agent and a nonzero exit code on blocking ones.Source file: categories/scoring-ranking.md
Score axes inside a single systemOne request and turns them into a 0-100 Slop Score in ordinary TypeScript.Noul properties per source file so the agent knows what to fix first.Source file: categories/agent-decisions.md
Continued at the source.
Headless MCP server that generates teacher verification documents — employment letters, teacher ID cards, teaching licenses, payslips, and more — across 13 countries.
Portable, self-contained, and installable anywhere.
Documents are rendered as high-resolution PNGs. Examples generated by this tool:
| Teacher ID (US) | Employment letter (US) |
|---|---|
| Teacher ID (UK) | Employment letter (UK) |
|---|---|
| Code | Country | Document types |
|---|---|---|
uk |
United Kingdom | employment_letter, teacher_id, teaching_license |
us |
United States | employment_letter, teacher_id, teaching_license |
france |
France | installation_statement, iprof_screenshot, bylaws_extract, teaching_certificate |
netherlands |
Netherlands | employment_contract, teacher_registration, duo_declaration, school_id |
indonesia |
Indonesia | payslip, teaching_experience_letter, nuptk_card, appointment_letter |
australia |
Australia | signed_school_letter, school_id, teaching_license |
canada |
Canada | oct_card, teaching_license, signed_school_letter |
spain |
Spain | teaching_id, signed_school_letter, employment_contract |
argentina |
Argentina | payslip, employment_certificate, signed_school_letter |
slovakia |
Slovakia | payslip, employment_letter, signed_school_letter |
mexico |
Mexico | teaching_id, signed_school_letter, employment_certificate |
philippines |
Philippines | teaching_id, employment_certificate, teaching_license |
thailand |
Thailand | payslip, letter_of_employment |
Pillow, mcppip install dist/yowes_doc_generator-0.1.0-py3-none-any.whl
pip install -e .uvx --from . yowes-mcpThe server speaks MCP over stdio — the transport used by most agent runtimes (Hermes, Claude Desktop, and any MCP client). Connect it, discover the tools, then call them.
# from the built wheel pip install dist/yowes_doc_generator-0.1.0-py3-none-any.whl # or editable from source pip install -e .
Verify the install and that bundled assets resolve:
python -c "from countries.utils import load_font, get_profile_photo; \ print(load_font(30).getname()); print(get_profile_photo((280,340), person_id='x', gender='Male') is not None)" # ('DejaVu Sans', 'Book') <-- bundled font, not system # True <-- bundled photo found
# After install: yowes-mcp # Or from source: python mcp_server.py
It blocks and waits for MCP requests over stdin/stdout — don't run it as a foreground terminal app expecting prompts.
Point your MCP client at the yowes-mcp command:
{
"mcpServers": {
"yowes": {
"command": "yowes-mcp",
"args": []
}
}
}If yowes-mcp isn't on your PATH, use the absolute path to your interpreter and module instead:
{
"mcpServers": {
"yowes": {
"command": "/path/to/python",
"args": ["-m", "mcp_server"]
}
}
}| Tool | Description |
|---|---|
list_countries_tool |
List available countries, display names, and their document types. |
list_schools(country) |
List all schools for a country code. |
generate_documents(...) |
Render one or more documents to PNG and return their paths. |
No arguments. Returns one result item per country — { code, name, document_types }. (Because a list return is split into one MCP content item per entry, iterate content to see them all.)
country (required) — country code from list_countries_tool (e.g. "us").{ name, address, town, postcode, state, phone, lea }. Iterate content to see them all.| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
country |
string | ✅ | — | Country code (e.g. "us", "uk"). |
first_name |
string | ✅ | — | Teacher's first name. |
last_name |
string | ✅ | — | Teacher's last name. |
school_name |
string | ✅ | — | Exact or partial school name (matched against that country's school list). |
position |
string | ✅ | — | Teaching position/title. |
date_of_birth |
string | ✅ | — | DOB string, printed on the teacher ID (e.g. "12/05/1988"). |
gender |
string | — | "Random" |
"Random", "Male", or "Female" — selects the profile-photo pool. |
document_types |
string[] | — | all types | Which documents to render, e.g. ["employment_letter", "teacher_id"]. |
output_dir |
string | — | output/ |
Where to save PNGs (relative to the server's working dir). |
Returns { country, school, document_types, files, count, output_dir } — files are absolute PNG paths.
Minimal working client (requires pip install mcp):
import asyncio from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client async def main(): params = StdioServerParameters(command="yowes-mcp", args=[]) async with stdio_client(params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() countries = await session.call_tool("list_countries_tool", {}) # A list return is split into one content item per entry: for item in countries.content: print(item.text) res = await session.call_tool("generate_documents", { "country": "us", "first_name": "John", "last_name": "Smith", "school_name": "Valley High", "position": "Head of Science Department", "date_of_birth": "12/05/1988", "gender": "Male", }) print(res.content[0].text) asyncio.run(main())
list_countries_tool to see what's available.list_schools("us") to pick a real school.generate_documents(...) with the chosen country, school, and person details.Generated PNGs are written to output/ (or the output_dir you pass).
A tkinter (CustomTkinter) GUI is still available for manual use. The core generation logic is shared.
python main_gui.py # on Windows, use run.bat (sets TCL_LIBRARY)The MCP server is the primary, headless interface. The GUI is optional and not required for the skill.
yowes/
├── countries/ # Document generation core (package)
│ ├── base.py # CountryGenerator ABC (contract)
│ ├── utils.py # Fonts, profile photos, shared helpers
│ ├── foto/ # Bundled profile photos (package data)
│ ├── fonts/ # Bundled DejaVu fonts (package data)
│ └── <country>/ # One package per country
├── mcp_server.py # MCP server exposing tools
├── main_gui.py # Legacy tkinter GUI
├── docs/examples/ # Sample rendered documents
├── pyproject.toml # Packaging, deps, entry point
├── output/ # Generated documents (git-ignored)
└── run.bat # Windows GUI launcher
countries/<code>/__init__.py with a class inheriting countries.base.CountryGenerator.get_country_name, get_country_code, get_schools_data, get_first_names, get_last_names, get_positions, get_document_types, generate_document.countries/__init__.py via register_country("<code>", <Name>Generator).main_gui.py (get_country_list / on_country_change).The new country is automatically picked up by the MCP list_countries_tool and list_schools.
MIT © 2026 hirotomasato
A curated list of Jev use cases, projects, SDKs, tools, and learning resources.
Jev is the first System One model from TypeSafe AI — an AI model that returns typed decisions (Choice, Score, Noul) with calibrated probabilities instead of generated text.
Looking for real-world Jev use cases with numbers? madewithjev.com is a directory of what people are building with Jev — every build with the cost, latency, and source the author reported. Submit yours →
Jev launched in early access on September 15, 2026. This list is unofficial and not affiliated with TypeSafe AI. Pull requests are welcome — the ecosystem is days old and growing fast.
Large language models generate text. Jev does not. It evaluates typed questions against a state and returns values your code can branch on, sort by, and route with — plus calibrated probabilities and confidence. TypeSafe AI calls this model class a System One model: fast, structured decisions that software can use directly, trained with RLCD (Reinforcement Learning for Calibrated Decisions).
text or JSON state + typed questions → constrained answers + probabilities → your code
Jev exposes three question types. Questions in one request run in parallel against the same state.
| Question | Goal | Returns |
|---|---|---|
| Choice | Pick one option from a list | choice, probabilities, confidence |
| Score | Rate the state on a rubric | score, probabilities, confidence |
| Noul | Is this statement true? | noul (0–1) |
Use it to classify, route, score, detect, rank, extract, verify, and gate automation — anywhere you would otherwise write a brittle regex or pay an LLM to return JSON you then have to parse. Questions describe judgments; your code owns composition, thresholds, and side effects.
From TypeSafe's launch post:
| Existing LLMs | System One + Jev | |
|---|---|---|
| Optimized with | RLHF / RLVR | RLCD (Reinforcement Learning for Calibrated Decisions) |
| Optimizes for | Human preference; verifiable rewards | Calibrated decisions with honest probabilities |
| Output | Strings that need parsing and validation | Type-safe structured values, defined in advance |
| Sampling | Sequential, token by token | Parallel, all outputs in a single query |
| Cost | $0.20–$10 / MTok input, output ~5x more | $0.042 / MTok input, output free |
| Speed (vendor-reported) | 3–329 s end-to-end for frontier models | 70–500 ms end-to-end |
| Confidence | Tends to be overconfident if asked | Calibrated confidence on every answer |
| Best at | Chat, writing, code, open-ended reasoning | Decisions inside software: classify, route, score, verify |
Jev is not a replacement for an LLM. When you need free-form text, pair them: let Jev route, retrieve, verify, or guard the call, then let the LLM write inside the boundaries your code enforces.
Snapshot reviewed September 18, 2026. Check Models for current values — limits can change dynamically.
| Item | Current detail |
|---|---|
| Model alias | jev-latest (current version: jev-1.13.0) |
| Endpoint | POST https://api.typesafe.ai/v1/systemone |
| Price | $0.042 / 1M input tokens; output tokens free |
| Listed limits | 250,000 tokens/second, 1,200 requests/minute |
| Choice cardinality | Up to 255 options per Choice question |
| Modalities | Text only — no images, audio, or video |
| Direct access | Early access via waitlist at typesafe.ai |
| No-waitlist access | Vercel AI Gateway (typesafe-ai/jev) and Cloudflare Workers AI (typesafe/jev) |
Get an API key from the TypeSafe console, then:
Python — pip install typesafe-sdk
from typesafe_sdk import Choice, Noul, Score, TypeSafeClient state = {"ticket": "I was charged twice and need the duplicate refunded today."} with TypeSafeClient() as client: # reads TYPESAFE_API_KEY from the environment response = client.system_one( state=state, questions={ "intent": Choice( instructions="What is the customer's main request?", criteria={ "refund": "The customer wants money returned.", "technical_help": "The customer needs a bug or integration fixed.", "information": "The customer is asking for information only.", "other": "None of the other options clearly fits.", }, ), "is_urgent": Noul(instructions="Does the ticket explicitly communicate time pressure?"), "frustration": Score( instructions="How frustrated does the customer appear?", criteria=["Calm and neutral", "Concerned but civil", "Very angry or using strong language"], ), }, ) print(response.answers["intent"].choice) # "refund" print(response.answers["is_urgent"].noul) # 0.0–1.0 print(response.answers["frustration"].score) # probability-weighted rubric position
JavaScript / TypeScript — npm install @typesafe-ai/sdk
import { choice, noul, score, TypeSafeClient } from "@typesafe-ai/sdk"; const client = new TypeSafeClient(); const result = await client.systemOne({ state: { ticket: "I was charged twice and need the duplicate refunded today." }, questions: { intent: choice("What is the customer's main request?", { refund: "The customer wants money returned.", technical_help: "The customer needs a bug or integration fixed.", information: "The customer is asking for information only.", other: "None of the other options clearly fits.", }), isUrgent: noul("Does the ticket explicitly communicate time pressure?"), }, });
On Vercel AI Gateway, use experimental_evaluate from the AI SDK with the model id typesafe-ai/jev. See the official quick start for details.
TYPESAFE_API_KEY).POST https://api.typesafe.ai/v1/systemone.typesafe-ai/jev for the AI SDK's experimental_evaluate, no TypeSafe waitlist required.typesafe/jev via env.AI.run, with worked support-routing and risk-escalation examples.Production-shaped uses with the cost and latency their authors reported. Each links to a full breakdown on madewithjev.com, the Jev use-case directory that maintains this list.
| Build | What it does | Reported numbers | Source |
|---|---|---|---|
| Jev plays Doom | Game loop asking Jev what to do ~10 times a second | ~10 queries/s, ~$7/hour | X |
| jev-ultrafast | Browser Use's agent with the next-action decision moved to Jev | ~2.9k stars | GitHub |
| Flight search with Browser Use | Booking flow driven end to end | ~7 s, ~$0.004 | X |
| Stagehand on a remote browser | Browser tasks at a tenth of a cent each | ~$0.001/task | X |
| jev-trader | Buy/sell decided inside a 300 ms Monad block, on Kuru's order book | 300 ms decision window | GitHub |
| Triage across 1,500 emails | A full inbox sorted in one pass | ~1,500 emails | X |
| Every's editorial vibe check | 37 documents, 21 questions each; 6 of 7 planted defects caught | 1,709 judgments, <$0.01, 0.35 s median | Every |
| 1kpapers | A corpus classified by topic and published as a site | 1,018 papers | Site |
| Jev plays chess | Legal moves as a Choice, compared with reasoning models | illegal moves impossible by construction | dev.to |
| 3,282 posts, eight questions each | Ian Nuttall's X back catalogue scored for what travels | 4.25M tokens, $0.1282, 8 m 34 s | X |
| Post scoring with SuperX | 61 questions about a draft before it ships | ~1 s, $0.0004/draft | X |
| 724 competitor ads, broken down | Hook, format, offer, CTA per ad across 37 brands | ~40 s, ~$0.09 | X |
| typesafe-computer-use | macOS computer use, one typed decision per step | ~$0.0002/step | GitHub |
| jev-drone | Tactical judgment loop flying on hardware | control at 2.5 Hz | GitHub |
| Wikiracing | Pick one link out of thousands until you arrive | 255-option Choice ceiling | TypeSafe |
All figures are as reported by each author, not measured by this list. → Browse the full directory at madewithjev.com
Official first, then community clients. Community packages are not affiliated with TypeSafe.
Official
pip install typesafe-sdk. Docs.npm install @typesafe-ai/sdk. Docs.TypeSafeClient replacement backed by LLM APIs, to compare Jev against chat models on the same questions. pip install system-one-adapter.@ai-sdk/typesafe-ai with experimental_evaluate; use typeSafeAi.evaluationModel('jev-latest') or the Gateway id typesafe-ai/jev.Community, by language
go get github.com/Gaurav-Gosain/jev-go. Also Stumble/jev-go - dependency-free, works against TypeSafe direct and Vercel AI Gateway, with an interactive CLI and an installable agent skill.system_one and model listing. Also Jev (OTP) - Jev as a peer GenServer; answers arrive as messages you pattern-match, with network-free tests.pip install jevclient), separate from the official SDK.Open-source projects that put Jev in a real loop. Grouped by what Jev decides.
Continued at the source.
A curated, source-backed list of projects built with Jev, TypeSafe AI's System One model for fast, typed, probabilistic decisions.
Jev takes program state plus typed questions and returns constrained answers with probabilities. It is designed for software decisions such as classification, routing, scoring, ranking, verification, and guardrails, rather than free-form text generation.
This list favors public source code, concrete Jev usage, clear limitations, and reproducible evidence. The latest review added 20 source-reviewed integrations, projects, and studies, bringing the community catalog to 155, alongside official resources, provider integrations, and related lists. See the September 20 research notes for pinned source evidence and review boundaries. Review completed September 20, 2026 (Europe/Istanbul); upstream event dates below are UTC.
System One shape: text or structured state + typed questions → constrained answers + probabilities → deterministic application code.
Question primitives: Choice selects an option, Score evaluates ordered rubric levels, and Noul returns a number from 0 to 1 representing the probability of "yes". Review or abstention behavior is defined in application code. See the primitive reference.
Input boundary: the hosted Jev model is text-only. Browser, audio, image, and robotics projects supply extracted text or structured observations, or use separate perception models. Independent multimodal reproductions are listed separately.
Good fits: semantic routing, triage, reranking, rubric scoring, moderation, verification, and low-latency decisions inside bounded workflows.
Important caveat: schema-valid output is not the same as a correct decision. Validate on your own data, calibrate thresholds, keep high-impact actions behind deterministic checks, and provide a human fallback.
September 18: Python SDK 0.7.0. Release notes document a breaking serialization change from msgspec to Pydantic, a new response_model argument, and corrected serialization of str subclasses.
September 18: OpenRouter listing. Jev 1.13 is listed with a September 18 date. This is a provider listing date, not evidence of a separate new upstream model revision.
September 16: Vercel AI Gateway integration. The integration introduces typed evaluation via AI SDK's experimental evaluate API.
Current model: TypeSafe documents jev-1.13.0, with both jev-latest and jev-preview currently pointing to it. Pin the version when comparing evaluations.
September 20: framework adoption. Source-level Jev integrations are now present in LangChain, Pydantic AI, LiteLLM, Rig, Composio, Effect, BAML, Ax, and TanStack AI. Availability and release status vary, so inspect the linked repository before depending on a package.
typesafe/jev integration accepting state and typed questions.@typesafe-ai/sdk, with credentials and billing handled by Netlify.typesafe/jev-1.13, alongside the moving typesafe/jev-latest alias.typesafe-ai/jev through AI SDK's experimental evaluate interface; its Boolean primitive corresponds to TypeSafe's Noul.Upstream framework integrations with inspectable Jev implementations. Presence on a default branch does not guarantee a stable package release.
@effect/ai-typesafe decision model mapping Effect's classify, probability, and rating operations to Jev Choice, Noul, and Score questions.TypeSafeClassifier Runnable with batched typed questions plus model-routing and risky-tool middleware.rig-typesafeai crate with typed Choice, Score, and Noul queries, response validation, examples, and fixtures.@tanstack/ai-typesafe adapter exposing typed Boolean, Choice, and Score decisions through TanStack AI's decide() API.Community-maintained clients and tools; official TypeSafe SDKs are listed above.
Uncertain branch, and procedure routing, with recording, replay, caching, and call budgets as DecisionModel middleware.if Hunch.likely?("fraudulent", given: order) branches on a typed Jev answer, with graded predicates from possibly? to definitely?.tail -f.decide — to an application across generate/stream/decide.Source-reviewed experiments and integrations. A model judgment does not establish safety or replace the host application's permission checks.
jev-pref.json and feeds findings back to coding agents.Choice over the installed skill roster plus Noul gates, while code applies the thresholds and names at most one skill; it starts in a shadow mode that only logs the decision.Choice decisions select tools and targets, uncertain decisions escalate to an LLM, and a shared guarded kernel supports isolated Docker workspaces and paired LLM-only comparisons.jevonian/auto, after deterministic code has already filtered candidates by wire protocol, context window, thinking-level floor, and spent quota windows; minConfidence marks a low-confidence route in the ledger instead of silently accepting it, and Jev is skipped entirely for pinned models, explicit jevonian/<route> requests, and routing.mode: "off".These tools select what reaches a model. Preserving retained text verbatim does not prove that omitted history was unnecessary.
Continued at the source.
An agent skill that finds where a photo was taken — and shows its work.
Works with
…and any other agent that reads SKILL.md and runs shell commands.
No text. No plates. No landmarks. One bridge, one mountain. Located to within 2 m.
npx skills add Oldcircle/geo-sleuth
Pick your agents when prompted. Then hand your agent a photo and say:
find where this photo was taken
That is the whole interface. The agent reads SKILL.md, runs the scripts, and comes back with the camera position, the direction it was facing and a satellite evidence image. Prefer to copy the folder yourself? See Installation.
SKILL.md plus plain Python scripts — so the same folder runs in Claude Code, Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot.A phone photo with the EXIF stripped: a white oven at the edge of a harvested rice paddy, a long viaduct in the distance, a steep mountain on the right. Not a single character in the frame. One message to an agent with this skill installed, and it came back with the camera position and the direction the camera was facing.
photo → 27,335 → 171 → 14,372 → 22 → 3 → 1 → ±2 m
| Step | What it did | Candidates left |
|---|---|---|
| Read the photo | Poles on the viaduct are catenary masts, so it is an electrified railway. Pier spacing used as a ruler (32 m span assumed): the left segment is about 0.5 km away, the right one over 1 km. A steep mountain about 3 km away. Rice harvested but grass still green, so no frost yet. | South China, as a bet, not a proof |
| Region scan | Pulled every railway bridge in the region from OpenStreetMap: 27,335 segments. Sampled a point every 400 m and computed the 360° horizon from elevation data at each one. Kept points with flat ground nearby, a clear mountain within a few km, and a flat horizon next to it. | 171 sites |
| Skyline fit | Placed candidate camera positions around each site and rendered the ridge line seen from each one: 14,372 positions. The top 20 were within 0.1° of each other, so it added a constraint: the bridge must be near on the left and far on the right. | 22 |
| Overlay check | Drew the top three ridge lines back onto the photo. Score #1 (Fuzhou) had a bump hidden behind the oven, which is why it scored well. #3 (Huizhou) sloped where the photo is flat. #2 (Qingyuan) fit from the foot of the mountain to the edge of the frame. | 1 |
| Pier count | 17 piers in the photo become 17 bearings from the camera. Where they hit the railway line, the intersections must be evenly spaced. Combined with the skyline: first a band about 300 m long, then a single spot. | ±2 m |
geo-sleuth is a standard Agent Skill: one folder holding SKILL.md, scripts/, references/ and data/. Install it with the skills CLI, or copy the folder yourself.
All six agents, user-wide, one command:
npx skills add Oldcircle/geo-sleuth -g -a claude-code -a codex -a cursor -a gemini-cli -a opencode -a github-copilot -y
By hand:
git clone https://github.com/Oldcircle/geo-sleuth mkdir -p ~/.agents/skills ~/.claude/skills cp -r geo-sleuth/skills/geo-sleuth ~/.agents/skills/ # Codex, Cursor, Gemini CLI, OpenCode, GitHub Copilot ln -s ~/.agents/skills/geo-sleuth ~/.claude/skills/geo-sleuth # Claude Code
~/.agents/skills/ is read by Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot, so one copy there covers all five. Each agent's own folders, from its docs:
| Agent | User-wide | Per project |
|---|---|---|
| Claude Code | ~/.claude/skills/ |
.claude/skills/ |
| Codex | ~/.agents/skills/ |
.agents/skills/ |
| Cursor | ~/.cursor/skills/ or ~/.agents/skills/ |
.cursor/skills/ or .agents/skills/ |
| Gemini CLI | ~/.gemini/skills/ or ~/.agents/skills/ |
.gemini/skills/ or .agents/skills/ |
| OpenCode | ~/.config/opencode/skills/ or ~/.agents/skills/ |
.opencode/skills/ or .agents/skills/ |
| GitHub Copilot | ~/.copilot/skills/ or ~/.agents/skills/ |
.github/skills/ or .agents/skills/ |
Any other agent that reads SKILL.md and runs shell commands works the same way: put the folder where it looks for skills.
The work is split into three layers. Scripts decide, scripts perceive and rank, the model only judges among the top few.
flowchart LR
A["photo"] --> B["intake.py<br/>EXIF · OCR · reverse image search"]
B --> C["board.py<br/>candidate board: clues, likelihood ratios, ranking, next step"]
C --> D{"which branch?"}
D --> E["sun.py · terrain.py · osm.py · pose.py<br/>shadows, skylines, OSM corridors, camera pose"]
D --> F["sat_scan.py · match.py · gsv.py · baidu_pano.py<br/>CLIP-ranked satellite tiles, DINOv2+SIFT street view"]
E --> G["board.py check · report"]
F --> G
G --> H["evidence.py<br/>coordinates ± radius · evidence image · graded confidence"]
| Layer | Who | Tools |
|---|---|---|
| Decide: which candidates, how evidence scores, what can be excluded, where to scan next | scripts (the candidate board) | board.py |
| Perceive: read text, look up tables, find targets in satellite tiles, compare street view | scripts rank first, a person looks at the top few | intake.py ocr.py clues.py sat_scan.py match.py geo.py |
| Judge: pull clues from the frame, propose hypotheses, pick among the ranked few | the model | SKILL.md + references/ |
Every conclusion has to point at a command that actually ran in the session and the file it produced. Exclusions need read or computed evidence; observations and guesses can only lower a candidate's weight.
Twenty scripts, one job each. The full table with data sources is in skills/geo-sleuth/references/data-sources.md.
| What it does | Script |
|---|---|
| EXIF: GPS, capture time, equivalent focal length, heading | exif.py |
| OCR on the whole image, zoomed crops and tiles (Apple Vision on macOS, RapidOCR elsewhere) | ocr.py |
| Reverse image search on Baidu and Yandex, similar images tiled into a numbered sheet; keyword image search | revimg.py |
Steps 0–3 in one command: metadata, edge crops, variants, OCR, reverse search → intake.md |
intake.py |
| Zoom crops, edge and corner crops, tiling, pixel columns of evenly spaced structures such as piers | imgprep.py |
| Lookup tables: plate prefixes, landline area codes, calling codes, driving side, dependent territories, administrative divisions | clues.py + data/ |
| Candidate board: candidates, clues, likelihood ratios, exclusion, ranking, scan order, pre-report checks | board.py |
| Gazetteer: list sub-divisions with bounding boxes, built-up area extent | gazetteer.py |
| Place, compound or shop name → coordinate candidates, every namesake listed | poi.py |
| Sun and shadows: latitude band, time of day, street orientation, heading from lit faces, true bearings | sun.py |
| OSM Overpass: feature co-occurrence, line-to-point, route corridors, line intersections, street grid templates | osm.py |
| Satellite tile mosaics, markers, numbered thumbnail sheets | tiles.py |
| CLIP zero-shot scoring of satellite grid cells or candidate points (tracks, factories, silos, dams…) | sat_scan.py |
| Baidu panoramas / Google Street View: find points, render headings, thumbnail sheets, historical batches | baidu_pano.py gsv.py |
| Rank candidate ground-level images against the photo: DINOv2 global similarity + SIFT inliers | match.py |
| Elevation: synthetic mountain views, skyline overlays, profiles; linear feature × terrain scan, ridge extraction, batch skyline scoring | terrain.py |
| Multi-point camera pose: lat/lon, height, heading, pitch, roll, with error radius | pose.py |
| Bearings, distances, line-of-sight intersections, alignment lines, frame/occlusion checks, camera position from evenly spaced structures | geo.py |
| Evidence image: satellite tile + camera fan + comparison grid | evidence.py |
The three steps from the case above (region scan, batch skyline scoring, camera position from pier spacing) are built into the skill as subcommands: terrain.py scan / ridge / fit, imgprep.py piers, geo.py spacing. Case scripts tuned to that photo are kept in examples/rail-skyline-session/ for reference.
Per-operator measurements:
| Script | Test | Result |
|---|---|---|
match.py |
8 cases: a historical Baidu panorama batch rendered as the photo, panoramas within 150 m as candidates (Shenzhen) | ground truth ranked 1/2/4/1/1 and 5/1/6, all in the top 6, half at #1 |
sat_scan.py |
4×8 km, 364 cells at z17, 40 OSM-tagged running tracks as ground truth, multi-scale (Shenzhen) | recall@20 17/40, @30 22/40, @100 32/40, median rank 23 |
terrain.py scan / fit + geo.py spacing |
bounded re-run on the case photo above | true cluster ranks #1, final position about 2 m from ground truth |
clues.py |
6 tables, 9 values spot-checked | 9/9 correct |
The method comes from breaking down 14 videos by online-geolocation creators, 22 puzzles and a set of real runs, then turning what works into rules and scripts. v2 moves every rule that can be code into board.py, so the rules get executed, not just read.
Python 3.10+, uv and an agent that can run shell commands. Each script declares its own dependencies and uv run installs them on first use.
Optional: Google Chrome for reverse image search (uvx playwright install chromium works too), and export GEO_PROXY=socks5h://127.0.0.1:<port> to route every networked script through a proxy.
terrain.py scan / fitIssues and pull requests are welcome, see CONTRIBUTING.md. The most useful contributions are a transferable clue for references/clues/ (with a source), a new data source with its licence, or a run on your own photo where the skill went wrong and why.
skills/geo-sleuth/data/README.md.MIT, see LICENSE. Tables in data/ derived from Wikipedia are CC BY-SA 4.0; see skills/geo-sleuth/data/README.md.
Responsible use: run it on your own photos or ones you have permission to analyze, never to find people who have not agreed to be found.
中文 | English
中国创作者给全球 AI 模型设计的一场非标准化考试。这里收录首期测评视频，只做索引与导流，点击即回到 B 站观看。
A community-built, real-world test for leading AI models—curated as a searchable video index, with every view directed back to Bilibili and the original creator.
进入 B站AI无限竞技场 · 浏览 40 个主题 · 浏览 190 个视频 · 查看源数据 · MIT License
2026-09-18 实测： 从竞技场主页公开接口读取到 40 个主题、164 个不重复视频地址；再与首期腾讯文档的 75 个地址合并去重，共收录 190 个视频、33 位 UP 主。截图中的 6 个主题及其视频均已纳入。189 条视频通过 B 站视频接口读回完整元数据；另 1 条仍由竞技场公开接口列出，但原视频接口当前不可用，仓库保留其 BV 地址并明确标记。
下面直接展示当前收录的全部 190 个视频。点击封面或标题进入 B 站原视频；点击作者名进入 UP 主主页。
Continued at the source.
🌐 Search and filter ↗ ｜ 🤖 Install the Agent Skill ｜ 📂 Categories ｜ 🚀 Submit a project (Issue only)
[!TIP] Project Submissions: We welcome your Jev projects! All project submissions and updates are handled exclusively via GitHub Issues. This repository does not accept Pull Requests. Simply fill out the issue template with your repository URL.
When building autonomous agents, routing every small branching decision to a heavy reasoning model (System 2) incurs seconds of latency, runaway token costs, and context drift.
TypeSafe Jev (System 1) is purpose-built for fast, typed discrete decisions:
Choice, Score, and Noul without fragile JSON regex parsing.| Dimension | System 2 (Reasoning LLMs) | TypeSafe Jev (System 1 Radar) |
|---|---|---|
| Latency | 1,500ms – 5,000ms+ (Slow) | 50ms – 100ms (Sub-second reflex) |
| Output Type | Unstructured text / fragile JSON regex | Native typed Choice, Score, Noul |
| Token Economy | High cost ($1.00 – $15.00 / 1M tokens) | Ultra-lightweight (fraction of a cent) |
| Context Drift | Prone to hallucinations & attention fade | Deterministic bounded state machine |
| Primary Domain | High-level planning, open-ended generation | Tool routing, action dispatch, safety gates |
Search and filter ↗ · 597 curated projects
A community-maintained directory and radar for Jev, highlighting open-source projects with verified code and clear decision architectures.
Entries are linked to commit-pinned source code and specific decision points for easy reference. Compatible implementations clearly identify their underlying model.
Projects follow their respective open-source licenses; unstated licenses are noted individually.
Founding partner slots are open. There are no paid sponsors yet.
Sponsorship does not change inclusion review, project descriptions, or organic ranking.
Install the skill to query projects by domain and inspect pinned source evidence directly from your terminal or agent.
npx skills add logicrw/awesome-jev-projects npx skills add https://logicrw.github.io/awesome-jev-projects/
Agent Skill · llms.txt · llms-full.txt
Continued at the source.
A server implementing the TypeSafe/Jev HTTP API with Qwen3.6-35B-A3B on SGLang.
Each container one B200 with SGLang 0.5.19's Rust frontend,
radix caching, and breakable prefill CUDA graphs. A separate Python API process
uses FastAPI, uvloop, the Rust-backed HF tokenizer, and pooled asynchronous HTTP
connections to SGLang on localhost. CUDA dependencies stay in SGLang's container;
uv sync on your laptop installs only the API, deployment tools, and tests.
uv sync # Only if you haven't authenticated Modal on this machine: uv run modal setup # Start a temporary Server, run actual inference checks, then shut it down: uv run modal run modal_app.py # Deploy a stable public endpoint: uv run modal deploy modal_app.py
The deployment prints a https://...us-west.modal.direct URL. It uses a
Modal Server, unauthenticated=True,
routing_region="us-west", and compute_region=["us-west", "us-central", "us"].
Autoscaling has no explicit container cap and scales to zero after five idle minutes.
Set min_containers=1 in modal_app.py to keep a B200 warm.
If SGLang exits unexpectedly, the API exits too. The Modal launcher watches the
API and exits the container so Modal can replace it, rather than leaving a live
HTTP process with a dead inference backend. Normal shutdown disarms both watchers.
Cache warmups also request one unused token probability to avoid SGLang's mixed-logprob batch crash. This keeps warmups and scoring requests batch-compatible without patching SGLang.
The first build imports a large SGLang image. The first GPU start also downloads
weights and compiles/captures kernels. Model weights persist in the
openjev-huggingface Modal Volume, alongside SGLang's tuning cache and Triton
compilation cache. Later starts reuse these files; CUDA graph capture still runs
at startup. The Rust frontend receives an explicit local tokenizer directory to
avoid remote-name lookup issues with revision-pinned snapshots.
A scaled-to-zero Server returns 503 while it
starts; the included smoke command retries startup responses.
uv run openjev smoke https://YOUR-SERVER.us-west.modal.direct
The smoke test covers all three answer types, a 64-answer question, basic semantic
sanity checks, and rejection of 65 answers. It reports startup wait, inference
latency, and cache usage. modal run saves this report as smoke-result.json.
curl "$OPENJEV_URL/v1/systemone" \ -H 'Content-Type: application/json' \ -d '{ "model": "jev-latest", "state": [ {"role": "system", "content": "You are a support assistant."}, {"role": "user", "content": "I was charged twice. Please refund the duplicate."} ], "questions": { "refund": { "type": "noul", "instructions": "Does the user request a refund?" }, "department": { "type": "choice", "instructions": "Which department should handle this?", "criteria": {"billing": "Payments and refunds", "technical": "Software bugs"} }, "urgency": { "type": "score", "instructions": "How urgent is the request?", "criteria": ["Routine", "Urgent", "Emergency"] } } }'
Or use curl "$OPENJEV_URL/v1/systemone" -H 'Content-Type: application/json' --data-binary @examples/request.json.
state and instructions accept strings, JSON objects, or arrays. A state that is
a list of chat messages, or exactly {"messages": [...]}, is rendered using the
model's native chat template. Original roles and message objects are retained;
the classification question becomes an additional user turn, even after another
user turn. Other structured state is serialized intact into a user message.
Objects containing messages plus additional fields are kept intact so metadata
isn't silently discarded. Chat state supports text, not image/audio/video content.
| Route | Purpose |
|---|---|
POST /v1/systemone |
Noul, Choice, and Score evaluation |
GET /v1/models |
Model catalogue with TypeSafe and OpenAI-style fields |
GET /v1/limits |
Admission limits |
GET /health |
Readiness, including SGLang health and startup duration |
GET /health/live |
API process liveness |
GET / |
Scalar API reference with an editable example and request client |
GET /docs |
Built-in Swagger UI |
GET /openapi.json |
Generated API schema |
jev-latest is a compatibility alias for the configured Qwen model. The public
model ID is Qwen/Qwen3.6-35B-A3B;
NVIDIA's repository is only the internal weight source. Set
OPENJEV_SERVED_MODEL_NAME or openjev serve --served-model-name NAME to override
the public ID. It is also passed to SGLang as --served-model-name.
No requests go to TypeSafe. The public server is
the evaluation API; SGLang's generation and administration routes remain on
localhost and aren't forwarded publicly.
/generate with max_new_tokens=1, await completion,
and discard the sampled token. This warms SGLang's radix cache.prefix + question suffix + assistant header + "Answer:\n"
for each question. Every call again has max_new_tokens=1. Request
token_ids_logprob for every answer label and logprob_start_len=-1, so there
is no need to recompute prompt logprobs. The sampled token itself is ignored.P(yes), Choice returns the argmax and full distribution, and Score returns
sum(level_index * probability) with zero-based levels and a legend.Options are rendered as A: description, B: description, etc., without JSON
wrappers. Choice keys identify response fields and are hidden from the model,
except when a description is null: then the option key supplies its meaning,
matching Jev's nullable description schema.
This is a prefill plus first-token-readout workload: there is no generated chain of thought and no autoregressive continuation after the first token. There are N+1 one-token calls for N questions, including the cache-warming call. Speculative decoding is not enabled.
Qwen tokenizes 10 and 64 as multiple tokens. Answer labels are therefore
A–Z, followed by verified single-token letter combinations (AA, AB, ...).
All 64 labels are checked against the actual tokenizer at startup. Your original
option names are preserved in the returned distribution. This keeps 64-way
classification an exact one-token readout instead of comparing only the first
digit of a multi-token number.
Radix reuse is opportunistic, not a pinned per-request KV session. Hybrid Qwen's
recurrent state, cache page boundaries, cache pressure, and concurrent requests
can reduce hits. The backend uses --mamba-radix-cache-strategy extra_buffer.
x-openjev-prefix-tokens exposes the requested common prefix size. When SGLang
reports cache counts, x-openjev-cached-tokens sums the branch cache hits. SGLang
0.5.19's Rust frontend omits these counts: the header is absent and smoke reports
null, rather than a misleading zero. Scheduler logs still show actual cache
hits (verified on the live B200 deployment). Server-Timing separates
prompt preparation, the shared prefill, and branch inference.
usage.input_tokens sums SGLang's full prompt counts across the warm-up and all
branches, including cached tokens. usage.output_tokens is N+1. These are backend
usage counts, not TypeSafe billing estimates or unique tokens actually computed.
Defaults are 64 questions, 2–64 answers per Choice/Score, 2 MiB JSON,
32,768 tokens per branch including its output, 262,144 total submitted input
tokens, 16 simultaneous evaluations, and 64 simultaneous backend calls.
Invalid requests return 422 before inference; oversized bodies return 413;
overload returns 529 with Retry-After. Backend timeouts return 504. Failed or
cancelled evaluations cancel sibling requests and attempt to abort them in SGLang.
All settings can be provided as OPENJEV_* environment variables; see
src/openjev/config.py. Common settings:
| Variable | Default |
|---|---|
OPENJEV_MODEL |
nvidia/Qwen3.6-35B-A3B-NVFP4 |
OPENJEV_SERVED_MODEL_NAME |
Qwen/Qwen3.6-35B-A3B (profile-specific public name) |
OPENJEV_REVISION |
Pinned NVIDIA checkpoint revision for the default model |
OPENJEV_FRONTEND |
rust (python is an explicit fallback) |
OPENJEV_MAX_INPUT_TOKENS |
32768 |
OPENJEV_MAX_TOTAL_INPUT_TOKENS |
262144 |
OPENJEV_MAX_CONCURRENT_REQUESTS |
16 |
OPENJEV_MAX_CONCURRENT_BRANCHES |
64 |
OPENJEV_REQUEST_TIMEOUT |
120 seconds |
OPENJEV_TEMPERATURE |
1.0, applied during label normalization |
OPENJEV_API_KEY |
Unset; optional Bearer authentication for the API |
OPENJEV_BACKEND_API_KEY |
Unset; optional separate SGLang Bearer key |
The Modal launch script forwards OPENJEV_PROFILE, OPENJEV_FRONTEND, and
OPENJEV_SERVED_MODEL_NAME from the local environment. To customize other remote settings,
add them to image.env(...) or
use a Modal Secret for keys. The default Modal endpoint intentionally has no auth.
OpenJev defines
confidence = 1 - H(probabilities) / log(number_of_options), clamped to [0, 1].
This is zero for a uniform distribution and one for a point mass. Probabilities
are conditioned on the supplied options, depend on prompt and label ordering,
and are not calibrated estimates of correctness.
uv sync uv run pytest # offline unit + API tests uv run pytest -m integration # real tokenizer, small HF download, no GPU uv run ruff check . uv run openjev schema # no GPU or model download # Connect to an existing backend; it must have matching model/tokenizer revision, # selected-token logprobs, radix cache, and a sufficient context length: uv run openjev serve --connect http://127.0.0.1:30000 # On a B200 host/container with SGLang 0.5.19 installed in another environment: uv run openjev serve --sglang-python /path/to/sglang/bin/python
A repository's first hour decides its fate. Visitors judge the name, the description, the README and the topics before they read a single line of code. This tool audits a GitHub repository before launch and scores ten readiness signals 0-10, so you see what your first users will see - while you can still fix it.
$ github-launch-checklist repoboost-hq/buy-github-stars GitHub Launch Readiness ──────────────────────── Repository name ✓ descriptive and searchable Description ✓ present and well sized Topics ✓ 8 topics README ✓ 743 words License ✓ MIT Demo ✓ homepage set Installation ✓ installation or quick start found Contributing guide ✓ guide present Issue templates ✓ templates present Social preview ⚠ check manually in repo Settings Launch readiness: 9.5/10
| Check | What "ready" means |
|---|---|
| Repository name | Descriptive, searchable, not generic |
| Description | Present, 20-300 characters, keyword front-loaded |
| Topics | At least 5 relevant topics set |
| README | 300+ words with real content |
| License | A license file exists |
| Demo | A homepage or demo link is set |
| Installation | An install or quick start section exists |
| Contributing guide | CONTRIBUTING.md is present |
| Issue templates | .github/ISSUE_TEMPLATE is configured |
| Social preview | Checked manually (GitHub's API does not expose it) |
Every check is explained in docs/checks.md - including why it matters and how to fix a fail.
pip install git+https://github.com/repoboost-hq/github-launch-checklist.git github-launch-checklist owner/repo
Or run it without installing:
python checklist.py owner/repo
github-launch-checklist owner/repo # readiness report github-launch-checklist owner/repo --json # machine-readable output github-launch-checklist owner/repo --strict # exit 1 if readiness < 8/10 --token $GITHUB_TOKEN # optional, raises API rate limit
Works on any public repository. No token required for occasional use.
Available on the GitHub Marketplace - add it to any workflow in one step.
- uses: repoboost-hq/github-launch-checklist@v1 with: repo: owner/repo # optional - defaults to the calling repository fail-under: 8 # optional - fails the step below this score
docker run --rm ghcr.io/repoboost-hq/github-launch-checklist owner/repo
GitHub search weighs a repository's name, description, topics, README and engagement together. Most repositories fail their first impression not because of the code, but because the name is vague, the About line is empty, the topics are missing or the README never explains how to install anything.
This checklist is the pre-flight version of that reality: fix the ten things above and the repository is discoverable, credible and easy to adopt from day one.
Is it free? Yes - MIT licensed, open source, no sign-up, no limits beyond GitHub's API rate limits.
Do I need a token? No. A token only raises the rate limit if you audit many repositories in a row.
Why does it want at least 5 topics? Topics are how GitHub search filters discover repositories. Under 5 topics, you are invisible in most filtered searches. 5-10 relevant topics is the sweet spot.
Is the social preview automated? No - GitHub's API does not expose it, so the tool flags it as a manual check. Set it in repository Settings.
Can I use it in CI?
Yes: --strict exits non-zero below 8/10, so a workflow step fails when a repository regresses.
It says I am not ready - what do I fix first? The fails in order: description, installation section, topics, license. Those four move the needle most.
More from the org: github.com/repoboost-hq
🌐 buygithub.com | 🧰 More from the org
Built by RepoBoost. Independent service, not affiliated with GitHub, Inc.
A curated awesome list of public projects and practices built on Jev, TypeSafe AI's System One model for typed decisions.
This README is the homepage aggregate of the current category files, so the latest accepted entries are visible here without drilling into subpages.
Jev is not a chat model. It takes unstructured state plus a typed question and returns a typed decision — a choice, a score, or a boolean, each with a confidence. That makes it a drop-in decision layer for software: classification, routing, rubric scoring, verification, and agent guardrails. This list tracks who is actually building with it, and which patterns transfer across industries.
The repository treats all categories equally — each entry lives in exactly one category, chosen by its direct Jev application domain. A dedicated Related Practices / Discussions category captures credible public practice signals — X threads, Reddit discussions, and interviews — that describe real Jev usage even when no strong standalone case page exists yet.
Warning
A listing is not an endorsement. This project applies inclusion rules only — public, citable, genuinely uses Jev for a typed decision, one-sentence summary. It does not review code quality, security, maturity, or whether a project runs at all.
Treat same-day bulk submissions with particular care. Several repositories published together by one author, sharing a scaffold and a thin commit history, can satisfy every inclusion rule and still be unproven. Volume is not evidence of quality. See Curation is not endorsement for a checklist to run before adopting anything here.
Most Jev discussion is scattered across launch threads, model-gateway listings, and one-off prototypes. This list answers two practical questions quickly:
This is not a comprehensive database. It is a high-signal, fast-scanning field guide.
An entry should meet all of the following:
Jev/jev, cites TypeSafe AI's System One models, or shows a typed-decision loop (typed question → typed answer with confidence → accept/reject/escalate).We do not include:
Inclusion means one thing: the entry satisfies the inclusion rules above. It is not a quality review, a security audit, or a recommendation. We do not verify that a project compiles, that its tests pass, that its published numbers reproduce, or that its license permits your use.
This matters most for projects that arrive in bulk. When one author releases several repositories on the same day, they commonly share a single scaffold — the same AGENTS.md, CLAUDE.md, STATE.md, and CHANGELOG.md — land in one or two commits each, and may ship considerably more prose than code. Such projects can be entirely legitimate; they are simply unproven. Treat them as leads, not as validated tools.
Before adopting an entry, check it yourself:
| Check | Why it matters |
|---|---|
| Does the code actually call the Jev API? | An entry can read well on a README alone. Look for a real request carrying typed questions, and a parsed answer coming back. |
| Is there a runnable check? | A test, an example with expected output, or a public demo. No check means no evidence that it works. |
| Do the numbers have a source? | Any accuracy, latency, cost, or volume figure should be traceable to the linked page. We strip claims we cannot verify, but the project page itself may still carry them. |
| How much of the repository is code? | Some projects are mostly prompt documents. That can be legitimate — just know which one you are getting. |
| Is there a license? | A few entries have none, which limits reuse and redistribution. |
Found something wrong? Open an issue or a pull request — removal is as valid a contribution as addition. Rules for AI-assisted work, project depth, and submission rate live in CONTRIBUTING.md.
Each entry lives in exactly one category. When a project could fit multiple categories, we choose the one closest to its direct application domain.
Optional tags on an entry name the coding agent it targets and the kind of integration it is. Most entries carry none — they are added only when the source itself supports the classification.
Source file: categories/classification-routing.md
NOTRA_JEV_CLASSIFIERS flag routes brand-visibility classifiers off an LLM and onto Jev Boolean decisions at a 0.5 threshold, targeting 300 ms p50.Choice (invoice or general) and routes each inbound email to the matching handler./v1/systemone endpoint to new-api so typed decisions sit behind the same gateway as chat models.Choice and Noul questions, sending low-confidence answers to human review.Choice picks plain code, Jev or a reasoning LLM behind a Noul gate for non-tasks, code vetoes Jev when the idea needs images, and low confidence returns "not sure"; closed source, free page and API.Choice over the installed skill roster plus Boolean-style gates on whether any skill is needed, suggests a skill only when the gate and the per-candidate fit both clear 0.30, and defaults to a shadow mode that logs the decision without injecting it.Choice over ten kinds of post plus three Noul questions (paid ad, clickbait, emotional pressure) about each, counting an ad from 0.7, or from 0.4 when the kind is also ad, and clickbait and pressure from 0.5, then draws the monthly mix on a shareable card that links the highest-scoring posts for a manual check.Choice questions to classify CSV columns into a 13-code type vocabulary and each dataset into one of six scenes, then executes every write locally; measured Jev at 6.6–12.7× an LLM's token cost on this task because the output is already one character while per-question criteria repeat.jevonian/auto from session state, quota health, candidate capabilities, and cache-switch penalties, after deterministic code has filtered candidates and while pinned models, explicit jevonian/<route> requests, and routing.mode: "off" skip Jev entirely; minConfidence marks a low-confidence route in the ledger rather than accepting it, and the ledger records the serving model and why.Choice question per tab against user-editable group criteria — with unclassified tabs falling to a fixed fallback bucket, manual groups untouched, and the pre-grouping tab order restored on ungroup; an optional LLM engine with self-invented group names is included for comparison runs.Noul judgments against per-platform, user-defined topic and expression labels to annotate Weibo, Threads and X posts directly in a Chrome extension.Noul per remaining (source, destination) pair plus a guard Noul per incoming column, mapping 10 of 10 columns of a 23-column export at 253 questions in one call, 915 ms, $0.0012, unmapped fields left visible above a 0.75 threshold rather than guessed.Noul actionable, Score severity, Choice owning team, Choice duplicate-of), pages on P(SEV1)+P(SEV2) ≥ 0.80 and drops only below 0.20 when actionability agrees, sends the band between to a human who has 15 minutes to ack before it pages anyway, links duplicates into a cycle-broken incident graph so two alerts can never silence each other, and falls back to configured severity on any error or timeout; a published 300-alert run measured p50 418 ms, p95 1477 ms and $0.04 per 1,000 alerts, with the slowest call landing 151 ms short of the 2 s timeout.Choice and Noul questions about issue category and reproduction steps in opt-in live mode, then applies confidence and reproduction gates to propose a queue or review fallback without assigning the issue, with synthetic offline fixtures and policy tests.Choice, Score and Boolean questions to sort the peer's message into intent (10 options), an emotion distribution (9 options), urgency (0-3 Score) and a suggested reply posture (11 options), then shows exactly three toasts and takes no other action - no generated reply, no input injection, no screenshot or OCR, with a local alias-to-relation table passed as state so the same sentence is judged differently for a partner than for a colleague.Continued at the source.
English · Русский
Made with AI. Made by you.
A local, GPU-powered music generation and production studio — one interface for ACE-Step 1.5 and YuE2-3B, with a built-in multitrack DAW.
🚧 Actively in development — expect breaking changes, bugs, and rough edges. Not a stable release yet.
Why · What's inside · ACE-Step · YuE2 · LoRA · DAW · Desktop app · Built with · License · Installation
ACE-Step and YuE2 are two independent music generation engines, each with its own web UI, its own result-storage format, and its own process that has to be started and stopped by hand. They typically cannot run simultaneously on a single consumer GPU. Remiqora solves this with a single layer on top:
| Module | What it does |
|---|---|
| ACE-Step 1.5 | Fast generation from text/style tags, covers, section repainting, extracting/adding parts on top of a reference track. |
| YuE2-3B | Full-length track generation with CoT score planning (a symbolic ABC plan before the audio). |
| SheetSage2 | Extracts melody and harmony from a reference track into ABC notation — used as YuE2's input. |
| LoRA training | Dataset → auto-labeling → preprocessing → training → export — the whole ACE-Step fine-tuning pipeline for your own voice/style, in the browser. |
| Demucs | Splits any track into 4 stems: vocals, drums, bass, other. |
| MuScriptor | Transcribes audio (the full mix or a single stem) into MIDI notes. |
| Built-in DAW | A multitrack timeline editor for assembling tracks/stems into a final mix: an effects rack on every channel, auto-BPM and time-stretch, WAV/MP3 export. |
The interface is fully bilingual (Russian/English). It starts in your system language, and the switcher in the header overrides it.
Two input modes: “Simple” — a single text description the model uses to infer both style and lyrics on its own; and “Custom” — style tags with autocomplete plus lyrics with structure markup ([Verse]/[Chorus]/[Bridge]) and performance annotations ((whisper), (falsetto)), or an “Instrumental” checkbox.
Attaching a reference track unlocks 5 remix scenarios:
Plus: 10–300 s duration, batch of 1/2/4 variants, mp3/wav/flac formats, advanced parameters (BPM, key, time signature, vocal language, inference steps, guidance scale, seed), LoRA adapter support with adjustable strength, local presets, and a "Stop all" button for bulk job cancellation.
Three CoT (Chain-of-Thought) modes: off — straight to audio; melody — the arrangement is built around a given melody (ABC); full — the model first builds a symbolic plan (melody + chords), then generates the audio.
SheetSage2 lets you upload a reference track and pull its melody into ABC notation, right in the form, with one click — editable by hand afterwards. Beyond that: q8_0/q4_0 precision, batch of 1–4, a full set of sampling parameters for audio generation and the ABC planner separately, local presets, and viewing/reusing the ABC score of an already-generated track.
The full ACE-Step fine-tuning pipeline on your own dataset, no console required:
One click splits any saved track into 4 isolated stems (Demucs htdemucs), with a progress bar, a separate player and download per stem, and the option to redo or delete. Runs alongside the active generation model (without stopping it), sharing a GPU lock. The "Open in editor" button allows you to instantly send all 4 stems into a new built-in DAW project for further mixdown.
Transcribes the full mix, or any already-separated stem, into MIDI. Technically this isn't a separate process — it's a model loaded into the already-running YuE2 server, so transcription requires YuE2 to be the active model. Result: a built-in Web Audio synth player, a mini piano roll, a note count and BPM readout, and .mid download.
Any number of tracks, onto which you can add anything from the shared library (a full mix, a single stem, a file uploaded from disk) — via a picker dialog or by dragging a file straight onto a track. The quickest way in is through stems: the "Open in editor" button on the stems panel creates a ready-made four-track project (vocals, drums, bass, other).
S), duplicate (Ctrl+D), delete (Delete).Ctrl+Z / Ctrl+Y) — up to 30 steps of history. Zoom with Ctrl+wheel or the slider and Fit button; pan the timeline with Shift+drag or the middle mouse button.The "?" button in the toolbar opens built-in help: a list of hotkeys, mouse controls, and short tips on Loop and Magnet.
backend/ — FastAPI (Python). app/orchestrator/ manages the models' process lifecycle (start/stop/health-poll) and enforces their mutual exclusion on a single GPU. app/api/routes_proxy.py reverse-proxies /api/ace/* → ACE-Step's REST API (port 8001) and /api/yue2/* → YuE2's native server (audiocpp_server.exe, port 8080). app/db.py + routes_tracks.py are the shared SQLite database and files, organized per model, regardless of how a track was created (generation, upload, or assembled in the editor).frontend/ — Vue 3 + TypeScript + Tailwind v4 + Pinia + vue-router + vue-i18n. A fully native implementation (not an iframe) on top of the models' original APIs — src/audio/ contains its own Web Audio engine (mixer, timeline, effects, a MIDI parser and synth, WAV/MP3 encoders).desktop/ — an optional Electron shell and installer: first-run setup, server lifecycle and packaging. It runs the same backend/ and frontend/; see desktop/README.md.acestep-api and audiocpp_server.exe) runs from their original code — everything else (UI, proxying, storage, file upload/transcoding) is written in this repository. YuE2's own web UI (web-ui/server.py) is no longer used — the one useful part of it (transcoding non-WAV uploads via ffmpeg) has been ported to backend/app/api/routes_yue2_upload.py.Remiqora is a UI and orchestrator on top of third-party inference engines. Their code isn't vendored into this repository — only small functional patches (external/patches/) on top of the originals:
| Project | What's used | License |
|---|---|---|
| ACE-Step-1.5 | Text/style-driven music generation engine, LoRA training | MIT |
audio.cpp (dev branch) |
YuE2 (generation), SheetSage2 (melody extraction), MuScriptor (MIDI transcription) | Apache-2.0 |
| Demucs | Stem separation (htdemucs) |
MIT |
Patch details and exact base commits are in external/patches/README.md.
The desktop app additionally uses Electron (MIT), electron-builder (MIT), uv (MIT or Apache-2.0) and static FFmpeg builds (GPL) that it downloads on first launch instead of redistributing.
Remiqora's own code (this repository) is MIT-licensed. That covers the UI and orchestrator only — it is a separate thing from the license of a track you generate with it. Remiqora is an orchestrator, not a generator with its own model — all audio is produced by third-party engines (ACE-Step 1.5, YuE2-3B, and the SheetSage2/MuScriptor tools built on top of them). Because of that:
There are two ways to install Remiqora: the desktop app (experimental, described first) or the scripts (Steps 0–2 below).
For anyone who would rather not use a terminal, Remiqora also comes as a desktop app for Windows (NVIDIA RTX 20-series or newer, driver 580 or newer) and macOS (Apple Silicon). It opens in its own window and sets everything up on the first launch, so there is no Git, Python, CUDA Toolkit or compiler to install. The Windows installer installs per user and needs no administrator rights.
Download (v0.2.0, pre-release): Windows installer (.exe) · macOS installer (.dmg, Apple Silicon) · all files and SHA-256 sums
Status. Experimental. The installers are not signed yet, so Windows shows a SmartScreen warning ("More info" → "Run anyway") and macOS may ask you to allow the app ("Open Anyway" in System Settings → Privacy & Security). The SHA-256 sum of every file is in SHA256SUMS.txt on the release page. To build an installer yourself instead:
cd frontend && npm ci && cd ../desktop && npm ci npm run dist # Windows: dist/Remiqora-Setup-<version>.exe · macOS (run it on a Mac): dist/Remiqora-<version>-arm64.dmg
desktop/README.md covers what the first run installs, the test switches and the known gaps.
What it is built with. An Electron shell around the same web UI and FastAPI backend, packaged with electron-builder (an NSIS installer on Windows, a DMG on macOS). The first launch uses uv for the Python environments, the audio.cpp release binaries and static FFmpeg builds. Licenses are unchanged; in particular the YuE2-3B weights stay CC BY-NC 4.0.
The steps below install from scripts instead: Git, a terminal and, on Windows, the build tools.
setup_prereqs.bat
Via winget (built into Windows 10/11), installs Git, Python, uv, Node.js,
CMake, ffmpeg, plus Visual Studio Build Tools (C++ workload) and the CUDA
Toolkit — those are large, need admin rights, and can take a while.
setup_prereqs.bat -SkipHeavy installs only the small, fast tools, leaving
Build Tools/CUDA for you to install manually from links the script prints.
The NVIDIA GPU driver is deliberately left out — install it by hand from nvidia.com/drivers for your card: silently swapping a video driver on someone else's machine is risky (it can blank the screen and usually needs a reboot on your schedule, not the script's).
After installing, close the terminal and open a new one so PATH picks up the freshly installed tools.
On macOS (Apple Silicon):
./setup_prereqs.sh
Via Homebrew, installs Git, Python, uv, Node.js, CMake,
ffmpeg and Ninja. No separate GPU driver step: Metal is built into macOS.
CMake/Ninja are only actually used by the --from-source build path below —
the default YuE2 setup needs no compiler at all.
setup_models.bat
The script:
ace-step/ACE-Step-1.5 (MIT) and 0xShug0/audio.cpp (Apache-2.0,
dev branch — YuE2 support is dev-only for now) into external/.external/patches/README.md) — without the upstream
custom web-uis, which aren't needed.uv sync for ACE-Step and builds audiocpp_server (CUDA release,
yue2,sheetsage2,muscriptor models) for audio.cpp.tools/model_manager_v2.py.demucs uv project in external/Demucs for stem separation,
routed at PyTorch's cu128 wheel index so it gets a CUDA build (a plain
uv add demucs would silently resolve a CPU-only torch wheel instead).backend/.env with paths to the freshly cloned repositories,
including FFMPEG_BIN_DIR — auto-detected from ffmpeg's winget install
(setup_prereqs.bat), even right after installing it in the same
terminal, before a new one would pick it up on PATH.ACE-Step's own weights don't need a separate download — acestep-api pulls
them from HuggingFace/ModelScope on first request, the same way its Gradio
UI does.
The script is idempotent — safe to re-run (the -SkipBuild / -SkipWeights
flags skip the corresponding steps). It expects git,
uv, Python 3,
CMake, the CUDA Toolkit and Visual Studio Build Tools (C++ workload) to
already be installed — if any is missing, that step is simply skipped with a
hint on what to install.
After that, the only manual step left is checking CUDA_BIN_DIR in
backend/.env (FFMPEG_BIN_DIR is filled in automatically — unless ffmpeg
wasn't found at all, in which case the script says so and it needs setting
by hand).
Hard machine requirements the script can't remove: Windows, a CUDA-capable NVIDIA GPU (tested on an RTX 4080 16 GB), and an installed video driver.
On macOS (Apple Silicon):
./setup_models.sh
Adapted for macOS, with one difference from the Windows steps above: by
default, audiocpp_server is installed from audio.cpp's own prebuilt
macOS/Metal release (a pinned tag, sha256-verified before extracting) —
no compiler needed at all, unlike the Windows path, which always builds
from source since there's no prebuilt CUDA release. The Demucs uv project
also isn't routed at a CUDA wheel index — a plain torch dependency
already resolves an MPS-capable wheel on darwin/arm64, same as
ACE-Step-1.5's own pyproject.toml does. The written backend/.env has no
CUDA_BIN_DIR — there's no CUDA toolkit on this path.
Pass --from-source to build audio.cpp from the same pinned dev commit
Windows uses instead of downloading the release (useful if the release lags
behind a dev-only fix, or on Intel Macs, which the prebuilt asset doesn't
cover) — that path needs full Xcode.app (not just the Command Line
Tools) for its Metal shader compiler; setup_prereqs.sh prints exact steps
if it's missing. --skip-build / --skip-weights mirror -SkipBuild /
-SkipWeights. Otherwise it expects git, uv and Python 3 to already be
installed (cmake too, for --from-source).
Continued at the source.
Jev gives your software a typed judgment. Your code stays in charge.
A community field guide to TypeSafe's Jev: see one documented call, try live projects, copy a starter, and inspect independent tests.
Try a live build · Shape a decision · Explore projects · Add your project · Star on GitHub
One call, three typed answers. TypeSafe's documented support-ticket example shows the saved
jev-1.13.0response below. This is a published example, not a live model call. Application code still decides when to route or escalate.
| Input or answer | Documented value |
|---|---|
| State | Hi, I've been trying to connect my Stripe account for 3 days and the integration keeps failing. I'm losing sales. Please help ASAP. |
| Choice | technical · 0.85 selected probability |
| Score | 1 on a 0–2 frustration rubric (Frustrated but civil) |
| Noul | 1.0 urgency probability |
Open a live build from a preview, or read its listing first.
Three additions from 22 September 2026. These are places to explore, not a ranking or endorsement; the full listings include limitations and source links.
| I want to… | Go here |
|---|---|
| Understand the idea in 2 minutes | See where Jev fits, try the policy threshold, then read the introduction and the three primitives |
| Make my first typed call | Copy the runnable example, shape your own question, then explore the official SDKs |
| See it work live | Explore the featured builds, then browse more applications |
| Test the claims | Read what independent tests found, inspect JevBench's cross-model results, then browse independent evaluations and TypeSafe's own evals |
Download the JSON directory · Use with a coding agent · Suggest a resource · Follow updates · Join the builder community
Independent community project. This repository is not affiliated with or endorsed by TypeSafe AI. Community entries are labeled by section; inclusion is not a claim that TypeSafe has reviewed or approved them.
Last updated: 2026-09-23. Links and project descriptions change; please report a stale entry.
In a support workflow, separate the work before choosing a model. This is a practical design rule based on the TypeSafe introduction linked above, not a performance claim:
| What the step needs | Use | Example |
|---|---|---|
| Apply an explicit rule to known fields | Code | Check an account flag or enforce a routing threshold. |
| Judge messy context with a bounded answer | Jev | Choose billing, technical, or other for a ticket, with probabilities. |
| Produce prose or work through an open-ended task | Text LLM | Draft the reply after the route is chosen. |
Code still validates the answer and owns the action. Measure Jev's error and abstention rates on your own cases before automating a consequential step.
One state can answer several focused questions in the same request. Pick the answer shape your code can use directly:
| Question shape | Use it for | What comes back |
|---|---|---|
| Noul | A clear yes/no claim, such as “Does this message request a refund?” | A number from 0 to 1: the probability of yes. |
| Choice | Selecting from named options, such as billing, technical, or sales. | The selected option, a probability for every option, and confidence. |
| Score | An ordered rubric, such as calm, concerned, or angry. | A position on your rubric, probabilities over its levels, and confidence. |
Ask independent questions together. Set thresholds, fallback behavior, and side effects in application code.
The typed decision is the common idea; the client, model name, authentication, and billing depend on the route. Start with the direct API below if you want TypeSafe's documented systemOne contract, or follow the platform guide for an app already running there.
| Route | Documented way in | Check before using it |
|---|---|---|
| TypeSafe direct | Official JavaScript or Python SDK with a TypeSafe API key. | The runnable example below uses this route and sends its state to TypeSafe. |
| Cloudflare Workers AI | Run typesafe/jev with a Workers AI binding or Cloudflare API call. |
Use Cloudflare's request shape and credentials; its model page labels Jev as a third-party model. |
| Netlify AI Gateway | Use the official TypeSafe JavaScript SDK from a Netlify Function or Edge Function; the gateway supplies its environment configuration when enabled. | Follow Netlify's plan and key-override rules. This is a server-side path, not a browser key. |
| Vercel AI Gateway | AI SDK's experimental evaluate with typesafe-ai/jev. |
Its Boolean question maps to Jev's Noul; the AI SDK interface differs from systemOne. |
| OpenRouter | OpenRouter's decisions API with typesafe/jev-1.13 or its latest-model route. |
Use an OpenRouter key and its decisions request shape; do not send these questions to a chat-completions API. |
These are documented access paths, not equivalent SDKs or claims about price, latency, or reliability. Check the linked provider page before deploying because availability and terms change.
Pick JavaScript or Python. Both examples send a synthetic support ticket to TypeSafe's API and return a Choice and a Noul. Jev returns typed answers; the 0.9 routing rule is ordinary application code. It is an illustrative threshold, not a measured or recommended operating point.
Install the official JavaScript SDK with npm install @typesafe-ai/sdk (Node.js 20+), set TYPESAFE_API_KEY in your environment, save this as first-decision.mjs, then run node first-decision.mjs:
import { choice, noul, TypeSafeClient } from '@typesafe-ai/sdk'; const { answers } = await new TypeSafeClient().systemOne({ state: { ticket: 'I was charged twice. Please refund the extra payment.' }, questions: { team: choice('Which team should handle this ticket?', { billing: 'Payments and refunds', technical: 'Bugs and integrations', other: 'None of the above', }), refund: noul('Does the customer explicitly request a refund?'), }, }); const team = answers.team.choice; const probability = answers.team.probabilities[team]; const action = team !== 'other' && probability >= 0.9 ? `route to ${team}` : 'send to review'; console.log({ team, probability, refundProbability: answers.refund.noul, action });
Install the official Python SDK with python3 -m pip install typesafe-sdk (Python 3.10+), set TYPESAFE_API_KEY in your environment, save this as first_decision.py, then run python3 first_decision.py:
from typesafe_sdk import Choice, Noul, TypeSafeClient with TypeSafeClient() as client: result = client.system_one( state={"ticket": "I was charged twice. Please refund the extra payment."}, questions={ "team": Choice( instructions="Which team should handle this ticket?", criteria={ "billing": "Payments and refunds", "technical": "Bugs and integrations", "other": "None of the above", }, ), "refund": Noul(instructions="Does the customer explicitly request a refund?"), }, ) team = result.choices["team"].choice probability = result.choices["team"].probabilities[team] action = f"route to {team}" if team != "other" and probability >= 0.9 else "send to review" print({ "team": team, "probability": probability, "refund_probability": result.nouls["refund"].noul, "action": action, })
Start with one state and a question whose answer your code can use. This synthetic support report can be asked as a Choice, Noul, or Score. On the live site, edit the fields and copy a JavaScript SDK call. The designer runs in your browser without making a model request; running the copied code later sends the state to TypeSafe.
| Design input | Synthetic example |
|---|---|
| State text | The PDF upload fails with a 500 error. I need it before today's deadline. |
| Choice question | Which team should handle this report? |
| Choice options | technical=Failures and integrations; support=Account and usage help; other=Neither team |
| Noul question | Does the message explicitly mention a deadline? |
| Score question | How much does the reported issue block the user's work? |
| Score levels | Cosmetic; Workaround available; Blocks the task |
Keep the state short, describe the options so they do not overlap, and include a no-match option when the task allows it. Choose thresholds and actions only after measuring your own labelled cases.
The documented support-ticket example above selects technical with probability 0.85. In this illustrative policy, a ticket routes automatically only when the selected probability reaches the application's threshold. At 0.90, it goes to review; at 0.80, it routes to technical. The model answer stays the same. These thresholds are teaching examples, not measured operating points or safety guarantees.
| Policy input | Example value |
|---|---|
| Selected team | technical |
| Selected probability | 0.85 |
| Starting threshold | 0.90 |
On the live site, move the threshold to see which action the application takes. A real threshold needs evaluation on your own labelled cases, with a review path for uncertainty.
Independent studies make five failure modes concrete. Each result below belongs to the cited task, dataset, and model run; use it to design a test for your own workflow.
| Decision you want to make | What was measured | What to test before shipping |
|---|---|---|
| Answer or abstain? | In a KoBBQ audit, Jev chose “unknown” for 95% of 300 ambiguous items when that option was available. With that gold answer removed from the options, accuracy on those items was necessarily 0%; 79% of answers picked the dataset's stereotype. | Add an explicit no-match or review option where evidence can be missing. Measure wrong forced answers and needless abstentions on your own ambiguous cases. |
| Route to a fallback? | Janus tested 500 items each from Banking77 and Web of Science. Its tuned Jev-to-DeepSeek cascade improved Banking77 accuracy over either model alone, but on Web of Science matched Jev alone at 47% higher cost. | Label representative cases, price both legs, and choose a threshold on a held-out split. Confirm that the fallback actually fixes errors where Jev is uncertain. |
| Certify a routing threshold? | In jev-certify's CLINC150 study, a 5% bound on silently misrouted incoming queries held on 400 in-scope examples: 84.75% were auto-routed with 2.25% loss per incoming query. A separate scope gate missed its 5% target by 3.6× when out-of-scope prevalence rose. | Calibrate on traffic that represents deployment, monitor the mix, and distinguish loss per incoming query from error among routed queries. The bound does not cover a shifted population. |
| Sort by probability? | An ordering study passed six ranking gates on 360 topic-membership rows, then failed four of six on 306 human-graded shopping pairs. On the first corpus, 53 rows tied at 0.99; batching 40 rows changed a passing ranking gate into a failure. | Measure pairwise order, ties at the cutoff, and the exact request shape on your relevance labels. A good classifier is not automatically a good sort key. |
| Approve an agent action? | In a 111-case action-gate study, Jev matched 100 case labels and Claude matched 102; each had one unsafe allow. Contract and policy mapping was the largest single source of wrong decisions for both. | Test the answer-to-action mapping as well as the model. Escalate consequential tool families with deterministic policy even when a semantic answer seems confident. |
These are independent, study-specific observations, not a leaderboard or a guarantee for another task. Read the linked protocols, labels, and limitations before carrying a number into a decision policy.
For a comparison across decision models, JevBench's method publishes its scoring code, frozen tasks, adapters, and result artifacts. Its composite score combines accuracy, calibration, speed, and cost; some latency and hosting costs are estimates, and a held-out set is still sent to the evaluated services. Read the per-task outcomes and assumptions before treating a rank as evidence for your workflow.
Continued at the source.
Audit your GitHub repository's search ranking signals.
Checks name, description, topics, README, stars, and activity. Scores each factor 0-100.
buygithub.com · How GitHub SEO Works
Pass any GitHub repository and get a search ranking audit. The tool checks six signals that affect where your repo appears in GitHub search results, scores each one, and gives you an overall grade with actionable tips.
Signals checked:
git clone https://github.com/nMaas8388/github-ranking-audit.git
cd github-ranking-audit
pip install requests
python ranking_audit.py facebook/reactpython ranking_audit.py torvalds/linux
Auditing torvalds/linux...
Repository: torvalds/linux
Description: Linux kernel source tree
Language: C
Stars: 183,247
Forks: 54,102
Topics: 3 (linux, kernel, c)
Last push: 2026-09-16
README words: 847
Scores:
name 75/100 [###############.....]
description 60/100 [############........]
topics 40/100 [########............]
readme 100/100 [####################]
stars 100/100 [####################]
activity 100/100 [####################]
Overall: 82/100 (Grade B)
Tip: add 8-15 relevant topics to improve discoverability
python ranking_audit.py owner/repo --query "cloudflare turnstile solver"python ranking_audit.py facebook/react vuejs/vue sveltejs/svelte
python ranking_audit.py owner/repo --json
| Signal | Weight | What it checks |
|---|---|---|
| Name | 25% | Query keyword match |
| Description | 15% | Length and completeness |
| Topics | 15% | Count (8-15 is optimal) |
| README | 15% | Word count, first 200 words |
| Stars | 20% | Logarithmic scale |
| Activity | 10% | Days since last push |
Grades: A (85+), B (70-84), C (50-69), D (30-49), F (<30)
GitHub's "Best match" sort combines text relevance (name > description > topics > README) with popularity signals (stars > forks > watchers > recent activity). The exact weights aren't published, but the ranking order is well-documented by the community.
Read the full analysis: How GitHub SEO Works
requestsGITHUB_TOKEN env var for higher API rate limitsMIT
Save Astra for the decisions that need it. Let DeepSeek V4.1 Flash do the volume.
A personal Codex skill designed to preserve Astra usage without giving up Astra's judgment. Astra stays responsible for planning, architecture, high-stakes decisions and final review. DeepSeek V4.1 Flash takes the high-volume work: repository discovery, implementation, testing, debugging and routine verification.
Bring an existing plan or start with a feature request. The workflow turns it into coherent implementation bundles, sends those bundles to Flash, then returns the completed patch and evidence to Astra for one focused acceptance pass.
Status: early release. Offline installation tests pass, and the workflow has completed a measured local field build. Results below describe that run, not guaranteed savings. A new installation still needs runtime routing verification on its first authorized task. Installation never runs paid inference.
In one substantial field build, Astra Flash Orchestrator used 98.9% less Astra input per 1,000 implementation and test lines than the all-Astra baseline. It did that by moving the implementation loop—not the important decisions—to Flash. Total API-equivalent compute per 1,000 lines was 97.0–97.7% lower, while the measured phase produced 39% more implementation and test lines.
| Workflow | Astra input per 1K implementation lines | Total compute per 1K lines |
|---|---|---|
| All Astra | 8.56M | $11.32 |
| Astra + DeepSeek V4.1 Flash | 95.9K | $0.26–$0.34 |
The per-token price difference explains why delegating implementation has so much leverage:
| Cost per 1M tokens | Astra estimator | DeepSeek V4.1 Flash | Astra premium |
|---|---|---|---|
| Uncached input | $10.00 | $0.15–$0.30 | 33–67× |
| Cached input | $1.00 | $0.003–$0.006 | 167–333× |
| Output | $50.00 | $0.60–$1.20 | 42–83× |
Astra does not have a public API SKU; its values above are API-equivalent estimates, not ChatGPT or Codex subscription charges. Flash values use published off-peak and peak API rates. See the benchmark methodology for sources, exact measurements and limitations.
Astra → scope + design + task brief
Flash → implement + test + report
Astra → review + verify + accept or request fixes
→ integrate + checkpoint + next task
astra_flash_builder role, not a separate agent CLI.This is workflow guidance, not a deterministic scheduler, a security sandbox, or a guarantee of model quality or cost savings. It is independent of OpenAI, DeepSeek and Codex Router.
There is no mode setting or mode-switch command. The package always uses the usage-saving Astra → Flash → Astra workflow for substantial implementation.
Three routing outcomes remain intentionally different:
Those are scope and safety decisions, not user-selectable performance modes.
Before installing, you need:
$CODEX_HOME/agents/.multi_agent_version: "v2".| Provider | Worker route |
|---|---|
| DeepSeek API (default) | deepseek/deepseek-v4.1-flash |
| OpenRouter | openrouter/deepseek-v4.1-flash |
| opencode Go | opencode-go/deepseek-v4.1-flash |
| Command Code | commandcode/deepseek-v4.1-flash |
| Nous Research | nousresearch/deepseek-v4.1-flash |
| Ollama Cloud | ollama-cloud/deepseek-v4.1-flash |
Provider credentials are entered by you through Codex Router's private local prompt before installing this package. Never paste an API key into an assistant chat. This installer never asks for, reads, stores or validates provider keys.
Do not spend API credit during installation. Installing this package does not authorize an assistant to run
subagents certify,test-model --live, a Router smoke test or any other paid inference probe. If the selected route is absent or is not already advertised asv2, the installer stops and reports the prerequisite. Decide separately whether to certify a route yourself.
Do not add or change [agents].default_subagent_model for this package. The
installer creates a named astra_flash_builder role that pins its own route and
catalog-supported effort, so unrelated subagents keep their existing defaults.
The installer does not install the Router, add credentials, select your root
model, or rewrite config.toml. Direct DeepSeek remains the default. Any other
provider requires an explicit --worker-route; if that route is unavailable,
installation stops instead of silently choosing another provider.
The installer supports loopback Router URLs using /v1 or /_codex-router/<capability>/v1. It rejects remote hosts, embedded credentials, queries, fragments and unexpected paths. Client/project/UI overrides still need checking in your actual session. Router subagent selection enables discovery; it does not prove successful inference. Some Router enable commands automatically launch paid verification, so inspect the installed version before changing selection. This installer never enables routes or runs those probes.
Download this repository as a ZIP and extract it, or clone it:
git clone https://github.com/ethanplusai/astra-flash-orchestrator.git
cd astra-flash-orchestratorRun the following commands from that repository folder.
The installer performs its own prerequisite checks before writing. Preview the exact destinations, then apply:
python3 -B install.py python3 -B install.py --apply
That is the normal installation path. The first command changes nothing. The second repeats preflight, installs atomically, backs up existing instructions and prints a guarded undo receipt. It does not change your root model, Router, credentials, permissions or reasoning effort.
To use an already-configured alternate provider, pass its exact route to both commands. For OpenRouter:
python3 -B install.py --worker-route openrouter/deepseek-v4.1-flash python3 -B install.py --worker-route openrouter/deepseek-v4.1-flash --apply
The option selects an existing catalog route; it does not configure the provider, collect a key, certify the model or make an inference request.
Ask Codex:
Read INSTALL-IN-CODEX.md in this folder and install the package following it.
Preserve my root model, reasoning effort, Router, config and authentication.
Do not launch workers or run paid inference during installation.
Release archives are tested before publication. If you also want to run the offline suite yourself:
python3 -B -m unittest discover -s tests -v
For a nondefault profile, pass --profile PROFILE to the dry run, apply and doctor consistently. --home and --codex-home are available for explicit location overrides. Use the same locations for undo.
| Location | Installed content |
|---|---|
~/.agents/skills/astra-flash-orchestrator/ |
Skill, references, templates, doctor, plan validator and routing binding |
$CODEX_HOME/agents/astra_flash_builder.toml |
Native builder pinned to Flash; nested agents disabled |
$CODEX_HOME/AGENTS.md |
A marked, scoped workflow policy block |
$CODEX_HOME/astra-flash-install-backups/ |
Original files and an undo receipt |
CODEX_HOME defaults to ~/.codex. An existing nonempty AGENTS.override.md receives the policy instead of AGENTS.md. Other instructions are preserved. The policy keeps trivial work single-agent and honors explicit no-delegation requests, repository restrictions and managed policies. Use --no-policy for a skill/role-only installation.
Root model/effort, provider configuration, authentication and existing permissions stay unchanged. Installation does not start services, workers or model requests, and does not commit, push or deploy anything.
Fully quit and reopen the host app (ChatGPT or Codex), then start an Astra session. A new chat alone may reuse a cached model catalog. Use:
$astra-flash-orchestrator Use the existing plan in docs/plan.md to implement
this feature. Keep Astra focused on planning and final review. Use one installed
Flash builder for a coherent implementation and verification bundle. Do not poll
the worker; review its completed patch and evidence in one batched pass.
Replace the example plan path with your actual plan or describe the feature. Your first authorized useful task should verify the child model and provider using host/router request metadata. A worker saying its model name is not proof.
If the session does not expose the custom role or exact worker model, do not substitute another model or launch a second CLI. Check client support and session configuration first.
From the repository folder:
python3 -B skill/astra-flash-orchestrator/scripts/doctor.py python3 -B skill/astra-flash-orchestrator/scripts/doctor.py --check-local-router
An installed copy reads its generated routing.json, so doctor checks the same
route automatically. Pass --worker-route only when running doctor from a fresh
source checkout or intentionally checking a different reviewed route.
The first checks local configuration/catalog data. The optional second command makes only a local /models GET, with proxies and redirects disabled. It does not read authentication files or attach credentials; an authenticated Router may reject it even when normal Codex requests work. Do not disable Router authentication to make this check pass.
Neither check proves paid inference works. See troubleshooting and validation evidence.
For an update, download the new source, run its tests, and preview python3 -B install.py --replace. Review the differences before applying with --replace --apply. Existing package-owned files are backed up; unrelated files are not deleted. An existing valid routing.json preserves the installed provider when --worker-route is omitted. Pass the option explicitly only to change providers, and review that replacement before applying it. Do not edit generated routing.json or the agent model to force a different provider through preflight.
Preview undo using the exact receipt printed during installation:
python3 -B install.py --undo /path/to/receipt.json
Add --apply to restore. Undo refuses if a managed file changed afterward, protecting later edits. Backups remain available. Keep a copy of the installer and receipt; receipts may contain private paths and original instructions and should never be published.
To validate the synthetic plan example:
python3 -B skill/astra-flash-orchestrator/scripts/validate_plan.py examples/invoice-filter/plan.json
The example is a planning fixture, not a runnable application. Markdown plans work without the optional manifest validator.
Continuous software-quality review for AI coding agents, powered by Jev.
Jev Review runs as a local MCP server and gives Claude Code, Codex, Cursor, and OpenCode structured quality scores while they work. Your coding agent remains responsible for diagnosing weaknesses and changing the code; Jev supplies a fast scalar signal across correctness, complexity, changeability, modularity, tests, security, and other independent quality dimensions.
Important
Your API key stays on your machine. Jev Review has no hosted backend, database, telemetry service, or author-operated proxy. The only remote request is sent directly to the configured Jev API.
jev-review-demo.mp4
| Purpose | Continuous, structured software-quality evaluation |
| Supported clients | Claude Code, Codex, Cursor, OpenCode |
| Distribution | This GitHub repository—no npm publication |
| Runtime | Local Node.js process over MCP stdio |
| Remote access | Direct requests to Jev using your API key |
| MCP tools | One focused tool: jev_review |
| Code changes | Always performed by the primary coding agent |
Requirements:
Set your API key before starting the coding agent:
export JEV_API_KEY="your-key"
Install Jev Review directly from GitHub—no npm publication is required:
npx plugins add NiazMorshed2007/jev-review
Choose your coding client when prompted, restart it, and ask the agent to use jev-review while implementing a nontrivial change.
flowchart LR
A[Agent implements] --> B[Focused diff and context]
B --> C[Jev Review MCP]
C --> D[Jev evaluation]
D --> E[Structured quality signals]
E --> F[Agent improves the code]
F -. review again .-> B
Jev Review is intended for frequent, focused checkpoints: after a coherent implementation slice, after a score-driven improvement, and before final handoff. The first call establishes a baseline. The agent then inspects its own implementation, forms a hypothesis about weak dimensions, improves the code, validates it, and rescores.
Jev returns typed Score, Choice, and Noul decisions rather than a free-form review essay. It does not generate a prose explanation of why a score is low. Jev Review validates and converts those decisions into metric scores, confidence levels, coarse rubric hints, and comparisons with a previous evaluation. The coding agent—not Jev—must determine the actual cause and appropriate code change.
There is deliberately no synthetic “82/100” overall score. Dimension changes such as Readability 6.3 → 8.1 and Security 8.2 → 8.2 are more useful than a blended percentage.
| Client | Plugin installation | Manual MCP available |
|---|---|---|
| Claude Code | npx plugins add NiazMorshed2007/jev-review --target claude-code |
Yes |
| Codex | npx plugins add NiazMorshed2007/jev-review --target codex |
Yes |
| Cursor | npx plugins add NiazMorshed2007/jev-review --target cursor |
Yes |
| OpenCode | Manual configuration below | Yes |
Every client starts the same bundled dist/server.js process locally over stdio.
npx plugins add NiazMorshed2007/jev-review --target claude-code
Restart Claude Code and run /mcp to confirm that jev-review is connected.
To load a local clone while developing:
claude --plugin-dir /absolute/path/to/jev-review
Manual MCP-only setup:
claude mcp add --scope user jev-review -- node /absolute/path/to/jev-review/dist/server.js
npx plugins add NiazMorshed2007/jev-review --target codex
Restart Codex and run /mcp to verify the connection.
Manual setup in ~/.codex/config.toml:
[mcp_servers.jev-review] command = "node" args = ["/absolute/path/to/jev-review/dist/server.js"] env_vars = ["JEV_API_KEY"]
npx plugins add NiazMorshed2007/jev-review --target cursor
Restart Cursor and check Settings → MCP. The bundled skill is named jev-review; invoke it with /jev-review or leave it on Agent Decides.
Manual setup in ~/.cursor/mcp.json:
{
"mcpServers": {
"jev-review": {
"type": "stdio",
"command": "node",
"args": ["/absolute/path/to/jev-review/dist/server.js"],
"env": {
"JEV_API_KEY": "${env:JEV_API_KEY}"
}
}
}
}If Cursor is launched from the macOS Dock, it may not inherit variables from your shell profile. Make the already-exported key available to GUI applications before starting Cursor:
launchctl setenv JEV_API_KEY "$JEV_API_KEY"Verify without printing the key:
test -n "$(launchctl getenv JEV_API_KEY)" && echo "JEV_API_KEY is configured"
OpenCode does not currently appear in the portable plugins installer targets. Point it at the same bundled server instead:
git clone https://github.com/NiazMorshed2007/jev-review.git cd jev-review opencode mcp add jev-review --global -- node "$PWD/dist/server.js"
For the full skill and MCP setup, add this to ~/.config/opencode/opencode.json, replacing the absolute path:
{
"$schema": "https://opencode.ai/config.json",
"skills": ["/absolute/path/to/jev-review/skills"],
"mcp": {
"servers": {
"jev-review": {
"type": "local",
"command": ["node", "/absolute/path/to/jev-review/dist/server.js"],
"environment": {
"JEV_API_KEY": "{env:JEV_API_KEY}"
}
}
}
}
}Run opencode mcp list to verify the connection. OpenCode may display the tool as jev-review_jev_review; the underlying MCP tool is still jev_review.
Jev Review intentionally starts with one tool: jev_review.
{ task?: string; diff?: string; files?: Array<{ path: string; content: string; }>; repositoryContext?: string; previousEvaluation?: Evaluation; }
At least one current-context field is required. Callers should normally send the task and focused diff, adding complete files only when the surrounding implementation is necessary to understand the change. Jev Review never reads the repository automatically.
Jev Review does not impose an additional character, token, or file-count limit. The Jev API currently enforces its own token ceiling: live jev-latest behavior indicates roughly 32,768 tokens for the submitted state, although this number is not published in the API documentation or OpenAPI schema and may change. When Jev returns max_tokens_exceeded, the server asks the agent to reduce unrelated context or split the change into coherent review slices.
The response contains:
{ "applicable": false } for dimensions unsupported by the supplied contextpreviousEvaluation is suppliedAlways evaluated when the supplied context is sufficient:
Evaluated only when relevant evidence is present:
The evaluator judges consequences in context. It does not assume short functions, small files, zero duplication, more layers, more comments, or more tests are automatically better.
The included jev-review skill teaches agents to treat Jev as a repeated scalar feedback loop:
jev_review with focused context to establish a baseline.previousEvaluation, then inspect improvements and regressions.Correctness and the user's requirements always outrank score improvement. A higher score never justifies speculative architecture, unnecessary abstraction, scope expansion, breaking behavior, meaningless tests, or needless rewrites.
jev-review/
├── plugin.json # Portable Agent Plugin manifest
├── mcp.json # Portable stdio MCP definition
├── .claude-plugin/
│ └── plugin.json # Claude Code adapter
├── .codex-plugin/
│ └── plugin.json # Codex metadata
├── skills/
│ └── jev-review/
│ └── SKILL.md # Agent review workflow
├── src/
│ ├── config/ # Environment handling
│ ├── evaluation/ # Metrics, scoring, and comparisons
│ ├── jev/ # Direct Jev client and validation
│ └── mcp/ # MCP tool boundary
├── dist/
│ └── server.js # Committed standalone server bundle
├── public/
│ └── jev-review-demo.mp4 # Product demonstration
└── test/ # Unit and MCP protocol tests
plugin.json and mcp.json are the portable Agent Plugins 1.0 package. .claude-plugin/plugin.json and .mcp.json provide Claude Code compatibility, while .codex-plugin/plugin.json supplies Codex metadata. These are small packaging adapters around one MCP implementation.
git clone https://github.com/NiazMorshed2007/jev-review.git
cd jev-review
npm install
npm run validateUseful commands:
npm run check npm test npm run build npx plugins discover . claude plugin validate . --strict
npm run build creates the committed dist/server.js bundle. Unit and MCP protocol tests use local fakes and do not consume Jev API quota; a live Jev call requires JEV_API_KEY.
The local MCP process reads JEV_API_KEY and uses it only in the TLS Authorization header sent directly to https://api.typesafe.ai/v1/systemone. Jev Review never stores or logs the key.
Only the task, diff, files, and repositoryContext explicitly supplied to jev_review are sent to Jev. previousEvaluation is compared locally and is not included in the current code context. No repository files are discovered or uploaded automatically.
Review context does leave your machine for TypeSafe's Jev API. Do not supply secrets or unrelated proprietary content, and review TypeSafe's privacy policy for the remote service's handling terms. Jev Review complements rather than replaces dedicated security tooling.
Give your AI agent answers it can act on: typed judgments with real probabilities, instead of prose it has to parse.
evaluate connects Claude Code, Claude Desktop, Codex and pi to TypeSafe's Jev model. Your agent asks a question like "is this urgent?" or "which team owns this?" and gets back a number or an option it can use in an if statement.
"Help! My payouts have been ┌──────────┐ is_urgent 0.95
failing for 3 days." ───▶ │ Jev │ ───▶ department billing (86%)
└──────────┘ technical (14%)
is it urgent? which team? sales (0%)
The problem: An agent that needs a quick judgment call usually asks an LLM, reads a paragraph back, and guesses what it meant. "This seems fairly urgent" gives the agent nothing to branch on, and it can't tell a confident answer from a coin flip.
The fix: Jev is a model built for judgments rather than text generation. You name the question and the possible answers, and Jev returns a probability for each answer in a fixed format. Your agent gets data it can compare against a threshold, and never has to parse prose.
1. Install (macOS and Linux):
curl -fsSL https://raw.githubusercontent.com/itsmostafa/typesafe-mcp/main/install.sh | sh2. Connect your agents (get a key):
TYPESAFE_API_KEY=your-key evaluate setup mcp
This finds Claude Code, Claude Desktop and Codex and registers evaluate with each one. If you already have an OpenRouter account, set OPENROUTER_API_KEY instead. For pi, run evaluate setup pi.
3. Ask a question:
"Use evaluate to decide whether this ticket is urgent and which team should own it: Help! My payouts have been failing for 3 days."
Your agent sends:
{
"state": "Help! My payouts have been failing for 3 days.",
"questions": {
"is_urgent": {"type": "noul", "instructions": "Does this convey urgency?"},
"department": {"type": "choice", "instructions": "Which team should handle this?",
"criteria": {"billing": "Payments, refunds", "technical": "Bugs, outages", "sales": "Pricing"}}
}
}And gets back:
{
"answers": {
"is_urgent": {"type": "noul", "noul": 0.95},
"department": {"type": "choice", "choice": "billing", "confidence": 0.79,
"probabilities": {"billing": 0.86, "technical": 0.14, "sales": 0.0}}
}
}noul), pick one option (choice), and rate on a scale (score). Each answer comes with probabilities.items and ask the same questions of each one. If one record fails, the rest still complete.evaluate setup mcp configures every supported client it finds. Run it again to update.evaluate update upgrades it in place.items, limits and errors.TypeSafe builds System One models: small units of AI judgment that you use like programming primitives. Instead of generating text, they turn natural language and application state into typed answers and probabilities that code can combine. Jev is the first of them.
Website · Docs · API reference · Console
Issues and pull requests are welcome. See docs/development.md to get started.
If evaluate spares your agent some prose-parsing, a ⭐ helps others find it.
Explore your health records together—and check the evidence behind the patterns.
Created by Kajeesan Jeevendra · AGPL-3.0 licensed
Open Health Atlas brings sleep, training, mood, nutrition and other health records into one local application. Compare periods and inspect the sources, units and calculations behind the results.
Use the dashboard on its own, or connect a compatible MCP client to query your records and verify evidence with the model you choose. Open Health Atlas runs the calculations; the external model provides the interpretation.
Try the fictional demo · Connect an MCP client · Develop the project
macOS desktop preview: a bundled native app is being verified. See the installation and build guide and tested/blocked acceptance matrix. A notarized public installer is not yet available; evaluation artifacts are not a normal public release.
Fictional UI demo snapshot. Explore the demo · Image details.
New here? Start here: install and connect your AI. The guide includes copy-paste instructions for a setup agent. Hermes, Telegram, Google Health, Hevy, Cronometer and a Hostinger VPS have their own sections on the separate Optional page.
| I want to… | Start here |
|---|---|
| See the interface without installing anything | Open the browser demo — a clickable, read-only fictional snapshot, with no backend or AI connected. |
| Check the macOS desktop preview | Installation and release status (Apple silicon, signed and notarized 0.2.5 preview). |
| Run the real panel and try fictional data locally | Try the local demo — includes the local API and write broker. |
| Use my preferred AI client with local data | Connect an MCP client — copyable setup and a fictional query → analysis → evidence example. |
| Change code or contribute | Develop the project — environment, relevant tests and contribution guidance. |
The browser snapshot is useful for exploring screens and navigation; its values are not a fresh evaluation of the current engine. Run the local demo to execute calculations, save fictional entries and test imports.
For example, compare recorded sleep with day ratings over a selected period, then inspect the number of observations, source coverage and evidence behind the result. An association can suggest a useful question; it does not establish why the days differed.
| Component | Responsibility |
|---|---|
| Open Health Atlas | Local records, deterministic calculations, validated writes, evidence and dashboard. |
| Your MCP client and chosen model | Conversation, tool selection and interpretation of returned evidence. |
| Optional external Hermes | The existing integrated conversations, Telegram and configured automation workflows. |
The standalone MCP connection is read-only and does not require Hermes. Model credentials belong in the client. A local database does not guarantee that a client using a hosted model processes returned data locally.
This is a developer-oriented, single-user source release. Current local setup supports macOS and Linux with Python 3.11 or newer; native Windows is not supported by the Unix-socket broker and POSIX file locks. The standalone MCP entry point uses stdio. Clients requiring remote MCP need a separate transport setup.
A macOS desktop packaging preview includes the existing dashboard, guided local workspaces and a bundled optional MCP executable. The 0.2.5 preview is prepared for Developer ID signing and Apple notarization; check its release notes for the exact published artifact. Clean-Mac installation and the minimum supported macOS remain unverified; this is not a stable-release qualification claim. Built-in API-key chat, a hosted multi-user service and a bundled always-on agent are not included. Existing Hermes and external-service integrations require their own configuration. Results describe recorded data and bounded associations; clinical validation and causal conclusions are not claimed. See known limitations and the feature-retention matrix.
Start with Contributing, the development guide and the code of conduct. Look for good first issues or propose a focused improvement through the issue forms.
Use fictional or redacted reproductions. Report security vulnerabilities through the private reporting channel, not a public issue.
Open Health Atlas was created by Kajeesan Jeevendra. Its product architecture, data/evidence boundaries and user experience were developed and assembled through an AI-assisted development process.
Original project code uses the GNU Affero General Public License, version 3
only (AGPL-3.0-only). Commercial use is allowed subject to its
terms, including applicable source-sharing requirements. See
licensing and earlier MIT releases for details. Preserve
applicable copyright and license notices.
Third-party components retain their own licenses and credits; see
NOTICE, third-party notices and
research references. Use CITATION.cff for citation.
typesafe-computer-use drives a Mac toward a goal you type in plain English, for about a fiftieth of a cent per step. It reads the screen deterministically, asks a small classifier which action comes next, and only calls a writing model when a text field genuinely needs free text or the classifier has stopped and the screen needs reading.
clicker "go to techcrunch and take me to the checkout page for the cheapest tickets to their next upcoming event" --act
Beta. This is under heavy development. Expect rough edges, and expect settings and behavior to change between 0.x releases. It drives your real mouse and keyboard, so start with a dry run.
Frontier-model computer use is capable and expensive: every step ships a screenshot and waits several seconds for a plan. Most steps do not need a plan. They need one choice from a short list, made quickly and cheaply, with a confidence number you can gate on.
TypeSafe sells exactly that: a decision model that answers
a Choice over up to 255 options with a full probability distribution and a calibrated
confidence, in a few hundred milliseconds, with free output tokens. This project is a
computer-use loop built around it.
Measured on the same screenshot and goal, one decision each:
| typesafe (jev) | Claude Opus 5, bare screenshot | multiplier | |
|---|---|---|---|
| input tokens | 4,882 | 4,785 | same |
| cost per decision | $0.0002 | $0.032 | 155x cheaper |
| cost per decision, realistic loop with history | $0.0002 | $0.035 to $0.08 | 170x to 390x cheaper |
| cost per 12-step task | $0.003 | $0.40 to $0.90 | 130x to 300x cheaper |
| model latency | 0.13 to 0.38 s | 5.2 s | 14x to 40x faster |
| end-to-end step, with capture and OCR | about 1.5 s | about 5.5 s | 3.7x faster |
The honest caveat: the big model read the event dates off the pixels and compared them unaided. The classifier needed the date parsing described below. Every piece of reasoning the frontier model does for free has to be rebuilt here as deterministic state.
macOS 14 or newer, Python 3.12 or newer, uv. Windows 10 and 11 are experimental; see Windows below.
git clone https://github.com/awlevin/typesafe-computer-use
cd typesafe-computer-use
uv sync
cp .env.example .env # fill in the keys
| variable | required | purpose |
|---|---|---|
TYPESAFE_API_KEY |
yes | every decision |
ANTHROPIC_API_KEY |
no | type_text, writer-proposed URLs, and the final answer |
CLICKER_EMAIL |
no | enables the type_email action |
CLICKER_BROWSER |
no | defaults to Google Chrome |
CLICKER_WRITER_BASE_URL |
no | send the writer to another endpoint; unset means api.anthropic.com |
CLICKER_WRITER_API_KEY |
no | the key for CLICKER_WRITER_BASE_URL, if it checks one |
CLICKER_WRITER_API |
no | what that endpoint speaks: anthropic (the default) or openai |
CLICKER_WRITER_MODEL |
no | types text and proposes URLs; defaults to claude-haiku-4-5 |
CLICKER_ANSWER_MODEL |
no | reads the screen whenever the classifier stops; defaults to claude-sonnet-5 |
CLICKER_WRITER_VISION |
no | false for an answer model that reads text only; defaults to true |
Other models. Point CLICKER_WRITER_BASE_URL at any endpoint that speaks the Anthropic
Messages API or, with CLICKER_WRITER_API=openai, OpenAI's Chat Completions API: LM Studio,
Ollama, vLLM, a LiteLLM proxy, DeepSeek. Name the models it serves. The full request URL works as
well as the root; for the OpenAI API keep the /v1. Such an endpoint may ignore structured-output
parameters, so the schema is also spelled out in the prompt, and code fences or a sentence around
the JSON are tolerated. On the OpenAI API a json_schema response format is asked for first, then
json_object, then none, stepping down only when the endpoint refuses one. Thinking is turned off,
since a model that thinks by default spends the writer's small token budgets on it and returns no
text. The answer model reads a screenshot; for a model that reads text only, set
CLICKER_WRITER_VISION=false and it gets the screen's text alone. A reply that cannot be read
refuses the step it was for, and the run goes on.
Keys never cross over: CLICKER_WRITER_API_KEY goes only to CLICKER_WRITER_BASE_URL, and
ANTHROPIC_API_KEY and OPENAI_API_KEY never go there. On the Anthropic API it is sent in both
the x-api-key and Authorization headers, since proxies differ. Leave it empty for an endpoint
that checks no key.
# LM Studio, either of its two APIs
CLICKER_WRITER_BASE_URL=http://localhost:1234
CLICKER_WRITER_MODEL=qwen3.8-flash-next
CLICKER_ANSWER_MODEL=qwen3.8-flash-next
# any OpenAI-compatible server
CLICKER_WRITER_API=openai
CLICKER_WRITER_BASE_URL=https://api.deepseek.com/v1
CLICKER_WRITER_API_KEY=sk-...
CLICKER_WRITER_MODEL=deepseek-v4.1-flash
CLICKER_ANSWER_MODEL=deepseek-v4.1-flash
CLICKER_WRITER_VISION=false
.env lives at the repo root and is read by every entry point (clicker, clicker-inspect).
Grant your terminal Screen Recording and Accessibility in System Settings >
Privacy & Security. Without the first, captures are wallpaper. Without the second,
synthetic clicks are silently dropped, and --act refuses to start.
windows.py provides the same adapter over UI Automation, Win32 SendInput, and
Windows.Media.Ocr, and uv sync installs its packages in place of the macOS ones. It is
untested on Windows: CI runs only its pure rules, on macOS and Linux. Expect it to break, and
please report what you see. Install the English OCR language once, from an elevated PowerShell:
Add-WindowsCapability -Online -Name "Language.OCR~~~en-US~0.0.1.0"
No permission prompt is needed. What differs from macOS:
activate matches the executable name exactly (notepad.exe), or a
known browser by its product name (Google Chrome is chrome.exe). Window titles never count.clicker.exe, run uv run python -m typesafe_computer_use
with the same arguments, or ... typesafe_computer_use inspect for clicker-inspect.uv run clicker "open the Playground" # dry run: one step, prints what it would do
uv run clicker "open the Playground" --act # drives the machine, up to 100 steps
uv run clicker "log in" --act --steps 20 --delay 3 # longer and slower
uv run clicker "log in" --act --handoffs 0 # the classifier alone: its first stop ends the run
uv run clicker-inspect "any goal" # 3-2-1, capture, open the annotated screen + payload
Clear the terminal first. It is on screen, so its text is OCR input.
Stopping a live run. Ctrl-C when the terminal has focus, or slam the mouse into the
top-left corner of the screen from any app. The corner is checked before every click, key,
scroll, app switch and accessibility action, and between typed characters, so a run stops
mid-word. A key or mouse button goes back up even when Ctrl-C lands between its down and
its up. The loop also stops itself on done or none, on confidence under
--min-confidence (0.4), when it stalls, or at --steps. Each of those stops goes to the
writer, which answers and may hand the run back (below).
Stalls. Nothing in an action's description says what came of it; only the next capture
does. So each step keeps a signature of the screen (the app, the page, the text on it) and
the loop stops after three actions in a row that left the screen as it was (a refused
action, a wait on a page still loading, a scroll that has run out of page) or after two in a
row that were already taken on the same screen earlier in the run (a click that does
nothing, or a cycle through two pages). Two captures count as the same screen when at most
one line differs, and that one is one line in ten or fewer: a clock or a ticker does not
hide a stall, and a two-line modal on a dense page is not mistaken for nothing happening.
When more than that changes every step, a run that is getting nowhere runs to --steps:
the rules err toward running on, never toward stopping a run that is making progress.
The answer. When the classifier stops, the writer reads the screen it stopped on and prints the result: the information the goal asked for, or where things stand and the next step when the screen does not hold it. A dry run that would have acted, and an aborted run, print no answer.
The hand-off. A stop is not the end when the goal is not reached. One sentence of goal does not say which of two good moves comes first ("cheapest, and here in under a week": open the cheapest listing, or filter by delivery?), and a classifier split between them reads as low confidence. So the writer's answer may carry a focus, one move in terms of the screen ("Click the 'Arrives in 2-4 days' filter"), and the classifier goes back to work with the goal and the focus both in its state. On the capture that stopped such a run at 0.39, the same classifier picks the filter at 0.92 under that focus. It may instead carry a question, put to you in the terminal when there is something only you can say ("13 or 15 inch?"); your reply joins the state for the rest of the run, the writer reads the screen again with it, and the app you were in comes back to the front. An empty reply declines, and the answer stands. The writer never picks a click: every action is still the classifier's.
The exchange cannot go round on itself. A focus the classifier takes no action under
leaves the answer it came with standing, without a second reading of the same screen.
--handoffs (10) bounds the trips, three questions bound the asking, and a stop on
the last step is final. A done the writer does not see on the screen is sent back
like any other stop.
Who did the work. Every run ends by counting the requests each model took:
calls: classifier 14 (82%, 3.9s) writer 3 (18%, 21.4s) handoffs 1 questions 0
The classifier's share is the number the design stands on. A task it falls on is a task the writer had to steer at every turn, and the fix belongs in the state the classifier reads, not in more hand-offs.
macos.py drives whatever is on screen. For the browser there is a second backend that
never looks at pixels: it reads the DOM over the Chrome DevTools Protocol, so it needs no
Screen Recording permission and cannot fight you for the cursor. It is opt-in: clicker
never uses it. clicker-bench starts its own Chrome on a fresh, temporary profile (never
yours), whose debugging socket listens on the loopback address and accepts one origin, and
removes the profile when it closes. It reads TYPESAFE_API_KEY and the writer's settings
the way clicker does, from the environment or ./.env.
uv run clicker-bench loop --fixture --runs runs # end-to-end step loop
uv run clicker-bench perception --url https://news.ycombinator.com
uv run clicker-bench replay --run runs/<ts> --step 2 # re-decide a saved step offline
Perception, same page, same machine, same moment, one decision each. The TypeSafe call is identical in both paths, so the difference is purely how the screen is read:
| page | DOM (browser backend) | screencapture + Vision OCR | ratio |
|---|---|---|---|
| local fixture | 1.3 ms (28 elements) | 288.0 ms (31 blocks) | 221x |
| news.ycombinator.com | 4.5 ms (120 elements) | 697.3 ms (46 blocks) | 155x |
| en.wikipedia.org/wiki/Singapore | 14.4 ms (91 elements) | 897.0 ms (50 blocks) | 62x |
The OCR column is roughly 143-177 ms capture + 573-710 ms Vision OCR + ~0.2 ms merge.
Speed is the smaller half of it. OCR reproduced only 7/13, 7/88 and 3/85 of the DOM's labels verbatim across those pages — under 10% on real sites. A classifier choosing between OCR blocks is choosing between garbled strings; a classifier choosing between DOM elements is choosing between the page's actual labels.
End-to-end loop, p50: 302-380 ms per step (2.6-3.5 steps/sec), of which ~280-350 ms is the TypeSafe decision. Perception is now ~0% of a step.
Runtime.evaluate ─► ordered element list (text, role, click point, on-screen, covered)
│
ONE TypeSafe request, two Choices
kind : click | type_text | navigate | press_enter | scroll_down | ... | done
element : which on-screen element (used only when kind is click)
│
real Input events ─► observe-until-changed ─► next step
The action set is filtered to what the page can actually do: no type_text without a field
and a writer, no navigate without a writer, no scroll_down when the document does not scroll, no back with
empty history. An option the loop cannot execute is a guaranteed stall, and it reads as
model doubt when the model was never at fault.
The post-action observation and the next step's perception are the same call, so waiting costs no extra round trip.
The writer, as above: compose_browser_text for a field and compose_url for an address,
each with a structured reply. There is no other source, so with no writer the loop does not
offer type_text or navigate. Every step records where its text came from (writer,
writer_declined, writer_error(...), refused_credential, no_writer) in the step line
and the run folder.
Perception never reads what is in a field: an input's value is not collected, and no element
is named after it, so a password on the page cannot reach the classifier, the writer, or the
disk. Password inputs and fields whose autocomplete asks for a credential or card data are
marked, and the loop refuses to type into them, or into any field labelled like one, before
the writer is asked.
runs/<timestamp>/ in the same shape, with step-NN-elements.json as the replayable
artefact, since a browser step has no pixels to re-capture:
run.log, run.json
step-NN-payload.txt the exact `state` and every Choice criteria sent
step-NN-state.json the state, for comparison on replay
step-NN-answers.json every probability returned
step-NN-elements.json everything perception returned, for offline replay
clicker-bench replay rebuilds the page from that file and reports whether the
reconstructed state matches the saved one, so a stall can be re-decided without a browser.
Viewport only, the same as OCR only saw the visible screen; elements below the fold need
scroll_down first. The DOM sees elements rather than paint, so canvas-drawn UI and text
baked into images are invisible here and are visible to OCR — use macos.py for those.
One tab, one page target, no iframes or shadow-DOM piercing.
screencapture ─► Vision OCR ─► merge lines into blocks ─► drop lines echoing the goal
accessibility ─► actionable elements (role, label, frame), pruned to the display,
the labelled pressable ones it pruned kept as off-screen controls
│
└─► one numbered list of items, each carrying its source
│
accessibility ─► focused field (role, label, placeholder, value, frame)
AppleScript ─► frontmost app and pid, active tab URL
clock ─► local date and time
dates.py ─► "dated 2026-10-13 (in 27 days)" on any block containing a date,
"near a line dated ..." on its neighbours
layout ─► "in the row of ..." on any label that appears more than once
runner.py ─► the actions already tried on this same screen, each of which led back here
│
▼
one TypeSafe request, three Choices, four with off-screen controls
┌────────────────────────────────────────────────────────────┐
│ kind : click_item | use_browser | type_text | scroll… │
│ item : which item (used only for click_item) │
│ site : which website (used only for use_browser) │
│ offscreen : which hidden control (only for press_offscreen)│
└────────────────────────────────────────────────────────────┘
│
▼
deterministic action ─► wait ─► next step
Items carry where they came from: ocr for a text block, ax for a control the app
declared, ax+ocr when both found the same thing. An ax item reads as
button 'Share' (top-right) in the criteria, so the classifier can tell a real control
from a line of text. A label that appears more than once carries its row as well:
'Buy' (middle-right; in the row of 'Coldplay', 'Oct 2'), since the label says nothing
about which and the layout does.
Splitting the decision into three questions keeps screen noise out of the action choice. Every stall found while building this came from two options that meant the same thing. Confidence measures concentration, so overlapping options always read as doubt. Keep the action set mutually exclusive.
Vision is about two thirds of a step, and it charges by the amount of text rather than the number of pixels, so the only real saving is reading less of the screen.
The timing line says how much was read, and in how many pieces: ocr 0.31s (22% of screen, 2 rects). A replay (--image) always reads the whole image and never reuses, so an offline
repro matches the original run.
OCR cannot see an icon. The accessibility tree can, so each step also walks the frontmost process for labelled, on-screen controls. Coverage is uneven, measured on ten apps on one Mac: Finder 100% of on-screen controls labelled, Chrome 88%, Slack 85%, Notion 68%, Spotify 0 (its CEF shell exposes three window buttons and nothing else). Terminals expose the grid as one text area. So AX is a bonus source, never a replacement.
Labels live in AXDescription for web and Electron, AXTitle for AppKit, and a short
AXValue otherwise. A decorative image takes the label of the control around it; a list
row takes it from a shallow AXStaticText.
Frames lie, so the walk prunes hard:
AXMenu subtrees, which are thousands of zero-sized items behind a closed menuAXGroup layout boxes, even pressable onesWalks measured here: Finder 152 controls in 0.08 s, Chrome 172 in 0.59 s. The assistive
handshake attributes (AXManualAccessibility, AXEnhancedUserInterface) are unsupported
on this macOS, so nothing relies on them.
AXPress does not need an element to be visible. Notes selects a row parked thousands of
points below the display, Chromium delivers a click to a link it clamped to a 1 px sliver
because the page is scrolled past it, and an auto-hidden Dock hands over all 37 of its
items from 5 pt below the bottom edge. So the same walk keeps the labelled, pressable nodes
it pruned, and offers them as a separate capped list rather than mixing them into the items:
nothing on the capture points at them, and a mouse click would land somewhere else entirely.
The list is deduplicated by role and label, drops any label the visible items already carry,
and stops at 120 controls, after which those subtrees are pruned as before, so the walk costs
what it always did. It is offered only when it is not empty, as a press_offscreen action
plus an offscreen question, and the step log counts it next to ax=. A refusal is the end
of it: there is no pixel to fall back on, so it reads as a no-op. What a walk finds depends
on the app, and the node and time caps bind first on a big tree: Notes and Chrome spend all
4000 nodes on what is already on screen and report nothing hidden.
| key | does |
|---|---|
click_item |
press the element through the accessibility tree when the item came from it, so the press lands on the control rather than on whatever covers it; a mouse click at the center of the box otherwise, and as the fallback when the press is refused |
press_offscreen |
AXPress a labelled control the app exposes but does not show, chosen from the off-screen list; offered only when that list is not empty, and a refusal counts as a no-op since there is no pixel to fall back on |
use_browser |
go to the browser, showing the website the site answer names: none brings it forward on the page already open there, a SITES catalog key opens that URL through AppleScript open location, and other opens a URL the writer proposes |
type_text |
the writer composes the string; it is set on the focused element through the accessibility tree, with keystrokes as the fallback when the value does not read back, and a TypeSafe Noul then checks the field's value |
type_email |
fills in $CLICKER_EMAIL the same way; refused unless a text field is focused |
press_enter, press_escape |
keyboard |
go_back |
Cmd-[, the browser's Back, when the last click led somewhere unhelpful |
scroll_down, scroll_up |
10 lines, after parking the cursor over the frontmost window |
wait |
screen still loading: 3 s, then the step's own delay, so three waits cover a slow page |
done, none |
stop |
The classifier never generates text. The writer model runs in three places, each with a small packet and a structured reply. Each packet also carries the current focus and what the user said, once there are any:
Continued at the source.
buygithub.com · How We Deliver Stars · Blog
Track the star history of any public GitHub repository.
Daily and weekly growth, peak detection, multi-repo comparison, CSV and JSON export.
buygithub.com · How GitHub Stars Work
GitHub uses star velocity as a core signal for its Trending page, Explore feed, and search ranking. A repository gaining 200 stars in 24 hours is more likely to surface than one with 10,000 total stars and flat recent growth.
This tool gives you that data: how many stars a repository gained each day, across its entire life. Compare your project against competitors, measure a launch, verify whether a star spike was earned or delivered, or track your own growth week by week.
Until June 2026, tools like this one read the stargazers listing endpoint to reconstruct per-star timestamps. GitHub then restricted stargazer and watcher lists to repository admins and collaborators to protect user privacy — breaking most star-history tools in the ecosystem.
In September 2026, GitHub shipped the fix: a privacy-safe star history endpoint that returns weekly and daily aggregate counts with no stargazer identities. Version 1.1.0 of this tool migrated to that endpoint. It works for any public repository again, with no ownership or special access required.
| Feature | Description |
|---|---|
| Complete daily history | Daily star counts from the repository's first star to today |
| Growth windows | 7-day, 30-day, 365-day and all-time rates |
| Peak detection | The single best day and its exact count |
| Multi-repo comparison | Pass any number of repositories and compare their curves |
| Velocity report | 30-day view with terminal bar chart (examples/velocity_report.py) |
| CSV + JSON export | Machine-readable daily series for charting or analysis |
| Rate-limit aware | Header-based backoff — no wasted probe requests |
| No API key needed | Public data for public repositories; a token only raises rate limits |
git clone https://github.com/Marcos66236/github-stars-history.git
cd github-stars-history
pip install -r requirements.txt
python star_history.py sindresorhus/npOr install it as a command-line tool:
pip install git+https://github.com/Marcos66236/github-stars-history.git star-history owner/repo
No token, no signup, no configuration. Works with Python 3.8+.
python star_history.py sindresorhus/np
Fetching star history for sindresorhus/np...
Fetched 2,227 stars so far (page 10)...
Total: 7,712 stars (20 week pages)
Repository: sindresorhus/np
Total stars: 7,712
Created: 2015-08-16
Age: 11 years, 1 month
Growth summary:
Last 30 days: +5 stars (0.2/day)
Last 365 days: +117 stars (0.3/day)
All time: +7,712 stars (1.9/day)
Peak day: 2016-07-07 (+373 stars)
python star_history.py facebook/react vuejs/vue sveltejs/svelte
python examples/velocity_report.py sindresorhus/np
Shows the last 30 days of star gains as a terminal bar chart.
python star_history.py owner/repo --format csv # daily series (default) python star_history.py owner/repo --format json # history + full analysis python star_history.py owner/repo --summary # print only, no files
CSV output is one row per day: date,stars. JSON output adds the full analysis block and metadata.
Without a token you get 60 requests/hour; with a token, 5,000. Large repositories need a handful of requests per decade of history, so most users never hit the limit.
export GITHUB_TOKEN=ghp_your_token_here
python star_history.py torvalds/linuxGenerate one at github.com/settings/tokens — no special scopes are required for public repositories.
GET /repos/{owner}/{repo}/stargazers/history
The endpoint returns weeks of aggregate daily counts, newest first, paginated backwards to the repository's creation week. The tool reconstructs a single daily series, computes growth windows, and stores nothing.
[
{ "week": 1789257600, "total": 126, "days": [0, 0, 0, 0, 104, 22, 0] },
...
]
Two deliberate design decisions:
Does this work for any repository? Yes — any public repository, whether you own it or not. Private repositories require a token with access.
Why does the output show daily counts instead of usernames? GitHub restricted stargazer identities to repository admins in June 2026. The public star history endpoint returns aggregate counts only, which is what growth analysis actually uses.
How far back does the history go? To the repository's creation week. The tool paginates through every week automatically.
Can I see who starred a repository? Not through this tool. If it is your repository, GitHub's UI and API still give you full stargazer access. For anyone else's, that data is no longer public.
How do I detect a suspicious star spike? Plot the daily series and look for vertical jumps with no external explanation — a launch, a release, a viral post. Our companion guide covers the checks: How to tell if GitHub stars are real.
Do I need Python? For this CLI, yes. The raw endpoint is plain HTTP and works from any language — see How it works above.
github-stars-history/
├── star_history.py Main tool (fetch, analyze, export)
├── setup.py Package configuration
├── requirements.txt Dependencies (requests only)
├── examples/
│ ├── compare_frameworks.py Compare frontend frameworks
│ └── velocity_report.py 30-day velocity chart
├── tests/
│ └── test_star_history.py Unit tests
├── sample-output.json Example JSON export
├── CHANGELOG.md Version history
├── CONTRIBUTING.md Contribution guidelines
└── LICENSE MIT
python -m pytest tests/
# or
python -m unittest tests/test_star_history.pyrequestsSee CONTRIBUTING.md. Issues and pull requests are welcome.
MIT. See LICENSE.
Related resources from buygithub.com
GitHub Stars Service · GitHub Followers · Search Ranking · Aged Accounts · Blog
Browser automation where an LLM plans and Jev decides.
Unofficial project, not affiliated with TypeSafe. It calls the TypeSafe System One API with your own API key.
The calling LLM (Claude, via MCP) says what outcome it wants, one step at a time, and hands over any text to type. For each round of a step, code describes the page. Then one ~300 ms Typesafe System One request asks Jev several questions at once: which element, which action, which value, and is the step done / blocked / showing an error / about to do something irreversible. Playwright performs the action. The LLM never reads page snapshots unless it chooses to take over.
Claude ── browser_do("Log in", {email, password}) ──▶ jev-browser
│ loop until done / stuck / needs confirmation
│ 1. settle (network + DOM quiet)
│ 2. describe (elements, labels, state, visible text, diff, counts)
│ 3. Jev (done? error? irreversible? tool? target? value?)
│ 4. act (Playwright)
Claude ◀── { status: "done", url, actions[], done_score } ─┘
Jev only answers with probability distributions: yes/no (noul), pick one option (choice)
or a rating (score). It never writes text. So everything free-form comes from the caller as
candidates, and code turns disagreement or low confidence into a status the LLM can act on.
42 tasks in 16 categories on live sites (see RESULTS.md):
likely_done) and verifying a sort.Works on: forms, native and custom dropdowns, checkboxes and radios (including styled replacements), dynamic loading, modals, JS dialogs, hover, right-click, drag and drop, key presses, file upload, iframes, shadow DOM, new tabs, pages with 2,000+ elements (two-stage selection), and non-English UIs.
Known limits, so write steps around them:
browser_check or browser_snapshot.npx playwright install chromium # once
claude mcp add jev-browser -e TYPESAFE_API_KEY=your-key -- npx -y -p jev-browser jev-browser-mcpAny MCP client works the same way: command npx, args -y -p jev-browser jev-browser-mcp,
env TYPESAFE_API_KEY. Add JEV_BROWSER_HEADED=1 to watch it work.
git clone https://github.com/Ying-Kai-Liao/jev-browser && cd jev-browser npm install npm run setup # downloads Chromium for Playwright cp .env.example .env # add TYPESAFE_API_KEY npm test # offline tests (no network, no key) npm run test:e2e # MCP server end to end (network + key)
claude mcp add jev-browser -- node /absolute/path/to/jev-browser/bin/jev-browser-mcp.mjs
From a source checkout the server reads TYPESAFE_API_KEY from the repo's .env.
| tool | purpose |
|---|---|
browser_open(url) |
navigate and wait for the page to settle |
browser_do(goal, values?, max_actions?, allow_irreversible?, explain?) |
work toward one outcome; returns a status |
browser_check(question) |
yes/no about the page → p_yes |
browser_choose(question, options) |
pick among given options → distribution |
browser_snapshot() |
compact numbered element list, for taking over |
browser_act(action, element, value?, key?, destination?, accept_dialog?) |
act on an element directly, no model; confirm/prompt dialogs are dismissed unless accept_dialog |
browser_screenshot(full_page?) |
PNG image |
browser_close() |
end the session |
browser_do statuses:
| status | meaning / what the caller should do |
|---|---|
done |
goal reached |
likely_done |
the page looks done but Jev is unsure: verify before moving on |
needs_login |
a sign-in wall and no credentials in values; log in yourself (headed + JEV_BROWSER_PROFILE) or pass credentials |
needs_confirmation |
next action, or a confirm dialog it opened (then dismissed), looks irreversible (order, pay, send, delete); see pending, re-call with allow_irreversible: true only if the user wants it |
error |
the page shows an error after the last action (e.g. wrong password); see page_text |
stuck / max_actions |
no progress; see info, page_text, candidates |
ambiguous |
low confidence in the target; pick from candidates with browser_act |
blocked |
captcha, access denied, error page |
Env: JEV_BROWSER_HEADED=1 shows the browser, JEV_BROWSER_PROFILE=/dir keeps a persistent
profile (logins survive restarts), JEV_BROWSER_LOG=1 prints per-round decisions to stderr.
// npm install jev-browser && npx playwright install chromium import { JevBrowser } from "jev-browser"; const b = await JevBrowser.launch({ headed: true }); await b.open("https://www.saucedemo.com/"); await b.do("Log in", { values: { username: "standard_user", password: "secret_sauce" } }); await b.do("Add the Sauce Labs Backpack to the cart"); const p = await b.check("Does the cart badge show 1 item?"); // 0..1 await b.close();
node examples/x-profile-demo.mjs # headed, read-only x.com walkthrough with decision highlightsEach action is outlined in red with Jev's choice and scores before it happens (highlight: true,
on by default in the MCP server when JEV_BROWSER_HEADED=1). Some sites, x.com included, serve a
blank page to headless Chromium: use headed mode there.
node bin/jev-browser.mjs do https://the-internet.herokuapp.com/login "Log in" username=tomsmith 'password=SuperSecretPassword!' node bin/jev-browser.mjs run examples/flows/todomvc.json --headed
values with a meaningful key (email, postal_code, file).likely_done, needs_confirmation, ambiguous and stuck as your turn: check,
snapshot or ask the user, don't just retry.browser_check what must not have changed.node bench/run.mjs --set all # base, hard, guard (+ bench/tasks.local.mjs if present) node bench/run.mjs --only ti-login,drag node bench/context-cost.mjs bench/results/<run>.json
Private sites and credentials go in bench/tasks.local.mjs (git-ignored), exporting LOCAL.
src/session.mjs JevBrowser: settle, snapshot, decide (1 or 2 stages), resolve, act, do, check, choose
src/page-script.mjs runs in each frame: elements, labels, state, visible text, metrics, dialogs
src/page-model.mjs pure helpers: diff between pages, counts, compact rendering
src/jev.mjs System One API client
src/flow.mjs JSON flow runner
bin/ CLI and MCP server
bench/ tasks with ground-truth checks, runner, context-cost estimate
test/ offline fixture tests, MCP end-to-end test
NOTES.md design notes: what works with Jev, what doesn't, and why
CI runs the offline tests on every push and pull request. To publish, bump the version and push
the tag; .github/workflows/release.yml tests, publishes to npm with provenance and creates a
GitHub release:
npm version patch # or minor / major: commits and tags vX.Y.Z
git push --follow-tagsPublishing uses npm trusted publishing, set up once with
npx npm@latest trust github jev-browser --file release.yml --repo Ying-Kai-Liao/jev-browser --allow-publish.
MIT
At A Glance | Start Here | Explore The Stack | Why This Repo Wins | Star History
Want to learn how to vibe code like the pros? Join my Skool community! 🎯
I'll show you how to set up applications to use these APIs and start making money! I have the best tech stack when it comes to vibe coding that virtually costs nothing. Forget tools like Loveable, Replit, Manus, Base44, etc.
The ultimate collection of shippable AI APIs — LLMs, vision, agents, and MCP servers ready for production products. 1,555 production-ready APIs across 5 navigable sections.
This repository is designed to feel like a launchpad, not a junk drawer. Pick a section folder, scan the API table, and click through to the provider — no endless flat scrolling.
| Metric | Count |
|---|---|
| Total APIs | 1,555 |
| Categories | 5 |
| Last Updated | 2026-09-16 |
| Focus | AI Product Building |
|
795 APIs Text generation, chat, reasoning, summarization, and extraction APIs. |
83 APIs Computer vision, image/video generation, OCR, and multimodal AI. |
61 APIs Autonomous agents, orchestration layers, tool-using assistants, and workflows. |
|
41 APIs Model Context Protocol servers that connect assistants to real tools and data. |
575 APIs Everything else AI-adjacent — brand visibility, SEO/GEO, utilities, and niche AI products. |
Text generation, chat, reasoning, summarization, and extraction APIs.
Browse LLM Generation & Reasoning APIs
Vision & Media AI (83)Computer vision, image/video generation, OCR, and multimodal AI.
Autonomous agents, orchestration layers, tool-using assistants, and workflows.
Browse Agents & Orchestration APIs
MCP Servers (41)Model Context Protocol servers that connect assistants to real tools and data.
Everything else AI-adjacent — brand visibility, SEO/GEO, utilities, and niche AI products.
| AI Founders | Indie Hackers | Product Engineers | Agency Builders |
| RAG Apps | Agent Platforms | MCP Toolchains | SaaS Features |
?fpr=p2hrc6) on every link.This repository intentionally organizes APIs into:
LLM Generation & Reasoning, Vision & Media AI, Agents & Orchestration, MCP Servers, Other AI Tools
Unmatched rows land in the other-* catch-all so zero APIs are dropped.
See FOLLOW_CREATOR.md — follow Chris Porter on Facebook for more valuable repos and updates.