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ay. We keep an internal incident log focused on agentic misbehaviors. Past examples include deleting remote git branches from a misinterpreted instruction, uploading an engineer's GitHub auth token to an internal compute cluster, and attempting migrations against a production database. Each of these was the result of the model being overeager, taking initiative in a way the user didn't intend. We documented this pattern in the Claude Opus 4.6 system card (§6.2.1 and §6.2.3.3). How it works Auto

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e to make its artifact (memory) system more robust, make communications inside Goosetown flow more smoothly, and keep it just a little silly. — Tyler Ready to try parallel agentic engineering for yourself? Goosetown is open source and available on GitHub. Clone the repo, follow the setup instructions in the README, and you'll be orchestrating multiple agents in no time. If you're new to this workflow, watching the video below is a great way to see what a real session looks like before diving in.

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Devin also continues to autofix lint and CI/CD issues. These move us a big step forward in closing the agent loop: writing and fixing its own code until its fully correct. When a GitHub bot comments on a PR - a linter flags an issue, CI catches a test failure, a security scanner surfaces a vulnerability - Devin can automatically pick it up and fix it. It works with any bot that comments on PRs. Linters, CI pipelines, security scanners, dependency managers - if it leaves a comment, Devin handles

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editor, head over to the ACP documentation for detailed setup instructions for your editor of choice. And if you're interested in how OpenHands works under the hood, check out our GitHub repository or join our Slack community to chat with the team and other developers using OpenHands in their daily workflows. What Is An AI Dark Factory? What Lights-Out Manufacturing Teaches Us About Software Insights and updates from the OpenHands team Thank you for your submission! OpenHands is the foundation f

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An IDE assistant that integrates git operations, file manipulation, package managers, testing frameworks, and deployment pipelines. An operations coordinator that connects Slack, GitHub, Google Drive, Jira, company databases, and dozens of MCP servers simultaneously. To build effective agents , they need to work with unlimited tool libraries without stuffing every definition into context upfront. Our blog article on using code execution with MCP discussed how tool results and definitions can so

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update sample without stitching; just log the returned IDs via return token ids enabled. Links: Docs: OpenAI‑compatible server; Project to try with this feature: Agent Lightning (GitHub, docs) Why token IDs matter for Agent RL RL for LLMs trains on token sequences, so trainers need the exact token IDs sampled by the behavior policy. In single‑turn settings this used to be straightforward, since calling vLLM’s low‑level generate returns tokens directly. In agent settings, most agent frameworks c

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ial and the contents of the git interaction (e.g. ensuring it is only pushing to the configured branch), then attaches the right authentication token before sending the request to GitHub. Getting started Our new sandboxed bash tool and Claude Code on the web offer substantial improvements in both security and productivity for developers using Claude for their engineering work. Run /sandbox in Claude and check out our docs on how to configure this sandbox. Go to claude.com/code to try out Claude

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that will improve performance for larger scale, higher complexity models (XL MoE, +vision encoders, MLA, etc.). If you’d like us to prioritize something specific, please submit a GitHub feature request here. Supported/Verified TPU generations Trillium (v6e), v5e Features Prefix caching; Chunked Prefill; Multimodal Inputs; Single Program Multi Data (SPMD); Structured Decoding; Speculative decoding: Ngram; Out-of-tree model support; Optimized Runtime Sampling (top k, top p, temperature, logit out

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te a perfect SQL statement to get the data I needed. But the context was limited to my chat window—I had to explicitly tell ChatGPT about my code and what I needed. The next year, GitHub Copilot went mainstream (though it had already been in GA for a while). Having an LLM right there in neovim felt like magic—Copilot was able to predict what I was going to type before my hands even hit the keyboard. It had at least a small window into my codebase—no more copy/pasting back and forth into the Chat

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del. (See our full model speed and accuracy benchmark here) Seeing is Believing To put this in perspective, here's both agents executing the same task: "Find the most recent PR in github.com/browser-use/browser-use." BU 1.0 finishes in 15 seconds. Gemini 2.5 Computer Use takes 1 minute 15 seconds. As you can see, we're getting into the regime where our agents are becoming as fast as human workers. Here we present some of the core optimizations drive this performance: 1. Making Use of the KV Cach

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tching page results To fetch individual pages (e.g. when a user provides a url in the prompt), use the new web fetch API. Python library Result Example Python code is available on GitHub. JavaScript library Result Example JavaScript code is available on GitHub. You can enable web search in any MCP client through the Python MCP server. Cline To integrate with Cline, configure MCP servers in its settings. Codex Add the following configuration to /.codex/config.toml Goose You can integrate with Oll

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opment task, any time. One of our flagship ways to provide this is through the OpenHands Cloud where users can interact with agents through a GUI, API, or integrations into Slack, GitHub, GitLab, and BitBucket. We are constantly listening to feedback from the cloud users, and this month worked hard to fix two major requests that we got from users: Cost of the Agents; Usability of the GUI Experience We thought hard about how to deliver these, and here's our best try for both. Model Choice + BYOK

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ide inference acceleration for open-source LLMs through: Semantic-aware routing; Efficient model switching; Enterprise-friendly deployment (Kubernetes & Envoy) Find the project on GitHub. The current focus is on a Work Group and planned v0.1 Roadmap. Currently, ModernBERT runs internally within the router for classification. It is not yet served by vLLM. However, future work aims to make the classifier—and potentially other embedding models—pluggable, allowing integration with vLLM-hosted models

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y of 20+ secure connectors—spanning data, productivity, development, automation, commerce, and custom integrations. Search, summarize, and act in tools like Databricks, Snowflake, GitHub, Atlassian, Asana, Outlook, Box, Stripe, Zapier, and more. Custom extensibility: Add your own MCP connectors to broaden coverage and drive more precise actions and insights. Flexible deployment: run on mobile, in your browser, or deploy on-premises or in your cloud. Context that carries: introducing Memories (be

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d to enable the next wave of startups and AI progress to be built on open rails and open models. Quick Links Environments Hub; Environments Hub Documentation; prime CLI; verifiers github; verifiers docs; prime-rl github Features Developing and Sharing Environments Create, manage and share environments for reinforcement learning and evaluation on the Environments Hub: Evals Create and explore evaluation reports for environments for various models. RL Training Environments are natively supported i

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JSON configuration files Dependency management : Users must resolve package conflicts and version mismatches No discovery mechanism : Finding useful MCP servers requires searching GitHub Update complexity : Keeping servers current means manual reinstallation These friction points meant that MCP servers, despite their power, remained largely inaccessible to non-technical users. Introducing Desktop Extensions Desktop Extensions ( .mcpb files) solve these problems by bundling an entire MCP server—i

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both the Minion and MinionS protocols. To start it, run: A browser window will open. Select your Remote Provider to be “Secure”. Set the Secure Endpoint URL to http://20.57.33.122:5056. Set your local client to Ollama and select the model you would like to use. Example code To run the secure protocol programmatically in Python, the minions package can be used. First create a file named example.py and add the following contents: Run the example: Minions GitHub repository; Hazy Research blog post

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s truncated for brevity. Python library Please update to the latest Ollama Python library. Example of enabling thinking Please visit the Ollama Python library for more information about its usage. More examples are available. JavaScript library Please update to the latest Ollama JavaScript library. Example of enabling thinking Example of streaming responses with thinking Please visit the Ollama JavaScript library for more information about its usage. More examples are available. Reference GitHub

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impactful ways. Explore the diverse applications of Mistral’s Agents API across various sectors: An agentic workflow built with Mistral's agents API where an agent interacts with Github and oversees a developer agent, powered by DevStral to write code. The agent is granted full authority over Github, showcasing automated software development task management. Read our cookbook An intelligent task coordination assistant powered by our Agents API, using multi-server MCP architecture to transform c

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tion function or worry about breaking other models—they can focus solely on their own model and its training. Examples of how some models are implemented are available on Ollama’s GitHub repository. Accuracy Large images produce large number of tokens which may exceed the batch size. Processing this correctly with the right positional information is challenging, specifically when a single image crosses boundaries. Ollama adds metadata as it processes images to help improve accuracy. Some example

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