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accessible, high-quality, open source safety tools for digital organizations of all kinds in the age of AI. Established in 2025 by a diverse group of leading technology companies, philanthropic organizations and academic institutions, ROOST believes that solutions for online safety can best be achieved by providing organizations with innovative open source tools and technical support. Reference OpenAI blog; OpenAI gpt-oss-safeguard developer cookbook; ROOST’s model community repository on GitHub

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er. This is what the documentation statesabout the configuration: “Allow network egress to package managers only (default): Claude can access approved package managers (npm, PyPI, GitHub, etc.) to install necessary software packages. This balances functionality with security, but some advanced features may be limited.” The default setting is called “Package managers only”: That list of allowed domains is short and documented here as “Approved network domains”: This list seems quite limited. At s

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lysis of standard data, we also conducted 1-on-1 comparisons of practical performance against Claude 4.5 Sonnet and several open-source models: These test sets will be released on GitHub this week. To make it easier for everyone to use Agent-related capabilities, we have launched our M2-powered Agent product in China and upgraded the overseas version. In MiniMax Agent, we offer two modes: Lightning Mode: A high-efficiency, high-speed Agent for instant output in scenarios like conversational Q&A,

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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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our premium tier model—that's a game-changer for our unit economics.Jon Noronha Co-Founder, Gamma Our early testing shows that Claude Haiku 4.5 brings efficient code generation to GitHub Copilot with comparable quality to Sonnet 4 but at faster speed. Already we're seeing it as an excellent choice for Copilot users who value speed and responsiveness in their AI-powered development workflows.Matthew Isabel Distinguished Product Manager Safety evaluations We ran a detailed series of safety and ali

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ript Library Install Ollama’s JavaScript library Pull the model Example non-streaming output with image Example streaming the output with image Ollama’s JavaScript library page on GitHub has more examples and API documentation. Python Library Install Ollama’s Python library Pull the model Example non-streaming output with image Example streaming the output with image Ollama’s Python library page on GitHub has more examples and API documentation. API The model can also be accessed directly on oll

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transcript. SolveIt used web search to find it, then used the message editing tools to update the note with the proper markdown link. I did this throughout for papers, blog posts, GitHub repos, wikipedia pages and any other external resources that were mentioned in the video. In this screenshot, we can see at the top a note message containing part of the transcript. Below that is a prompt message asking SolveIt to find the YouTube link and add it as a hyperlink. The AI’s response shows it used w

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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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where the model’s predicted answer letter is compared directly to the correct one. Two other popular methods exist that involve log-probability scoring. I implemented them here on GitHub. (As this builds on the concepts explained here, I recommended checking this out after completing this article.) The following subsections illustrate how the MMLU scoring shown in figure 2 can be implemented in code. 1.2 Loading the model First, before we can evaluate it on MMLU, we have to load the pre-trained

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n development. running the test suite is slow as each LLM call take 100’s of ms to run; llm responses are non deterministic which makes assertions difficult; ci/cd pipelines (like Github Actions) need access to API keys to run tests As we build most of our software in notebooks non-deterministic responses create an additional problem. They add significant bloat to notebook diffs which makes code review more difficult 😢. Why cachy? Although LLMs are relatively new these challenges are not, and an

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ements on longer horizon tasks. It reinforces why many developers using Cursor choose Claude for solving their most complex problems.Michael Truell CEO Claude Sonnet 4.5 amplifies GitHub Copilot's core strengths. Our initial evals show significant improvements in multi-step reasoning and code comprehension—enabling Copilot's agentic experiences to handle complex, codebase-spanning tasks better.Mario Rodriguez Chief Product Officer Claude Sonnet 4.5 is excellent at software development tasks, lea

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fr, and Hellaswag-fr while preserving competitive English performance. All training code, evaluation scripts, and the Luth-SFT dataset (338 million tokens) are openly available on GitHub to support further French NLP research. Availability & licensing Liquid Nanos are available today on the Liquid Edge AI platform (LEAP) for iOS, Android, and laptops. Developers can use them out of the box, integrate via SDKs, or compose them into agents. Models are also available on Hugging Face. We’re making t

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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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utor), Mark Saroufim (PyTorch), Kyle Krannen (NVIDIA, Dynamo), and Ashish Vaswani for reading pre-release version of this blog post and providing feedback! References vLLM https://github.com/vllm-project/vllm; "Attention Is All You Need" https://arxiv.org/abs/1706.03762; "Efficient Memory Management for Large Language Model Serving with PagedAttention" https://arxiv.org/abs/2309.06180; "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model" https://arxiv.org/abs/2405

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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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