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e to make contributions, provide feedback, or offer feature requests, please join our community Slack. What is Docent? Docent is our tool for understanding complex behaviors in AI agent transcripts. It enables users to explore large collections of transcripts for interesting or unwanted behaviors, and precisely measure how often particular behaviors occur. For instance, a user might start with a vague question like “is my model reward hacking.” Docent first helps the user explore potential insta

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and run Ollama’s Python library Then make a request using ollama.web search: Example output JavaScript Install and run Ollama’s JavaScript library Example output Building a search agent Use Ollama’s web search as a tool to build a mini search agent. The example uses Alibaba’s Qwen 3 model with 4B parameters. Result Recommended models: These models have great tool-use capabilities and are able to have multi-turn interactions with the user and tools to get to a final result. qwen3; gpt-oss Recomme

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mini to summarize and clarify any page, with Workspace support coming soon Zoom launches AI Companion 3.0 with cross-platform meeting intelligence Notion 3.0 introduces autonomous Agents that work inside your docs and databases Workday ships new HR and Finance agents focused on real business tasks Zoom’s AI Companion 3.0 pushes agentic AI across meetings, chat, and support workflows Gemini arrives in Chrome for U.S. desktop users Summary: Google is rolling out Gemini in Chrome on Mac and Windows

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At OpenHands, our number one mission is to build a highly autonomous and accessible coding agent that can be used for any software development 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 Ag

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latency, and sustainability. One-Sentence Summary GPT-5: enterprise routing for smarter inference; vLLM Semantic Router: technical-first routing for open-source LLMs; Edge future: context-aware, minimal-compute inference that works seamlessly Related Posts Announcing vllm-metal: Concurrent Serving on Apple Silicon vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M5 prefill acceleration.

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across more TerraTorch models and modalities and beyond, making installation seamless. Longer-term, we envision IO Processors powered vision-language systems, structured reasoning agents, and multimodal pipelines, all served from the same vLLM stack. We're also excited to see how the community uses IO Processors to push the boundaries of what’s possible with vLLM. We also plan to continue working with and contributing to the vLLM community to enable more multimodal models and end-to-end use case

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RL environments are the playgrounds where agents learn. Until now, they’ve been fragmented, closed, and hard to share. We are launching the Environments Hub to change that: an open, community-powered platform that gives environments a true home. Environments define the world, rules and feedback loop of state, action and reward. From games to coding tasks to dialogue, they’re the contexts where AI learns, without them, RL is just an algorithm with nothing to act on. Environments sit at the center

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single task. Amp is better, faster, and cheaper when used this way. A notice will appear when you hit 20% of the context window to remind you of this. Longer threads are more expensive, both because each iteration of the agentic loop sends more and more tokens, and because requests with more than 200k tokens are roughly twice as expensive per token in Anthropic's API pricing. Note: the screenshot shows 968k tokens because the context window is composed of 968k input tokens and 32k output tokens.

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Over the past 12 months, tens of thousands of developers have built and productionized industry-leading voice agents using our frontier speech models, Sonic and Ink. In serving these developers, we found that even the most sophisticated teams struggled to build great voice agents with the tools available today. Voice agents are uniquely hard to bring to production: they require maintaining natural, low-latency conversations with advanced reasoning across millions of calls. A few challenges come

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leaders. Google adds AI image editing features in Slides and Vids OpenAI weighs encryption for temporary chats to strengthen privacy Salesforce partners report slower adoption of Agentforce in the enterprise Apple Intelligence features surface in the iOS 26 public beta, shaping user expectations at work Google adds AI image editing to Slides and Vids Summary: Google Workspace introduced background removal and background expansion for images in Slides and Google Vids. The weekly recap also highl

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estion, and it will break it down, gather credible sources, and build a structured, reference-backed report that’s easy to follow. It’s powered by our tool-augmented Deep Research agent (in preview), but designed to feel simple, transparent, and genuinely helpful, as though you’re collaborating with a well-organized research partner. Vocal mode lets you talk to Le Chat just like you’d talk to a person — no typing needed. You can brainstorm ideas while walking, get quick answers while running err

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One of our most important objectives at OpenHands is to make state-of-the-art software development agents accessible to any developer no matter what your circumstances. As an open source AI coding agent, developers often use OpenHands locally through a GUI or CLI with an API key to their LLM provider of choice. However, many OpenHands users find managing API keys for multiple LLM providers a nuisance, and low rate limits leading to a sub-optimal experience. This is why we are announcing a new Op

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There's a lot of news in agents nowadays, ones that code, ones that browse the web, ones that perform customer service, ones that wash your socks. However, while there is a lot of excitement around agents, there is also a lot of disappointment. When you actually try them out, they often make simple mistakes, get stuck in loops, and don't get the job done. At OpenHands, we have been working hard at making agents that actually work, and doing it in the open so you can actually understand why they

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Code is powered by four models that are state of the art in coding: Codestral for fill-in-the-middle / code autocomplete Codestral Embed for code search and retrieval Devstral for agentic coding And Mistral Medium for chat assistance Critically, customers can fine-tune or post-train the underlying models on private repositories or distill lightweight variants—capabilities that simply don’t exist in closed copilots tied to proprietary APIs. Mistral Code is proficient in 80+ programming languages

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globally distributed setting, we can achieve even higher speeds: Can it be used in HPC settings? Despite the fact that PCCL does not directly optimize for high-performance local networks, PCCL remains competitive with PyTorch’s Gloo over Ethernet. Usage in our prime framework If you want to see how we use PCCL, refer to the open source prime repository, which is our production-ready open source implementation of DiLoCo and async DiLoCo. ‍ Rewriting Prime Agent in Rust On the Nature of the Swarm

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It can sometimes be hard to tell whether Devin (or any other coding agent) is likely to succeed with a task. When Devin tries to tackle a task that's outside of its current capabilities, this sometimes leads to wasted time and ACUs. And, one of the biggest blockers to successful sessions is simply that initial prompts are underspecified—they don't contain enough detail for Devin to use. Today, we’re announcing some major changes in Devin 2.1, with these issues in mind. Devin now reports its conf

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Yannick Schnider from the Spyre team on vLLM for pluggable scheduler design and implementation, and other contributors, including yancong for extendable quantization method design and implementation, Aviv Keshet for extendable SamplingParams. Related Posts Announcing vllm-metal: Concurrent Serving on Apple Silicon vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M5 prefill acceleration.

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PVCs at a fraction of the cost without sacrificing quality or latency. As a result, we’ve been able to make PVCs available self-serve. Whether you’re building virtual avatars, AI agents, or assembling a private voice library, PVCs are now easily accessible through our website or API (no Sales contact needed). Fine-tune One—or Many We built our PVCs in response to overwhelming demand from businesses that need bespoke AI voices for private voice libraries and virtual personal avatars. Our custome

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waits by shifting memory-bound operations toward compute-heavy execution; Reducing Memory Footprint: Investigating techniques to minimize memory requirements during decoding, including lighter cache management and strategic re-computation; Enabling Asynchronous Execution: Designing naturally asynchronous inference protocols that can tolerate variability in node availability, latency, and bandwidth without sacrificing throughput References Rewriting Prime Agent in Rust On the Nature of the Swarm

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we first shared Devin 1.0 with the world last March. Since then, we've been working hard to improve Devin based on your feedback. Today, we're excited to announce Devin 2.0: a new agent-native IDE experience for working with Devin, along with a flexible new plan starting at $20. Now you can: Spin up multiple parallel Devins, each equipped with its own interactive, cloud-based IDE. This means you can easily multitask, tackling numerous tasks concurrently and stepping in to steer when needed; Coll

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