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uctivity: New engineers take 3–9 months to fully ramp (estimates); Senior engineers lose 5+ hours per week onboarding others (source); Stripe found legacy maintenance to be the 1 drag on productivity on their customers (source) This is the frontier that AI coding tools haven’t yet solved. Onboarding isn’t even a onetime cost, you pay it every time you switch context and codebases. The faster and better you understand your codebase, the faster and better you’ll be able to fix it yourself, or prom

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built on Temporal, which guarantees consistent behavior across retries, long-running tasks, and chained calls. The runtime manages large payloads, offloads documents to object storage, and generates static graphs that make execution paths auditable and easy to share. Every execution emits telemetry and evaluation data that flow directly into Observability for measurement and governance. AI Studio supports hybrid, dedicated, and self-hosted deployments so enterprises can run agents wherever thei

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resources and interacts asynchronously with the Agent Runner through the shared data protocols; Data Store — Serves as the central repository for managing all data exchange and storage within the Agent RL ecosystem. It provides standardized interfaces and unified data schemas to ensure interoperability among heterogeneous components. Through this design, the Algorithm and Agent Runner can communicate indirectly yet effectively, enabling flexible and scalable collaboration. For instance, using th

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general-purpose agents into specialized agents that fit your needs. Building a skill for an agent is like putting together an onboarding guide for a new hire. Instead of building fragmented, custom-designed agents for each use case, anyone can now specialize their agents with composable capabilities by capturing and sharing their procedural knowledge. In this article, we explain what Skills are, show how they work, and share best practices for building your own. The anatomy of a skill To see Ski

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werful new hardware plugin unifying JAX and PyTorch under a single lowering path. It is not only faster than the previous generation of vLLM TPU, but also offers broader model coverage and feature support. vLLM TPU is a framework for developers to: Push the limits of TPU hardware performance in open source; Provide more flexibility to JAX and PyTorch users by running PyTorch model definitions performantly on TPU without any additional code changes, while also extending native support to JAX; Ret

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on to vision-based models while accurately capturing what DOM-based agents can actually do. The Speed Advantage In OnlineMind2Web, BU 1.0 agent completes tasks in 68 seconds on average. For context: Gemini 2.5 Computer Use: 225s; Claude Sonnet 4.5: 285s; Claude Sonnet 4: 295s; OpenAI Computer-Using Model: 330s We're significantly faster than other state-of-the-art models - taking on average 3 seconds per step with our strongest model. (See our full model speed and accuracy benchmark here) Seeing

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ations are better organized and accessible. Full screen conversations: Easily hide the Action Panel, enabling a full screen conversation experience. Expandable input area: Easily drag and expand the input area, giving you a bigger canvas to write prompts. Status area: Easily see runtime status (on the left) and agent status (on the right). Git controls: Easily push, pull, or create PRs. Clicking these buttons will trigger a prompt for OpenHands to perform the git action. These are just some of t

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eyond. To support this, vLLM integrates Triton kernels from Flash Linear Attention, and adopts a hybrid KV cache manager to manage both linear and full attention layers, avoiding fragmentation and maximizing GPU utilization. In order to manage state for hybrid models like Qwen3-Next, vLLM automatically tunes the “logical” block size of the full attention layers to ensure that the state for the full attention layers and linear attention layers occupy the same amount of “physical” GPU memory. This

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vLLM as pooling models. Pooling is a technique that is commonly used in deep learning models to reduce the spatial dimensions of feature maps. Common types include max pooling, average pooling and global pooling, each using different strategies to aggregate information. In vLLM, pooling can be applied to tasks such as embedding vector calculation and classification. In addition, vLLM supports identity poolers, returning the model's hidden states without applying any transformations - exactly wha

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elp. They surface trends, suggest summaries, and point out moments worth revisiting, all based on your own data, and all editable. It’s a simple way to turn memory from passive storage into active signal. Download Le Chat on the App Store or Google Play to try memory on mobile. We’re continuing to improve how memory works: Trimming noise, speeding up recall, and making it easier to organize long-term. You’ll see updates soon that let you sort memories into categories, instantly forget something,

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ze, 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 (beta). Highly-personalized responses based on your preferences and facts. Careful and reliable memory handling: saves what matters, s

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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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rsor agents the same way you already assign issues to teammates. Many teams already use both Linear and Cursor, and this integration ties them together at the point of maximum leverage: inside Linear, where teams already manage their work and where the full context lives to spin up and manage high-quality agents. Today, Cursor agents can take on coding tasks, open pull requests, and update progress back in Linear. The benefits of this tight integration will only grow as agents continue to improv

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across millions of calls. A few challenges come up repeatedly—more reasoning introduces higher latency, small changes to one component can cause unpredictable quality swings, and fragile infrastructure across multiple providers make scaling unpredictable. That’s why we built Line. Line is the modern voice agent development platform, making it easy for developers and businesses everywhere to build best-in-class voice agents. Line is code-first The best products are built with code. We realized th

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th the hybrid KV cache manager, vLLM can dynamically share the KV cache space between the full attention layers and sliding window attention layers, reducing the potential memory fragmentation down to zero. gpt-oss includes built-in support for powerful tools, such as web browsing and Python code interpreter. When enabled, the model autonomously decides when and how to invoke these tools, interpreting the results seamlessly. vLLM natively supports these capabilities by integrating the OpenAI Res

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macOS and Windows. An easier way to chat with models Ollama’s macOS and Windows now include a way to download and chat with models. Chat with files Ollama’s new app supports file drag and drop, making it easier to reason with text or PDFs. For processing large documents, Ollama’s context length can be increased in the settings. Note: this will require more memory. Multimodal support Building on Ollama’s new multimodal engine, images can be sent to models that support them, such as Google DeepMin

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easily test our latest small language models right on their phones. We hope developers enjoy building with these tools as much as we do.” Built for Developers... Teams blocked by fragile or underperforming small model; Cloud API users frustrated by cost, privacy, or connectivity; Devs who want the fastest path to edge AI models that just work ...By Developers No ML expertise needed: Integrate local edge models into your mobile app as easily as calling a cloud API; Open and Interactive: Get the b

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esome MCP Extension", "version": "1.0.0", "description": "A brief description of what this extension does", "long description": "A detailed description that can include multiple paragraphs explaining the extension's functionality, use cases, and features. It supports basic markdown.", "author": { "name": "Your Name", "email": "yourname@example.com", "url": "https://your-website.com" }, "repository": { "type": "git", "url": "https://github.com/your-username/my-mcp-extension" }, "homepage": "https

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n delegate any task, and have a pretty good idea that the agent will deliver for you. Today, we're announcing two first steps towards this goal: First, VersaBench, a new broad-coverage benchmark for evaluating agents on many different tasks from software development to more general business-relevant tasks; Second, OpenHands Versa, an agent that can not only capably solve software engineering tasks, but cover a much broader spectrum of use cases OpenHands Versa is quite capable, delivering perfor

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opilots at the proof-of-concept stage: Limited connectivity to proprietary repos and internal services. Minimal customisation of the underlying models or prompts. Shallow task coverage that ends at “autocomplete” instead of finishing multi-step work. Fragmented SLAs spread across one vendor for the plug-in, another for the model, and a third for infra. Mistral Code addresses those pain points with a single, vertically-integrated offering: models, plugin, admin controls, and 24X7 support—so platf

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