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OpenWorker -- an open source agent that doesn't just chat but completes tasks on your laptop -- just released a new version with many features for security workflows. After our initial release, many users found it especially useful for cybersecurity. Attackers are already using AI; OpenWorker is committed to giving defenders the same leverage. Running an agent requires both (i) A model and (ii) A harness (the software around the model). Because the OpenWorker harness is fully open source, securi

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LPX, measuring a world-class interactivity of 3,431 output tokens/second. The technologies that power this performance unlock the ability of the Vera Rubin platform to serve multiagent systems powered by 2T+ parameter models with high interactivity and long context, through pairing Groq 3 LPX with Vera Rubin NVL72. Agentic sessions are characterized by multiturn inference. At the end of each turn, the agent’s output is appended to the continually growing context that is fed into all subsequent

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cross-node daemon coordination, lifecycle management, and failover; Weight update without reload: in-place weight refresh for RL / online updates, with the daemon as the delivery agent; Cross-GPU & fleet sharing: peer-copy and fleet-fill so a cluster cold start pays roughly one disk read per shard group; KV cache restore: preserve and remap KV cache across restarts / failover (KV reuse, handoff to standby) so in-flight context survives recovery instead of being recomputed from scratch; Rest of

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Today, we’re excited to announce that Grok 4.6 is now generally available on Amazon Bedrock. Grok 4.6 is our latest flagship model, built for long-running agents and ambitious interactive and visual work. It offers 500k context window and configurable reasoning efforts (low, medium, high, xhigh). Now, Amazon Bedrock users can leverage the model through Amazon Bedrock. Capabilities & Pricing Grok 4.6 is available for: Input : $2 per 1 million tokens Cached input : $0.50 per 1 million tokens Outpu

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Your agent runs local ComfyUI and builds workflows around your GPU, your models, and your custom nodes. Works from Claude, Cursor, Codex, or any MCP client. Today, Comfy MCP runs on your own machine. Connect Claude, Codex, Cursor, or any other agent to the ComfyUI installed on your local setup. This is the number one thing you asked for after Cloud MCP shipped in June, and the MCP server is fully open source. Comfy MCP is now one MCP with both local and cloud. Everything the Cloud MCP connection

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Contents The Miles RL Loop; Rollout; Fast Agentic Rollout by SGLang; Fully Async RL; Agentic Environments; Token-In-Token-Out (TITO); Efficient Rollout Routing Replay (R3); Training; Low-precision Training; Memory Efficiency & Disk Offload; Two Training Backends; Weight Update; Verified Day-0 Model Support; Other Post-Training Recipes; LoRA RL; On-Policy Distillation (OPD); Zero-KL Alignment; Code Quality Principle; Miles-Diffusion; Multi-Hardware Support; Example: Training GLM-5.2 on Terminal-U

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in anything customer-facing. There are two levers on value per token. One is the context engine: better context means better answers in fewer tokens. The other is the harness: the agent loop, the tool surface, session management, everything that turns model capability into finished tasks. We started by building the industry's leading context engine, and now we've made a major investment in improving the harness. Earlier this year we rebuilt the Auggie CLI's harness from the ground up. On SWE-ben

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now live in GitHub Copilot for the millions of developers who work in VS Code and across GitHub every day. Getting started GitHub Copilot covers several products, including cloud agents, the Copilot CLI, and the VS Code IDE. Open the model picker and select "Grok 4.6" to start using our latest coding model. Some businesses and enterprises will need to enable the model from Copilot settings. Grok 4.6 is also available from the SpaceXAI console at $2 per million input tokens and $6 per million ou

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Table of Contents Agents are only as capable as the environments they run in. Fast, reliable development environments allow agents to take ambitious, long-running tasks from start to finish. Until now, every cloud session began with extensive setup: boot a machine, clone the repositories, and run the install script. On a large, complex repo, this just-in-time boot could take several minutes before the agent started executing. Today we're introducing builds: ready-to-use copies of your developmen

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oE) architecture with a hybrid of full and linear attention, a context window of up to one million tokens, and an output length of up to 128K, designed for demanding reasoning and agentic workloads. Deploying a 2.4T parameter open-weight model requires data-center-scale accelerated compute. Inference at this scale depends on extreme co-design across chips, system architecture, and software. NVIDIA is working with the open-source ecosystem to bring the model to multinode deployments through optim

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ps. Sakana Namazu, meanwhile, has been upgraded from its original release to a new generation of the model. It surpasses the previous version in both Japanese response quality and agentic execution capability. Sakana Namazu is developed with an emphasis on taking Japanese instructions as they are and returning natural Japanese documents and materials, making it a great fit for the tasks that come up again and again in daily work. For more details on Sakana Namazu, please visit Namazu Product Pag

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cky decisions: take the native search some labs build in, or wire up a third-party engine like Exa, Parallel, or Perplexity? Is one search enough, and if not how long do I let the agent keep searching? Are more search turns worth the quality they buy? We built live leaderboards to help you decide the best search configuration with data. See the data on our new Benchmarks page. We benchmark all the combinations to find strengths and weaknesses When setting up a search request, you have four decis

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removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Learn how NVIDIA NeMo Switchyard routes agent workloads across specialized and frontier models to balance performance, cost, and efficiency. AI-Generated Summary Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Building an AI agent does not end with choosing a single model. Each

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te as their needs evolve. "As the neutral execution layer and trusted system of record at the core of the global travel industry, Amadeus bridges raw foundational data with modern Agentic AI to improve the traveller experience for everyone everywhere. In an AI-driven world, capacity, deployment control, and operating continuity become increasingly important for all enterprises. Equally crucial, at the same time, is ensuring businesses have the confidence to make long-horizon commitments at scale

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Introducing Hy3D WorldClaw——an agentic workflow that generates large scale 3D open worlds from text prompts.🚀🚀🚀 Not video, Not Gaussian Splatting, Every scene generated by WorldClaw is freely explorable and built entirely from editable, game-ready 3D assets with high-quality geometry and textures. Project Page: https://t.co/KJXFWHLb1S

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and more. Today we’re excited to open it up to the rest of the world. How Grok Bot is different A computer of its own Early access users say Grok Bot feels less like prompting an agent, and more like giving work to a highly capable teammate. Bots share a computer of their own in the cloud, so jobs do not stall when you step away. They can sign in and work across apps, tools, and websites, including platforms with no clean API or MCP, and come back with the work finished. Inside SpaceXAI, that a

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Contents SGLang is excited to announce Day-0 support for NVIDIA Nemotron 3.5 Lightning, a customizable open model built to power always-on agents across local systems, the edge, the datacenter, and the cloud. Always-on agents gather context, reason, use tools, and adapt across multi-step workflows. Frontier models handle complex orchestration, while smaller models efficiently manage high-volume, specialized tasks. Nemotron 3.5 Lightning is built to handle these high-volume tasks. Distilled from

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e're excited to partner with Meta Superintelligence Labs to bring Day-0 support for Muse Glimmer to SGLang, with dedicated optimizations tailored for high-performance inference of agentic workflows on local hardware. Highlights Model: Muse Glimmer is a 30B-parameter multimodal dense model with a 128k+ token context window that delivers competitive performance on end-to-end agentic workflows from local hardware; Performance: SGLang delivers up to 1,452 tok/s of total output throughput and 236 tok

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We changed the Dial. Link a ChatGPT subscription and low, medium, and high only use OpenAI models: as the main agent, as the oracle, as the thread reader, as code review. Every model behind those modes is an OpenAI model, billed to your subscription. Before this change, you'd still pay for tokens with a subscription linked, because parts of the work ran on other models: low and the thread reader on GLM-5.2, the high oracle on Claude Fable, code review on Haiku — all billed to Amp credits. That c

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benchmarks. It combines mixed-length Attention work, precision-sensitive routing, and sparse expert execution within the same latency-sensitive path; long-context, multi-turn, and agentic workloads further widen the distribution of live KV lengths. Serving performance therefore depends not only on raw matrix-multiplication throughput, but also on workload balance, numerical fidelity, and overhead control. These constraints surface in three performance-critical stages of MoE model serving. During

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