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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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🚀 Hy3 is now live in WorkBuddy global—free for users worldwide through August 31, 2026 (PT). Research, analyze data, create documents and presentations, and handle workplace communications in an agentic AI workspace—no setup required. Hy3 is also available through Tencent Design Miora and Tencent Cloud TokenHub. TencentHy Hy3 WorkBuddy Miora TencentCloud

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Alongside the models and workflows, the project releases training data and evaluation methods intended to help the broader research community build and study open agentic systems. Artificial intelligence is rapidly moving beyond static question-answering toward autonomous agents that can plan, reason, and act across complex, multistep environments. These systems can fix bugs in complex codebases, navigate the web on a user’s behalf, and manage workflows involving calendars and email. While there

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friendly and experienced engineering friend - no eval experience needed. Tl;dr Ask a question, get an answer (with proof) To use Ori Eval, you can simply tell your favorite coding agent: Your agent hands the request to Ori Eval. Ori Eval explores your codebase, and it comes back with questions. Before it runs any evaluations, it works with you to help figure out what’s important to you — is it cost, performance, latency, speed, tool call accuracy, etc. It then chooses the 5 latest models that fi

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ting the model since Sakana Chat first launched, and this version improves on earlier ones both in the quality of its Japanese responses and in its ability to carry out work as an agent. For the technical background at the time of the Sakana Chat release, see this blog. Why we built it Ever since Sakana Chat launched, we have heard the same request many times over: people wanted to use this model from their own products and workflows through an API. At the same time, the choice of LLM APIs that

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e testing Anthropic took more than two months to alert cops; AI systems are fully capable of carrying out nightmare attacks against infrastructure and nobody's ready Meanwhile, AI agents move robotic arms, 'make OT device operator screens lie'; Nvidia found $1B under the couch to help secure American scientific computing dominance Commitment comes as GPUzilla prepares to fortify Uncle Sam's arsenal with at least seven AI-optimized supers; TSMC taps GlobalFoundries to bolster US silicon interpose

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y 26% and delivers up to 2.5x greater token efficiency; MAI-Voice-2-Flash now powers Dynamics 365 Contact Center, where customers like T-Mobile and EasyJet build their call center agents, reducing GPU costs by up to 89%; MAI-Transcribe-1.5 now serves Dragon Copilot’s multilingual workflow across 58 languages — a solution used by 170,000 medical providers that processed 28 million patient encounters last quarter, where our tests show a 50% relative in reduction transcription and language-identifi

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ter if based on advanced heuristics or a small model that reads each turn and picks which LLM to use, a router will always be less capable than the model it’s choosing for. Replit Agent lets the model decide instead. The main agent, or core loop, chooses its subagents’ tier and effort, and adjusts its own as the task unfolds. Given that freedom, GPT-6 Astra hands routine implementation to less costly subagents and decides for itself where its tokens are worth spending. On both DeepSWE and Termin

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re their conclusions agree and diverge. According to Perplexity comms chief Jesse Dwyer, Model Council functions as a skill that tells an orchestrator model to spin up parallel subagents, each acting as a full agent harness and tackling the problem independently. “A wait barrier holds until every perspective finishes, then the model the user selected acts as the ‘chair,’” Dwyer told us in an email. “It reads all the perspective outputs and synthesizes them, explicitly surfacing where the models

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Announcing Mastra Factory From issue to production, run by agents.Sam Bhagwat· Jul 27, 2026· 2 min read Software production is becoming a hierarchy of loops. Engineers move “down” into lower-level loops to debug reliability. They move “up” to design better processes. The highest-value human work is deciding which loops to create. So today we’re launching Mastra Factory, a system for agents to take software from issue into production. In Mastra Factory, specialized agents triage issues, write and

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t rather than per token, so there is no position axis to shard, and the KDA layers stay TP-sharded by head. The whole feature is one flag, --dcp-size N. What this buys shows up on agentic traffic, where hundred-thousand-token sessions pile up in the cache. Replaying real coding-agent sessions on 2×4 GB300, with a host-memory KV tier on both arms and DCP as the only difference: DCP removes the active-set wall. The host tier rescues re-prefill traffic, but at 16 concurrent sessions the active work

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level intelligence, the top open model in the world, at a fraction of closed-model cost. It is 1 at frontend code, strong at writing, and it reliably finishes the long, multi-step agentic tasks where other models stall. Michele Catasta, the President of Replit, told us, "Kimi K3 on Replit Design exceeded our expectations. Unprecedented product design and UI capabilities, at a fraction of the cost we've come to expect from frontier models." Silas Alberti, SVP of Research at Cognition, highlighted

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Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you

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we introduced our hill–climbing machine, our integrated data, model, and harness flywheel. Today we are excited to share two examples inside Microsoft: MAI models specialized for agentic workloads in GitHub Copilot and Excel. Early results are promising. In our live product deployment, we see that our MAI model deployed in Excel is on par with GPT-5.6 for the most common tasks while being more cost-efficient. The figure below shows how MAI-Code-1-Flash, post-trained within the GitHub Copilot ha

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