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retch of late nights. Longevity Tab A new Longevity tab analyzes a user’s longitudinal health data for deeper insights into heart health, sleep, mental wellbeing, movement health, metabolic health, hearing, and nutrition. The tab includes Health Age, a new feature that uses Apple Watch data to show users how their metrics are tracking relative to their chronological age, informed by the best available science. The algorithm can factor in metrics from Apple Watch like VO2 max, resting heart rate,

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g a model from a lab based in the US or EU doesn’t guarantee that a data center in that region serves the request. At global endpoints, models from OpenAI, Anthropic, Google, xAI, Meta, NVIDIA, Thinking Machines, or Mistral can run anywhere the provider serving the request operates. A data residency review asks where your request was decrypted and where inference ran, so a US team using GPT-5.6, Claude Opus 5, or Gemini 3.6 Flash on a global endpoint can still fail a compliance review. OpenRoute

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g a model from a lab based in the US or EU doesn’t guarantee that a data center in that region serves the request. At global endpoints, models from OpenAI, Anthropic, Google, xAI, Meta, NVIDIA, Thinking Machines, or Mistral can run anywhere the provider serving the request operates. A data residency review asks where your request was decrypted and where inference ran, so a US team using GPT-5.6, Claude Opus 5, or Gemini 3.6 Flash on a global endpoint can still fail a compliance review. OpenRoute

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ing to a smaller batch size. The park / resume arrow is the handoff between those two owners: Parking is the ownership boundary for mutable batch state. Python changes per-request metadata, token buffers, KV page tables, and possibly the pointers for a new batch size while C++ waits. C++ snapshots that state only after resume, then owns it for the next decode step. This keeps the megakernel from observing a half-updated batch. The current trade-off is simple: prefill pauses decode, so the engine

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pic. One weakness of this analysis is that we’re talking about Microsoft’s Fiscal Year 2027, which actually began in the middle of 2026. Most other hyperscalers (including Amazon, Meta, and Google) align their financial years with the calendar years. Nevertheless, I think it’s fairly illustrative of the problem. To estimate the contribution — and be incredibly fair! — I have assumed OpenAI and Anthropic’s Microsoft spend will be linear (at $52.5 billion) across fiscal year 2028, and then halved

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B-Instruct 许可证或 Llama 许可证。DeepSeek 曾为 DeepSeek V3 使用自定义许可证，后来 R1 将其改为 MIT，这导致许多（中国）模型厂商在 2025 年采用 MIT 或 Apache 2.0 许可证。 2026 年，开放模型比以往任何时候都更具竞争力，这带来了两个有趣的发展：西方模型厂商采用开放许可证，Google 和 Meta 都转向了 Apache 2.0。然而，处于前沿的中国模型厂商正变得越来越严格：Kimi K3 附带的许可证要求提供推理或微调服务者签订商业协议，MiniMax M3 则要求超过收入门槛的协议，并规定了禁止使用的场景。 最新的例子是智谱的 GLM-5.3，它从 MIT（GLM-5.2 及更早版本）改为自定义许可证，对推理和微调服务提供商有如下条款： 如果被许可方或其任何关联方经营模型即服务（Model as a Service）业务，且被许可方及其关联方在任何连续 12 个月内的总收入超过 100 亿美元（或其他货币的等值金额），则被许可方在使用本软件或其衍生作品用于任何商业目的之前，必须通过 Z.AI 的安全审查。安全审查

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-device agents 🏪 Grok Bot Marketplace - Publicly available Grok Bot builds for agent teams ⚡️ Gemini 3.8 Flash - Google’s fast, low-cost reasoning model 🗣️ Muse Voice Transcribe - Meta’s real-time speech-to-text model Get more from Claude – Your style, voice, and data, supercharged. Unlock Claude’s full potential with custom skills. Download the guide . U.N. rights chief Volker Türk said he shares fears that “advanced AI could pose an existential risk to humanity,” adding that a few men hold “al

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ntainer do not affect the workspace file. How do I download a file the model created? Use the container files endpoints under /api/v1/containers/{container id}/files to list, read metadata, and stream content. To keep a file for the long term, promote it to a workspace document and it gets a new or file id. What is the difference between openrouter:shell and openrouter:bash? openrouter:shell is the OpenAI-compatible tool and runs commands server-side by default. openrouter:bash is the Anthropic-

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ate slots, and block tables in the serving engine. Optimizing Communications With DP attention and EP, the TPU GroupedGEMM implementation all-gathers token activations and routing metadata before the experts run, then uses reduce-scatter to sum their weighted outputs and return each token’s result to its attention rank. The backend originally gathered the selected expert IDs and routing weights separately. Google combined these into one all-gather, removing an extra collective whose latency can

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t The Rundown University . How do you use AI? Tell us for a chance to be featured. 🌟 GPT-6 Astra - OpenAI’s next-gen frontier model, now available on paid plans 🪄 Muse Spark 1.3 - Meta’s new model for long-horizon coding and agentic work 📶 Claude Fable 5.1 - Anthropic’s top-ranked frontier model 🎶 Lyria 3.5 - Google’s AI music model with upgraded vocals, now in Gemini Claude agents wrote the first computer-verified proof of Fermat's Last Theorem in 11 days, a job mathematicians had budgeted year

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d this. Like, if you believe truly in the value of data, instead of doing mech interp, you’d probably just be building the environments to do mech interp. Similarly, on a somewhat meta point, if you believe in the value of RL environments, you should probably not be making RL environments, but indeed making RL environments to make RL environments. (I guess you would do this if you believed in RSI, and not otherwise.) Another perhaps non-obvious point to make is about how valuable different types

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times use on-demand Python code to read and manipulate files at times, but Astra does it really quite excessively for me. Now here is an important disclaimer: this project is very meta here because I worked on the CPython interpreter. But I can assure you that I have seen this model do weird Python things even in TypeScript code in Pi. But I have the most evidence of odd code from when I had the thing work over the weekend with zero oversight from my slop factory. That it writes Python is not in

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ription, an eye-popping number even by STAR Market average. Enflame is one of the four domsetic chip upstarts to complete a mega-raise this year, after Moore Threads ($8 billion), MetaX ($4.2 billion), and Biren ($897 million). Unlike those rivals, it’s betting on domain-specific chip architecture over general-purpose GPUs, and it will need that raise to wean itself off heavy revenue dependence on backer Tencent. (SCMP/Reuters/Bloomberg) Tencent’s frontier model made the week’s biggest jump, and

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le product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models! Related Stories MAI-Transcribe-2 is the fastest, most accurate and cheapest speech recognition model in the world MAI-Code-1.1-Flash: Better, faster, at a quarter of the cost MAI-Image-2.6 launches at No. 2 on Arena ahead of Google, Meta and xAI

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define the Auth Gateway interface. Add Rego policy evaluation with the OPA Envoy Istio example . Review Authorino for an integrated reference covering JWT and API-key validation, metadata enrichment, policy decisions, and trusted upstream headers. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, ope

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nts explore the impact of different numbers of European and Japanese samples on elastic net performance when applied to variants discovered through a large-scale European GWAS. b) Meta-analysis experiments integrate population-specific GWAS results into the generation of variants used for elastic net model development. c) PRS-CSx experiments fit the PRS-CSx model on a variety of population-specific sample sizes. Sample sizes in UKB and BBJ for the eight traits explored in this study. For each ex

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neers has a single engineer run each project, and says the approach works well. Will this approach be adopted elsewhere, especially at other full-remote startups?; Industry Pulse. Meta moved over to Slack for better agent interoperability, layoffs at Uber and PagerDuty, Anthropic upsets users by calling a rate limit decrease an “increase”, token usage explodes on OpenRouter, AI drives surging demand for Apple’s Mac Mini & Mac Studio, and more 1. New trend: moving to open models at tech companies

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typically includes the trajectory (the timeline of events), observations (sensor data like images, point clouds, and force feedback), the robot state, actions taken, and relevant metadata. From my perspective, it really comes down to two main categories: human data (often called robot-free data) and robot data. Human data simply means data collected through human without involving robots, including web video, egocentric footage, universal manipulation interface (UMI), and motion capture (mocap)

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ur upcoming Machine Earning AI Summit will explore agentic commerce, intelligent money, enterprise finance operations, the evolving consumer and shopping experience, and the major meta-themes including fraud, risk, compliance, and security. The summit will be held on September 29 in San Francisco. Read on for a story and a market map examining a subset of the companies who are working on agentic shopping, smart payments, and the next-gen infrastructure rails that will power transactions. This ye

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reduced the cost of caching inputs for Fable by 75%, making API usage about 25% cheaper than before. OpenAI’s Astra is also coming soon (rumours say today). Another new model from Meta - Muse Voice transcribe looks really insane for transcription use cases. I wish it were open-source. Gemini models can now decide which bits of a video are worth watching . Instead of sampling a video at a fixed frame rate, it searches across frames, audio and transcripts for the moments it needs. 88% fewer tokens

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