搜索“AWS”

a with violating a privacy order that was filed after the 2019 settlement. It’s also been repeatedly hauled before Congress over its harms to minors, has lost or settled multiple lawsuits over the same topics, and has seen various whistleblowers outing its abuses. These careless privacy practices could ultimately see Meta lose the battle to be the consumer AI of choice… unless, of course, consumers decide that the “devil you know” is better than the one you don’t. (Especially when the latter are

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multi-GPU execution, or multi-node serving. Thirty-seven tasks come from a single upstream pull request. The other 16 combine two to six related changes. In total, the benchmark draws on 83 merged SGLang pull requests. The SGLang team, a launch partner for SWE-Serve, contributed ideas for identifying challenging tasks, suggested particularly demanding pull requests, and helped shape our approach to verifying correctness. Each task gives the agent an instruction and a containerized SGLang checkou

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y simulate and dream the physical consequences of actions before they happen. This technology is the key to revitalizing Japan’s Monozukuri legacy. By perfectly internalizing the laws of physics and logistics, these models will serve as the cognitive engines for the next generation of industrial manufacturing, allowing Japanese industry to optimize supply chains and robotic deployments without costly real-world trial and error. To achieve this, Jürgen will help guide our newly formed RSI Lab. Th

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pecific pooled investment vehicles — which should be read in their entirety, and only to those who, among other requirements, meet certain qualifications under federal securities laws. Such investors, defined as accredited investors and qualified purchasers, are generally deemed capable of evaluating the merits and risks of prospective investments and financial matters. There can be no assurances that a16z’s investment objectives will be achieved or investment strategies will be successful. Any

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Trump has either made the calculation that China isn’t that salient to voters and it just doesn’t matter, or he just doesn’t care. He wants to do this, and he views the political laws of gravity as applying to other people. Either way, some Republicans in battleground states will have to do some tap dancing. But the core question — and we’ll run the natural experiment in real time — is whether Americans do care, whether Trump’s high-risk gamble on the political timing is in fact right. That is i

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do this Tell Codex to test the app in the simulator. When it opens, prompt: “Scan for App Store readiness, list what’s missing, then fix it and package it for Xcode” The Rundown: AWS re:Invent returns to Las Vegas, Nov. 30 to Dec. 4, with 2,200+ sessions and 70% hands-on, giving builders the option to design a week that matches how you work. Stack workshops, code talks, and builders’ sessions back to back, or leave room to go deep on one track. While onsite, you can: Map a personalized agenda a

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d reports scaling up to 100 parallel agents in Section 8.12. @scaling01 called it the first lab report of its kind. @maksym andr highlighted it as evidence on multi-agent scaling laws. ProgramBench caveats. ProgramBench author @OfirPress flagged that Anthropic’s near‑100% solve rate comes from a 166/200 subset. That subset likely excludes the hardest programs, such as FFmpeg and the PHP compiler. He also flagged a metric mismatch ( @OfirPress , @OfirPress ): Anthropic reports average test pass r

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cording — it’s Chrome resizing the page, not the handoff from the static composer to the real one (that measured 0 px on all 49 of your loads today). On the new-tab page, Chrome draws its own 56 px footer below the page (“Managed by anthropic.com · Customize Chrome”). When the tab navigates to claude.ai the footer goes away and the page gets 56 px taller, but only 100 ms after our first paint; /new puts the greeting and composer 18% of the page height from the top, so they drop 0.18 × 56 ≈ 10 px

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dicts rank inversion via entropy collapse under RLVR. arXiv:2606.18487; Busbridge, D., Shidani, A., Weers, F., Ramapuram, J., Littwin, E., & Webb, R. (2025). Distillation scaling laws. ICML 2025, PMLR 267. proceedings.mlr.press/v267/busbridge25a; Kim, M., Shrestha, A., Shrestha, S., Nepal, A., & Ross, K. (2025). Reinforcement learning vs. distillation: Understanding accuracy and capability in LLM reasoning. arXiv:2505.14216; Li, J., Lin, H., Lu, H., Wen, K., Yang, Z., et al. (2025). QuestA: Expa

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由于基于评判器的评估器会返回每个被调用技能的结果，多技能运行仍然可诊断：你可以识别出是哪个选择结果或指令遵循结果拉低了聚合分数。如果没有调用任何技能，基于评判器的评估器不会产生分数。当回归测试具有已知的路由要求时，将它们与 SkillInvoked 搭配使用。 先决条件 Python 3.10 或更高版本；一个具有 Amazon Bedrock 访问权限的 AWS 账户，以及具有评判器模型 InvokeModel 权限的凭证 要跟随 Strands Evals 部分，请安装这些 SDK： 你还需要一次已记录的智能体运行。技能评估器接受 Strands Evals Session 或原始消息列表作为轨迹。在发布时，技能提取可识别来自 Strands AgentSkills 插件、Claude Code、Claude Agent SDK、OpenAI Agents SDK、Codex、Gemini CLI、OpenHands、Google ADK 以及通用 SKILL.md 文件读取的信号。 要跟随 AgentCore Evaluations 部分，你需要： 一个托管在 Amazon Be

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fying. The design move at billion scale vector search is tiering - hot vectors in RAM-backed HNSW, cold vectors quantized on disk or in object storage, whereas some solutions like AWS S3 Vectors and Turbopuffer still allow to enjoy both worlds – large scale vector search at decent performance and low cost. Because the engines have converged on the same algorithm and the same compression toolkit, the self-managed versus managed decision is no longer about capability. Both can do HNSW, both can qu

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nd $125 billion for short-lived equipment/chips.” Yet arguably the most revealing thing I could find was a quote from CEO Andy Jassy on Amazon’s Q1 2026 earnings call from April: …AWS has to lay out cash for land, power, buildings, chips, servers and networking gear in advance of when we can monetize it, typically 6 to 24 months before we start billing customers depending on the component. So, if we assume the number is roughly $374 billion, plus (at minimum) $50 billion from Microsoft, plus ano

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used this for my devices tweet in the intro. Exa Snapshot - Let agents search the past versions of the web. People don’t want to build . Epoch audited 15 AI benchmarks and found flaws in nine. Natural General Intelligence - a call to work on AI models for the planet to avoid natural disasters and more. Accenture’s evaluators will work inside Anthropic to check AI safety, with access similar to its employees. Speeding a 4B model to 730 tokens/sec on an M5 Max . Underdog is really interesting; I j

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pecific pooled investment vehicles — which should be read in their entirety, and only to those who, among other requirements, meet certain qualifications under federal securities laws. Such investors, defined as accredited investors and qualified purchasers, are generally deemed capable of evaluating the merits and risks of prospective investments and financial matters. There can be no assurances that a16z’s investment objectives will be achieved or investment strategies will be successful. Any

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ts. For some, that means keeping sensitive data in-country. For others, it means controlling who can access systems, where workloads run, how models are governed, and which local laws apply. The HPE Sovereign AI Factory lets customers in highly-regulated industries keep sensitive data, models, and operations under strict local control, using customized, validated infrastructure integrated with HPE services from deployment to operational support that helps customers with security, compliance, and

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e to me. Jordan Schneider: And as we discussed earlier, when you’re the second mover, you’re already that much further down the Moore’s Law curve. Plus, we have ten other Moore’s Laws running at the same time to make this intelligence cheaper. Token spend should not be a limitation the government gets blocked on anytime soon. Garrett Berntsen: No. I work for a large services company, and most of our business is actually commercial — 92% commercial, only 8% federal. That’s a big upside for us in

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provides that foundation for GPT-6 Sol and GPT-6 Luna through a high-performance inference engine built for security and reliability at scale. You can govern model access through AWS Identity and Access Management (IAM) policies and audit every invocation through AWS CloudTrail. Virtual private cloud (VPC) endpoints powered by AWS PrivateLink help keep traffic within your network boundaries. Inference runs on hardware-isolated infrastructure with zero-operator access, so even AWS operators cann

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ow down AI’s pace of progress over safety concerns, a call Altman and Musk backed. Why it matters: The move clarifies the U.S. approach on AI: back the industry, lean on existing laws, and prioritize beating China. Trump pitched AI as a quarter of the U.S. economy — and floated renaming it “Superior Intelligence” — framing the race as too valuable to slow, even as AI labs and a good chunk of the public urge caution. The Rundown: In this guide, you’ll build a simple ChatGPT writing setup that tur

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of Amazon Bedrock AgentCore, handles service-to-service authentication. Managed agent runtime . AgentCore runs agents in Firecracker microVMs, the same isolation technology behind AWS Lambda. Reactiv packages their Strands agent graph as a Docker image, pushes it to Amazon Elastic Container Registry (Amazon ECR), and deploys it to AgentCore. Agents spin up when a schedule triggers and shut down when done. No Amazon ECS clusters, scaling policies, or idle compute. “We don’t manage containers, orc

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ference workloads. We use the vLLM Deep Learning Container for SageMaker AI, configured through SM VLLM environment variables: VLLM IMAGE = ( f"763104351884.dkr.ecr.{region}.amazonaws.com/" f"vllm:0.19.1-gpu-py312-cu129-ubuntu22.04-sagemaker" ) environment = { "SM VLLM MODEL": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "SM VLLM ENFORCE EAGER": "true", required for Mamba-Transformer hybrid "SM VLLM TENSOR PARALLEL SIZE": "1", single GPU "SM VLLM GPU MEMORY UTILIZATION": "0.85", "SM VLLM MAX MO

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