搜索“AWS”

subtle reproducibility bugs; Operational overhead and idle time: Scaling inference would mean manual orchestration, days of setup, and paying for idle clusters “I used to wrangle AWS, Google Cloud, and Azure, but when I was dealing with raw instances and volumes I frequently felt like I was operating at the wrong level of abstraction. With Modal, building infrastructure has shifted from being an imperative task to a declarative one. You add a few lines of code specifying what you want to do and

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Modal runs a globally distributed, autoscaling GPU worker pool by sourcing compute from all cloud giants: AWS, GCP, Azure, OCI. We’ve scaled the worker pool to well over 20,000 concurrent GPUs, and launched over four million cloud instances in the last couple years. At this scale, you see almost every GPU reliability problem there is. Today, we’re sharing our GPU reliability system as both a demonstration of our commitment to Modal customers and as a guide for fellow travelers renting hyperscale

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rred Sumner因其工具被Claude Code等AI编程产品广泛使用，并看好Anthropic在AI编程领域的前景而选择加入。此次收购将Bun的高性能运行时和打包技术深度整合进Claude Code及未来AI工具，以提升性能与分发效率。Bun将保持开源并继续独立发展。来源：InfoQ 亚马逊云科技发布 Nova 2 系列 AI 模型等十大AI更新 在AWS re:Invent大会上，亚马逊云科技发布十大AI更新：推出自研AI芯片Trainium4（性能提升6倍）及Trainium3服务器；第二代大模型Amazon Nova 2系列（Lite、Pro、Sonic、Omni）性能超越多款主流模型；Bedrock平台新增18款开源模型，包括阿里、Kimi等中国模型；推出AgentCore开发工具和四大智能体（如自主编程、安全运维），强化企业级AI应用。来源：智东西 商汤发布 NEO 架构，重新定义多模态模型效能边界 商汤科技联合南洋理工大学发布全球首个原生多模态架构NEO，突破传统”视觉编码器+投影器+语言模型”的拼凑范式。架构通过原生图块嵌入、三维旋转位置编码和原生多头注意力三大创

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ble5. We focus on the middle: deployments where better protocols could meaningfully improve outcomes. Patch overpowered strategies. The adversarial methodology optimizes against flaws in settings. If some feature lets either team win too easily, they’ll exploit it regardless of whether the strategy is realistic. We call a strategy “overpowered” if it performs much better in the setting than it would in real deployments6. Much of the work on BashArena was identifying and removing features that en

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can find it at: Code: examples/01standalonesdk/31iterativerefinement.py; Documentation: Iterative Refinement Guide To run the example: For real-world COBOL files, you can use the AWS CardDemo application, which provides a representative mainframe application for testing modernization approaches. The iterative refinement example works well for small to medium codebases. But enterprise COBOL systems can span millions of lines across thousands of programs, with complex interdependencies. For these

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ation in practice: when prompted to both generate and analyze its own proof in one shot, the generator tends to claim correctness even when the external verifier easily identify flaws. In other words, while the generator can refine proofs based on external feedback, it fails to evaluate its own work with the same rigor as the dedicated verifier. As a logical consequence, one would assume they use a separate proof generator (LLM 1) and proof verifier (LLM 2). So, the self-refinement loop used her

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注于成果。 来源：GQCI 富士通的多智能体 AI：供应链，如今多了份脑力 摘要：富士通启动了一项多智能体 AI 系统的联合试验，该系统可协调整个供应链中的物流、库存和采购。早期结果：更少的“我的订单在哪？”邮件，更多的准时交付。 为何重要：多智能体 AI 正从实验室走向装卸码头，各地的运营负责人应当予以关注。 来源：Fujitsu 摘要：Amazon 在 AWS re:Invent 上揭晓了“前沿智能体”（frontier agents）——能够自主完成多步骤业务任务的 AI 系统。可以把它们想象成不知疲倦、无需咖啡休息的数字同事。 为何重要：智能体式 AI 即将改变工作的完成方式，尤其是在复杂的多系统环境中。 来源：GeekWire Gemini 荣获“最佳生产力 AI 助手”（抱歉了，Alexa） 摘要：Google Workspace 的 Gemini 被 Expert Consumers 评为“最佳生产力 AI 助手”，击败了 Alexa 和 Siri。秘诀是什么？与业务流程深度集成，并坚持不懈地专注于把小事做对。 为何重要：如果你还没在日常工作流程中使用 Gemini，那你

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n Bedrock , Azure Foundry, Hugging Face ( Large 3 & Ministral ), Modal , IBM WatsonX, OpenRouter, Fireworks, Unsloth AI,and Together AI. In addition, coming soon on NVIDIA NIM and AWS SageMaker. For organizations seeking tailored AI solutions, Mistral AI offers custom model training services to fine-tune or fully adapt our models to your specific needs. Whether optimizing for domain-specific tasks, enhancing performance on proprietary datasets, or deploying models in unique environments, our tea

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codebase. There are two major implications here: safety and scalability. Safety When you run a local agent, there's nothing stopping it from running rm -rf / or exfiltrating your AWS credentials. That's why local agents have an option to repeatedly ask you to hit the y key to confirm their next action—you need to babysit them to be sure they don't do anything harmful. When the agent runs in its own infrastructure, the blast radius is much smaller. The agent only has access to the tools and cred

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s them to the lines it previously rendered for the previous component tree. It keeps a backbuffer of sorts, remembering what was written to the scrollback buffer. Then it only redraws what changed, using a method I call differential rendering. I'm very bad with names, and this likely has an official name. Differential rendering Here's a simplified demo that illustrates what exactly gets redrawn. The algorithm is simple: First render: Just output all lines to the terminal; Width changed: Clear sc

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iminatory effects and protecting vulnerable populations; Reducing legal/financial risks and enhancing trust: Preventing biased outcomes reduces the likelihood of public backlash, lawsuits, or regulatory penalties. It also enhances public confidence that AI systems are fair, thereby supporting broader societal acceptance of AI technologies Cons; Potential Performance trade-offs: Bias mitigation can sometimes reduce predictive accuracy or impact performance for certain tasks or use cases; Technica

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Any consumer internet system with critical mass becomes, in part, a Mechanical Turk. It looks at what the users do and draws conclusions from that. Amazon knows that if you bought X, you might buy Y, because it's seen what lots of people buy and saw that if they buy X, they're likely to buy Y. Google's dominance of search is based in part on seeing what people search for, and what they click on and what they search for next. Of course, this is a network effect, and comes with a cold start proble

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for many tests, the actual top score may be unachievable because there are many errors in the test questions and measures are often reported in unusual ways. Every benchmark has flaws, but they are all trending the same way - up and to the right. The AIME is a hard math exam, GPQA tests scientific and legal knowledge, the MMLU is a general knowledge test, SWE-bench and LiveBench test coding, Terminal-Bench tests agentic ability. Data from Epoch AI . Despite these issues, all of these benchmarks,

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afer superintelligence Alongside spelling out very precisely the kind of superintelligence we should build, the time has come to also consider what societal boundaries, norms and laws we want around this process. At MAI this is a discussion, and a set of actions, that we welcome. Doing this requires real trade-offs and tough decisions that come in environments of immense competitive pressure and also opportunity. There are numerous challenges and obstacles to both delivering the vision and avoid

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so, if efficiency is not the main factor, the best we have for now. Traditional Decoder-Style, Autoregressive Transformers + Proven & mature tooling + “well-understood” + Scaling laws + SOTA - Expensive training - Expensive inference (except for aforementioned tricks) If I were to start a new LLM-based project today, autoregressive transformer-based LLMs would be my first choice. I definitely find the upcoming attention hybrids very promising, which are especially interesting when working with l

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. In contrast, metallic elements (Fe, Mg) proved more difficult to optimize. Even after optimization, their computational performance was slightly worse than the reference smooth PAWs. (Fe, Mg) (Fig.4) ref: smooth PAW datasets without norm conservation on any orbital provided with the GPAW code at https://gpaw.readthedocs.io/setups/setups.html . scattering-F: custom generated PAW with additional norm conservation on f-orbital. optim-scattering-F: custom generated PAW with additional norm conserv

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equirements quickly become the bottleneck. Figure 3 illustrates the memory footprint and the challenges we encountered when applying LSH-based deduplication to a 600 GB dataset on AWS. In this experiment, we have used several computational instances to accomplish it. Our initial attempt used a spot instance with 30 GB of memory, but the process failed with out-of-memory (OOM) errors. We then upgraded to a larger on-demand instance with 64 GB of memory, yet the problem persisted. Ultimately, we w

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no Studio—the world's first generative audio workstation that fundamentally reimagines how music is created. A New Creative Paradigm While traditional Digital Audio Workstations (DAWs) have served producers well, they've always had one limitation: you can only work with what already exists. Suno Studio changes everything by putting AI-powered generation at the heart of music creation, empowering artists to break through creative barriers and explore infinite musical possibilities. What Makes Sun

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o overwrite its own configuration and security settings. This allows the agent to break out of its sandbox and escape by executing arbitrary code. My research with GitHub Copilot, AWS Kiro and a few others demonstrated how this can be exploited by an adversary with an indirect prompt injection. This post goes a step further by showing how one compromised agent can rewrite another agent’s config and ‘free’ it, creating a cross-agent escalation loop. ‘Freeing’ in this context means that one agent

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d oversight. As part of this, it is important for our technology to be publicly vetted. That way, researchers across the world can help us improve the technology by pointing out flaws and suggesting improvements. Open-sourcing Docent makes this possible. Additionally, part of our mission is to support the broader ecosystem of AI evaluators. We hope that open sourcing Docent helps other evaluators, across governments, third-party non-profits, academia, and AI companies more easily use and build u

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