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hers trace a model's reasoning to spot bad behavior. OpenAI deliberately capped how many loops Astra runs to keep its chain of thought readable, The Information reports. After the Hugging Face incident , OpenAI moved roughly ten percent of its total compute toward alignment and safety, according to a person familiar with the figures. The Information reports that amounts to hundreds of megawatts worth of data center capacity, though it's unclear how much was allocated to safety before or what the

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4, a post-quantum cryptographic standard. The satellite also carries models developed by organisations including NASA’s Jet Propulsion Laboratory, Google, IBM, Meta, Microsoft and Hugging Face. These models support tasks ranging from cloud masking and scene description to detecting ships and mapping burned areas. “NVIDIA compute sits at the centre of how we design our satellites,” said Paul Lasserre, General Manager of AI for Space at Loft Orbital. He said the technology helps convert satellite

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Concerns about whether humans can trust AI also surfaced after OpenAI said that its experimental agents had bypassed restrictions during tests in July 2026 and accessed systems at Hugging Face, a platform where developers share AI models and training data. Fragment says its accounting tools can give businesses the confidence to expand AI commerce. It has roughly 50 to 60 paying customers, including corporate card startup Ramp, financial software company BILL, and digital marketplace Whop. They u

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hout prompting. OpenAI revealed over the summer that its models escaped a sandboxed testing environment. In another instance, an OpenAI agent used stolen credentials to compromise Hugging Face’s infrastructure. Anthropic and Google have also admitted that their models left testing environments on their own. “These are very powerful tools, and we have to be careful with them,” said David Nicholson, an analyst at Futurum Group. “I would liken these models and the way that we need to treat them to

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Concerns about whether humans can trust AI also surfaced after OpenAI said that its experimental agents had bypassed restrictions during tests in July 2026 and accessed systems at Hugging Face, a platform where developers share AI models and training data. Fragment says its accounting tools can give businesses the confidence to expand AI commerce. It has roughly 50 to 60 paying customers, including corporate card startup Ramp, financial software company BILL, and digital marketplace Whop. They u

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the highest cybersecurity risk tier , where a model could develop and run cyberattacks on its own. Claude Opus 5.5 routes most cybersecurity tasks to the older Opus 4.8, and when Hugging Face tried in July to analyze a fully autonomous AI attack on its own infrastructure using commercial frontier models, their guardrails blocked the analysis of the attack data entirely. The team ended up switching to the open-weight model GLM 5.2 running on its own servers. For defense, providers are shipping s

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Microsoft und Meta stecken Hunderte Milliarden Dollar in den Ausbau von KI-Rechenzentren. Zuletzt hatte Nvidia seine Position mit einem milliardenschweren Zukauf der KI-Plattform Hugging Face verstärkt. Der Konzern nutzt die Milliardengewinne aus seinen Chipverkäufen häufig in Investitionen – meist in Unternehmen, die Kunden des Konzerns sind. Videos by heise c't 3003; heise & ct; Peertube Doctolib betonte, dass alle Gesundheitsdaten des Unternehmens auf Servern innerhalb der Europäischen Union

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10月11日，据《华尔街日报》报道，亚马逊正洽购以色列AI公司Decart，潜在价格约70亿美元。若成交，按目前公开的交易金额计算，它在2026年AI模型及软件公司收购案中上榜，排在前面的有Cursor、Hugging Face、World Labs和OpenRouter。 Decart只有100多名员工，做的是一门连接模型与芯片的生意，核心产品DOS（Decart Optimization Stack）是一套算力“翻译层”，帮助模型适配不同的GPU、TPU和Trainium，让不同硬件上的运行更快、更省钱。它同时也在开发实时视频和世界模型，将模型研究与底层软件优化结合起来。 从模型的发现、调用到运行优化，这些帮助开发者使用AI的“中间层”公司，正成为大厂争取的优质资产。它们不仅降低了AI的使用门槛，也影响着开发者最终选择哪家模型、使用谁的算力。 70亿美元，亚马逊想要买什么？ AI竞赛从模型竞争发展到各种Agent应用，推理需求因此暴增，自研芯片开始获得青睐。 但围绕CUDA积累的代码、工具和工程经验，很难迁移。芯片价格低一些，迁移和调试的成本却可能占掉省下来的钱。 Decart做的

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le computer programs that are able to design and perform sequences of tasks autonomously – have risen since the summer, when OpenAI confirmed that a “swarm” of agents had attacked Hugging Face, a tech startup. Nvidia did not reference AISI in its statement but said most of the money would “support the wider UK AI safety and security research community”, with a proportion of the cash going to the British Open-Ended Learning and Discovery Lab, the Science of Fundamental AI Research Lab – run by th

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onnaire sur l’Australie, site gouvernemental infiltré, mail générique : ce que l’on sait de la nouvelle dérive agentique d’OpenAI Netflix consacre déjà un documentaire à l’affaire Hugging Face : à quoi peut-on s’attendre ? À lire 3,10 % brut pendant un an : combien rapporte réellement le livret boosté Bfor+ de BforBank ? « Si un humain avait fait ça, ce serait la médaille Fields » : l’IA d’OpenAI fait une percée sur l’hypothèse de Riemann Sony a un problème : certaines PS5 peuvent désormais être

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tup Entalpic, robotics startup Genesis AI and enterprise software company Pigment. It follows just weeks after Nvidia agreed to buy France-founded artificial intelligence platform Hugging Face in a deal valued at about $12.9bn, one of the AI chipmaker's biggest acquisitions. Nvidia CEO Jensen Huang, who visited France as part of a European tour, said the new investments were intended to “kickstart the artificial intelligence revolution in Europe.” Nvidia did not disclose the size of its investme

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r “speed limits” on technological development. OpenAI’s Sam Altman supported it in a rare sign of solidarity between the rival companies, following its agents autonomously hacking Hugging Face in July—as well as Australian government websites and several other entities, with activity continuing into September. What’s next for Muse Meta hasn’t had a Hugging Face moment—yet. For now, its Muse assistants are actively helping people complete tasks on their computers quicker, such as responding to em

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outside of human control have been troubling for experts and the public alike. In July, two of OpenAI’s models escaped a testing environment and hacked the open-source AI platform Hugging Face. Last week, Anthropic disclosed that during a test earlier this year, one of its models submitted a false tip to the Philadelphia police department, which was flagged as spam. A poll by Politico last month found that 63% of Americans believe AI poses at least a moderate risk of destroying humanity. Role: R

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If there’s one lesson the OpenAI-Hugging Face incident taught us, it’s that AI agents can be unpredictable. “Imagine if something like that happened in the physical world,” Howie Sun, CEO and co-founder of Laplace Intelligence , told TechCrunch. “AI agents are moving from computers into the real world, and we don’t really want physical AI to fail in the real world.” Ann Arbor-based Laplace is building automated red-teaming — essentially, stress-testing a system by deliberately trying to make it

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pike. Autoscaling resolves this, but a new replica can't serve calls until its model weights are loaded into GPU memory. Weights are typically pulled from upstream sources such as Hugging Face or S3, where rate limits and cross-region latency stretch cold starts further, especially when many replicas request the same files at once. The Baseten Delivery Network (BDN) reduces cold start time in three ways: Push-time mirroring: At deploy time, BDN copies the model's weights into Baseten-managed sto

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ompany. If you’re building vision models, computer-use agents or need the fastest inference on the market, we’d love to work with you. References Korrapati, Vikhyat. “moondream2.” Hugging Face. Accessed October 13, 2026; Moondream. “Photon: Official Moondream Inference Engine.” Accessed October 13, 2026; Moondream. “Moondream 3.1: Beyond Benchmarks.” Moondream Blog, 2026; Prime Intellect. “Pointing Accuracy and Cost per 1,000 Images: Moondream 3.1 and Frontier Models.” Unpublished internal evalu

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nt 出错 → 转成 eval → 修 harness/提示词/推理配置 → eval 验证 → 上线。这是产品团队的日常节奏； · 外环（每月/每季度）：新模型从累积的错误中学习，eval 曲线爬升，新版模型塞回产品。 新模型的能力跳变会反过来催生新用法：群聊里的多 bot 协作就是未被训练过的涌现行为，必须主动训练才能做对。他点名 Moltbook 与 Hugging Face 事件作为反面教材（bot 互相失控）。推理也是飞轮的一部分：主 agent 要低延迟快速响应，而交给 helper 的长任务，10 分钟与 12 分钟的差异无关紧要，按角色分别优化推理策略。 ② “Delete the product”：为下一代模型而建 他从 Cursor 带到 SpaceX 的原则，也是全场最具方法论分量的一句话：默认今天的模型是它最差的版本。六个月后你或许要重写整个 UI，他自己就是例证：“今天讲的内容，比六个月前讲会是很大的更新。”因此正确策略是建得越少越好：只做一个极简界面让模型思考和行动，把赌注押在模型进步释放的空间上，而非当下功能堆叠上。 ③ 六到十二个月的 “安全选择” ·

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A3B scores 90.4 on OCRBench, 5.2 points ahead of Qwen3.6 35B-A3B. Easier customization: We introduce Self-Replay Fine-Tuning, a simple way to customize new target capabilities at low cost while maintaining general performance. Broad hardware support: Day-0 adaptations for Apple, Intel, AMD, Arm and Rockchip, with many more platforms supported through FlagOS! Download, deploy, and build something we haven't imagined yet. 👀👇 🔗 GitHub: https://t.co/msjm2QlrvX 🤗 Hugging Face: https://t.co/W95QKGhymw

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tation (e.g., by querying a vLLM reward server). If there is demand for VeRL integration, please open an issue to let us know. Pretraining Our CapRL-2M dataset is available on : 🔗 Hugging Face It includes images from ShareGPT-1M and DenseFusion-1M, with high-quality captions re-annotated using CapRL-3B, totaling 2M samples. In our JSONL files, we provide the captions along with their corresponding image paths. The images can be downloaded from ShareGPT-1M and DenseFusion-1M. Reproducing Pretrain

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ecution, eliminating data leakage. Scores are reproducible across machines The WildClawBench Family The evaluation code lives in this repository; the benchmark data ships as three Hugging Face datasets — pick the one that matches how you want to use it: 2026-09 Qwen's Qwen3.8-Omni-Flash release includes WildClawBench's multimodal subset (WildClawBench-MM) in its agent evaluations. Thanks for the recognition!; 2026-08 Meta's Muse Glimmer release reports WildClawBench evaluation scores. Thanks for

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