正在读取
正在准备公开内容。
这是读取过程，不代表当前没有内容。页面准备完成后会自动显示。
正在连接内容来源
正在读取
这是读取过程，不代表当前没有内容。页面准备完成后会自动显示。
正在连接内容来源
每日公开源信号
公开页不会暴露原始候选池。新闻必须经过 AI 自动审核或人工复核并确认纳入趋势报告，才会在这里出现。
2026-07-07
日期、来源、摘要、可信度和合规说明均来自通过完整审核门禁与哈希校验的每日公开快照；周报发布后由同期完整发布包接管。
Exa MCP search metadata: Introducing Muse Image and Muse Video. # Introducing Muse Image and Muse Video Published: 2026-07-07T20:03:45+00:00 meta.com) Introducing Muse Image and Muse Video # Introducing Muse Image and Muse Video July 7, 2026• We’re excited to launch Muse Image and preview Muse Video, the first media generation models developed by Meta Superintelligence Labs. Muse Image is our most advanced image generation model yet: it follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. It also brings agentic tool use capabilities and integrates with Muse Spark. Muse Video, built on the same pretraining base, ... 来源：Exa MCP。Exa MCP public search metadata via the Agent Reach documented route; no cookies, login state, or full article body stored.
打开公开来源Exa MCP search metadata: Agent Skills for .NET Is Now Released | Microsoft Agent Framework. Agent Skills for .NET Is Now Released | Microsoft Agent Framework July 8, 2026 ### Agent Framework’s Orchestration Patterns Reach 1.0 July 9, 2026 ### Agent Harness: Scaling the claw or harness capabilities harness-capabilities You can now give your .NET agents reusable packages of domain expertise – instructions, reference documents, and scripts they load only when a task needs them – through a stable, production-ready API. Agent Skills for .NET in Microsoft Agent Framework has moved out of experimental preview – the`[Experimental]` attribute is removed and the API is stable. Teams can build skills,... 来源：Exa MCP。Exa MCP public search metadata via the Agent Reach documented route; no cookies, login state, or full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：From Hugging Face to Amazon SageMaker Studio in one click。Today, we’re excited to announce a deep-link integration between Hugging Face and Amazon SageMaker AI. Developers can now go from model discovery to hands-on experimentation in SageMaker Studio with a single selection. Whether you fine-tune a foundation model (FM) from Amazon SageMaker JumpStart or deploy it to an Amazon SageMaker Inference endpoint, you can now land directly inside the relevant SageMaker Studio workflow. Your selected model is pre-loaded, and the environment is fully configured and ready to go. Previously, getting started on SageMaker Studio after discovering a model on Hugging Face required navigating mul... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot。Update on GitHub For most teams, models and datasets live in a bucket in one region of one cloud. The GPUs you can get, whether for development, training, or serving, increasingly sit on a different cloud than your data. The moment those two come apart, you pay a cross-cloud transfer tax just to read your own data onto your own GPUs. Together with Hugging Face, we've joined the two halves: your models and datasets stay on the Hub, and SkyPilot runs the compute (dev, training, or serving) on whatever cluster has the GPUs. Mount a Hugging Face Bucket or any Hub repo into a SkyPilot job with one h... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：LeRobot v0.6.0: Imagine, Evaluate, Improve。Update on GitHub This new release is about closing the robot learning loop: policies that imagine the future before acting, reward models that tell you when your robot succeeds, a deployment CLI that turns failures into training data, and six new simulation benchmarks to measure it all. It also brings depth sensing, VLM-powered dataset annotation, custom video encoding, cloud training on HF Jobs, and a much leaner install. TL;DR LeRobot v0.6.0 introduces world model policies (VLA-JEPA, FastWAM, LingBot-VA) that learn to imagine the future, a wave of new VLAs (GR00T N1.7, MolmoAct2, EO-1, EVO1, Multitask DiT), and a new reward models API (... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Hugging Face Models on Foundry Managed Compute。At Microsoft Build 2026, we announced Foundry Managed Compute and Hugging Face models on Foundry — a curated catalog of open-weight models from the Hugging Face ecosystem, refreshed weekly, deployable in one click onto Foundry Managed Compute. Weights are pre-staged in Azure, runtimes are built and scanned by Microsoft, and every model in the Collection ships with the same enterprise security, governance, observability, and billing that applies to every other model on Foundry. The Platform: Microsoft Foundry and Managed Compute Microsoft Foundry is a platform for building and operating agentic AI applications. Foundry starts with the ... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源日期归档
只列出至少包含一条已复核公开信号的日期。