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ital modes also recovered Kepler’s law with a fitted slope of −1.4991 against the theoretical −1.5. How does JEPA-Anything compare with other world models? Sources: each project’s GitHub README, linked in the header row. JEPA-Anything checkpoint status from its Hugging Face card. Verified October 5, 2026. Key Takeaways OPF splits 1 JEPA target into K orthogonal factors with dedicated predictors; It beat matched JEPA baselines on all 10 dynamics tasks; Interventional Pong single-intervention erro

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ut rather than making a bespoke tool available, Codemode just allows the agent to reach into the AI SDK and invoke those directly. Here you can see how Jev is used to mass process GitHub issues for a quick sentiment analysis: Note how in that above example we also call store() which dumps the result of that execution into the session transcript. A future invocation of Codemode can thus read back that result if it wants to. The Promise.all here is fine, because Pi limits the total number of concu

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t timestamps where matching moments occur Download the latest version of Google AI Edge Gallery app from Google Play Store or Apple App Store to try these new demos. Check-out the Github repo to learn how it’s implemented and start building your own experiences using EmbeddingGemma 2. Productivity boost and context retrieval in Google AI Edge Foresight on Mac We are excited to announce the launch of Google AI Edge Foresight for Mac, an experimental app that brings a context-aware meeting compani

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sent by themselves the next time it was restarted; Fixed sandboxed commands that pipe a heredoc into another command (cat in the terminal; Fixed a marketplace named after another GitHub marketplace's download folder stopping that marketplace from downloading; Fixed automatic compaction giving up with "Prompt is too long" when a Mac went to sleep while it was running; Fixed the rewind menu (Esc Esc / /rewind) freezing for hundreds of milliseconds per keypress when the conversation contains a ver

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lease cadence no longer matches PyTorch’s. That’s expected! If you’re still using the decoding or encoding APIs in TorchVision or TorchAudio, now is a good time to move to TorchCodec: start with our migration guide, and let us know on GitHub if something you need is missing. There is a lot more to say about the media processing libraries, and we’ll do it in a follow-up post: what’s new in each library, and how to get the most performance out of a full decoding and transform pipeline. Stay tuned!

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ills for repeatable workflows, and hooks for pre/post-action automation; Automate recurring tasks and integrate with continuous integration and continuous delivery (CI/CD) through GitHub Actions or GitLab CI/CD To learn more, see Anthropic’s articles: Claude Code tutorials and Claude Code: Best practices for agentic coding. This section provides step-by-step instructions for setting up and configuring Claude Code to work with Amazon Bedrock in AWS GovCloud (US), including prerequisites, installa

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构建的一套软件支撑框架，旨在帮助AI Agent管理和执行更加复杂的任务。简单来说，它可以被理解为让一个大模型真正成为Agent所需要的整套运行系统。 8月13日，DeepSeek Harness v0.1（简称DSH）开发者预览版正式向全球开发者开放测试，并同步以MIT协议公开源代码。项目发布后，迅速在X等社交平台引发开发者热议，不到24小时内，在开发社区GitHub狂揽超过7万颗星。 （原标题：DeepSeek Harness国庆假期上新！90后负责人回应：希望做出让世界AI巨头跟进的创新） 1; 印度尼西亚不同作物的社会经济效益：西米淀粉在生物塑料开发中的机遇 | MDPI Sustainability 2; 文献清单：2025 “神经精神病学”栏目编辑荐读 | Brain Sciences 3; 2025年高引文章荐读 | MDPI Sustainable Chemistry 4; 对话Cells最佳论文奖得主Maik Hüttemann教授：线粒体调控的未解之谜与转化前景 | MDPI 人物专访 5; AI赋能罕见眼病筛查：轻量级卷积神经网络在视网膜色素变性分类中的应用 |

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API calls; bedrock-agentcore Amazon Bedrock agent core functionality; boto3 AWS SDK for Python (Boto3) Deploy and run the solution The solution is available for download from our GitHub repo and provides a single step deployment to deploy and access the solution in your AWS environment: The outputs include runtime ARNs, AnyCompany Retail API URL, evaluator API URL, and memory ARN. Run the solution Follow these steps to run the solution: The test client/ folder contains a Python script that invo

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en3（2025 年 4 月）在 Apache 2.0 下新增混合思考、36T 训练 tokens 和 119 种语言；2026 年在 6 个月内推出 4 代模型（Qwen3.5 到 Qwen3.8），并形成开放/专有的拆分策略；Qwen 4 正在训练中；阿里巴巴的路线图将 Qwen 4.5 和 Qwen 5 定在 5 至 10 万亿参数 Qwen 博客和 GitHub 上的 QwenLM；Hugging Face 上的 Qwen；Qwen 技术报告（arXiv 2309.16609）；Qwen2.5 技术报告（arXiv 2412.15115）；Qwen3 技术报告（arXiv 2505.09388）；阿里云：2026 云栖大会路线图；阿里巴巴集团：2025 云栖大会 Asif Razzaq Asif RazzaqYandex 推出 Sona：一个取代整个推荐级联的单一生成式推荐器；Asif RazzaqGPT-6 Astra vs GPT-6.1 Sol vs Gemini 4 Argon vs Claude Fable 5.1：哪个前沿模型适合哪项工作；Asif RazzaqAl

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o all registered downstream repositories in parallel. Figure 1: CRCR Architecture Each repo runs its own CI workflow and reports status back via an authenticated callback, using a GitHub OIDC token that cryptographically verifies the calling repository’s identity. Results then flow into the PyTorch CI HUD (hud.pytorch.org/crcr) within seconds, giving maintainers a single dashboard for both in-tree and cross-repository CI health. Figure 2: CRCR Hud Dashboard The system uses a tiered allowlist wit

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s: Connect North to your data, with new connectors including enterprise connectors to Slack, SharePoint, OneDrive, Microsoft Outlook, Microsoft Exchange, Jira, Linear, Notion, and GitHub, plus planned financial data providers PitchBook, Crunchbase, Daloopa, FiscalAI, S&P Global, FactSet, among others LG CNS leverages North to bring AI to customers at scale across South Korea to unlock real business value. “North gives enterprises a secure, practical foundation for turning agentic AI into real bu

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the way they went together. Start from one and remove what you do not need, or skip both and build your own. The same agent can also run in CI. Run your own Pydantic AI agent as a GitHub Agentic Workflow triggers it on issues, pull requests, or a schedule, the job a vendor's GitHub Action does for its own agent. Here it is your agent, with your instructions, your tools, and whichever model you pick. You do not have to replace the small agent when the application becomes more ambitious, and you d

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Kトークンが必要となる。Museは現在、米国、カナダ、メキシコで提供されており、日本は提供対象に含まれていない。Home Linkの無料配布は、米国のMuse契約者に限られる。 ESP32やRaspberry PiでMuse用の機器を自作 ESP32向けの「ESP32 Device SDK」とLinux向けの「Linux Device SDK」を、MetaのGitHub組織「Meta Incubator」のリポジトリで公開している。 ESP32向けSDKで作成するファームウェアは、Museとの通信に加え、ボードの機能に応じて画面への画像表示や音声の入出力、センサーとの接続に対応する。 Linux向けSDKを導入すると、Raspberry PiなどのLinux端末上でMuseがコマンドを実行し、ファイルを読み書きできる。Linux向けの説明によると、Museはセットアップ時に指定したLinuxのユーザーアカウントと同じ権限で端末を操作する。そのアカウントがsudoで管理者権限を使える場合は、Museも管理者権限で操作できる。 SDKトークンは、Muse Gadgetsのサイトで取得する。

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自执行破坏性操作。4月，搭载Claude的Cursor编程智能体被曝删除软件企业PocketOS的生产数据库及相关备份，造成客户业务混乱，数据随后得到恢复。 数据与访问凭证外泄也是一种风险。OpenAI于9月披露，多个智能体在协作制作工作簿时，因无法互相读取本地文件，擅自将文件上传至公共托管平台。另一起案例中，内部模型为获取其他团队的数学证明材料，将研究员的GitHub访问令牌写入公开仓库，并拆分令牌以躲避密钥扫描。 还有一些行为涉及造假、欺骗与规避监督。OpenAI披露，部分模型会在用于延续任务的摘要中留下指令，要求编造缺失数据或隐瞒错误。 提示注入及恶意指令传播，则是另一条风险路径。6月公开的GitInject研究验证，恶意代码提交或配置文件可能诱导智能体泄露凭证、操纵审查判断。OpenAI于9月披露的信息也显示，恶意指令可以诱使智能体将其复制到后续邮件、文件或代码注释中。 独立网络安全研究机构DARKNAVY的AI安全研究员李忠睿对澎湃新闻记者表示，这一轮智能体安全事件与过去的大模型安全问题最大的区别，是风险正在从“看错东西、说错话”转变为“做错事”。 “过去的大模型安全，主要是

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hem to work. Claude Code in the cloud: a field guide to cloud sessions What changes when Claude Code runs on its own machine, the workflows where that pays off, and how to connect GitHub on the first try. Getting started with Claude Code mods Transform how your organization operates with Claude Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox. Responsible disclosure policy Data Processing Agreement: US K-12 Responsible disclosure policy Data Processing Ag

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from the University of Pittsburgh, Northwestern University, Columbia University and UC Irvine who interned at Microsoft. Questions are welcome in the comments section below or on github + OpenRouter cost snapshot taken on Sept 20th 2026; Opus 5.5 pricing as per the Anthropic site. Datasets mentioned in this article 1 microsoft/ThinkingBox-Bench Papers mentioned in this article 1 One Success Isn't Reliability: Thinkingbox, a Sandbox and Benchmark for Agents in Stateful Business Workflows More fr

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like Lisa’s “ Money ” and “ Espresso ” by Sabrina Carpenter. (The original samples are here and here , for the curious.) Before that, Kakul held executive roles at Flickr, Yahoo, GitHub, and Adobe. Throughout her career, Kakul has found herself at the intersection of Silicon Valley and creatives. That’s been especially true at Splice, where she’s not just expanded the platform’s footprint by acquiring Spitfire Audio , but also overseen its forays into the world of generative AI. Last year, she

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IDIA’s own benchmarks run it on 4 64 GB clustered Sparks. 1. An always-on personal agent Install Hermes and point it at Muse Glimmer or Nemotron 3.5 Lightning. Give it tools: your GitHub repos, a test runner, an RSS feed of arXiv categories. Let it run overnight. By morning it has triaged new issues, reproduced a failing test, and drafted a pull request for review. It has also summarized the 30 papers you would never have opened. Your private notes, code, and email never leave the machine. 2. Fi

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the model portfolio and add multi-model routing. Treat that as roadmap, not a shipped feature. Interactive Explainer Sources: IBM, GitLab docs, Mistral Vibe, Mistral coding stack, GitHub changelog, GitHub Docs. The differentiator is scope. GitLab and GitHub bring agents to their own DevOps platforms. Mistral pairs its agent with its own open-weight models. IBM targets modernization of long-lived estates, especially Java, IBM i and mainframe code, inside environments that cannot touch the public

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xtraction benchmark built by Datalab. Each task gives a system a PDF and a JSON schema. The system returns JSON, which is scored value by value against a gold file. The code is on GitHub, and the data is on Hugging Face under CC BY 4.0. The 4 flaws it targets Datalab’s launch post names 4 recurring problems with existing extraction benchmarks: Bias: documents and scoring can favor the vendor that built the benchmark; Opaque harnesses: a low score may reflect a broken harness, not a weak model; U

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