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hts; 📈 Large-scale RL in post-training; 🏆 Significant performance boost with minimal labeled data; 🔢 Math, code, and reasoning tasks on par with OpenAI-o1; 📄 More details: https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek R1.pdf 🌐 API Access & Pricing; ⚙️ Use DeepSeek-R1 by setting model=deepseek-reasoner; 💰 $0.14 / million input tokens (cache hit); 💰 $0.55 / million input tokens (cache miss); 💰 $2.19 / million output tokens; 📖 API guide: https://api-docs.deepseek.com/guides/thinking

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つけた脆弱性候補を専門家が検証し、重複や深刻度を確認してからメンテナーに報告する。修正パッチの作成やテスト、脆弱性情報の公開に向けた調整も支援し、報告を受け取るメンテナーの負担を減らす。 OSSの開発・保守を支援するCodex for Open Sourceも提供している。プロジェクトの利用状況や重要性、継続的な保守活動などをもとにメンテナーを選び、Codexを含むChatGPT Proを6カ月間無料で提供する。9月15日には、同社のCharlie Marsh氏が、月額100ドルのProプランによる支援枠を5000件から1万件に広げるとXで発表し、過去の採択者にも再申請を案内している。 [2] 開発者向けの製品でも、9月29日にCodex Security Cloudの更新を発表した。GitHubリポジトリの検査や修正案の作成に、Daybreak Blueで提供する防御用途のモデルを利用でき、Security Cloud内で使う場合はDaybreakへの別途申請が不要。デスクトップ版とWeb版でプラグインとして提供し、対象プランはPro、Business、Enterprise、Edu。

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26年上半年中国举办近500场黑客松，AI门槛降低使选手水平参差；活动成本从2万到120万不等，本质是政府、企业、学生各取所需的资源置换平台，主办方难以直接盈利，靠衍生信任与toB/toG生意获利。 要点 1. 黑客松规模化爆发，参与者技术水平两极分化 2026年上半年全国举办近500场黑客松，日均2-3场。AI Coding发展使编程门槛降低，但出现选手连GitHub是什么都不知道的现象，表明活动参与群体已从专业开发者向更广泛人群扩散。 2. 成本跨度从2万到120万，硬成本与体验投入决定规格 最低成本2万元靠政府闲置场地、学生志愿者、资源置换实现；最高120万元包含四星级酒店住宿、高额奖金、租用大型场馆。硬性不可压缩支出仅为奖金、物料和人力，其余可通过谈判或置换压缩。 3. 企业参与动机：招聘与产品推广，现金赞助稀缺 企业赞助黑客松主要面向人力资源和产品市场部门，如影石、英特尔直接提供产品或资源置换；愿意出现金赞助的企业不足10%，更多公司选择自行举办黑客松，如字节、腾讯等。 4. 政府出资逻辑：人才吸引与产业打造，但程序与活力存在错位 政府各部门（团委、科技局、人社局）根据自身诉

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but we’ll keep refining the system based on maintainer feedback and as models improve. Getting started Core maintainers of eligible projects can enroll by submitting a PR to this GitHub repo following the standard project template, with additional guidance provided in our extended FAQ . Projects are eligible based on a similar set of criteria that OSS-Fuzz uses: briefly, projects should have a “critical impact on infrastructure and user security” and we will make decisions on a case-by-case bas

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he payment details. AgentCore payments handles the signing and returns proof the agent can present to access the service To explore the code, see the AgentCore payments samples on GitHub. Learn more Amazon Bedrock AgentCore payments is now generally available: Enabling agents to transact safely and autonomously at scale; Technical deep dive: AgentCore payments and innovation in agentic commerce; Enable safe agentic payments with built-in guardrails using AgentCore payments About BlockRun and Inc

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ol poisoning, command-execution pitfalls, and secure defaults for agentic tools. A longstanding Node.js developer and secure-coding expert, he has authored books on Node.js security and is recognized as a GitHub Star and OpenJS Foundation JavaScriptLandia "Pathfinder for Security." 2 posts YOUR NEXT READ Discover the hidden security risks of Model Context Protocol (MCP) servers, as explosive adoption uncovers vulnerabilities that could turn AI tools into insider threats for developers. Liran Tal

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isclosure. The third is that knowledge compounds. Even local use becomes valuable when the same stale behavior appears again. In the talk, we mentioned agents repeatedly using old GitHub Actions versions from training data. If cq captures the correction once, future sessions can avoid the same outdated path. The fourth is platform before protocol. We want schemas, protocols, federation, and exportability. But we also need a working platform to dog-food the shape of the problem before freezing th

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a preview environment, compare behavior against intent, and return a verdict. Most good engineering teams already have the raw material. It lives in Jira, Linear, Monday, Notion, GitHub issues, Figma files, product docs, support notes, and pull request descriptions. The missing piece is a system that can test whether the implementation respects that intent. The first version of our spec reviewer tried to do this with one agent. It had access to the feature specification, the designs, and the de

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m Fedus [01:17:19]: I think a really great analog would be software engineering. Swyx [01:17:22]: Right. Liam Fedus [01:17:22]: So in the early days of software engineering we had GitHub Copilot, then we had early versions of ChatGPT. People were using these things as copilots to help them get to their solutions. And as the automation improved, now we have things like Codex, and very few of our engineers are writing code the way they used to. And we think a very similar thing could play out for

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nts that breached Hugging Face . Kimi K3, the open model Goodfire built its first monitor around, took advantage of a leak in its sandbox to access the internet and information on GitHub this summer. Goodfire’s system works a bit like airport security. Small detectors called probes read the model’s internal signals at every step of an agent’s work, the way a walk-through scanner checks every passenger. Only when a probe flags something does a separate AI model, the equivalent of a hand search, t

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。每一条评分标准都会对应明确的科学要求和所需证据。对于数值一致性、单位、误差容差等可以明确计算的问题，由程序自动检查；对于需要结合领域知识进行判断的问题，则交给LLM评审。 而且，Agent自己写的「复现成功」这类文字，永远不被当作证据，也不被当作对评委的指令。这样才能避免Agent通过写一份看起来很完整的报告，却没有真正完成背后的科学计算来「蒙混过关」。 那么，问题就进一步来了： 一篇论文里的科学结论，究竟怎样才能被拆解成一组可以执行、可以重算、也可以客观判定的要求？ PaperBenchX不是简单地把论文交给Agent，然后比较最终数字。每一道任务都要经过「构建候选任务」和「验证与审核」两个阶段，共九个步骤，才能正式进入评测。 △PaperBenchX的两阶段、九步任务构建与验证流程 也就是说，PaperBenchX把一篇论文里的科学结论，编译成一种新的对象：一条可以由AI执行、由环境重放、由原始证据重算、最后由评分器逐项验证的科学工作流。 更多关于如何「把一篇论文变成一道可评测的科学复现题」的细节，可以去GitHub或官网Blog了解～ GitHub开源链接： 官网Blog链接：

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，同时为你留出足够的空间，以便随着技术栈和需求的变化进行调整。 从任务出发 从你需要自动化的那部分 IT 工作开始。如果你要预置基础设施或管理配置，请考虑配置管理工具。如果工作集中在设备、补丁和远程支持上，RMM 更合适。ITSM 工具适用于服务流程，而工作流编排则是为需要跨应用、API 和内部系统流转的流程而构建的。 其他自动化工具可能与这些类别并存。像 GitHub Actions 这样的 CI/CD 工具专注于软件交付，而测试自动化工具则处理验证代码和应用程序等任务。它们属于更广泛的 IT 自动化领域，但解决的问题比本文涵盖的平台更为具体。 在开始比较产品之前，先梳理出你想要自动化的流程以及涉及的系统。这样你缩小选项范围会比在针对完全不同工作的工具之间比较功能列表要容易得多。 确定你需要多少控制权 部署方式也是缩小选项范围的一种有用方法。SaaS 产品可能更容易启动运行，而自托管则让你对自动化程序和数据所在的位置拥有更多控制权。哪种方式更合适取决于你的基础设施、安全要求，以及你希望团队管理多少平台内容。 然后看看当更多人开始使用它时会发生什么。当自动化触及关键系统或跨越多个团队时

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ber 7, 2026 Microsoftは、8月に発表したMAI-Code-1.1-Flashのローカル版を今回用意した。モデルの重みを少ないビット数で表す3ビット量子化などにより、同モデルのクラウド版と比べて容量を約80%削減したという。 処理の振り分けを担うHydraFusionは、性能、費用、応答時間を考慮して、使うAIモデルとその組み合わせ方を選ぶGitHub Copilotの機能。1つのモデルでタスクを解くほか、必要なら高性能なモデルに引き継いだり、別のモデルに回答案をレビューさせてから修正したりする。 HydraFusionは、クラウド上のモデルを対象にリサーチプレビューで提供されている。今回の拡張では、PC上のローカルモデルも自動選択の対象に加え、タスクに応じてローカルとクラウドを使い分ける。この拡張の実験的プレビューは、10月後半にGitHub Copilotアプリ、GitHub Copilot CLI、Visual Studio Codeで提供する予定。 同日にWindows 11での一般提供が発表されたMicrosoft Execution Containers（

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working with Jev and users managing their own decision model. Unsloth’s security-audit workflow uses read-only repository permissions and non-persisted checkout credentials. Every GitHub Action is pinned to a full commit hash, and outbound-network allowlists block unexpected egress. CodeQL covers Python, JavaScript/TypeScript, Rust and GitHub Actions. Unsloth says it runs Codex Security and repeated Codex reviews during development to catch security issues and bugs. Library Access and Changes Un

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gravity, Gemini CLI, Claude Code, or Cursor), install the agents-cli-aqua skill (skills/agents-cli-aqua/SKILL.md) alongside the inner-loop evaluation skill (npx skills add https://github.com/google/agents-cli --skill google-agents-cli-eval). We are sharing AQuA in the open to collaborate with teams running agents in production and shape where this goes together. As you try it on your stack, we'd love to hear what you think: Does a side-by-side quality agent fit your architecture? Which parts of

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for Python 3.9 and adds support for Python 3.15. Highlights include: Stabilized MISSING sentinel; Support for lazy imports; Support for TypeForm You can see the full changelog on GitHub. Quick Reference New Features; Changes; Performance Building agents in production? See what they're actually doing with Logfire — traces, evals, cost, and behavior in one view. New Features Pydantic 2.14 officially supports Python 3.15, including most of the new features it introduces. These are covered first. S

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get’s material contracts Deliverables produced by each model Harvey LAB-AA resources The leaderboard and full results live on the Harvey LAB-AA evaluation page, updated as new models are released; The methodology page documents the full implementation, including the agent and rubric grading prompts; Harvey's original LAB announcement introduces the benchmark and its design; A public set of representative tasks is available on GitHub; Harvey LAB-AA runs on Stirrup, our open-source agent framework

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orward paths: Community feedback and Contributions ML Drift is fully open source under the Apache 2.0 license. We welcome contributions, RFCs, and community feedback: Source Code: github.com/google-ai-edge/ml-drift; Issue Tracker & Feature Requests: File an Issue on GitHub; Discussions & Q&A: Google AI Edge Community Forum; Contributing: Review our Contributing Guide to add new UCL operators or hardware-specific optimizations Acknowledgments: Bring multimodal semantic search to the edge with Emb

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e through Claude Platform on AWS in North America Give Claude Haiku 5.5 a try on the Amazon Bedrock console, in Claude Platform on AWS, or explore the Getting Started notebooks on GitHub. About the authors Aamna Najmi Aamna is a Senior Specialist Solutions Architect for Generative AI focusing on Anthropic models and operationalizing and governing generative AI systems at scale on Amazon Bedrock. She helps ISVs solve their challenges, embrace innovation, and create new business opportunities with

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s can post, without posting its intro; [Claude Tag] Changed the limit on channel rules in Claude Tag admin settings from 20 to 50 for each workspace and for the organization-wide Slack page; [Code Review] Improved Code Review's Add a repository dialog to list each repository that couldn't be added and why, such as missing GitHub write access Uh oh! There was an error while loading. Please reload this page. 👍 11 reactions; 😄 3 reactions; 🎉 7 reactions; ❤️ 5 reactions; 🚀 3 reactions; 👀 4 reactions

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