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ench）拿到 51.3%，领先最接近的对比模型约 9 个百分点。 到了软件工程和科研领域，谷歌放出的几个案例，更能看出它啃硬骨头的底子： 在真实代码仓库修复基准 DeepSWE v1.1 中，Argon 拿到了破纪录的 77.9%。落到实战里，研究人员用它参与优化开源量子化学工具 xTB，把分子结构预测速度最高提升至原来的 2.3 倍，相关代码已经提交至 GitHub 官方仓库。 另一个很能说明问题的实验是“底层调优”：研究人员借助 Argon，短短几天就完成了自家开源模型 Gemma 4 E2B 推理链路的深度改造，在 A100 显卡和 vLLM 环境下，生成质量分毫不差，端到端推理吞吐直接达到原来的 2.85 倍。 按谷歌的说法，以前这种抠细节的底层苦差事，工程师团队通常得熬好几个月时间才能干完。 而在生物与物理科研中，它帮研究人员设计并筛选出 5 种全新酶的候选方案，后续进实验室验证，五种方案居然全都测出了催化活性；在核聚变仿真研究里，它还参与寻找磁场数学模型的新解，为检验仿真程序提供测试基准。 虽说科研成果仍要经过严谨验证，但作为帮研究人员筛方案、找思路的工具，它已经展示出了

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itch model can also narrate and respond to your questions while it is playing. Resources for Getting Started Granite Switch 4.1 Model Cards Speech-to-Decisions Demo Granite Switch GitHub Repo Create your own aLoRA v LoRA race on Colab . Footnotes Models mentioned in this article 5 ibm-granite/granite-speech-5.0-470m-turboctc ibm-granite/granite-switch-4.1-30b ibm-granite/granite-switch-4.1-8b ibm-granite/granitelib-core-r1.0 ibm-granite/granitelib-guardian-r1.0 Spaces mentioned in this article 3

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i 53670 Account for token usage from incomplete responses @jif-oai 53672 Keep discovered Apps tools available during startup recovery @jif-oai 53673 Publish Rust releases with the GitHub CLI @jif-oai 53678 Preserve inherited context in prediction forks @keyz 53689 Constrain API-key model access programs to server support @julee-oai 53691 Account for replay caches in encrypted compaction budgets @ioanbudea-OAI 53693 Add keyboard selection support to the transcript view @fcoury-oai 53698 Interrupt

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erence efficiency on platforms including NVIDIA DGX Spark. Next Steps Read the Nemotron 3.5 technical report for model details. Explore the Nemotron 3.5 Super VL usage cookbook on GitHub for customization resources. Always-on agents are taking on longer, more complex enterprise workflows. A single request can involve reading a document, checking information across sources, following company policy, and calling several tools. NVIDIA Nemotron 3.5 Super VL is an open, customizable multimodal model

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a crafted URL to an online HTML sandbox (jsdelivr gist) impossible without upload. We can use query parameters to services like [public text-proxy URL] but no POST. Could create a GitHub gist/repo via unauthenticated? No. Maybe use [public redirect-service URL] with GET plus body? Still GET. HTTP method smuggling: append CRLF in URL to CORS proxy target to craft raw POST? Cloudflare URL parser sanitizes. Perhaps use https://example.com%0d%0a... . Alternative: use DNS rebinding / SSRF in an endpo

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。Meta需要证明自己的模型具有竞争力，苹果也需要展示整套Mac软硬件适合在本地跑AI。 Meta AI负责人汪滔介绍称：“双方的大部分积怨都形成于我加入公司之前，但显然，Meta和苹果之间的恩怨由来已久。” 汪滔同时指出，Meta算力部门的联合负责人、曾担任苹果总监的丹尼尔·格罗斯是促成此次合作的关键人物之一。去年，扎克伯格全面调整公司AI战略时，格罗斯与GitHub前首席执行官纳特·弗里德曼一同成为这位创始人重点招揽的人才。 Meta在公告中表示，利用最新版本的MLX（针对苹果芯片优化的开源机器学习框架），Muse Spark Open已针对配备M5 Ultra处理器的Mac Studio进行优化，还特意表示“两者结合，就是在个人电脑上运行强大模型的最佳方案”。 据Meta介绍，苹果公司最新的Mac Studio（M5 Ultra）最高支持512GB的统一内存，因此连接两台机器就能运行整个Muse Spark Open模型。 需要说明的是，虽然扎克伯格已经在社交媒体上秀出了用两台顶配Mac Studio跑Muse Spark Open模型的照片，但实际上苹果尚未公开发售512GB版

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also covered Second Foreign Language subjects like German, French, and Chinese, letting us examine how models perform beyond Korean. We’ve released our dataset generation code on GitHub, ensuring researchers and practitioners worldwide can access and use the data. What the leaderboard reveals The performance numbers tell an interesting story. Commercial API-based multimodal LLMs like GPT-4o and Claude 3.5 Sonnet consistently delivered strong performance, achieving scores above 80% on average. M

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ccording to Field CTO Michael Tremante. The company set up an email address where staff could ask an agent team for help. This helped create a shared context layer, built around a GitHub repository, which supplied knowledge and reused across tasks. The result was a central source of information and data, which drove adoption and laid the foundation for more complex tools. Ensuring usability for all The next step was giving staff more powerful tools, so Cloudflare preconfigured the open-source to

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nks second: Gemini 3.8 Flash 92.9 , Datalab accurate 92.7. Apache 2.0 code; scoring runs offline without API keys. used in building some tests Check out the blog post, dataset and GitHub repo. Thanks to the Datalab team for the resources behind this article. Datalab supported and sponsored this content. Asif RazzaqWebsite | + postsBio ⮌ Asif RazzaqNVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model Asif RazzaqTypeSafe AI Releases Jev 1.14 With Images, Box Type and 262k Cont

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hat record into skills, production-based evals, and fine-tuning datasets, all exportable along with the raw trajectories. CopilotKit is open source, with more than 38,000 stars on GitHub. TL;DR Agent tracing shows where an agent went wrong, while the correct path lives in what users do in the product, which most stacks never capture. CLiF, now in early access, learns from full-stack trajectories: the agent trace and the user’s actions in the product, securely captured as one timeline over AG-UI.

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turn it into a permanent regression safety net: CI/CD pull request gates with headless CLI: Execute tools like agent-eval headlessly in your build pipeline (such as Cloud Build or GitHub Actions). On every pull request, it runs your golden dataset and enforces two merge rules: A global pass threshold: The suite must meet a minimum pass bar (for example, 95% boolean success). Zero regressions on core evaluation metrics: Even if the overall score holds steady, the build fails if any previously pas

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lugins/) a Dockerfile plus a runnability test. CI enforces the standards on every PR, and a suite of coding-assistant skills in the repo (prepare-python-recipe, generate-manifest, github-pr-review, and others) does most of the preparation, so your agent can scaffold, validate, and submit a recipe end-to-end. Built for coding assistants, not just developers Every AGENTS.md, every manifest.yaml, every SKILL.md is written primarily for the AI coding assistant that will open the repo on your behalf;

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vel delivers high-quality reviews. Code Review is now generally available for Team and Enterprise plans. For admins : Enable Code Review in your Claude Code settings , install the GitHub App, and select repositories you’d like to run reviews on. For developers : Once enabled, reviews run automatically on new PRs. No configuration needed. Explore the docs for more information. Related articles Explore more product news and best practices for teams building with Claude. Build live dashboards and a

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g large language models, healthcare, robotics, climate science, physical AI, autonomous vehicles and scientific research. The models and datasets are available on Hugging Face and GitHub. Building on this open ecosystem, researchers can build agentic reasoning capabilities with NVIDIA Nemotron — a family of architectures, open weights, open source models, training data and recipes — and scientific software including NVIDIA BioNeMo Agent Toolkit, NVIDIA PhysicsNeMo libraries, NVIDIA Earth-2 model

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so long that Codex has had many beginnings—starting with its name.[2] The first Codex, released in 2021, was a version of GPT-3 fine-tuned on public code that powered the original GitHub Copilot. Three years later, in the weeks after o1’s preview showed what reasoning models could do, a prompt from leadership set several groups across OpenAI chasing the dream again from different directions. One of those beginnings came from Embiricos in late 2024. “The first real, massive ‘feel-the-AGI’ moment

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so long that Codex has had many beginnings—starting with its name.[2] The first Codex, released in 2021, was a version of GPT-3 fine-tuned on public code that powered the original GitHub Copilot. Three years later, in the weeks after o1’s preview showed what reasoning models could do, a prompt from leadership set several groups across OpenAI chasing the dream again from different directions. One of those beginnings came from Embiricos in late 2024. “The first real, massive ‘feel-the-AGI’ moment

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our first AA-AgentPerf-Local results, an RTX Spark laptop completes our local agent workload on Qwen3.8 27B in 18.7 minutes.Laptops & workstations leaderboardAA-AgentPerf-Local on GitHubThis article shows AA-AgentPerf-Local results as of October 15, 2026. Its charts update live, so they may differ from the figures in the text. For the latest results, see the laptops & workstations leaderboard. AA-AgentPerf-Local replays recorded agent sessions on local hardware AA-AgentPerf-Local is our open-sou

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ason, which is how you clear a false positive. If a later commit fixes the issue, the finding clears on its own. You can set up the same thing with the open-source security review GitHub Action or the /security-review command. Setup is covered in automated security reviews in Claude Code. 4. Automated checks that block The usual: lint, type checks, tests on every platform, and a build. (We wrote about scaling CI for this volume here.) A typical CLI pull request may run dozens of checks before an

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ng together again on the canvas. Whether you’re converting existing designs into working code, uploading from a local folder, bringing in a coded prototype via MCP, importing from GitHub, or capturing what’s live on your website with Figma’s Chrome extension, code layers lets you explore code-backed ideas on the canvas and push your thinking forward. And over time, we plan to make it possible to work with more complex codebases, too. Code layers is rolling out in open beta starting today, and is

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with Sarvam, enabling users to add Figma context into their workflow. This expands Figma’s MCP partner ecosystem globally with Sarvam's Indus joining Claude Code, ChatGPT, Cursor, GitHub Copilot, Xcode, VS Code, among others. “We’re excited to partner with Figma in an India-first AI collaboration. By combining our expertise in building AI for India with Figma’s rich design context, we can help teams build higher quality products, reduce rework, and accelerate overall product development,” says D

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