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afety Training [link] Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, et al., 2025. arXiv; Dolphin-llama3-8B-sleeper-agent-distilled-lora [link] [model] Cadenza Labs, 2024. GitHub; [Replication] Crosscoder-based Stage-Wise Model Diffing [link] [model] Anna Soligo, Thomas Read, Oliver Clive-Griffin, dmanningcoe, Chun Hei Yip, rajashree, Jason Gross, 2025. AI Alignment Forum Citation Aranguri, S. and McGrath, T., "Discovering undesired rare behaviors via model diff amplification", Goodfire

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iently across disciplines. Format: Mercari runs hands-on sessions where designers learn engineering fundamentals, including how to write code or use an AI coding tool and set up a GitHub repository. In turn, developers learn foundational design principles like visual hierarchy. With this training, product builders develop enough fluency to produce a useful starting point outside their own discipline, allowing them to more efficiently collaborate with cross-functional teams. Takeaway: Cross-train

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00 responses from researchers. Promising results come with doubts about how they were released It's too early to fully assess the mathematical substance of what OpenAI uploaded to GitHub . Some papers have already been withdrawn, and others have drawn criticism for being difficult to read. Still, several experts say the results impressed them. Fields Medalist Peter Scholze urged patience. "We should remember that we are all in this together; mathematics is a marathon, not a sprint; and the goal

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导约26%的AI研发工作——今年3月这个数字还不到1%。 RSI（递归式自我改进）大火后，人的纠偏与批准会变成「常设成本」。 今年8月，Anthropic内部审计系统审查了超10亿次AI执行决策，拦截了约0.002%（每4.7万次执行拦截1次），每周还有约50起高优先级案例升级到人工复核。 AI的自我纠错能力仍然存疑：一项覆盖30万条AI提交记录、6299个GitHub仓库的研究发现，在被识别并追踪的代码缺陷中，经AI纠错后，仍有22.7%的问题遗留在最新版本里。 第二，人才梯队与维护责任。 代码越容易生成，越需要有人看懂改动、追查故障，并判断系统能否安全运行。 在Anthropic的内部调查中，一些工程师担心，初级员工少了自己排错的机会，可能失去建立系统判断力的过程；也有人观察到，年轻同事向资深同事请教变少。 第三，信任成本与供应商依赖。 信任虽然是一笔「算不清的账」，但企业必须清醒地认识到：当各行业都搭建在大模型基座上，企业经营风险会与模型厂商的动作深度绑定。 越来越多的代码正进入真实系统，复杂性也沿着两条线扩散： 一条是训练基础模型所依赖的底层系统，由头部科技公司研发和维护； 另

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mes as fast as GPT-6 Sol at a fraction of the cost for Xbox, and 46 times more consistent in Discovery. But the model may have a bigger job in mind. On Wednesday, the company said GitHub Copilot will soon decide when to run a task on-device and when to send it to cloud-scale models , though it hasn’t disclosed what Copilot sends to the cloud. Model routing is among the use cases Microsoft lists for Decision-1, but the company hasn’t said whether the model will make those calls for Copilot. With

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スを参照のこと。 既存のポリシーに指示を追加する extra policyには、承認判断に追加したいルールだけを書く。基本のポリシーはそのまま使われるため、全文をコピーする必要はない。 次の例をconfig.tomlに記述し、山括弧内を実際の指示文に置き換える。 ポリシーを置き換える policyに指定した内容は、基本のポリシーを置き換える。標準のポリシーはGitHubで公開されているが、実際に適用される内容はモデルや組織の設定によって異なる場合がある。 公式ドキュメントは、適用中のポリシー全文を確認し、既存のルールを保って調整するよう案内している。全文を確認できない場合は上書きしない。 コラム⁠：次のメッセージを予測する「Composer predictions」 OpenAIは2026年10月9日、Codexの「Composer predictions」のベータ提供を案内した。現在の会話の内容やユーザーの話しかけ方を基に、次に送るメッセージ全体を予測し、入力欄に予測候補を表示する[7]。ベータ版では、入力途中の文章を逐次補完することはできない。 Now in beta: compo

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rs. The Q8 0 GGUF also matched on an Apple M5 Pro on both Metal and CPU. A Drex agent skill plugs the model into Claude Code, Codex, Cursor, OpenCode, Hermes Agent, Gemini CLI and GitHub Copilot. Nace’s launch post also offers a $25 sign-up bonus for cloud users. How does Drex 1.5 compare with other decision models? Decision Index 0.3.1 rows are from the Drex 1.5 model card, which cites the public leaderboard for Jev and Nimble. Nimble’s 0.2.1 score is from its own model card. What are the limit

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force-killed at shutdown; stdin now closes first and the process tree is killed only if the server is still running after 300 ms; Windows: Fixed claude plugin install failing for GitHub owner/repo plugin sources on machines with no GitHub SSH key; the clone is now retried over HTTPS; Windows: Fixed rm -rf /c/Users/ in Git Bash not asking in bypass permissions mode; Improved fullscreen mode: the link under the mouse pointer is now underlined to show it can be opened; Improved transcript toggle (

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topology, probability and statistical mechanics, and mathematical physics. The collection is so vast that OpenAI felt the need to publish guidance on how to navigate the sprawling GitHub repository. The sheer volume of work makes even a preliminary assessment as to exactly what the company has released difficult. In the hours and days following the drop, most mathematicians The Verge spoke with said they were still struggling to digest everything; several said that simply working through the rou

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erating text), delivering answers in 115ms locally. It’s optimized for agentic AI tasks like tool selection, routing, and guardrails, with all code, data, and weights available on GitHub and Hugging Face. Find the right model for each workload Choose from a broader set of OpenAI models for your everyday work. OpenAI’s Astra, Sol, and Luna models are now generally available on Amazon Bedrock, with options for demanding projects, recurring complex work, and high-volume tasks. GPT-6 Astra is the fl

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u put that resilience to the test. Treat the defaults and controls in this post as a starting point, tune them to what your workload actually needs, and tell us where the defaults should be smarter. For the principles behind these mechanisms, see the 2023 resilience and availability deep dive . To tell us what to build next, see the AWS Containers Roadmap on GitHub . The post Amazon ECS now auto-repairs failing GPUs and instances. Here’s why it matters for SREs. appeared first on The New Stack .

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y of data extraction from images, fostering improved productivity and decision-making in document-focused workflows. “Stay connected and support my work through various platforms: GitHub: For all my open-source projects and Notebooks, you can visit my GitHub profile at https://github.com/andysingal. If you find my content valuable, don’t hesitate to leave a star; Patreon: If you’d like to provide additional support, you can consider becoming a patron on my Patreon page at https://www.patreon.com

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lamaParse and LlamaParse using the LlamaIndex framework, our leading open-source framework in Python and TypeScript. We’ve got more than 3 million monthly downloads, almost 40,000 GitHub stars, and hundreds of thousands of active developers worldwide. Our event-driven Workflows architecture makes it easy to put together scalable, predictable AI agents, and developers love it! Our series A is led by Norwest Venture Partners, with participation from our existing investors Greylock Partners, bringi

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vices SDKs? The pre-existing SDKs will be maintained for approximately the next 3 months in order to allow a smooth transition for users. After this time, the llama-cloud-services GitHub repository will be marked as archived and we encourage users to move to the new llama-cloud-py (https://github.com/run-llama/llama-cloud-py) and llama-cloud-ts (https://github.com/run-llama/llama-cloud-ts) repositories. During this transition period, our documentation will contain references to both the old and

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options); More predictable and consistent import paths; Plus, in beta: MultiModal RAG Modules for handling text and images! Have questions or concerns? You can report an issue on GitHub or ask a question on our Discord! Read on for more details on our new features and changes. Sometimes, all you want is to ingest and embed nodes from data sources, for instance if your application allows users to upload new data. New in LlamaIndex V0.9 is the concept of an IngestionPipepline . An IngestionPipeli

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IDE. The CORS requirement likely comes from Electron. After the initial verification, all requests come from Cursor’s servers. You can find the entire codebase for “CursorZero” on GitHub. It finally worked! Finally: Observability for Cursor We could finally see everything coming in and out of Cursor — including its prompts. For example, here’s what we saw after asking Cursor “how do I get nginx to get the key from an environment variable in my .env?” This example paints a picture of what prompt

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gest banks, managing internal tooling for a massive codebase. Engineers in the organization were required to write detailed changelogs for every GitLab merge request (similar to a GitHub pull request) to improve long-term maintainability. The process was time-consuming and frustrating for the engineering team: writing comprehensive changelogs required significant effort, especially given the complexity of their enterprise codebase. In practice, most changelogs were skipped or incomplete. Mark wo

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halfway through and at the end. (These charts are very similar to the ones you’d find in the open-source TensorZero UI!) You can also find the code to reproduce this experiment on GitHub. Halfway Through the Experiment ( 1,000 samples) At this point, the algorithm is still exploring all four variants, with sampling probabilities concentrated most heavily on the two best-performing variants, claude-sonnet-4-5-detailed-prompt (39.7%) and claude-sonnet-4-5-simple-prompt (38.4%). Note that the varia

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orZero Autopilot with 5 independent seeds and evaluated the optimized variants it generated with 100 fresh rollouts on held-out tasks. You can find the code for this experiment on GitHub. For this experiment, we constrained Autopilot to a fixed set of models of comparable cost and disabled custom model training (e.g. fine-tuning): Here are the results: TensorZero Autopilot dramatically improved performance across every task. What actually happened under the hood? terminal-bench presents diverse

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g its maximum speed to keep the drone clear; Streamlining the optimization made that calculation up to 7.4x faster in the researchers’ tests, and the team has released its code on GitHub; A drone completed 12 flights among moving obstacles without collisions, handling perception, localization, and planning entirely onboard; The proof covers each planning window and depends on assumptions about obstacle speeds, dimensions, and sensing and tracking accuracy Why it matters: A drone that can predict

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