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covers yes/no questions. Is the cluster setup properly? Is software installed? Is it up to date? Do utilities work as expected? It takes about 15 minutes to run. It’s available on GitHub or via {uv} pip install clustermax. The audit checks the configuration before we put it under load. It covers hardware inventory, software and firmware versions, GPU access, containers, scheduler configuration, networking, storage, health monitoring, and security. We check which components apply to the environme

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classifier, and not bother with training your own model, check out together/Tev1-4B-experimental on Together’s serverless platform. The first thing we need to do is clone the tev1 GitHub repository. Once the repository is cloned, we’ll need to install the necessary dependencies using: uv sync --locked The final setup step is to create an .env file that will hold the necessary environment variables. Copy .env.example : cp .env.example .env Edit the new .env file and add your TOGETHER API KEY . Yo

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ined by the time you're in production. And it's a fair concern. One of the category's first popular open source voice-agent frameworks in the LLM era, Vocode, amassed nearly 4,000 GitHub stars before it stopped shipping updates in late 2024. Both LiveKit Agents and Pipecat pass. Here are some signals worth considering, and where to check them yourself. The figures below are refreshed regularly, and the links let you check them yourself at any time. LiveKit Agents3.6M; Pipecat1M Pipecat16.3k; Liv

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erence implementation: Read Part 1: Build zero-trust AI agents with Google's Agent Development Kit; Clone the repository: Check out the open-source zero-trust-agents-2 codebase on GitHub; Run the CLI demo: Execute ./demo/run part2 demo.sh to walk through the four attacks locally; Explore the dashboard: Run python3 -m http.server 8000 to interact with the browser dashboard; Deploy on Gemini Enterprise Agent Platform: Review the Agent Platform governance documentation to enable Model Armor, Semant

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ions. Next Steps Review the Memory-Driven Chief of Staff recipe to adapt the memory design for your own NemoClaw example. Read the design proposal in the NVIDIA/nemoclaw-community GitHub repo for deeper architectural details. Learn more about NVIDIA NemoClaw and NVIDIA OpenShell to explore the underlying frameworks. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Enterprise work spans messages, decisions, projects,

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Middle: How Language Models Use Long Contexts, TACL 2023 / arXiv:2307.03172; Thariq Shihipar — The New Rules of Context Engineering for Claude 5 Generation Models, Claude, July 24, 2026; Boris Cherny and Diana Hu — Boris Cherny: Building Claude Code, Y Combinator Startup School, July 27, 2026; Xiaoyu Zhang (BBuf) — AI-Infra-Auto-Driven-SKILLS, GitHub; Tianyu Zhang, Yusong Gao, Yun Zhang — Pushing the Limits of Serving DeepSeek-V4-Pro on Compute-Constrained NVIDIA H20, LMSYS Org, August 19, 2026

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ome much more attentive to security fundamentals: least privilege (regarding network access, credentials, available features, etc.), logging, and active monitoring. Further, we can limit the time agents have to operate and ensure a pristine environment for each use. X LinkedIn GitHub Mastodon Hacker News Related Posts 1Password's AI patching benchmark is misleading September 15, 2026 1Password’s benchmark misrepresents AI patching. We share real-world data on human and agent patch quality from …

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Steps Download the Cosmos Edge policy weights and data to begin post-training; Explore Cosmos Edge on Jetson for deployment guidance and examples; Get the Cosmos framework code on GitHub to access post-training and deployment scripts Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for learning physical interactions, but their size can make on-device deployment difficult. This changes with t

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into putting some reasoning where it should not go — but only when native reasoning is disabled. Fun fact: this blog post triggered safey checks Hilariously enough I was unable to use GPT 5.6 terra for spell and grammar checking on this blog post because of safety filters. Had to switch to Kimi. Content licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. Contact me via mail, bluesky, x, or github. You can sponsor me on github. Color scheme: auto, light, dark.

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entic RL、开发者实践和高校教学的案例，帮助开发者从更小、更直观的任务出发，逐步把后训练方法迁移到自己的真实场景中。 07 欢迎下载使用 Tiny，也欢迎参与贡献 AReno Ling-3.0-tiny 已正式开源，提供 BF16、FP8 和 INT4 版本。欢迎根据自己的设备与任务需求下载使用： 🤗 Hugging Face 也欢迎访问 AReno GitHub 仓库（https://github.com/inclusionAI/AReno），复现这次 Spark 后训练实验，提交 Issue、参与讨论，或者贡献 Tiny 个性化训练 Case。 如果你对以下方向感兴趣，欢迎参与 AReno 开源社区： 模型进入本地只是第一步，能否根据业务反馈持续适配，才决定它能否真正进入流程。 Ling-3.0-tiny × ASystem AReno 此次验证的，不是一个已经完成的行业解决方案，而是一条轻量、透明、可复现的任务适配路径：从跑起来，到训起来，再到回到任务中验证是否真的变好。 我们希望开放的不只是模型权重和训练工具，更是一种可被验证、批评、改造和继续创造的方法。 Open in

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PUs without waiting for a download. The data never leaves the LeRobot format, and it never leaves the Hub. Try it using the sample application The full Strands Robots sample is on GitHub at strands-labs/robots in examples/notebooks/05 streaming data loop.ipynb . It walks you through the full loop cell by cell: record, render, sync to a bucket, stream back, train, and load the checkpoint. Every cell runs in simulation on the mock policy, so no GPU, no Docker, and no Hugging Face credentials are n

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parent agent and translates it into Snowflake SQL. It runs against our Panther data warehouse, which ingests audit logs from across the company: AWS CloudTrail, Okta system logs, GitHub audit events, GCP audit logs, osquery endpoint telemetry, Workshop/Santa events, Wiz findings, and about a hundred other tables. The parent agent calls it the way you’d ask a colleague to run a query: “Find the most recent Okta logins for user X in the past 48 hours” or “Check what processes user Y has been runn

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th passes agree. Validated findings pass one last human filter. A duplicate check happens before anything is opened. Only bugs that are confirmed upstream and not already found in GitHub’s issue backlog are drafted for submission. Figure 4: The full workflow Kevin Valerio used to find every bug in Rust Since its release, /goal has been a powerful tool for amplifying the bug-hunting work that we do. Codex can create custom security infrastructure that takes a security researcher weeks to build in

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nce alignment as well as the data tooling we use for these in a future post. Build something with PRX, or find a bug in our pipeline? PRX is Apache 2.0 and the model code lives at github.com/Photoroom/PRX . We integrated PRX into the diffusers library and there is a Hugging Face Space to try out our latest version PRX Pixel . Come talk diffusion models and data with us on Discord . Qwen2.5-VL-7B-Captioner-Relaxed CMMD, "Rethinking FID" (Jayasumana et al., 2024) DINOv2 (Oquab et al., 2023), the f

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ing work being done by Patch the Planet matters. Every new capability that helps us find bugs faster is just as available to an attacker, so the advantage goes to whoever finds the bugs and fixes them first. Patch the Planet If you enjoyed this post, share it: X LinkedIn GitHub Mastodon Hacker News Related Posts How we use /goal to find bugs in Patch the Planet July 28, 2026 Over the first few weeks of Patch the Planet, our engineers found three techniques for using /goal, which hands Codex an …

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ihao Liu, Ligeng Zhu, Zijian Zhang, Zenus Zhang, shinan6, DYZhang, Chao Liu, Zhou Yaoyang, gyy0592, AcrossForest, Emin, Qiming Chu, jiaxiaoyu, tastynoob, and zhenwei. 8. References SGLang GitHub Repository; SGLang .claude/skills; SGLang diffusion .claude/skills; AI-Infra-Auto-Driven-SKILLS; AI-Infra-Auto-Driven-SKILLS prompts; Kernel Design Agents (KDA); KernelWiki skill; ncu-report-skill; KDA-Pilot; SGLang Diffusion Advanced Optimizations, LMSYS Blog; OpenAI Codex Prompting: Goal mode; Humanize

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the increasingly popular Cline, but more on that later. The reason why I am primarily using Qwen-Code when working with Qwen models is that: it is open-source, like Codex (https://github.com/openai/codex) but unlike Claude Code; Qwen models have been specifically optimized for the Qwen-Code harness (more information below); I can run both Codex (with the latest GPT model) and Qwen-Code with a local Qwen model side by side on the same machine without having to switch manually back and forth betwe

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evious results in log files can help the model stay consistent and reflect on previous executions of the workflow. Examples include: standup-post — aggregates your ticket tracker, GitHub activity, and prior Slack → formatted standup, delta-only; create- -ticket — enforces schema (valid enum values, required fields) plus post-creation workflow (ping reviewer, link in Slack); weekly-recap — merged PRs + closed tickets + deploys → formatted recap post 5. Code scaffolding and templates These are ski

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ses. USE CASES To make this approach more concrete, below are some example use cases where I think using HTML files make more sense than Markdown. You can also follow along with a GitHub gallery of these use cases, here. Specs, planning, and exploration HTML is a rich canvas for Claude to dive into a problem. When I start working on a problem instead of a simple Markdown plan I expect to make a web of HTML files. For example, I might start with asking Claude Code to brainstorm and create some ex

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n. The agent produced all the code needed to deploy void-model on Together's Dedicated Container Inference (DCI) infrastructure, essentially on release day. The output lives here: github.com/blainekasten/together-void-model-container ‍ The whole setup took three steps. Step 1: Install the Together dedicated containers skill. npx skills add togethercomputer/skills That pulls in the together-dedicated-containers skill , which gives Goose the specific knowledge it needs to work with Together's infr

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