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r post-training of open-source models. Halo delivers up to 2.8x the throughput of stock TRL with less peak memory, while models stay in their native HuggingFace format. Star us on GitHub: https://t.co/3mAiUljdrN pic.twitter.com/NDoydDUBWa— White Circle (@whitecircle) September 21, 2026 既存モデルを活用し⁠、複数GPUで追加学習 大きなモデルの追加学習では、モデルの重みに加え、学習中の計算結果やパラメーターの更新に使う情報にもメモリが必要になる。1基のGPUに収まらない場合は、複数のGPUに処理を分ける分散学習を使う。ただし、別の学習基盤へ移す際に、モデルの実装や保存形式を合わせる作業が必要になることがある。 Haloは、Hugging Faceの既存モデルに分散学習の機能を加えることで、モデル全体を書き

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atrick Esser, Dominik Lorenz, et al. 2026. Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis. arxiv.org/abs/2603.06507; Chen, Xiao. 2026. Awesome Astra Embodied AI. GitHub repository. github.com/zjwzcx/Awesome-Astra-Embodied-AI; Du, Yilun, Mengjiao Yang, Bo Dai, et al. 2023. Learning Universal Policies via Text-Guided Video Generation. arxiv.org/abs/2302.00111; Ebert, Frederik, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine. 2018. Visual Foresight: Model-Based

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generated Python against unit tests, providing executable verification, but no tool call and no environment to act on. SWE-bench is where the shift becomes clear: resolving a real GitHub issue means navigating a codebase, writing a patch, and passing the suite — file-read, search, and edit calls in sequence. The score measures the outcome, but the trajectory underneath consists entirely of tool calls. In many cases, then, the benchmarks you already run for general capability are already exercisi

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tions apply across languages and toolchains. Upgraded infrastructure and tooling⁠ Some of our earliest gains required almost no optimization of CI itself. Moving our workloads off GitHub Actions to third-party runners with faster CPUs, higher-performance storage, and better cache infrastructure gave us faster machines to run the same pipeline on. In a like-for-like comparison of the two days either side of the switch, jobs ran 34% faster on average, with some workloads like tsc dropping 52%. Sep

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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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み込み機能として実装 Thariq氏は今回の追加投稿で、AGENTS.md対応が「Claude Code mods」に基づく組み込み機能であると説明した。modは、Claude Codeの処理中に発生するイベントへ関数を登録し、動作を変更するプラグイン。AGENTS.mdの読み込みを実装したmodのソースコードも公開されている。 同氏は、今後は利用者もmodsを使い、プロジェクトの指示を扱う仕組みを自作できるようになると述べている。 コラム⁠：Claude Modsではテトリスの作例も AGENTS.md対応に先立つ9月9日、Claude CodeのGitHubリポジトリにあるIssueでは、「⁠Claude Mods」という名称と、環境変数を設定して試用する方法が案内されていた。 Claude Codeの開発に携わるBoris Cherny氏もXへの投稿で、Claude内でテトリスを動かすmodが作られたことを紹介している。同Issueには、画面共有を想定し、Claude Code Desktop上の機密情報を通常は伏せ、マウスポインターを重ねたときだけ表示するデモも掲載されている。

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the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to co

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y the prover, and need to be validated carefully. ↩︎ Most of these findings were due to the fact that almost all procedures in the core library were part of the public API, which meant that third-party developers could call them without proper validation. ↩︎ X LinkedIn GitHub Mastodon Hacker News Related Posts Circomspect has been integrated into the Sindri CLI February 26, 2024 Our tool Circomspect is now integrated into the Sindri command-line interface (CLI)! We designed Circomspect to help …

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tributions from community developers. You can enter the following request directly in your usual office agent: Help me install the core, api, and omni-related plugins from https://github.com/QwenLM/Qwen-MM-Plugins. Alternatively, install from the command line: Run the official guided installer: curl -fsSL https://raw.githubusercontent.com/QwenLM/Qwen-MM-Plugins/main/install.sh | bash In the menu, select: Install The agent harness you use The Omni plugins you need Plugin Capability core Read imag

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nt levels. Comments were broadly positive about open-sourcing the method and suggested maintainers such as Bartowski might adopt it for public quants. One commenter criticized the GitHub README as AI-written/over-marketed and asked for clearer technical wording. A commenter asked how Voodoo Quant can use gradient descent when quantization levels are discrete rather than continuous, specifically questioning the claim that it “runs all the quant levels of a model at the same time, for every tensor

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the generator compiler itself, the AGPL guarantees those improvements remain open to the community. You can explore the new Gemini Interactions API today using our updated SDKs. If you want to generate your own client libraries, CLIs, or MCP servers, you can find the Speakeasy generator on GitHub and run it against your OpenAPI specs. Thanks for reading! Keep on building. AI; Announcements; Learn Optimizing a 200,000-line Python codebase overnight with Gemini 4 Argon Gemini 4 Argon for Robotics

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sics via ovphysx, renders review views with ovrtx, and runs SimReady validation as the acceptance gate before handoff to Isaac Sim or Isaac Lab . Next Steps Explore Omniverse Labs GitHub repo for agent-callable samples across USD, rendering, physics, storage, and validation. Try SimReady Foundation to understand validation profiles and simulation-ready USD requirements. Learn how NemoClaw helps build specialized agents for robotics scene preparation. Powered by NVIDIA Nemotron. AI-generated cont

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e build commands, server configuration, and MLPerf client configuration 1. Clone TensorRT Edge-LLM and initialize its submodules. git clone --branch release/0.9.1-mlpinf \ https://github.com/NVIDIA/TensorRT-Edge-LLM.git cd TensorRT-Edge-LLM git submodule update --init --recursive 2. Download the calibrated NVFP4 checkpoint. huggingface-cli download \ centml/Qwen3.6-27B-NVFP4-W4A4-mlpinf \ --local-dir "$WORK/Qwen3.6-27B-NVFP4-W4A4-mlpinf" 3. Follow mlperf/README.md to build TensorRT Edge-LLM, exp

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easy as it is in Python: change the kernel, run the test, and the new binary is already in place. The tilegym-converting-python-to-rust agent skill , shipped in the NVIDIA/TileGym GitHub repo, is built around one design decision: the agent that loads it does no engineering work at all . Reading SKILL.md turns the top-level agent into a pure orchestrator whose only authority is routing; the work happens in specialized subagents it spawns, each loading only the reference documents its stage needs.

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r. What matters is that communication across the team as a whole runs smoothly. What tools do you use? Slack, Claude Code, Codex, Figma, Confluence, Google Workspace, Jira, Devin, GitHub, Typeless, Adobe Creative Cloud, among others. Given the nature of the Product Team, we actively encourage trying out new products, and it’s easy to bring in the tools you want to use. Autonomy and Roles How much autonomy do I get? This is the question engineers ask most often. The short answer: a lot. That said

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the optimistic counterpart, common in code-generation papers. Consistency gap. Mean@k − Pass^k, in percentage points. Linked artifacts / references ALTK-Evolve open source repo — github.com/AgentToolkit/altk-evolve Technical report — arXiv Consistency guideline demo — 2 min video More from this author IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license 60 September 9, 2026 Real-Time Intelligence with IBM Time Series Models on Confluent 53 September 2, 202

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. OpenAI has moved all its documentation inside of the source code, which makes it easier for agents to understand more of the code. Codex also has access to: Git repositories and GitHub; Slack and Notion; Internal data sources: Databricks, Datadog, internal logs, etc; Internal Codex skills – some of which are maintained by OpenAI’s Codex implementation itself! Codex is so “plugged” into OpenAI that new engineers are directed to ask Codex any questions they have during onboarding because it has

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版本： “使用post-patch-validation，依据 中的漏洞报告，验证HEAD中的补丁相对于 是否正确。” review-walkthrough帮助工程师审查他们负责合并的补丁。它将一个分支的完整差异转化为交互式演练，按合乎逻辑的阅读顺序解释各项更改。审查发现会显示在相关代码旁边，工程师可以在那里检查这些发现并起草自己的评论。该演练还可以准备一份GitHub审查以供提交。按照快速入门为你的分支生成演练。 这些发布加入了我们其他用于改进安全补丁的公开智能体技能： 我们公开这些方法，以便其他团队可以用它们来检查和改进自己的补丁。 一个有用的补丁基准测试应当衡量智能体是否帮助开发者产出正确的修复，以及这些修复需要多少审查。我们在2018年评估模糊测试研究指南中的原则在此适用： 我们对AI对防御者的实用性持乐观态度。通过Patch the Planet，我们投入工程时间，与维护受影响软件的人员一起修复漏洞。我们检查失败，以便改进我们的方法。 我们将继续让智能体处理困难的安全问题，并公开工具和经验教训。我们希望其他团队检验我们的结论，并将这些方法推进一步。我们的目标是让没有专职安全团

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kspace/run-evals.py --test=N” to compare the skill against a no-skill baseline and print a pass-rate summary. Keep “-test=N” to run the added test Pro tip: Give Claude any skill's GitHub repo and ask it to build an evaluation that checks whether the skill changes the result. Use the same pattern for every skill you install. The Rundown: Enterprise AI is only as valuable as the context behind it. Now, you can securely engage with Box content within ChatGPT, bringing enterprise content directly in

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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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