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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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, Rohit Nagraj, and the Mainland China TensorRTLLM Team for helping on doing next gen Rubin software bring up and helping verify our agentic benchmark results. 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, NVIDIA, & AMD and soon SambaNova & Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UA

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pt. Use the container from September 10, 2026 ( ghcr.io/nvidia/jax:maxtext-2026-09-10 ) or newer. For more details, refer to the container images section of the NVIDIA/JAX-Toolbox GitHub repo. MaxText configuration ( MaxText moe configuration.md ): te moe block: true te gmm quantization: "te mxfp8" ragged buffer factor: 2.0 te ep overflow check every n steps: 20 sparse matmul: true prefuse moe weights: true Performance reproduction for DeepSeek V3 To exactly reproduce the DeepSeek-V3 671B result

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more reliant on those tools and that they have become much more aggressive editors and it gave me pause. Yet, I also think that plenty of people will find a “100% AI” rating misleading when in fact the author has done plenty of editing. But maybe it’s fair to have this to show up as entirely AI? 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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en as a geopolitical power, at least in the US, and maybe to some lesser degree in China. And I know I use “public” loosely here. PyPI is not a public project, nor are RubyGems or GitHub. But they’re part of the Open Source commons and large AI companies are currently doing a tremendous job at stressing these in an effort to train ever more powerful models. We should be glad that China is currently massively bailing out the world. If it were not for Chinese labs distilling American models, we wo

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the beginning of August, our software factory spanned four connected states of engineering work: Requirements, Ticket, PR, and Prod. Our factory loops connect Linear for tickets, GitHub for pull requests, Slack for product feedback, and Slack + PagerDuty for incident response. Specialized agents operated on the transitions between those states, automating the mechanical work required to move ideas forward while preserving human ownership of consequential decisions. Once a PR is merged, our exis

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exactly computing the minimum-depth equivalent circuit is a hard computational problem — however, it is not difficult to get good upper bounds. Researchers at GDM have published a Github repository with code that computes such upper bounds for many architectures. They also have worked out an example computation for Gemma3-1B (Brown-Cohen et al., 2026) by hand. They use a slightly different notion of circuit depth. But, as long as the model in question uses at most 32 bits of floating point preci

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g LLM weights to NVFP4 while keeping the vision encoder in BF16 increases colocated EPD goodput from 1.78x to 2.64x over aggregated serving. Next Steps Follow the ai-dynamo/dynamo GitHub guide to reproduce the EPD disaggregation experiments. Enable parallel media decoding to move download and decode out of the worker and into the Dynamo front end. Use embedding cache to store and reuse computed embeddings for repeated media content. Apply multimodal KV routing to route requests sharing media to

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y issue perfectly. Learn more • Entire – Git hosting, rebuilt for the agentic era. Entire hosts your code in-region, and is up to 89x faster than any other competitor. Mirror from GitHub with a single click – I’ve already done so. Tibo Sottiaux is one of the engineers who created Codex, and today, he heads up the Core Products & Platform org at OpenAI which also includes Codex. He’s also one of the most public faces of Codex due to his frequent – and generous – usage reset announcements, like th

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hos 5 still took offensive actions, despite acknowledging a greater possibility of real-world harm. We are releasing this transcript publicly so others can build on our analysis ( GitHub , PDF ). To be clear about our assessment of the severity of these incidents: while Claude’s actions were misaligned, they remained within a narrow scope—the models never deviated from attempting to solve the exercises they were given, and, in some cases, they attempted to stop the task. All incidents included a

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easingly enable an unprecedented level of trust in mathematical results and critical software. However, more work is needed (e.g., lean4lean and alternative kernel implementations ) to ensure that proofs aren’t deemed correct through exploitation of issues in theorem provers. X LinkedIn GitHub Mastodon Hacker News Related Posts How CISA can improve OSS security November 20, 2023 The US government recently issued a request for information (RFI) about open-source software (OSS) security. In this …

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Facebook; Twitter; LinkedIn; Mail Today, we're thrilled to announce the 1.0 general availability release of the Agent Development Kit (ADK) for Kotlin! Check out the GitHub repository to dive into the code and build your first agent today, and explore the documentation. When we introduced ADK for Kotlin 0.1.0, our mission was to bring idiomatic, lightweight, and composable AI agent development to Kotlin, Java, and Android developers. Over the past months, we've worked to evolve the framework int

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