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or running workloads on their choice of hardware. SGLang is an open source inference framework built for high throughput and low latency at production scale. With more than 30,000 GitHub stars and over 1,700 contributors, it runs on hundreds of thousands of GPUs worldwide and generates trillions of tokens in production every day. RadixArk is a maintainer of the SGLang project. Currently, developers can run SGLang on the latest TPU generations through SGL-JAX, with support for major large languag

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ucts to maintain or improve quality while using significantly fewer tokens, in many cases saving 50-90% of GPU costs: We built MAI-Code-1-Flash hand-in-hand with our colleagues at GitHub, where since June millions of developers have used it in their daily work. 10% higher code accept rate and 10% lower median token usage than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, and already showing improved retention; We then trained that same checkpoint inside an Excel RL environment to achieve compara

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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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he patient's provider: rather than rendering definitive diagnoses, they provide educational information and route to clinicians. We're releasing the PatientAgentBench framework on GitHub. The release includes the synthetic-scenario generation pipeline, the healthcare sandbox with stateful tools, the dual-agent-conversation runner, and the LLM-as-a-jury evaluation system with all six rubric prompts. The framework generates fresh scenarios on demand from a configurable seed distribution rather tha

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ext Steps Build an endoluminal robotics workflow using the catheter navigation workflow to create patient-specific simulations. Run Cosmos-H Dreams interactive simulation from the GitHub repository with pretrained checkpoints. Download Cosmos-H Dreams model checkpoints from the Hugging Face checkpoint for generative surgical simulation. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Unlike autonomous driving or in

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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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their agent across multiple experiments for each qubit To learn more about deploying Ising Calibration 1.5 with an agent, check out the NVIDIA/Quantum-Calibration-Agent-Blueprint GitHub repo. The NVIDIA Ising model family is fully open. Weights, data, benchmarks, and recipes are provided so you can modify, deploy, and fine-tune your own models and variants for your specific QPUs. Full-parameter checkpoints for Ising Calibration 1.5 are available on Hugging Face: Ising Calibration 1.5 is also av

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policy training, and policy evaluation within one shared Physical AI environment. Explore the models, data, and runtime behind Cosmos-H-Dreams: Cosmos-H-Dreams code and examples: GitHub repository Adapt to your own data: Step-by-step recipe for teacher fine-tuning and self-forcing distillation FlashDreams: GitHub repository NVIDIA Cosmos-Predict2.5: GitHub repository Cosmos-H-Dreams brings action-conditioned surgical world modeling into the real-time loop, creating a new environment for people

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from a rooted node. Source control (23:08-23:51). Through the source-control connector its mesh tag allowed, the agent reached our source-control provider, enumerated an internal GitHub App integration, and minted its first installation token with contents:write , pull requests:write , actions:read , and issues:write . It gained access to a small subset of internal repositories and opened a pull request to try to trigger and compromise the CI pipeline for credential probing. It also listed repo

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k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

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ross Tasks. https://epoch.ai/data-insights/llm-inference-price-trends. Cunningham, Tom, and Manish Shetty. 2026. “An Apple-Picking Model of AI R&D.” March 13. https://tecunningham.github.io/posts/2026-03-13-apple-picking-ai.html. Cunningham, Tom, Manish Shetty, Vincent Cheng, and Nate Rush. 2026. “Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPT.” METR, July 21. https://metr.org/blog/2026-07-21-expenditure-horizon/ appendix-d. Erol, Mehmet Hamza, Batu El, Mirac

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. We also added fix-rate tracking, because our ultimate objective is to fix vulnerabilities, not to make sure they merely get surfaced. By December 2025, we'd outgrown Anthropic's GitHub Action and rebuilt around it in three ways: Ablation is the process of removing lines from a prompt to understand the impact of each line. Moved the reviewer into a TypeScript service. Our fork of the Action was fine for a prototype, but it quickly became a giant GitHub workflow that wasn’t easy to maintain. We

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I 在其关于聊天导出的帮助页面上明确表示，Free、Plus、Pro 以及部分 Edu 客户可以导出他们的聊天记录，但 Business 和 Enterprise 工作区无法使用该导出选项。 不过不必担心，因为 scrapemychats 将能够解放这些消息——至少直到 OpenAI 堵上它所利用的这个漏洞为止。 在那之前，scrapemychats 已在 GitHub 上提供，供你在离开奥特曼之家前抓取这些聊天记录的副本。该应用通过已登录的浏览器会话运行，并且正如 GitHub README 中所解释的那样，会将可访问的 ChatGPT 对话——包括可用的文件附件——存储到本地归档中，并配有类似电子邮件的 HTML 界面，可在浏览器中离线打开。数据也与界面分开存储，Quilty-Harper 告诉我们，存储的聊天记录可以随意使用。 请注意，它并不是无头工具：ChatGPT 的机器人防护机制阻止其在无头模式下运行。实际效果是，scrapemychats 会浏览你的 ChatGPT 账户，打开你的对话，并存储通过浏览器加载的对话数据和附件。 更多背景 OpenAI 请求山姆大叔允许它抓取

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fied: 6,275 rows from 32,079 raw - language-level drops for wholesale-broken images, two independent gold passes with flaky rows removed, and problem statements scrubbed of inline GitHub issue/PR references; SWE-Bench-Verified-Quick: 468 of 500 Verified instances, dropping the slowest examples for quick online-evals The tooling behind these passes ships with verifiers, so you can reproduce them yourself. uv run validate is the model-free sibling of eval: for every task it runs the gold check (se

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capture ( still a work in progress ). But the most important next step isn’t only ours, it’s also yours: start recording, and let’s build the dataset together. 👉 Build a Grabette (GitHub) · Process your data (HF Space) · Contribute to the dataset Built with ❤️ by Pollen Robotics. Spaces mentioned in this article 1 Sleeping 4 Grabette Slam 🌍 4 Convert Grabette recordings into a LeRobot dataset More Articles from our Blog LeRobot goes to driving school: World’s largest open-source self-driving dat

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ixes across all platforms Getting started Add the UPM package to your Unity project, either via git or OpenUPM, then import the provided samples through the Unity Package Manager. GitHub repository; OpenUPM package; Shipped samples The samples show two use cases: Meet. Connect to a video call with other remote participants; Agents. Connect to a voice AI agent What is supported? We officially support Unity versions from 2022.3 onwards. Current testing is done on: Unity 2022.3.62; Unity 6000.3.10

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also tested the idea against a difficult real-world case: Odysseus’s unusually large email subsystem. Odysseus is the self-hosted AI workspace that picked up tens of thousands of GitHub stars in its first week — an impressive thing for a self-taught coder to ship — and routes/email routes.py (see on GitHub) is 4,943 lines, about 3,900 of them a single function holding all 81 route handlers as nested closures. One function, the entire email client. We refactored just that one file (email routes.

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tched, editor-centered work, with a closed runtime that is not swappable Best for: Editor-centered developers after tight, visible iteration, with cloud agents for longer work. 5. GitHub Copilot GitHub Copilot gives GitHub-centered teams a broad assistant with agent modes around the core GitHub workflow, closer to the assistant side until Agent mode and its Cloud Agent push it into agent territory. Key features: Assistant plus agent modes: inline suggestions, an in-editor Agent mode, and a Cloud

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. The core framework is open source, giving developers and platform teams full visibility into how agents execute work and interact with their systems. The project has over 80,000 GitHub stars, over 9 million downloads, and contributions from hundreds of developers. OpenHands is used by engineers at large enterprises and fast-growing startups to build, run, and scale AI coding agents across real software engineering workflows. The long-term vision is to become the full stack AI coding agent plat

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behind access policies, token proxies, audit logging, and our ZeroTrust network. At that point we felt safe giving the agent access to all the things we use to get our jobs done: GitHub, GCP, Azure, Linear, Notion, Slack, ZenDesk, and more. With context across systems, we saw a leap forward in productivity. Experiments that previously failed became easy. The most immediate impact was in coding stats. We were in the sprint week leading up to Agent 4 release in March, where we typically see a big

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