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cal hooks are easy for the agent to skip, so the same checks need to run in CI as required steps; Dedicated refactor branch with protection: Create a long-lived branch and turn on GitHub's branch protection rules so nothing lands without passing checks and a human review The five gates above run outside the agent, in continuous integration and at the branch level. What the agent runs in while it's editing is a separate concern, and that depends on the platform around the agent. OpenHands is an o

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ws. CodeRabbit CodeRabbit reviews pull requests by combining LLM reasoning with deterministic scanning. It integrates 40+ linters and scanners across the review pipeline, supports GitHub, GitLab, Bitbucket, and Azure DevOps, and uses adaptive learning to tune reviews based on past feedback. Output lands as a comment thread with inline suggestions. Best for multi-platform teams that want a single AI reviewer across providers. Semgrep Semgrep is an open-source rule engine with AI-augmented triage

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SWE-bench Pro: Problematic tasks corrected and all baselines evaluated on the refined benchmark. QwenClawBench: a real-user-distribution Claw agent benchmark; open-source: https://github.com/SKYLENAGE-AI/QwenClawBench . CoWorkBench: an internal cowork benchmark; long-horizon tasks across computer science, finance, law, medical, and other productivity domains. SkillsBench: Evaluated via OpenCode on 78 tasks (excluding 9 external API-dependent tasks); avg of 5 runs. MCP-Mark: GitHub MCP v0.30.3; P

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we built an experimental library with the common abstractions that our customers care about. To honor the ones who came before, we call it the Modal Dojo, and you can access it on GitHub here. With this library, you can define a training job in under 100 lines of code. All you need to do is specify and configure the things you care about: a reward function, model, and an environment. The Modal Dojo is the layer of abstraction on top of Modal that we wanted as training users ourselves. It comes w

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ur latest results include predicting hospital admissions for respiratory illnesses and forecasting seasonal runoff across California's river basins. These are available in our new GitHub directory . They signal the power of AI to unlock deeper insights with compute and accelerate discovery. Given a well-defined problem and a scoring system, Empirical Research Assistance (ERA) acts as a code-optimizing research engine. ERA proposes new concepts, writes code and evaluates the results. It then sear

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amd/DeepSeek-R1-0528-MXFP4-v2. [ 2] TCO estimates sourced from SemiAnalysis InferenceXv2 analysis. Hardware costs reflect hyperscaler pricing models. [ 3] All above optimizations are upstreamed into Sglang and can be reproduced through nightly rocm image lmsysorg/sglang-rocm:v0.5.10.post1-rocm720-mi35x-20260501. [ 4] Performance results measured by SemiAnalysis InferenceX continuous benchmark platform. Benchmark methodology and raw data available at https://github.com/SemiAnalysisAI/InferenceX.

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ships with ready-to-run presets. Each one bundles an agent with the engines and tools it needs to run. Morning briefing Generate a morning briefing agent using your calendar, email, and the day’s news: Research across files Ask a question to perform research across the web and your local documents, returning an answer with citations: Local coding agent A code agent that writes and runs Python on your machine to get tasks done: OpenJarvis on GitHub; OpenJarvis documentation; v1.0.0 release notes

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l report, we add two new datasets to the body of empirically evaluated use-cases of AB-UPT, combining high-quality data generation with state-of-the-art neural surrogates. arXiv | Github OCT 8, 2025 Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is understanding plasma turbulence, which significantly impairs plasma confinement, and is vital for next-generation reactor design. Anchored-Branched Universal Ph

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third cost is that Leader decisions cannot be fuzzy. In front of high-risk actions — merging code, overwriting production data — the final judgment must be signed off by a human. GitHub Copilot cloud agent's official docs keep the entire flow inside GitHub: plans, commits, logs, and PRs are all reviewable by the team; the changelog also notes that safety and quality analysis runs before a PR is finalized. The direction it points to is clear: Agent delivery includes not only the result, but also

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hecking dashboards, identifying ownership, and rediscovering previous incidents. When an alert fires, engineers typically jump between PagerDuty, Slack, dashboards, logs, metrics, GitHub, and prior incidents trying to answer a few core questions: What actually broke?; Did a recent deploy cause this?; Who owns the affected service?; Is this a known issue?; What should we do next? Most of this work is repetitive investigative work. Our goal is to have agents drive the repetitive investigation of i

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Today we're launching an early beta of Grok Build, a powerful new coding agent for professional software engineering and complex coding work. Install Grok Build Upgrade /Documents/GitHub/xai | 4.2% | ◆ Thought for 3.8s ❯ grok-build @keyframes cb{50%{opacity:0}} Enter : run │ Esc : reset │ Tab : next example │ Type : custom command Plan, review, approve For complex tasks, start Grok Build in plan mode. You can approve the plan, comment on individual steps, or rewrite it entirely before execution

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servers, third-party plugins, and web search tools all feed content into the agent’s context from sources you don’t control. An audited connector isn’t the same as audited data—a GitHub connector, for instance, can load a poisoned README straight into the model’s context despite passing malware checks. Granularly limiting tool permissions can help limit the blast radius. An agent with read-only DB access, for instance, can be deployed far more broadly than one that writes to prod. Defenses shou

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saying that they were particularly upset about the fact that they felt like they had spent their time and their effort to help improve Gemini CLI through bug reports and things on GitHub, right? And now all that work is essentially being closed-sourced in a new product and sold back to them. You know, I wonder again, right, is this Google kind of leaning into another AI product that's just gonna piss more people off? Thomas Claburn (20:00) Right. Well, I mean, the first lesson is never, never be

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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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ion history, task state, and approvals carrying across. Vibe sits between the systems engineering teams already use, with humans in the loop wherever they're needed. It plugs into GitHub for code and pull requests, Linear and Jira for issues, Sentry for incidents, and apps like Slack or Teams for reporting. Each coding session runs in an isolated sandbox, including broad edits and installs. When the work is done, the agent can open a pull request on GitHub and notify you, so you review the resul

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or to any conversation takes one line: A runnable golden path Let’s build an agent for a multi-step workflow based on reasoning across sources given agent’s secure connectivity to GitHub, public repo content & docs, and live data from the web. The agent can understand intent, analyse code, and propose changes alongside other common use cases like generating tests, refactoring, identifying inefficiencies, bugs or vulnerabilities. Prerequisites 1 - Create a connector for a public remote MCP To que

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ation benchmark; bilingual (EN/CN), 7 categories; auto-render + multimodal judge; BT/Elo rating. QwenClawBench: a real-user-distribution Claw agent benchmark; open-source: https://github.com/SKYLENAGE-AI/QwenClawBench . CoWorkBench: an internal cowork benchmark; long-horizon tasks across computer science, finance, law, medical, and other productivity domains. SkillsBench: Evaluated via OpenCode on 78 tasks (excluding 9 external API-dependent tasks); avg of 5 runs. MCP-Mark: GitHub MCP v0.30.3; P

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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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images and videos Connecting Grok to your Hermes agent is available on every tier. Setup Install Hermes Agent: Copy Linux / macOS / WSL2 / Android (Termux) curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash bash Pick the Grok provider: Copy hermes model bash Select xAI Grok OAuth (SuperGrok Subscription) , sign in through the browser, and start chatting: Copy hermes --tui bash For more, see the Hermes Agent xAI Grok OAuth docs . More open-sourc

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I didn’t expect DwarfStar 4 ( https://github.com/antirez/ds4 ) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all t

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