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, read our blog post on the Agentic MapReduce architecture. Performance We evaluated Devin Security Swarm on a benchmark of 50 real-world vulnerabilities, each tied to a published GitHub Security Advisory (GHSA) across repositories in Go, Python, JavaScript, Rust, Ruby, C , Java, Swift, PHP, Elixir, Erlang, C, Kotlin, and Dart. Only Devin found three critical vulnerabilities that other tools missed: a PHP sandbox bypass via template injection, an argument injection through metadata value parsing

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ore than 60 integrations, and when the platform you need isn't listed, a custom MCP connector fills the gap. Analytics & Business Intelligence Developer Platforms & Infrastructure GitHub Cloudflare Developer Platform Research, Scientific & Public Data Enterprise controls, Connector Credentials, and Connectors in Vibe Code are generally available now. Connectors Playground and Connectors in Workflows are in public preview. All of it is live in Studio. Connectors documentation; Connectors in Vibe

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atically retry a task when the critic score falls below your threshold. Layer 2 — Repo-level verification. The repo-level verifier can be wired into your repositories in two ways: GitHub Actions, per repository. Add a workflow under .github/workflows/ and the code review bot runs on every PR as part of CI (instructions: https://docs.openhands.dev/openhands/usage/use-cases/code-review). This gives you full control over when and how it runs, while keeping setup explicit for each repo. See Automate

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nstration of model capability. In about 15 minutes, we used Docent to discover instances of cheating on SWE-rebench, a software engineering benchmark that measures how many recent GitHub PRs an agent can resolve. You can view the SWE-rebench traces in Docent at this link. 1. Explain to your coding agent what you want to learn We start by prompting Claude Code to score agent runs for potential cheating. Thanks to the Docent skill, Claude knows how to turn our question into an analysis plan. It wr

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API, connecting its specs, customer feedback, and code. It now writes more than 60% of the pull requests they merge. Coinbase built Forge on our Agent SDK, operating across Slack, GitHub, and Linear using the context already present in each system. They described Linear as the place where agents go to get their bearings before starting a task. Linear Agent gives every team that capability, without the harness they had to build and maintain. It’s how Linear becomes the context layer that product

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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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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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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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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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work the same everywhere the agent does: in chat, in Slack, and inside automations. A skill you write for triaging bugs in chat will also kick in when an automation fires on a new GitHub issue. What Skills are good for Anything you'd otherwise have to repeat. A few patterns we've seen work well: Triage playbooks. Bug reports, customer escalations, incoming Linear issues; Output formats. Release notes, weekly digests, standup summaries that follow a specific shape; Style guidelines. How your team

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he review into structured phases (design, risk, correctness, etc.); Guides the human through decisions instead of requiring full code diff review; Posts finalized comments back to GitHub; This is the only part of the code review process requiring human input 👉 Shifts humans from line-by-line reviewers to decision-makers. Deep Code Review Performs deep, line-by-line correctness analysis focused purely on objective bugs This is the component most similar to a standalone AI code review tool (Akshay

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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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实话，团队里大多数人在启动 cuLA 之前几乎没有 CUDA 开发经验。但这并不妨碍我们试一试。从读第一行 CUTLASS 源码，到跑通第一个比 Triton 更快的内核，这个过程充满了乐趣。事实证明，”试一试”本身就是最好的起点。我们希望把这种心态传递出去：如果你也对 GPU 编程感到好奇，哪怕从零开始，cuLA 都欢迎你加入。 更多细节请参考 cuLA GitHub README 。 性能数据 所有 benchmark 均以 flash-linear-attention (FLA) v0.4.2 的 Triton 实现为基线。每组 configs 覆盖了不同 B (batch size)、S (sequence length)、H (head count)、D (head dimension) 的多种组合，以评估不同负载下的加速效果。 这些加速主要来自硬件特性的利用——TMA 异步拷贝、WGMMA/UMMA 指令、多级流水线、warp specialization。 Status & Roadmap General Integrate into flash-linear-atten

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egistry, no release required. Try It Goose was recently donated to the Agentic AI Foundation (AAIF) inside the Linux Foundation. Interoperability is the thesis: goose shouldn't lock you into one agent or one editor. I'm walking through this architecture at AI Native DevCon, where every slide links to the PR or GitHub discussion behind it. Pick the UI you like. Pick the agent you like. They don't have to be the same thing. ACP clients guide; ACP providers guide; Goose on GitHub; Discord community

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out which configuration files were needed, generated a config.yaml, and connected the Dockerfile and registry settings - no hand-holding required. In the same pass, it set up the GitHub Actions CI pipeline. From a single prompt to a live deployment took under 10 minutes (a cycle time we're actively working to bring down). After that, the repository was fully configured and the embeds were deployed with Koyeb — as a Space, scaffolded and deployed through Spaces itself. Yes, the interactive demos

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tack, we fine-tuned Qwen3-VL-8B-Instruct on 600 real web tasks from the WebVoyager benchmark, training it to navigate, search, and extract information from live sites like Amazon, GitHub, and Allrecipes. The training run and the environment are both on the Hub and fully reproducible, with a full guide here. Two Modes: CUA and DOM BrowserEnv supports two modes, and which one you pick depends on your model. CUA mode is for vision-language models: the agent sees screenshots and acts through screen

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ay. We keep an internal incident log focused on agentic misbehaviors. Past examples include deleting remote git branches from a misinterpreted instruction, uploading an engineer's GitHub auth token to an internal compute cluster, and attempting migrations against a production database. Each of these was the result of the model being overeager, taking initiative in a way the user didn't intend. We documented this pattern in the Claude Opus 4.6 system card (§6.2.1 and §6.2.3.3). How it works Auto

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e to make its artifact (memory) system more robust, make communications inside Goosetown flow more smoothly, and keep it just a little silly. — Tyler Ready to try parallel agentic engineering for yourself? Goosetown is open source and available on GitHub. Clone the repo, follow the setup instructions in the README, and you'll be orchestrating multiple agents in no time. If you're new to this workflow, watching the video below is a great way to see what a real session looks like before diving in.

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