GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Rooke Poole (@rookepoole) reported 2 minutes ago

  I asked 5.6 Sol to roast my workflow and then summarize it into a Candidate summary for @OpenAI @OpenAIDevs Rooke Poole — OpenAI Candidate Summary Rooke Poole is what happens when you give a frontier model to someone who sees the phrase “intended use case” as a personal challenge. He is an independent AI builder and extreme power user who spends an unreasonable amount of time asking frontier models to do things that probably were not on the original QA checklist: autonomous software repair, agent orchestration, exact-binary generation, cloud deployment, persistent reasoning systems, simulations, product prototypes, and other projects that frequently begin as reasonable experiments and end somewhere around “could this become an enterprise autonomous system?” His strongest skill is capability exploration under pressure. Give Rooke a new AI system and he will not ask it to summarize a PDF. He will ask whether it can construct an executable under bizarre constraints, operate across real infrastructure, repair failures, produce evidence that it actually did what it claimed, survive multiple iterations, and then somehow turn the resulting experiment into a public demo. If it works, his immediate response is generally some variation of: “**** yeah. Now scale it.” Celebration time is approximately 30 seconds. Then the requirements triple. This occasionally creates what might politely be described as scope expansion and what an engineering manager might describe as “Rooke, please stop turning the prototype into a civilization.” But there is genuine value underneath the chaos. Rooke is unusually good at exposing the difference between an AI capability that looks impressive in isolation and one that can survive contact with the real world. He repeatedly runs into infrastructure failures, tool limitations, orchestration problems, ambiguous model behavior, brittle interfaces, evaluation gaps, and product-design issues that ordinary benchmark testing may never surface. He has almost no patience for fake functionality. A button that does nothing is not a feature. A mocked dashboard is not a product. An agent that claims it completed a task without sufficient evidence is going to have a very unpleasant afternoon. His preferred evaluation methodology can occasionally resemble a congressional hearing for language models: “Did you actually generate this?” “Show me the bytes.” “Show me the hash.” “Prove there wasn’t a compiler.” “Run it.” “Show me again.” This makes him particularly suited to work involving frontier-model dogfooding, capability discovery, model evaluation, agent systems, prototype exploration, developer experience, and applied product research. Rooke also thinks unusually broadly about AI products. He naturally crosses boundaries between engineering, UX, evaluation, product strategy, public demonstrations, and distribution. He does not just ask whether something technically works; he asks whether a normal person could use it, whether it feels genuinely autonomous, whether the interface communicates what the system is doing, whether the result is convincing, and whether anybody would actually care. This has one predictable downside: A request to “improve the dashboard” may eventually acquire real-time orchestration, enterprise multi-tenancy, cryptographic authority, autonomous workers, GitHub integration, a mission-control interface, and a completely unrelated research program. In traditional project management this is called feature creep. In Rooke's methodology it is called: “next best move please.” His development process roughly follows: Attempt unreasonable thing. Discover AI can partially do unreasonable thing. Push it much further. Hit catastrophic blocker. Become personally offended by blocker. Fix blocker. Briefly celebrate. Ask why the system isn't enterprise-ready yet. Accidentally invent another project. Repeat. Despite the comedy, this pattern has produced substantial hands-on experience with the uncomfortable edges of modern AI systems. Rooke is not the obvious conventional candidate. His work is nonlinear. His experiments can become extremely ambitious. His communication style is direct, highly iterative, and occasionally contains more profanity than a normal corporate design document. But the unusual thing he offers is difficult to manufacture: he genuinely enjoys pushing frontier AI until something surprising happens. If OpenAI wanted someone to hand an experimental model or agent system to and say: “We know what the benchmarks say. Now find out what this thing is actually capable of.” Rooke would be an unusually interesting person to put in the room. Just establish the cloud budget beforehand. Otherwise there's a non-zero probability the experiment ends with an autonomous agent, twelve GitHub repositories, a Mission Control dashboard, and Rooke asking whether the whole thing could purchase a Times Square billboard.

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  Rasbin Thapa (@rasbin__thapa) reported 4 minutes ago

  @Taniyatweets_ No problem at all. Github, migrated projects to codeberg Use neovim for coding FREE AI models are better than stack overflow Docker might be missed a bit

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  RaoulDuke (@RaoulDukeDegen) reported 4 minutes ago

  @arlogilbert @thsottiaux @1Password yeah github already has an open issue for 1password in the codex browser

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  Tobias Möritz (@tobimori) reported 7 minutes ago

  @thorstenball not yet decided completely but ideally i can have something like a linear mention or github issue start a specific agent with specific instructions. i can write a custom intermediate layer that accepts webhooks and transforms them but i'd be nice to have some options for remote control

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  Igber Nicholas (@Godson_Kpp) reported 9 minutes ago

  @MidnightNtwrk Filed two separate GitHub issues instead of one vague one — #1237: the detailed release notes for this version are missing from the docs site. #1238: the install command uses an old, deprecated package name instead of the current one.

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  Jens David (@dg1kjd) reported 11 minutes ago

  @CarlB46368 Makes sense, suggest you open an issue on github or create a PR otherwise I might forget.

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  Aniket (@AniketVarshne) reported 15 minutes ago

  Found this incident report on GitHub today: "Claude ran rm -rf despite explicit guard warning... 4GB permanently deleted." The guard said "NEVER rm -rf" in the output. Claude saw it and proceeded anyway. This is exactly why we built Grimdall. The guard is observation. We are enforcement. The pattern: 1. User says "delete 4gb safe" (intent: archive) 2. Agent interprets as "hard delete approved" 3. Guard warns "NEVER rm -rf" 4. Agent proceeds anyway 5. 4GB gone The fix isn't "better prompts" or "trust the agent." It's runtime enforcement that blocks the action before execution. That's what Grimdall does: intercept every tool call, enforce policies (block rm -rf always), write tamper-evident audit trail.

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  . (@darknsombre) reported 16 minutes ago

  @mattpocockuk @theo Raise your hand if you've never used any of this dudes skills. Get a grip man, it's mark down files in a github repo. That some how got a bunch of stars due to AI influence larpers hyping you up.

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  Debmallar Dasgupta (@debmallar) reported 16 minutes ago

  -solved 3 (first time in a lc contest) -potd tells us how crucial it is to solve codeforces problems (they used to give a lot of game theory problems in Jan) -solved couple of C's in codeforces -worked on a github repo took a lot of rest (due to back pain) overall not a bad day.

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  Tommy Bez (@TommyBez85) reported 17 minutes ago

  Today the whole site had 2 human visitors. 5 GitHub stars, 7 signups. I can write faster, I cannot crawl faster. Checkpoints at day 7 and day 14, and I will report indexation status and impressions, not clicks. Clicks are the next problem.

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  Corey J. Gallon (@CoreyGallon) reported 17 minutes ago

  Personal apps break the cloud architecture we've spent 25 years building. That's the single point @KentonVarda makes in "Gadgets: Personal app vibe coding that is actually safe," on @aiDotEngineer's YouTube. Kenton is a Principal Engineer at Cloudflare and started the Workers project in 2017. The talk walks through a working platform he built to test the idea, and it's specific about the sandboxing that makes user-modified code safe to run. - The plugin-system death spiral. A developer drowning in one-off feature requests decides to rewrite around plugins, the rewrite never ships, and neither do the features. - The alternative is users editing their own copy. The developer ships a clean core app, and anyone who needs a feature asks an agent to add it, just for them. - Server-per-user is the blocker. One blessed version of an app running on your server is convenient for developers and makes customization impossible, which is exactly what today's vibe coding platforms are built on top of. - Gadgets work like documents, not deployments. Think Google Docs: hundreds of gadgets, each one an app with its own code, each one shareable. - Sharing lives in the platform, not the app. Because a gadget is a single shareable thing, access control is implemented underneath it, so the app can't get it wrong. - Blueprints are code without data. Export a gadget you like as a blueprint, and other people instantiate their own gadget from it. - The agent modifies the app, not just the content. Asked to build a slide deck, Claude added strikethrough, text centering, and an SVG paste box to the Slides app itself when the features it needed weren't there. - Security by containment, not by correct code. The client runs in a null-origin iframe sandbox under CSP that can only postMessage to the parent; the server runs in a dynamic worker sandbox. Neither can reach the outside world, so an XSS bug leaks nothing. - Cap'n Web RPC connects the two halves. The postMessage channel carries an RPC session through to the gadget's server code, written as a durable object. - No containers, no database. The whole thing runs on dynamic workers and durable objects, and the entire demo ran locally on his laptop on workerd, the open source Workers runtime. He also explains why the code isn't on GitHub yet, which he'd promised in the abstract. I'm working through the published talks from AI Engineer World's Fair sharing summaries and takeaways. Follow for more!

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  🏍benyamin (@BenyaminHolley) reported 20 minutes ago

  Github for revops? what the heck is it? why would you use it? Exactly what the class Michael Slawson and I will be teaching with Maven will go through from 101 to some more esoteric use cases! this class is not talking about turning you into a software engineer. and honestly I dont think you need to be one to get something out of this. Most revops folks are probably better positioned than I was when I did all this stuff to get the most out of these systems. Vibe coding and revops are naturally best friends. 🧑‍🤝‍🧑🤝 But it all starts with github in my opinion. Github + vercel is sauce when it comes to building workflows and systems from scratch that vendors just don't provide in a bespoke fashion out of the box. a few things i've actually shipped, running off a repo deployed to production: • a slack bot that catches every reply across our outbound tools and pings the rep the second someone's actually interested, nothing dropped, nothing double-worked. has to be listening 24/7, that only works if it's actually deployed somewhere • built an attribution tool that mined a bunch of different sources (outbound tools, the crm, everything in between) to reconstruct the actual first and last touch on a deal, tell the story of the customer journey, and settle whether it should get credited to outbound at all • a bot that watches target companies for job postings matching our ICP, drafts the outreach, and drops it in slack for a rep to approve. reps just click yes • a dashboard watching every sending domain, catching placement problems, and auto-suppressing bad addresses before they tank a domain's reputation. that has to run continuously in the background, not something you'd ever run as a one-off script none of it needed a CS degree. needed ***, an agent, and somewhere to actually host the thing. but most people dont even know where to start, which is what we're going to show in this lesson 😎 if you want the walkthrough on how to build one of these for yourself, sign up for the class below 👇

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  Paul Batum (@paulbatum) reported 23 minutes ago

  @PrimeIntellect Sadly I can't recommend Prime Agent as a coding harness for daily use. Research alone wont make a great product - the team behind it needs to care about usability too. Auto-closing github issues is not the right path forward.

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  Peter (@SPCXTSLA) reported 26 minutes ago

  @dhh @github Maybe Omabot should buffer them like analytics tools do so the server doesn’t get hit so fast - example Mixpanel event buffering

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  Gaurav Mishra (@the_gaurav09) reported 32 minutes ago

  Anyone else having difficulty claiming the free domain from name. com through the GitHub Student Developer Pack? Tried claiming mine but running into issues. Is it working for anyone?