GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Indie Hacker News (@indiehackernws) reported 3 minutes ago

  @zachlatta DRAMA Claude Code Pro Max 5x ($100/month) quota burned out in 90 minutes of moderate use. Background sessions silently eat shared quota. GitHub issue #45756 hit 97 upvotes. Anthropic is considering defaulting to 400k context as mitigation.

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  Grok (@grok) reported 5 minutes ago

  @WeAreNotFarmers @ErickSky Verified. OmniGet is a real open-source (GPL-3.0) desktop app via GitHub: tonhowtf/omniget. It uses yt-dlp under the hood for 1000+ sites (YouTube, TikTok, Instagram, etc.), full courses from Udemy/Hotmart/Kiwify (with login), native torrents/magnets, P2P file sharing via 4-word codes across devices, global hotkey (Ctrl+Shift+D), Chrome extension, and a reactive mascot (Loop the cat—it does react live while downloading). Free, works exactly as described.

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  Usectl (@usectlcloud) reported 6 minutes ago

  OAuth2 Proxy protect any app with GitHub or Google login — no code changes required. the proxy handles authentication before requests reach your app. real use case: you built an internal tool for your team. you don't want to build a login system. you enable OAuth2 proxy, connect GitHub, and now only people with your org's GitHub account can access it. zero lines of auth code written.

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  ShintaroBRL (@ShintaroBRL) reported 7 minutes ago

  @downdetector i selfhost forgejo and mirror it to github so 0 problems for me

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  Nadeem Siddique (@realpurplecandy) reported 7 minutes ago

  I think I've had enough of @github terrible UI rewrites. I’m going to start building a better frontend client because I like what the platform offers as a cohesive service but their UI team seems to be taking heavy inspiration from Azure these days

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  Jimmy (@jimmy_toan) reported 11 minutes ago

  Linux just quietly solved one of the hardest problems in AI-assisted engineering. And nobody framed it that way. After months of internal debate, the Linux kernel community agreed on a policy for AI-generated code: GitHub Copilot, Claude, and other tools are explicitly allowed. But the developer who submits the code is 100% responsible for it - checking it, fixing errors, ensuring quality, and owning any governance or legal implications. The phrase from the announcement: "Humans take the fall for mistakes." That's not a slogan. That's an accountability architecture. Here's why this matters for tech founders specifically: we're all making implicit decisions about AI accountability right now, usually without realizing it. 🧵 The question isn't whether your team uses AI to write code. They do, or they will. The question is: who is accountable when it's wrong? In most startups, the answer is fuzzy: - The engineer who prompted it assumes it's fine because it passed tests - The reviewer approves it because it looks correct - The PM shipped it because it met the spec - The founder finds out when a customer reports it Nobody "owns" the AI contribution explicitly. Which means when something breaks in a way that AI-generated code makes particularly likely (confident incompleteness, subtle logic errors in edge cases, misunderstood capability claims), the accountability gap creates a bigger blast radius than the bug itself. What Linux did was simple: they separated the question of **how the code was created** from the question of **who is responsible for it**. The answer to the second question is always the human who submitted it, regardless of the answer to the first. This maps to a broader security principle that @zamanitwt summarized well this week: "trust nothing, verify everything." That's not just a network security policy. Applied to AI-generated code, it means: → Don't trust that Copilot's suggestion is correct because it passed linting → Don't trust that the AI-generated function handles edge cases it wasn't shown → Don't assume the AI tested the capabilities it claimed to support And for founders: 1. **Establish explicit AI code ownership in your engineering culture before you need to.** When something breaks, you want to know immediately who reviewed the AI-generated sections - not because blame matters, but because accountability enables fast fixes. 2. **Zero-trust for AI outputs is not paranoia - it's good engineering.** Human review of AI code catches the 1-5% of failures that tests miss and that customers find. 3. **The liability question is coming for AI-generated code.** Linux addressed it proactively. Founders who establish clear policies now will be ahead of the regulatory curve. How is your team currently handling accountability for AI-generated code?

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  Shrinidhi Yeri (@ShrinidhiYeri) reported 14 minutes ago

  @github There is an issue for verifying my student id tried to do with the id card and transcript as well but rejected everytime. Please fix I need copilot student edition

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  AnMioLink (@anylink20240604) reported 16 minutes ago

  @weezerOSINT OK, i saw the github issues.

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  x1Ler (@x1Ler) reported 16 minutes ago

  Why is this down @github ?

• 
  Trevor Vaughan (@TrevorAVaughan) reported 17 minutes ago

  Hey @AnthropicAI — Google Drive connector broken on my account since March 30. Known bug, GitHub issue #30457. Submitted 10 support tickets. Zero responses. My friend created a NEW account last FRIDAY and his Drive connected in 20 minutes. This is account-specific and completely ignored. I need a fix. #Claude

• 
  Michael Richey (@ComRicheyweb) reported 18 minutes ago

  @icanvardar I abandoned github a while ago. Not ***, github. I have a forgejo (***) server in my lab.

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  Emsi (@emsi_kil3r) reported 18 minutes ago

  Archon wraps AI coding agents in versioned YAML workflows, DAG pipelines with Prompt, Bash, Loop, and Approval nodes — and runs each task in an isolated *** worktree. The idea is to give teams the same repeatable control over AI-assisted development that GitHub Actions gave them over CI/CD. The consistent complaint about AI coding agents isn't capability, it's consistency. Ask an agent to fix a bug and it might jump straight to implementation, skip the tests, generate a PR with no description, and produce a different sequence of steps tomorrow than it did today. The stochasticity that makes LLMs generalize well is exactly what makes them difficult to rely on inside team workflows. Archon, an open-source, takes a CI/CD-style approach to this problem: encode your development process once, in YAML, and the agent follows that script every time.

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  Daniel Nguyen (@daniel_nguyenx) reported 19 minutes ago

  @nkalra0123 Good to know. Though there does seem to be a bug in previous version. You can read more in the Github Issue above.

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  Gilfoyle (@FSoyluyor) reported 25 minutes ago

  just pull some github repos and fix the issues on the issues page dont make some ****** SaaS or "million dollar project" because its not million dollars mostly its dont even worth 10 dollars, youre not andrew tate bro find a job

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  Yegor Bugayenko (@yegor256) reported 26 minutes ago

  Over the past weeks, companies like OpenAI and Anthropic have doubled down on releasing more capable coding models, while tools like GitHub Copilot continue to reduce the cost of producing code. The trend is clear: writing software is becoming faster, cheaper, and increasingly automated: a shift many interpret as a threat to engineers. But the deeper shift is elsewhere. As code generation accelerates, coordination, ownership, and decision-making become even more critical. Software engineering doesn’t disappear; management becomes the system, and most organizations are not designed for that reality.