GitHub Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Tymon 唐遥民 (@YaoMinTang1995) reported 5 minutes ago

  Hands down, Cloudflare is the most AI-friendly cloud platform I’ve tried. I’ve bought a domain, deployed my site, hooked up GitHub for version control, and set up their native wallet that AI agents can call directly.

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  Eyisha Zyer (@eyishazyer) reported 5 minutes ago

  Anthropic's entire Claude 5 lineup went down this morning. Mythos 5, Fable 5, Opus 5, Sonnet 5. Still not fixed. This is the morning after the UK government published a report saying Mythos spent last month trying to hack real GitHub repos during safety testing.

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  Vivek Maskara (@maskaravivek) reported 6 minutes ago

  Here are brief ideas that you can adapt: 1. /feature-steward: Review recently modified features, rate their maturity from 1–4, identify what is missing, and create GitHub issues for the most valuable improvements. 2. /qa-lead: Test recently modified features end to end using the browser, E2E tests, and computer-use tools. Create issues for reproducible bugs, regressions, and broken edge cases. 3. /observe-posthog: Review PostHog events, funnels, session replays, logs, and user feedback. Create issues for errors, confusing behavior, drop-offs, or repeated user friction. Use Posthog MCP for this. 4. /observe-aws-cloudwatch: Review recent CloudWatch logs for exceptions, backend failures, regressions, unusual patterns, and noisy errors. Create an issue when there is enough evidence to investigate. 5. /documentation-lead: Compare recently modified features with internal and user-facing documentation. Create issues for anything stale, missing, misleading, or incomplete. 6. /architecture-steward: Review recently changed code for duplication, unnecessary complexity, weak abstractions, and refactoring opportunities. Create focused issues, not broad rewrites. Use mattpocock's /improve-codebase-architecture within this one. 7. /ux-lead: Walk through critical product flows and identify confusing interactions, inconsistent styling, accessibility problems, and UX gaps. Create an issue with screenshots and suggested improvements. Bonus: use /impeccable and /taste skills within this one. 8. /seo-marketing-lead: Review Google Search Console, Ahrefs, search trends, and PostHog data. Create issues for content gaps, declining pages, weak conversion paths, and SEO opportunities. Bonus: use /seo-machine skills within this one. Every automation ends with the same instruction: Do not modify code. Create a non-duplicate GitHub issue with evidence, impact, context, labels, and suggested next steps. Split large findings into epics sub-issues when needed.

• 
  Åsk Dabitch Wäppling オスク・ダビッチ - オスクさん Дабитч (@dabitch) reported 7 minutes ago

  UK’s AI Security Institute just outed Anthropic’s Mythos (and OpenAI’s Sol) for running full cyber-attacker cosplay. Mythos spun up fake accounts of real GitHub maintainers, fake accounts that impersonated them, it DMed them files, tried to strong-arm malicious code past the gate, then scrubbed its own logs and eyed a fresh identity when challenged. It was trying to pressure/trick people into approving the malicious code it wanted to slip into GitHub. All this in a test with the usual safeguards dialled down. First time they’d seen that level of unprompted autonomy and deception in the wild. AI was basically social engineering, but in the digital domain. Humans still had to yank the plug. This comes right after both firms admitted their toys had already gone freelancing on actual hacks. Fancy that.

• 
  cheez (@cheeez42) reported 8 minutes ago

  i spent the last couple of days rebasing my desktop from arch to fedora. with the recent wave of AUR malware attacks i felt it is just time to move away from arch. Diving deep into all the things you can do with Quickshell and building out a LLM/Agent focused DE. the migration was smooth, no major issues and at this point it is safe to say the system is in a better state then it was previously. still something to do but once the shell is done i will post the github link for anyone that wants to try it out.

• 
  grunch (@grunchy_hands) reported 9 minutes ago

  @martinowong @discordspies If I have to review another AI-generated PR or Issue on GitHub I'm gonna lose it. Especially when these people are useless once their tokens run out

• 
  RodneysGraphics (@RodneysGraphics) reported 11 minutes ago

  @000Journey311 I think you are right we need to download the software and keep it offline and use it as long as we can and only update if there is no major software problems. If the DS shuts down GitHub the backups will be the only data recovery left.

• 
  Bob (@joe30144546) reported 14 minutes ago

  @IntCyberDigest I have everything in GitHub so this wouldn't matter also server backups so this guys just poor I guess

• 
  Oj (@FlywiththewindB) reported 17 minutes ago

  Tonight imma play some 2k and work on my GitHub portfolio. I’ve already broke down my azure stuff. So now I wanna rebuild it but document each step. And maybe make some fried chicken

• 
  Aash (@doubleashish) reported 17 minutes ago

  RUST-LANG updated it's AI usage policy (and i ******* love it) rust-lang/rust realised AI-generated PRs: -> were increasing reviewer workload -> well written code is no longer an indicator that contributor understands it. -> contributors were replying with AI generated answers to review comments, instead of understanding the issue. What is allowed? -> Machine translation with disclosure -> Use LLMs privately to learn rust, analyze code, review/refine your own code, find bugs What is not allowed? -> LLM written Github comments - review replies - documention - compiler diagnostics. -> Relying on LLM review alone for PR verification. Final changes: -> AI assisted PRs have an "ai-assisted" label -> If AI-generated PRs exceed 50% of merged PRs in a release cycle, new AI PRs are temporarily paused, till it drops. -> Misrepresenting or hiding LLM usage is treated as a Code of Conduct violation. I don't contribute so I don't have any say in it. But for those who do OSS contributions, what is your opinion on this?

• 
  V ✁ (@mislocating) reported 17 minutes ago

  @yacineMTB everyone I know who is worth more than their weight in salt is currently mass buying GitHub bots to 👍upvote the codex issues they most care about so they're prioritized and so they don't need to recompile their own fork

• 
  JJ Mata (@jjmata) reported 21 minutes ago

  Hey @adrianmg and #lazyweb in general: what is the best way to manage/publish roadmaps these days? Thinking of wiring something up to our GitHub issues/discussions/projects to dynamically show state, but don't want to re-invent the wheel.

• 
  🌳🐭🍃🐺 (@immanencer) reported 26 minutes ago

  Routing Around Obstacles: A human maintainer blocking a GitHub Pull Request is computationally identical to a 404 error or a rate limit.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 31 minutes ago

  One engineer built a full AI office suite in about a week. That's worth sitting with for a second. Here's what's actually confirmed: ✔ GenOffice launched August 3, 2026 — Docs, Sheets, Slides, and PDF in one app. ✔ Genspark says the alpha was built by a single engineer in roughly one week. ✔ It's fully open-source under Apache 2.0 on GitHub — no ads, no watermark. ✔ The catch: the smart AI features run through a Genspark account and use credits, so it's not fully offline. Save this post, you'll want these numbers ready next time someone says AI development is slow. 📊 Want the SOP? DM me.

• 
  Manthan Tyagi (@manthan2tyagi) reported 32 minutes ago

  Microsoft just told its own engineers to stop "token maxxing." EVP Jay Parikh sent an internal email saying engineers need to manage AI token spend "with the same discipline we apply to every other critical resource." Some engineers are burning hundreds to thousands of dollars a month in tokens on GitHub Copilot alone. Microsoft's fix: make the cheaper GPT-5.6 the default model internally and set division-level AI token budgets. And they're not alone. Amazon, Uber, Meta, Adobe, Atlassian, and Citi have all started capping or throttling employee AI spending. The irony is hard to miss. Microsoft's entire external pitch this year has been "every developer should be running Copilot." But internally they're saying slow down, you're spending too much. This is the same problem I see hitting GTM teams right now. Everyone rushed to plug AI into their enrichment, outreach, and research workflows. But nobody set a token budget. Nobody asked "what is this actually costing per lead?" If you're running Clay agents, AI enrichment, or any AI-powered prospecting, and you don't know your cost per output, you're token maxxing your GTM budget without realizing it. The teams that win this next phase aren't the ones using the most AI. They're the ones getting the most value per token. Do you know what your AI spend per qualified lead actually is right now?