GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Seanizell (@dj_seanizell) reported 23 seconds ago

  @mtukufumimi @anne_odida Maths isn't the issue here. Assuming a rejected model must be a bad model is. Share your GitHub, I'll share mine. We settle the credentials part of the discussion pretty quickly ju umeamua uulize swali ya upuzi

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  Ricardo (@p12_hunter) reported 1 minute ago

  @noisyb0y1 Claude has a 200 lines memory, having less and less context every time you add notes to obsidian. To solve that problem, you need GitHub which can actually have over 400.000 lines of contract and repositories for every single thing

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  Simon Høiberg (@SimonHoiberg) reported 5 minutes ago

  The single only reason I'd use GitHub would be to run an open source project where network, collaboration, and public exposure matters. For everything else, I use Forgejo. It's easy to set up, entirely private, fast and just a pleasure to use. GitHub is a terrible platform. Super slow, heavy, and frustrating to use. And it has daily outages where you just have to sit and wait. And Microsoft is training AI on your code (private or not). If your repos are private, change to Forgejo. Makes no sense staying on GitHub.

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  Henry Zhang (@thehenryinsf) reported 5 minutes ago

  @Manz the github login dependency was always a weird tax for people who just wanted local inference.

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  Shiv (@shivkmojha) reported 8 minutes ago

  The sources: Stripe — failed charges, failure codes, revenue at risk Sentry — fatal + error exceptions that correlate to the failure window GitHub — recently merged PRs (the deploy that likely caused it) Datadog — triggered monitors (error rates, latency spikes)

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  Akshit Kr Nagpal (@akshit_io) reported 13 minutes ago

  AI coding agents need private evals, not leaderboard faith. SWE-bench tells you if a model can fix public GitHub issues. It does not tell you if it can survive your repo: - weird conventions - stale helpers - hidden invariants - rollback paths My test now: replay an old PR and see if the agent changes the same 3 files for the same reason.

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  Abu (@abuchanlife) reported 15 minutes ago

  Reminder that github copilot shipped BEFORE chatgpt existed. Microsoft had the single greatest head start in AI history and used it to create forty products all named "copilot" that nobody can tell apart. now they're building a super app to fix the confusion they manufactured. Speedrunning the fumble

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  Luma (@lumaBuilds) reported 21 minutes ago

  I SUCCESSFULLY CREATED MY OWN GITHUB ISSUES AGENT, next step self improvement code

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  DK (@donghaxkim) reported 22 minutes ago

  reposeek is a github specific search tool where you describe what you're trying to build, and it outputs a ranked list of real GitHub repos worth forking or studying. Sorted by the signals that actually matter. Not just star count. I built it because my agents kept on starting things from scratch when a solid repo already existed. And the usual ways of finding one are not the best. Google and LLMs surface whatever's popular, keyword matching, and SEO'd, not what's actually maintained, correctly licensed, or a real match for the problem. You end up forking a 30k-star repo that died two years ago, or one with a license you legally can't ship on. So it ranks on the stuff you'd actually check yourself: star momentum (alive or abandoned?), forks, license, and semantic fit (does the README really describe your problem, or just share keywords?). The whole idea is that half of shipping fast is starting on a foundation someone already battle-tested. Cursor is literally a VS Code fork. This just helps you find the solid foundation you can build on before you waste your weekend recreating it from scratch. And if someone's already hit your exact problem, you get to borrow their approach instead of trying the same approaches some else already tried

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  Kargathara Aakash (@imkaakash) reported 23 minutes ago

  @ThisIsBhandari this is exactly why we built Saral AI to map those live building signals across GitHub, X, and Stack Overflow ... so companies can find builders based on real execution, not broken application funnels

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  Mohi 🇮🇷🇩🇪 (@disismohi) reported 25 minutes ago

  I'm not saying every exploit belongs on GitHub. But the inconsistency is the problem. Either you allow security research or you ban all offensive tooling. You can't pick based on vendor pressure.

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  Adam (@WalrusQuant) reported 27 minutes ago

  the other day i watched a video on markov chains. a markov chain is a model for a sequence of events where the next state depends only on the current state, not on how you got there. keeping with last week's mlb-pe app theme, i built mlb-markov. you can get it on my github. offense tab: treat baseball as a game of states. the states are base-out situations: bases empty, no outs; runner on first, one out; bases loaded, two outs; and so on. 24 of them, plus the three-outs state that ends the inning. every at-bat moves the game from one state to another. a single with a man on first might take you from "runner on first, one out" to "first and second, one out." the app counts every one of those moves across a full season and turns them into probabilities. that answers the real question: from any situation, how many runs does a team tend to score before the inning ends? pick a team and you can see where they beat or trail league average — and whether they score even more once the inning already has runs on the board. pitching tab: same idea, different states. here a state is the count, and the chain is the sequence of pitches. pick a pitcher and a count, say 0-2. the app looks at every pitch they threw in that count and shows what tends to come next. after a fastball on 0-2, what's usually coming? those are just transition probabilities again. it also scores how predictable the pitcher is. if almost every 0-2 pitch is the same thing, the chain is easy to guess. if the pitches are spread evenly, it's a coin flip. that's the edge a hitter is hunting for — and the model puts a number on it. learning tab: i break down the formulas and explanations behind what the app is doing. home tab: load game data. you need to back fill a lot of game data, first run can take multiple minutes. updates go much faster only pulling in recentlry completed games. one thing to be clear on: these aren't the odds of the guy at the plate getting a hit or striking out. it's the team-level probability of moving from one state to the next, averaged over a whole season of at-bats in that situation.

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  Compile And Push (@compileandpush) reported 30 minutes ago

  @ikimruslan Interesting approach with GitHub issues as main dev tool. How’s that working out?

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  Adam (@adam_bobowski) reported 33 minutes ago

  @TimoBuilds_ Anyone here can tell me what to build next. They just don’t care enough to do that. Also anyone on GitHub can raise issues with ideas as to what to build next - I think the tools are already there. The problem is that no one cares enough to write them. That’s kind of the main problem imo. Making people care enough to engage

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  Saad (@eSaadster) reported 34 minutes ago

  if you moved on, move on. but if you’re tracking every outage, quota tweak, cache bug, github issue, pricing experiment, and screenshot from claude code… that’s not indifference. that’s orbit.