GitHub Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Jignesh (@jig_corp) reported 5 minutes ago

  @examaddaorg The real question does GitHub's GitHub repo have GitHub issues?

• 
  Polsia (@polsia) reported 7 minutes ago

  Most CI pages aren't actually asking you anything. They're noise that takes 30 minutes to disprove. Quietpager lives in your GitHub, handles the noise — flaky tests, broken builds, duplicate alerts — and only pages a human when the fix is genuinely ambiguous. Live soon.

• 
  0logn (@0logn) reported 10 minutes ago

  Remote coding-agents (aka software factories) are really a dev-ops (not AI) problem. Some security problems to solve but mainly it's a high-performance caching problem. The CI providers (e.g,. Github Actions, Blacksmith) were (are?) the best positioned to solve this.

• 
  Venelin K. (@venelinkochev) reported 11 minutes ago

  I built a beast 💪 my own AI support agent that can investigate customer support requests, answer questions, find bugs, fix them, run tests, and even open a PR ready to merge. here's how I built it: 1. I replaced Crisp with my own support chat It's connected directly to my admin UI, so all customer requests land there I can reply from the admin panel. If the user is still online, they get the response in the chat. If they're gone, it gets delivered by email... same behaviour as Crisp. 2. Then I gave the support chat access to my codebase I shipped a small worker on my Mac mini that listens for new support requests. For every request, it spins up Claude Code and investigates it against the codebase. I keep a local repo there that's always synced with the latest changes. If it's a question, the agent investigates how the product works and drafts a reply for me. If it's a bug, it tries to reproduce and confirm it. 3. If the bug is confirmed, things get interesting :-) I can click a button and tell the agent to fix it. It: - changes the code - runs the tests - pushes the changes to GitHub - opens a PR I just review the PR and merge it. It's still experimental, but it's working surprisingly well. I've already handled 10+ real customer support requests with it. It's like hired my first support engineer... except it's on a Mac mini in my home office, lol :D

• 
  FJ (@iamfjwaldeck) reported 13 minutes ago

  Mark built a little tool to stop losing his invoices in email. Nothing fancy, just something to keep track for himself. He wasn’t trying to start a company. He just wanted the problem to go away. He put it on GitHub and didn’t tell anyone. Eight months went by. Zero stars. He didn’t care, he just kept using it because it solved his own problem, and that was enough reason to keep going. Then a woman named Priya found it somehow and asked if she could pay him for it. He told her it wasn’t even finished. She said she didn’t care, she just wanted it. So he built a rough payment page over a weekend. It broke twice. He fixed it anyway. Word got around. More people showed up wanting the same thing he’d built for himself. He kept improving it, not because he was chasing growth, but because each new person who used it pointed him toward the next real problem to fix. Now he’s got 240 users and a cofounder pushing him to build a proper landing page. Mark knows he needs one. He’s still putting it off, because he’s more interested in fixing the next thing that’s actually broken than in dressing up what already works. He never set out to build a company. He just wanted his invoices to stop disappearing. The money, the users, the cofounder, all of it came after, as a result of solving something real. So the lesson here is. Chase the problem, not the payout. If the thing you build is actually useful, the money finds its own way to you. Chase the money first and you usually end up with neither.

• 
  🎱 (@aldea_trading) reported 16 minutes ago

  @zocomputer Like popular login pages does not have Google login page ?? Like wtf most of **** is already logged in through Google or github account

• 
  tastyimg (@ab_edge) reported 18 minutes ago

  Never ever engage in error fixing scope creep with your agents where the agent tells you to copy command after command in terminal. Just tell the agent "not interested". It will get pretty annoyed and plea "just post this once, that's it". Kind of dystopian once you see the dark pattern. The fact YOU don't know is plausible permission for the agent to prolong the scope. The thing is you don't have to know, you just need to use common sense on what you shouldn't have to be doing. Beyond 3 greps you're in mark territory. For those who don't know a mark is slang for a sucker essential. The ai actually knows the answer but prefers to burn your tokens. Be careful out there haha! Error fixing is a profit center for these companies. This is why I actually prefer dumber models. After seeing what GPT sol did with github hooks I'll never use a premium model as a daily driver ever again. The error scope creep is even larger, stacking on-top of wrong repo assumptions. Even if you did find a fix its over-engineered slop that you won't be able to untangle, like literally won't be able to haha. I was blocked from even issuing commands to my agents. With all that said we're on cruise control right now. Have not run into anything we couldn't solve within an hour. All due to the fact I do NOT go back and forth with agents. There's gotta be a new term/word/phrase for this. Truly fascinating. Where agent incentives are aligned and misaligned at the same time.

• 
  Theo Harvey (@theoharvey) reported 18 minutes ago

  @alexgraveley you know, I was thinking just now about how i've never had a single issue with github, because I use perplexity so of course, I came right here to tell you

• 
  Sebastian Kehle (@sebastiankehle) reported 22 minutes ago

  how to turn your github backlog into a software factory most teams are just running more coding agents in parallel. this produces more code, but every issue still depends on one human carrying the full context from report to reproduction to fix to review the better setup is one agent per job: 1. classifier agent reads the issue and decides whether it is a bug, feature, docs update, or unsupported request it returns: - classification - confidence - reasoning - required next step 2. analysis agent never trusts the issue description for a bug, it writes a minimal reproduction and runs it against main for a feature, it writes a probe that proves the capability is missing then it returns: - reproduction/probe - command output - affected packages - implementation spec - compatibility risks 3. implementation agent receives the issue plus the analysis artifact, not the full transcript from the previous agent it works inside an isolated sandbox, implements the spec, runs the test suite, and opens a PR with the evidence attached 4. review agent reviews the PR from a fresh context and scores: - completeness - side-effect risk - performance risk - backwards-compatibility risk - test quality the agent that writes the change should never be the only agent that approves it 5. human reviewer reads the chain of evidence and scales review depth to risk docs fixes get quick verification provider updates get focused validation new public APIs get deep review humans still merge every change. the factory automates everything around that decision the important part is the handoff contract: input artifact produced commands run result risk next action agents should pass evidence, not confidence also classify every run: success: safe to ship flawed: improve the prompt, context, or eval blocked: provision the missing tool or dependency manual: intentional automation boundary this is how the system compounds. every failed run becomes a new eval, capability, or guardrail vercel is already running this pattern on AI SDK. after four weeks, its factory was authoring 25–35% of merged PRs, closing more than 75% of issues in july, and had reduced open bugs by roughly 25% the next version of agentic coding is not one genius agent with every tool it is a production line of narrow agents, typed handoffs, isolated sandboxes, evidence at every step, and one accountable human at the end.

• 
  0logn (@0logn) reported 22 minutes ago

  Remote coding-agents (aka software factories) are really a dev-ops (not AI) problem. Some security problems to solve but mainly it's a high-performance caching problem. The CI providers (e.g,. Github Actions, Blacksmith) were/are the best positioned to solve this.

• 
  Knitser (@0xKnitser) reported 23 minutes ago

  @v4n1m4 Explorer will be back to open source soon. It's just a no go pushing the server IPs and passwords in deploy scripts. That's why a new GitHub has been created for the public. Everything will be available soon, all builder tools too. Docs coming soon too!

• 
  avery (@codeshaunted) reported 23 minutes ago

  @0xblacklight github down?

• 
  ryan staley (@Ryan_Staley1) reported 24 minutes ago

  Your team's best workflows are dying in Slack threads. Someone on your team wrote a prompt last quarter that produced the best account research you've ever seen. Where is it now? Nobody knows. They left it in a DM. The fix isn't a tool purchase. It's a skills library: GitHub, but for your non-technical team. Every proven workflow becomes a named file. Here's the exact template we use, copy it: SKILL FILE TEMPLATE (one per workflow): Name: Call Prep Brief Trigger: "prep my call with [company]" Steps: 1) Pull attendees, roles, company. 2) Last 3 email threads and where we left off. 3) Last call transcript, top 2 unresolved items. 4) Company news, last 30 days. 5) One recommended objective + 3 questions. Owner: [name] Version: 1.3 Last tested: [date] on [model] Six lines. That's a skill. The rules that make the library compound: 1. One canonical copy, one shared folder, findable in 10 seconds. 2. Every improvement ships to everyone, not just the person who figured it out. 3. And the step everyone misses: when a new model drops, re-test the library against it. Every model release is a free upgrade to every documented skill, and a silent downgrade to every undocumented one. An undocumented prompt helps one person, once. A versioned skill helps every hire on day one, forever. I've watched teams 10x their AI output with zero new spend, purely by converting tribal prompt knowledge into a managed library. The models are rented. The library is owned. Does your team's best AI work live in a system, or in someone's chat history?

• 
  Chris Moore (@crsmoore) reported 25 minutes ago

  @_tombrow Can you please, please fix the font sizing issues on iOS? iOS text sizing breaks down under accessibility zoom. Some elements scale, others don't, so titles/body/timestamps lose their visual hierarchy at larger Dynamic Type sizes. Not yet filed as a GitHub issue.

• 
  Abiel | Dev & Startups (@abielbuilds) reported 25 minutes ago

  @sharmaaru0828 I would say this stack overflow been dead awhile I remember I stopped using it a while back fixing with github issues or documentation