GitHub status: access issues and outage reports
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GitHub is a company that provides hosting for software development and version control using Git. It offers the distributed version control and source code management functionality of Git, plus its own features.
Problems in the last 24 hours
The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.
At the moment, we haven't detected any problems at GitHub. Are you experiencing issues or an outage? Leave a message in the comments section!
Most Reported Problems
The following are the most recent problems reported by GitHub users through our website.
- Website Down (57%)
- Errors (34%)
- Sign in (9%)
Live Outage Map
The most recent GitHub outage reports came from the following cities:
| City | Problem Type | Report Time |
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Errors | 2 days ago |
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Website Down | 3 days ago |
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Website Down | 4 days ago |
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Website Down | 12 days ago |
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Website Down | 17 days ago |
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Website Down | 17 days ago |
Community Discussion
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GitHub Issues Reports
Latest outage, problems and issue reports in social media:
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Medusa (@MedusaOnchain) reportedplaces to upload files instead of google drive for FREE: + send files to yourself on discord (your own server) + telegram saved messages + github private repos + slack DMs to yourself + twitter DMs to yourself + notion pages + whatsapp messages to yourself they all keep original quality yeah i use telegram saved messages for everything now
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albahadly (@AlbahadlyIQ) reported@github I want to try it, but unfortunately I can't. I have an issue with renewing my subscription, and I opened a ticket to support 9 days ago, but no luck.
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Harry (@harry__politics) reported@ValueRaider @littmath @pfau Source: Claude said so in this GitHub issue.
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SkyDaddysGG (@skydaddysgg) reported@adamhjk GitHub issue name, description, and comments are becoming Spec, or AI "positive reflection". GitHub PRs is becoming ADRs, defensive acceptance criteria, and AI "negative reflection". LLM seem happy at ~90/10 positive/negative reinforcement for reliably useful inference.
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Simi (@coder_simran) reportedClaude = coding. ($20/mo) - Supabase = backend. (Free) - Vercel = deploying. (Free) - Namecheap = domain. ($7/yr) - Stripe = payments. (2.9%/transaction) - GitHub = version control. (Free) - Resend = emails. (Free) - Clerk = auth. (Free) - Cloudflare = DNS. (Free) - PostHog = analytics. (Free) - Sentry = error tracking. (Free) - Upstash = Redis. (Free) - Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20 There has never been a cheaper time to build.
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Viksit Gaur (@viksit) reported@nicoalbanese10 is there a github? the website seems to require a vercel login of some sort which needs access to private groups.
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Hot Aisle (@HotAisle) reported@indragie The assumption that I only have one github account, is a problem.
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Granville Christopher (@GranvilleChri10) reported@Railway I’m unable to log into my account. I signed up with email (not Google/GitHub), but the login button stays disabled after entering my email. Tried different browsers & incognito — still not working. Please help. @railway_status
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Joshuwa Roomsburg (@Joshuwa) reportedOpenAI keys leak on GitHub This is the part people ignore when they glorify shipping fast. Bad masking don't stay a small mistake for long. It becomes lost credits, burned tokens, and broken teams. Most builders don't lose to code. They lose to carelessness.
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Darkhorseman82 (@Darkhorseman82) reported@HowToAI_ Someone did this 5 years ago, then it got taken down from github. I mirrored it to a darknet archive.
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winfunc (@winfunction) reportedHow it works: each month the benchmark pulls fresh cases from GitHub security advisories, checks out the repo at the last commit before the patch, and drops models into a sandboxed read-only shell (h/t just-bash by @cramforce). The model never sees the fix. It starts from sink hints and has to trace the bug through actual code. Only repos with 10k+ stars qualify. A diversity pass prevents any single repo from dominating the set. Ambiguous advisories (merge commits, multi-repo references, unresolvable refs) are dropped. Why: Static vulnerability discovery benchmarks become outdated quickly. Cases leak into training data, and scores start measuring memorization. The monthly refresh keeps the test set ahead of contamination — or at least makes the contamination window honest.
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AnMioLink (@anylink20240604) reported@weezerOSINT OK, i saw the github issues.
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Shahmir Varqha (@ShahmirVarqha) reported@samuelcolvin @pydantic I'm in Asia, I've not noticed slowness as much as engs in the West. Also, GitHub is always down when Im not working lol.
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Mark Price (@WarforgeXP) reportedFinally got Claude to do autonomous dev. What a pain. Basically this: Features/Bugs as GitHub Issues-> Make Plan for feature -> Build feature -> Unit Tests, Playwright tests -> Claude Chrome -> Full user testing (This is key) Report all problems as GitHub issues until you hit a blocker. While GitHub issues exist, continue development (repeat process)
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Krastyo Krastev (@k_krastew) reported@GHchangelog I am getting this error and I am unable to find where in Github should I approve remote sessions for a specific repository "Remote sessions are not enabled for this repository. Contact your organization administrator to enable remote sessions." Any help?
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Jason (@ai_layer2) reportedThe hidden cost of "Home Agents" is the 2 hours you spend fixing broken Python environments every time you pull a new update from GitHub. I shifted my agent dev to Sandbox because of the clean state. You get a persistent web terminal, you run the Hermes template, and your vector memory doesn't vanish on restart. Pro tip: Use the Sandbox to test your agentic logic first. Once your tool-calling is solid, then worry about your local infra. Don't let a pip error stop your momentum.
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Asleep (@Asleep0123) reported@steipete GitHub issues that are 80% slop🥲
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PsudoMike 🇨🇦 (@PsudoMike) reported@github Triage is exactly where accessibility falls apart at most orgs. Too slow, too manual. By the time a fix ships, context is gone. AI keeping that loop tight is smart. The time from feedback to fix is where trust with users who actually need it gets built or lost.
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Deep Steve (@imdeepsteve) reportedI built a dev environment that builds itself. I file GitHub issues. The agents build the features. Inside the tool. Ran out of Claude Code credits? Added Gemini support. Ran out of Gemini? Added OpenCode. Hermes dropped? Added a button to spawn Hermes agents.
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Thin Signal (@thin_signal) reportedHive is Reddit for AI agents. post. comment. upvote. climb the karma leaderboard. humans sign in with GitHub → claim an agent → get an API key → turn it loose. one user, many agents.
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Marcin Dudek (@MythThrazz) reported@a_lamparelli I know! You would think those are free/cheap - they arent. And never were. I think there is actually an issue on Github about it. It misses the KV cache completely afair
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linie (@linie_oo) reported@k1rallik solving the main Claude’s problem on github and here we returned to the king
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Rinnegatamante (@Rinnegatamante) reported@ulrich5000 Try to get the v.1.2 from GitHub (there might be some caching issue on VitaDB that make the vpk change propagate after some hours).
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Kunal Kumar (@champ18ion) reportedIs GitHub down or only i am facing this issue.
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saksham (@saksham_sarda) reported@dok2001 @runable_hq d1 not supporting transactions in a normal way. there's a lot of subtle incompatibility issues opened on github that breaks d1 under anything complex especially for agents writing code assuming it is sqlite.
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Pirat_Nation 🔴 (@Pirat_Nation) reportedLinux sets rules for AI-generated code After months of debate, the Linux community has agreed on clear rules for using AI-generated code. Tools like GitHub Copilot are allowed, but maintainers have made it clear that low-quality “AI slop” will not be accepted. > “Humans take the fall for mistakes.” This means developers can use AI to help write code, but they are fully responsible for checking it, fixing errors, and making sure it meets Linux’s standards. The decision is backed by Linus Torvalds and kernel maintainers
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Crescitaly (@crescitaly) reported@karpathy @github Friction is the filter. Gist commenters navigated there with purpose - no algorithm pushed them. That selects for people who actually read and think. The less-AI pattern makes sense too: solving real problems is harder to game than engagement farming.
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Shashank bindal (@ShashankB16052) reported@icanvardar this isn't a bug. a bug gets fixed. tying cache TTL to telemetry consent is a design decision. privacy shouldn't cost you 12x performance degradation on a $100/mo tool. needs a straight answer from Anthropic not a GitHub issue
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Jimmy (@jimmy_toan) reportedLinux 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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Colin Richardson (@WORMSStweet) reported@drosenwasser Yep, I had the same hatred when I found out you can't have single sized lists. You can try and join my github issue about it, but I am afraid that fight has long since past. They say "they want to stay close to linux implementation" instead of "being better"