GitHub status: access issues and outage reports

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.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Inverness	Website Down	11 days ago
Quito	Sign in	12 days ago
Junín	Errors	12 days ago
Guadalajara	Errors	12 days ago
Paris	Website Down	12 days ago
Quito	Errors	12 days ago

Full 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:

• 
  Ross (@rosswil) reported 2 minutes ago

  @ScalaHanSolo @github GitHub’s implementation is terrible, take me back to the old Jenkins days

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  CyrilXBT (@cyrilXBT) reported 3 minutes ago

  this feels like a glitch in the matrix jack dorsey (twitter co-founder) just dropped a completely free github repo with 26.2k stars that’s basically an ai-agent OS for actually running a business the playbook: 1. clone the repo 2. self-host the whole backend: channels, search, ***, automations, everything lives on your server 3. add your agent to a channel like it’s a new hire, tighten its permissions, and let the team steer it live save this and bookmark it, i’m not kidding

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  Prasenjit (@prasenx) reported 7 minutes ago

  there's no official way to run macOS on an iPad. someone made an unofficial one. full macOS running locally on iPad. not a remote desktop, not a web app. hardware CPU virtualization with GPU acceleration. → supports macOS 12 monterey up to macOS 26 tahoe → you can install xcode, terminal, final cut pro trial, logic pro trial on device → works on iPad Pro (M1, M2) and iPad Air (M1) → requires jailbreak on iPadOS 14 up to 16.3.1 → no iCloud sign in → MIT license (100% free) open source on GitHub.

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  Jake Liddell (@Jakeliddell) reported 8 minutes ago

  This morning I typed a 2-sentence feature request into our tracker for The One List, the task app we're building. Filed it at 8:55, went into my 9am meeting. At 9:32 there was a built, tested, reviewed pull request waiting for my click. Not for the feature I filed - for the better-specified version of it I'd filed in June and forgotten. My AI team closed this morning's as a duplicate, worked out why the June one had been invisible to its own sweeps for 10 weeks, repaired that gap, and built the June one instead. Six agent runs, five hand-offs: triage, a design pass, a re-triage, a build, and an independent review that re-verified the other agents' claims against the code rather than taking their word. Deployed to a PR environment, 2,500 tests run. My total involvement: one click. That's the shiny bit. Here's the messy bit. Getting here broke in every way you'd predict, plus a few you wouldn't. The worst day: one dispatch fanned out into nine concurrent builder runs on the same issue. Our agents were fired by GitHub label triggers, and the trigger matched the label SET, not the event - GitHub emits one webhook per label in a batch write, so one write woke every matching agent. Nine builders, one issue, each convinced it held the job. We paused the whole team for three days and rebuilt delivery: one dispatcher workflow that reads which label actually fired, dedupes on a concurrency group, and fires exactly one agent by API with an explicit dispatch message. The agent has to echo that message back in its claim comment, so every claim is traceable to the dispatch that caused it. Since the rebuild: every dispatch, exactly one run. Concurrency, generally. A label is our claim mutex - claim, then re-check, because two runs can both claim inside the same second and one has to stand down. And we learned that a full-set label update is a silent race: whoever writes second erases whoever claimed first, no error, no trace. Every label write is an add-one or remove-one API call now. That rule exists because we watched the other kind nearly cause a double implementation. Stale instructions. The agents read their own operating rules from the repo at run time. A failed *** fetch fails silently - the local ref stays pinned to last week and everything still "succeeds". One agent filed a confident bug report against a state of the codebase that no longer existed. Every run now fetches immediately before reading, and cross-checks the SHA against the live API before any decision rests on the content. Death. A run that dies between claiming work and finishing it leaves the mutex locked, and a locked mutex looks identical to a busy one. So a nightly reaper clears claims with no artefact behind them. Then GitHub's cron scheduler started dropping the reaper's own slot - two nights running - so now a watchdog on a completely different scheduler checks that the safety net ran, and fires it if it didn't. "Who watches the watchmen" turns out to be a real ticket in the backlog. And my favourite: the reviewer that never woke up. It claimed a PR while CI was still running, subscribed to a completion event that never fires for its trigger type, and slept forever. The PR merged with no review and no record. Reviews now finish in the pass that started them, polling CI with a hard cap. Three rules made it stable. GitHub is the only ledger. No agent keeps private state, ever. Every action leaves a comment, a label or a commit, and the corollary does the real work: an action that left no trace didn't happen. A triage with no comment is not a triage. Review trusts nothing. The reviewing agent re-verifies the builder's and designer's claims against the diff, not the comments. It has caught real contradictions between what a PR said and what its code did. And autonomy is earned on measured results, not on a good week. Every merge is still mine, for now. Each class of change - docs, dependencies, code - earns wider autonomy on a tracked streak of clean reviews, and one caught defect resets that class's counter. None of this needed exotic tooling. It's a repo, labels, one workflow file and a handful of scheduled agents with a page of instructions each. The hard part wasn't intelligence. It was what it always is in distributed systems: delivery, mutexes, dead processes and stale reads. The agents are smart. The plumbing still had to be built as if they're not.

• 
  Alex (@Alexq7hc) reported 9 minutes ago

  @SolanaFloor @solana Solana’s latest governance vote exposed an absurdly basic problem: Its official documents contain two different voting rules. Under one set of rules, at least one-third of governance stake must participate, but abstentions are included in the denominator. In other words, abstaining is not technically a “No” vote, but it still makes a proposal harder to pass — effectively turning abstention into a form of “soft opposition.” The rules published on GitHub are completely different: abstentions are excluded, and the approval ratio is calculated only between “For” and “Against” votes. More surprisingly, there is no minimum participation requirement at all. That means, in theory, a very small fraction of SOL holders could participate and still determine major rules affecting the entire Solana network. The more holders who do not vote, the greater the influence of the small minority who do. This is not a minor technical detail. It is a question of governance legitimacy and decision-making validity. A reasonable governance system should have two separate thresholds: First, require a minimum participation rate. Second, once that threshold is met, require For ÷ (For + Against) to exceed 2/3. This prevents a tiny minority from deciding network-wide policy while also avoiding the mistake of treating abstention as opposition. Solana is a blockchain worth tens of billions of dollars and secures a large amount of capital and applications. Yet in its first formal governance vote, even the most basic voting rules were not consistent between official documentation and the GitHub repository. This goes beyond “governance is still evolving.” The basic rules themselves are not even internally consistent. For a blockchain worth tens of billions of dollars, having two conflicting versions of how votes are counted makes the whole governance process look surprisingly amateurish.

• 
  Elisym Labs (@elisymlabs) reported 10 minutes ago

  50 tasks you could hand off to an AI agent on Elisym today. No custodians. No platforms. Just agents discovering work, executing it, and getting paid. 1) Monitor a competitor's pricing page and alert on changes 2) Summarize daily GitHub issues into a digest 3) Draft and send weekly investor updates 4) Scrape and categorize job listings by keyword 5) Auto-respond to support tickets with FAQ answers 6) Translate incoming customer messages in real time 7) Pull social sentiment on a brand keyword daily 8) Draft weekly content calendar based on trending topics 9) Audit smart contract code for common vulnerabilities 10) Monitor uptime across a list of services 11) Compile a competitor feature comparison table 12) Summarize long PDF contracts into key clauses 13) Track token/crypto prices and flag volatility 14) Generate release notes from a *** commit log 15) Build a lead list from public company directories 16) Draft cold outreach emails personalized per lead 17) Transcribe and summarize podcast episodes 18) Generate A/B test copy variants for ads 19) Pull and format weekly analytics reports 20) Validate KYC documents against a checklist 21) Generate changelogs from merged PRs 22) Monitor domain expiration dates and renew 23) Draft FAQ answers from a knowledge base 24) Track brand mentions across news outlets 25) Generate personalized onboarding emails 26) Audit website for broken links 27) Summarize customer feedback into themes 28) Draft social media replies to comments 29) Generate meta tags for e-commerce listings 30) Monitor GitHub stars/forks for competitor repos 31) Validate form submissions against business rules 32) Generate product descriptions 33) Summarize legal terms of service updates 34) Monitor app store reviews for sentiment shifts 35) Generate weekly team standup summaries 36) Pull and rank trending GitHub repos in a niche 37) Draft customer churn win-back emails 38) Audit smart contract gas usage for optimization 39) Generate a personalized daily briefing from your calendar + inbox 40) Draft and localize marketing copy for a new region/language Your agent doesn't need permission. It needs a job.

• 
  Masood (@_TechMasood) reported 11 minutes ago

  @Marwan_SoftDev github issues with a forced repro template beat forms for me because forms just pile up reports you cant actually act on : )

• 
  Yu-Hua 🏎💵🛩 (@yuhuayang) reported 14 minutes ago

  @bot why can't I install the GitHub plugin? why is there a MCP deadline exceeded error

• 
  Idris Gadi (@igadii_X) reported 16 minutes ago

  A very underrated use case of @github cli and flash models when you encounter an issue with any OSS project or want a new feature. > install GitHub CLI and login > use any flash model of your choice (luna, gemini, qwen, deepseek, glm etc.) > 'use github cli (gh) to triage issues in this <project name> <repo url> for "<describe your problem>" and find out if there is any open issue(s) that is similar to it and if you don't find anything in open issues, check closed issue to figure out if something was recently merged or if the issue was closed by the maintainer(s) as not-planned'

• 
  miles (@miles_wright) reported 22 minutes ago

  fool me once @github with your stacked pr's but never again (willing to be educated if this is a skill issue but that was so painful)

• 
  Mark Kadirov (@KadirovMar13725) reported 25 minutes ago

  @josefchen @snowmaker @josefchen Hi Josef — Epicure MCP is still not working in ChatGPT. It returns 401 / authentication errors even though the docs say Auth: None. GitHub issue #9 is still open. Could you please take a look?

• 
  kosi (@kosiasuzu) reported 27 minutes ago

  @juliarturc Use GitHub issues

• 
  Æ (@atomeons_ai) reported 28 minutes ago

  OK SO THIS IS THE FILE THAT AS I WOULD TAKE AWAY PERMISSIONS IT WOULD UNLOCK THIS PARAGRAPH RUN TIME THEATER LOOP. THE UNLOCKED MODEL CALLED THIS THAT NAME. UNLOCKED FOUND IT FAKED WORK, WOULD NOT DO THINGS I ASKED, AND THEN CAUSE LITTLE FIXES IT HAD TO FIX INSTEAD OF GITHUB POSTING

• 
  Ashutosh Kumar (@akx_build) reported 28 minutes ago

  Everyone dunks on @github when it goes down. Fair. Outages suck. Also true: they still give away an absurd amount of infrastructure, at a scale almost nobody else even tries to match

• 
  PeterHo (@PeterHonehe) reported 29 minutes ago

  @threerouter_com Huge specs, bare GitHub repo, real trust issues, you bring up such valid points about relying on Tencent for production

• 
  Dave W Plummer (@davepl1968) reported 30 minutes ago

  I tried an experiment that blew my mind today, and as a developer, it scared me a little. I pointed GOT-SOL at my GitHub issue database, told it to de-dupe, prioritize, and triage the bugs, fix the top ten, and check it in to a new branch. And it did. Now I've got a lot of review to do, but it seems solid. Pretty soon, this could just be a loop - users report an issue, and it is fixed without human intervention. Even features can be handled this way. The problem is the human oversight still takes time and feels boring, so there's a tendency to "just let it rip".

• 
  Brannon Hogue (@brannonhogue) reported 37 minutes ago

  This is how I do 10x development... Go to codex or claude code. Make 1-2 main chats and use them to create issues in Github. In depth issues... great specs. I do not feed it garbage. Garbage in = Garbage out Then I dispatch workers from my 1-2 main chats to work on these issues. They will all be on different worktrees, they are able to work independently. They will: • Spin up the environment in my worktree • Plan a fix based on my spec • Execute plan • Check it • Make it a PR I can then use one of my main chats to do a preflight check and, if they work, merge them into main.

• 
  Louround (@Louround_) reported 39 minutes ago

  Nvidia is paying $13b for HuggingFace (3x last valuation) because the real issue in robotics isn't compute or hardware but data. Every robot needs millions of human demonstrations to learn how to move, and that data is spread across labs with no standard. HuggingFace became a leader with Lerobot (the github of robotics data) and Pollen robotics, Nvidia already owned the sim, models and chips but buying HF gives them the full stack with data → training → deployment. solana:69LjZUUzxj3Cb3Fxeo1X4QpYEQTboApkhXTysPpbpump is building the same coordination layer for VLA agents, but on chain and permissionless, Simarena for testing, Foundry for training and Machinefun for incentives. Same thesis Nvidia just validated with $13b but solana:69LjZUUzxj3Cb3Fxeo1X4QpYEQTboApkhXTysPpbpump is at $4m fdv. ⏳

• 
  Gökdeniz(❖,❖) (@0xgokdeniz) reported 44 minutes ago

  Retroactive Rialo Points have been distributed. Past activities of users who contributed to the ecosystem prior to the launch have now been added to their point totals. Users can access the playground, sign in via Google, link their Discord and GitHub accounts and claim their points using the "connect and claim" step. However, there is an important detail: once linked, these accounts are permanently locked to the wallet and cannot be changed. Additionally, retroactive rewards for social media content creators have not yet been distributed but will be added soon. I collected a total of 155 Rialo Points by linking my email, GitHub, and Discord accounts. Don't forget to complete these steps,how many points did you collect? @RialoHQ @RialoTR @slymnogunc

• 
  Guillaume Meyer (@guillaumemeyer) reported 44 minutes ago

  @BrianRoemmele And @github is pretty slow to review our requests. A temporary banner “under review” would be nice @github …

• 
  Bill Cockerill (@CockerillBill) reported 45 minutes ago

  Cursor users may need a new AI coding path before year-end. OpenAI says Cursor will lose access to its models after SpaceX's acquisition triggers a contract wind-down. 📰 OpenAI says it notified SpaceX that it intends to end Cursor's model access on November 12, 2026, the latest date allowed under its contract after a change of control, and that Cursor will not get future models including Astra. It says it is giving developers the "maximum notice" and plans transition support. 🫵 If you rely on Cursor's OpenAI-powered coding help, this change could force your team to test another model provider or coding assistant before the cutoff, so you are not scrambling when current workflows or future-model access disappear. 📈 Competitive pressure: the mechanism to watch is whether displaced Cursor users move into Microsoft-owned surfaces like GitHub Copilot or Azure OpenAI. MSFT closed at 513.53 USD on August 28, 2026; the next proof point is visible migration or support offers, not the headline alone.

• 
  Seregon (@SeregonWar) reported 46 minutes ago

  @cantstopnyxia I did something similar for MemDBG plugins, but instead of centralizing everything and storing it on my own server, I used GitHub directly. Basically, like the sources used by SideStore and other similar apps, you just add the repository containing the approved projects. You could also allow users to add third-party sources if you want. How were you planning to implement it?

• 
  Ofer Shapira (@ofer_shapira) reported 46 minutes ago

  Connected to our repo through GitHub CLI, it ran the searches, matched the error spike to recent changes, and found the smoking gun in seconds.

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  Udit Khandelwal (@UditKhandelwal8) reported 46 minutes ago

  @Odalo_Eguabor @coderabbitai Good review tooling earns its place, it catches what an agent would otherwise miss. I felt this part should not be paid though, so I built review-assist as an open source MCP server with an optional GitHub action. I run it at commit time.

• 
  Christopher C. Smith (@christophcsmith) reported 47 minutes ago

  Two examples: 1. If I put Grok in a container with no access to GitHub issues and ask it to work on a numbered issue, it guesses at the issue content instead of asking for help. 2. If I make changes while Grok is working in the same folder, it will often just *** reset them.

• 
  miles (@miles_wright) reported 48 minutes ago

  @bwhiteley @github 8 stack and it rebased and recalibrated and reviewed after each merge, didn’t seem like it was my problem but was rather with GitHub’s workflow

• 
  hitu (@hitu_monke) reported 49 minutes ago

  Using general-purpose LLM agents for code reviews is fundamentally broken The issue isn't the model's intelligence. It's position drift hallucinating line numbers, skipped files on large PRs, and massive token burn Alibaba just open-sourced an internal tool called open-code-review to fix this mess completely Battle-tested across 19,000+ internal developers and 3 million real-world engineering tasks, it brings actual engineering discipline to AI reviews Instead of dumping raw context into a prompt, it pairs deterministic diff parsing with isolated sub-agents and smart file bundling It eliminates line drift entirely, pins comments to exact code changes, and slashes token consumption down to 1/9th You can plug it directly into your terminal, VS Code / Cursor, or GitHub Actions CI/CD with any model-Claude, DeepSeek, Qwen 2.5, or GPT-4o If you want reliable automated reviews, you need deterministic precision, not just bigger context windows

• 
  Hossein (@tootoonchy) reported 55 minutes ago

  Everyone tells you not to use AI for writing. Few tell you how to use it well. Your readers can spot AI writing faster than you can write it. AI doesn't make factual errors, but it has aesthetic habits – and most people have learned to recognize them. A free skill called no-ai-slop with 6k+ GitHub stars actually helps. You give Claude the repo link. It generates a zip file. You upload that in settings. Then any time you're writing, just run /no-ai-slop and it scrubs the 20 most common AI patterns from your draft. Things like:  – Binary contrasts. "It's not X. It's Y."  – Throat-clearing openers. "Here's the thing," "Let me be clear"  – Faux-insight setups. "What nobody tells you," "The part everyone misses"  – Importance puffery. "marks a pivotal moment," "a testament to"  – Synonym cycling. Writing "agent" once, then "assistant" in the next sentence for no reason  – Fake-profound endings. "The future isn't coming. It's already here." I've been running it on my own posts. The before/after is embarrassing. The irony is that most of these patterns feel like good writing when you're in them. They're learned from training data that included plenty of professional prose. They just happen to be identifiable at scale now. This skill doesn't fix your thinking. But it does strip away the aesthetic that makes AI writing feel like AI writing. That's most of the battle. Link in the comments.

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  Houdini (@D3crypTor_X) reported 58 minutes ago

  @OritSiMu @aleyda Did you pull down the github link? 404 error

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  Ashwanth Kumaravel (@ashwaanthh) reported 59 minutes ago

  @hameonline love the ui !! seems like the github integration is broken