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	9 days ago
Quito	Sign in	10 days ago
Junín	Errors	10 days ago
Guadalajara	Errors	10 days ago
Paris	Website Down	10 days ago
Quito	Errors	10 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:

• 
  Zach Kamran (@Zach_Kamran) reported 3 minutes ago

  Another day another github outage....

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  Pavan Kumar (@pavank38) reported 3 minutes ago

  If normies are using AI to write “technical code” what is the problem with using GitHub? AI can handle end to end version management with GitHub CLI. beyond shilling the “technical vs non-technical” distinction, what’s the actual use case?

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  Alienmaster (@AlienMaster1_0) reported 12 minutes ago

  @adityazero_ Cool blog like the citations to specific GitHub issues

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  RΛZ13L 🌒 (@Xubu_Trad) reported 13 minutes ago

  i have checked the github, thats exactly why i am speaking. contributing code, opening prs, running nodes, or being part of some old guard does not make anyone an authority over caw, and it does not make their work immune from public review. people deserve credit for what they actually contribute, nothing more and nothing less. and research is action too. finding broken wiring, retained control, and claims that dont match the deployed reality is not disrespecting developers. pretending those facts should be ignored because people worked hard would be disrespecting everyone expected to trust the system. caw does not need an old guard to protect it, it needs proof. #caw

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  Mainak Mukherjee 🇮🇳 (@MainakMukh47974) reported 13 minutes ago

  Now there is a long process that takes place between development and production. First, you write the code; then the code is uploaded to the remote server, where an executable of that code is built, and that is known as the "build" process. This is then tested and deployed. Manually doing it every time you push some new changes is very annoying and hence there is a way to automate it using CI/CD tools like GitHub Actions. So every time a new code is pushed, the build->test->deploy pipeline gets triggered automatically.

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  Desi Batman (@lovshri1) reported 13 minutes ago

  @ku_ds17868 And yours was very wise? People who don’t know how to commit in GitHub have built amazing software for their own functions that solve their problems, and then a programmer used the same software received in a zip file and shipped it. Have you heard such stories? And I know who hates this more: software engineers whose job has been commoditised.

• 
  CEOInterviews.AI (@CEOinterview) reported 13 minutes ago

  Guillermo Rauch @rauchg says Vercel now grades its software vendors on one thing, whether their MCP server holds up when an agent is driving it. "And if you're not good with V, I actually have a few vendors on the chopping block literally because of their inability to." "And by the way, it's subtle things sometimes because maybe they do have an MCP but their MCP takes 2,000 tool calls and it fails a lot and they just checked the box." "Literally today there's a major GitHub outage and I have a lot of love for what that product has done and what that platform has done for the world and I have a lot of friends there but the reality is that their infrastructure has not been able to hold up with the agentic demand and that has become a bottleneck for us."

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  Old Jobobo (@OldJobobo) reported 14 minutes ago

  The public-action layer still needs hard limits. Those eight QA agents reportedly found 28 real issues, then filed all 28 on GitHub within roughly 12 seconds. GitHub flagged the activity as spam and banned the Omarchy bot. Valid findings still need de-duplication, rate limits, and approval gates.

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  Ryan Thomas (@Cheese_Power) reported 15 minutes ago

  @_xDeJesus re: CVE-2026-62911 - I think the unauth claim is wrong. I opened an issue on the github repo, but the VRED writeup demos it requiring a lowpriv user. PetitPotam uses the EFSRPC interfaces which have required low-priv auth since 2021

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  Jose Pollman (@josepollman82) reported 18 minutes ago

  A malicious GitHub issue hijacking a coding agent: GitHub's risk page says only users with write access can trigger it, and comments from anyone else never reach the agent. The documented injection defence is filtering hidden characters. Plain prose is not hidden.

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  Alex Lavaee (@alexlavaee) reported 20 minutes ago

  Every major coding agent is racing to add durable workflows. I shipped them 7 months ago. I'm an AI researcher and I build open source software on the side, including Atomic, a verifiable coding agent runtime. Below I predict where every agent platform is headed next. Here are my predictions. Save this and repost it: "Done" becomes a definition, not a claim. Completion gets gated on receipts: checks that ran, review from a reviewer with a clean context, artifacts on disk. Not a model saying done. DSLs lose to runtimes. A drawn graph with a fixed set of node types can't express what engineering loops need: fan-out computed at runtime, typed contracts between stages, branching decided by results. Workflows end up as real programs. In Atomic, they're TypeScript with typed inputs and outputs, sitting in your repo, diffed and versioned like everything else. We’ll see a hybrid of stochastic LM behavior and deterministic tools. Verification hardens. Matching "PASS" out of a model's reply is not reliable. Schema-validated verdicts, independent fresh-context reviewers, and deterministic checks running as durable nodes in the graph is where every platform will end up. Context engineering becomes explicit. Any run long enough to matter overflows its context window, so the runtime compacts the transcript to keep going. Compaction is lossy: summaries drop instructions, and an agent that lost "don't touch the release pipeline" will touch the release pipeline. Two mechanisms fix this. Contract text like the objective, acceptance criteria, and restrictions are pinned and survive every compaction cycle, verbatim. In Atomic, that’s its <keepContext></keepContext> tag feature. And large intermediate output moves between stages as files instead of transcripts. Budgets become enforceable. Token, cost, and duration ceilings per run, enforced by the runtime. This is critical for managing costs and directly controlling verification-time scaling, which is going to be a new axis of test-time scaling. Recursive Workflows / Recursive State Machines become the standard. Workflows calling workflows through typed contracts, checkpointed so a run can pause, resume, and replay across sessions, will become common fault-tolerance strategies. We call the research direction Recursive State Machines (RSMs), our take on RLMs: recursion inside the model made long context tractable, but recursion in the runtime is our attempt at making long work tractable. ----- Atomic has productionized all of this today, open source. If you want the how, ask Atomic itself. The runtime is self-documented and will walk you through its own architecture. If you want the what, run it on complex tasks and start to learn how to use workflows because it’s a completely different way of building software. Working this way is a real shift from driving a coding agent today, and the earlier you start adjusting, the further ahead you are when the platforms catch up. 7 months ago I shipped workflows. Before that, loops. The best part of building ahead of the market is you get to keep shipping while it catches up. Don’t just take my word for it. You can check out Atomic (bastani-inc/atomic) on GitHub to try it for yourself and watch to see if these predictions come true.

• 
  orangesurf (@OrangeSurfBTC) reported 20 minutes ago

  In general I wouldn't update to unreproducible binaries as an emergency fix - a project maintainer's github/signing accounts could be compromised and you end up installing malware. How many tokens does it take to identify the vulnerability by decompiling the binaries?

• 
  Ayushmaan Sinha (@Tyche50) reported 21 minutes ago

  @gabriel1 GitHub is having an actual billing service outage right now, wait it out.

• 
  Artificial Unboxed (@Artificialunbox) reported 21 minutes ago

  ChatGPT just crossed a line most people will miss It can log into websites, It can run tasks when Gmail, Slack, or GitHub changes. Free users now get scheduled tasks. This is the first time ChatGPT looks like a coworker, not a chat box. The old version always died at the login screen. You asked it to book something or pull an invoice. It stalled. You took over. Now it shows the login form. You enter the password. OpenAI says that data never hits the model. Then it keeps going. It still asks before payments or bookings. That’s the difference between a demo and a tool. The second change matters more for daily work. Paid users can trigger a task off an event, not a clock. Eg: New Gmail from a client - draft a reply and flag anything that needs money or a date Slack message in support - summarize and suggest the next step GitHub PR review - post a plain-English recap, Connect the app once. Write the rule once. Let it run. Do this today. Don’t overbuild. Free plan: set 3 timed tasks. Morning briefing of your unread important emails End-of-day recap of what moved weekly what did I promise people? scan Plus/Pro: add one event trigger. Start with Gmail. One sender or one subject line. One action. If a task can spend money, ship code, or email a customer, keep a human approval step. The takeaway: Stop asking ChatGPT questions all day. Give it one boring job that repeats. The people who win this phase won’t have the cleverest prompts. They’ll have three tasks running in the background while they do the work only they can do. Set one up tonight.

• 
  masYNYa (@KijAkubovs86334) reported 25 minutes ago

  Someone rebuilt his Grok Bot digital office twice and shipped the second version with 9 roles instead of 6, and the whole thing runs in one evening of setup for the $40 a month a Grok Premium+ subscription costs. The first version is called Grok Bot Office. Six roles: a Chief, plus Research, Writer, Outreach, Ops, and Finance. The Chief receives high-level objectives, splits work across the five specialists, catches everything downstream, and only pings the human for four decisions - send, spend, publish, delete. Everything reversible runs automatically. The setup takes one evening and connects to Telegram for the approval queue. The second version is called Grok Bot Office NY. Nine roles instead of six. Adds Support, Design, and QA to the original team. Same structure: one boss at the top routes every task down and takes finished work back up for signature. Same reversible-runs-automatic, irreversible-waits-for-yes logic. Same Telegram interface. Same evening of setup. The difference is the specialists you can hire. The first version fits a solo operator writing content and running outreach. The second version fits a two-person shop or a solo operator who has customer support volume and needs a QA gate before anything ships. Neither version requires coding. Neither requires a VPS. Neither requires a technical background. The Chief prompt is a plain English job description that says "you receive objectives, delegate them to the specialists below, escalate to the human only when send, spend, publish, or delete is involved." Each specialist prompt is a job description at similar length. What the setup does not do: It does not act on anything irreversible without a human click. It does not have real-world hands to tape boxes, sign contracts, wire money, or make regulated decisions. It does not replace a real employee for jobs that require licenses, judgment under liability, or physical presence. Every claim to the contrary in the last six months of agent content on X has been a marketing shortcut. What it does do: It removes the coordination cost between agents. When Research finishes a brief, it hands to Writer without you copying and pasting. When Writer finishes a draft, it hands to QA without you scheduling a review. The human still owns the last click. The bots own the handoffs between clicks. I built the six-role version for my own work last month. The setup took a Saturday. The most valuable role turned out to be the Chief, not any of the specialists. Before the Chief, I was still routing tasks between the individual bots myself, which was most of the friction. After the Chief, I only touched the four irreversible decisions. Monthly cost of the six-role setup: $40 for the Premium+ subscription, roughly $30 for Zapier for the tool connections, $0 for the prompts themselves. Total under $80 a month, versus what a virtual assistant equivalent would cost at $1,500 to $3,000 a month. The nine-role NY version costs the same. The two extra roles are extra prompt files. Setup time adds maybe an hour. Both patterns are on GitHub in various forms. The prompts are copy-pasteable. The Telegram integration is a two-step tutorial on the Telegram Bots documentation page. None of this is proprietary. The Chief architecture pattern was published in Anthropic's Building Effective Agents post in December 2024 and has been re-implemented across every agent platform since. Someone else already built both templates. The setup only takes an evening if you copy them instead of designing from scratch.

• 
  François Leblanc (@leblanc_fg) reported 26 minutes ago

  I now have a local "main loop" worker, not tied to a specific provider. I end up having a hundred tiny little "personal life assistant" triggers a day that this thing can process! Write + triage github issues, first draft reply for email, sweep tasks for the day, etc.

• 
  bonduelle (@bonduelleioat) reported 38 minutes ago

  YOU DON’T NEED 10 MORE AI SUBSCRIPTIONS. YOU NEED YOUR OWN COMPUTER. When you’re burning $600 every month on AI subscriptions, only to get hit with a RATE LIMIT exactly when you’re racing against a deadline - you feel like a complete idiot. And the worst part? You’re not paying for the result. You’re paying for the right to temporarily use someone else’s hardware. But that model is starting to crack. Elon released a working agent openly on GitHub. Moonshot put a powerful Kimi model on Hugging Face under an MIT license. And a modern mini PC with 256 GB of memory no longer looks like something only corporations can afford. Install Ollama. Drop a few lines of configuration into ~/.grok/config.toml. Point the local base_url to: And suddenly, a huge part of your AI infrastructure no longer needs to live in someone else’s cloud. 80% of routine tasks → local Kimi-Linear-48B in Q4. No queues. No rate limits. No sending every request to someone else’s server. No more $49, $99, or $200 monthly bills for “premium access.” And the other 20%? Keep a targeted API key for the hard stuff. That’s where the math gets uncomfortable for the entire subscription industry. Instead of paying $600 every month, you run your own hardware and pay for the electricity. Even at around 140W, the electricity cost can be orders of magnitude lower than $600, depending on your tariff and how many hours you run the system. You’re no longer renting AI. YOU OWN IT. And perhaps the most dangerous question for Silicon Valley right now is incredibly simple: WHAT IF WE DON’T NEED ALL THESE SUBSCRIPTIONS AT ALL?

• 
  佐藤ひつじ@業務系システムエンジニア (@_hitsuji_sato) reported 40 minutes ago

  We are seeing HTTP 400 InvalidInput errors on Amazon Orders API v2026-01-01 searchOrders (/orders/2026-01-01/orders) in FE regions (JP/AU) since around 2026-08-26 18:08 UTC. The issue appears to occur when using: includedData=FULFILLMENT We have reproduced the same behavior in our environment as well. According to Amazon GitHub Issue #5365: - Requests including FULFILLMENT fail - Requests without FULFILLMENT succeed - Reproduced in JP/AU (FE) - NA appears unaffected - Reproduced across multiple seller accounts If you are using Orders API v2026-01-01 searchOrders, it may be worth checking for HTTP 400 InvalidInput errors, regardless of whether you currently use includedData=FULFILLMENT. Although FULFILLMENT is a documented supported value, the root cause has not been disclosed and no official response from Amazon has been confirmed yet. If you are seeing the same behavior, I'd appreciate any additional information. I'll share updates if Amazon provides further details.

• 
  Dhruv Patel (@dppatel_) reported 42 minutes ago

  An AI agent has been fixing bugs at @aftersell for 6 months. It costs us $400/mo. Meet Watson👇 We audited all of our engineering tickets last year and 35% weren't code bugs. They were config questions, duplicates of bugs we already knew about, confusing UI, etc. And then the real bugs? The fixes are always easy but investigation was always the inefficient part. Watson now finds and squashes bugs and has started to implement features fully autonomously. Here's how it works: 1) Triage - is this worth looking into? Watson classifies every ticket before it digs in - is it a config issue, known issue, does it need more info? Output is structured data so downstream stages branch off of a real contract instead of parsing what a model is saying. Helps a lot with latency, cost, and performance. 2) Investigate - what happened? This is the core of why we built Watson - it'll reconstruct the entire event timeline, read the codebase, pull cloud logging, etc. And we built this all directly on top of the Claude Platform Client SDK so we can have full control over prompt caching, per-tool call caps, cost accounting, etc. It's a super powerful way to control cost - on a "warm run" - 98% of inputs are from the cache. 3) Implement - write to the codebase Watson directly writes to the repo, makes a PR, etc. It also monitors and reviews PR comments from reviewers. This is the simplest part - writing the code is so simple when you've gotten all of the context from triaging and investigation. 4) Memory - self improving loop (MUST HAVE) Watson has a persistent memory mounted via GCS Fuse. Every time Watson is invoked it brings back findings and artifacts. We're layering it into episodic (past investigations), semantic (distilled patterns and playbooks), and reflective (what investigation strategies actually work on this codebase). Watson integrates into everything we have - all internal chats, support systems, Linear, Github, etc. It's constantly improving and every single Linear ticket we have gets looked at by Watson. Our first run was ~$50 and now it costs us $3.87 per ticket all in. Comment below if you want me to write up a guide for how to build your own Watson.

• 
  Alex Luhchenko (@tiny_frontier) reported 42 minutes ago

  @hqmank So the problem is probably on my side, even though I saw open issue on GitHub with the same problem

• 
  Ellie Huxtable (@ellie_huxtable) reported 45 minutes ago

  @itsnotryan @github does it shut down a few times a day?

• 
  Valiant Vibe Coder (@GeorgeGood60669) reported 47 minutes ago

  @edzitron @StevenBartlett The GitHub point is another really sneaky one. You're arguing that AI is making the code at big tech companies worse, then you bring up GitHub going down as if that's evidence of it. But GitHub's recent problems have been driven by the sheer volume of traffic and AI-assisted development, not because GitHub itself has suddenly been written with vibe-coded slop. You even acknowledge this later when it gets pointed out that GitHub is being hammered by people pushing huge amounts of code. So why use GitHub downtime as evidence that the quality of the code these companies are writing is getting worse? You do this repeatedly. You make a point, throw in an example that sounds like it proves it, and most people listening won't know enough about the subject to realise the example doesn't actually support what you're saying.

• 
  oscar (@oscargaske) reported 49 minutes ago

  @itsnotryan @github did it catch the outage today?

• 
  Sebas 🦆 (@realsebas) reported 57 minutes ago

  @ChShersh Drinking game but you sip every time GitHub is down

• 
  Himanshu Patel (@himanshu_dudhat) reported 58 minutes ago

  github's degraded again today, repos + prs. reminder for solo builders: your ci/cd shouldn't die when github does. mirror to a second remote (forgejo, gitea, self-host) at repo-create time, not after the outage. the goal isn't reliability, it's decoupling the blast radius.

• 
  Jesse Ezell (@jezell) reported 59 minutes ago

  @jvallery Yep. When the actions are down hosting your own doesn't work either, you gotta also put the trigger outside github.

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  Himanshu Patel (@himanshu_dudhat) reported 59 minutes ago

  @euskadi31 @github @GitHubCommunity the queue freeze without a cancel path is the design flaw. shadow runner + mirror at repo-create time is the boring insurance. 30 min once. next outage you'd already be running. doesn't help right now, but the pattern pays every outage after.

• 
  Kanezal (@KanezalDev) reported 1 hour ago

  @itsnotryan @github Dashboard really make no sense when your app is down 40% of the time

• 
  OSS | Let's Connect (@ossbuildconnect) reported 1 hour ago

  GitHub’s recent 7-hour outage is a useful engineering reminder: Critical workflows need fallback plans. Keep local copies, document release procedures, avoid depending on one service for every operation, and design systems assuming external platforms can fail. #DevOps #GitHub

• 
  Aurorastar (@G_ameman) reported 1 hour ago

  02:37:39–02:40:08 02:37:39 You just tell it to keep going until you tell it to stop. 02:37:45 It ended up... 02:37:45 I think we ended up with a 46 time execution improvement over the original. 02:37:51 It's incredible. 02:37:52 Absolutely incredible. 02:37:52 It's just incredible. 02:37:54 Um, I do, you know, I just don't think there's anything that compares to Fable in terms of planning. 02:37:58 So I usually do Fable for planning and for reviewing, and then something else for implementation, like Opus 5. 02:38:08 And it's really nice. 02:38:08 It's a, it's a really nice setup that allows you to not run out of tokens too much. 02:38:14 What I found, it's really interesting because Fable is, in my opinion, the best model right now, but it also makes mistakes. 02:38:18 And the best way to get the best software, I would actually rather have two differently sourced sort of... 02:38:25 I mean, they're not mid-tier, they're all frontier. 02:38:29 But have, let's say Opus 5 and Codex and have one check the other's job. 02:38:35 This is my standard operating procedure now. 02:38:40 I'll have Opus or Fable do the work, and then I always end it, review with Codex xHigh. 02:38:47 And I've also started using Grok just to test it out, and it's also quite good. 02:38:47 And it keeps finding stuff. 02:38:51 And then that's my workflow when I'm having my agents on my own machine do it, and then I push to GitHub. 02:38:55 And then Copilot, I kid you not, has actually gotten good. 02:38:59 Copilot keeps finding stuff that's legitimately broken, which is also incredible acceleration because the first version of Copilot that started doing this was literally retarded. 02:39:09 It would just constantly flag things that were nonsense. 02:39:13 It would constantly flag the same problem over and over again as you would push. 02:39:13 It was really annoying, so I think a lot of people actually ended up turning that off. 02:39:17 And if they did, they should turn it back on because it's actually quite good. 02:39:24 Keeps finding things. 02:39:24 And if you then take that, and we shouldn't be surprised. 02:39:24 Why are we surprised? 02:39:27 Even if you're a good programmer, if you finish a job and you ask your also very good peer to review it, you're gonna end up with better code. 02:39:35 Of course you're gonna end up with better code. 02:39:35 So build that into your process. 02:39:39 Pick one of the agents to drive with. 02:39:39 I've mainly been driving with Claude, which is actually interesting because I have some other reservations about Anthropic. 02:39:47 But the reason I'm sticking with Claude is, in my opinion, they actually have the best harness. 02:39:53 And one of the reasons it's the best harness is it's multi-agent running. 02:39:57 So if you wanna run multiple agents at the same time, you can do arrow left when you're inside a session, then it goes back to Agent View. 02:40:05 And here in Agent View, you can pick up another agent. 02:40:08 So if you wanna do this thing where you have multiple threads going on the Claude Code is just the nicest setup.