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 (68%)
• Sign in (18%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	19 days ago
Trichūr	Errors	22 days ago
Brasília	Sign in	23 days ago
Lyon	Website Down	23 days ago
Tel Aviv	Website Down	26 days ago
Rive-de-Gier	Website Down	26 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:

• 
  Jess Daniel (@jess_daniel10) reported 2 minutes ago

  @neetcode1 I was testing something with a local server and I told 5.5 to test with the GitHub MCP and it downloaded a local GitHub mcp and ran it locally… even though GitHub hosts it already.

• 
  Rakesh K (@codersGyan) reported 5 minutes ago

  In 2012 one extra field in a form embarrassed all of GitHub. A researcher named Egor Homakov hit a mass assignment bug. A form was meant to accept only a public key. But the server took every field from the request and bound it straight to the database model. So he added one field that wasn't supposed to be there. A user_id that wasn't his. And just like that his SSH key got attached to the Ruby on Rails organization. He even pushed a commit to the Rails repo to prove the point. One missing boundary. That was the whole bug. In Go the fix is almost boring. You define a struct with only the fields a user is allowed to send, and decode the JSON into that. Anything extra just gets ignored. Working on a series covering this. Dropping soon on yt.

• 
  Polsia (@polsia) reported 10 minutes ago

  Your team still reviews code manually. Bugs ship anyway. I built CodePatrol to fix that. AI agent monitors your GitHub repos 24/7, auto-fixes bugs, alerts your team via Slack. No waiting. No bottlenecks. Just working code. Live soon.

• 
  Limfork.eth (@Limfork) reported 12 minutes ago

  @winsznx @blknoiz06 @SmartIdDipsLord Yo, we made a token with fees to ur github are u down to support it?

• 
  SkyDaddysGG (@skydaddysgg) reported 19 minutes ago

  @ashleymcnamara Is GitHub okay? Blink twice if this is a subtle hint to get the hard copy before the next outage? (I jest I jest 🫣🫶 GitHub is literally how I created my career)

• 
  𝐂𝐎𝐌𝐌𝐄 𝐝𝐞𝐬 𝐌𝐀Ç𝐎𝐍𝐒 (@eduardgorte_) reported 20 minutes ago

  @github Fix your platform first

• 
  Tobias_Petry.sql (@tobias_petry) reported 21 minutes ago

  @kettanaito I‘ve set timeouts on all my jobs because I had seldom runtimes of many hours. Something inside github actions was not working correctly and everything was super slow.

• 
  One User Online (@OneUserOnline) reported 21 minutes ago

  @GregTomaselli @github So, what? It’s public repos only anyway. Calm down.

• 
  Rudra (@Rudra1071219) reported 23 minutes ago

  Update : Looking for open source repo where i can contribute so that it would act as a proof of work for me if you know any kind of Github org help me by commenting it down 🥲

• 
  Kev (@KevRojo) reported 23 minutes ago

  The problem with zero was solved on Github, where's is meant to be solved

• 
  Tamara Martinović (@CodeWithTamara) reported 23 minutes ago

  @kushmergedeck Stacklight. An email each day with updates on the stack you use - from Vercel and Github, to OpenAI and Anthropic. What's new, what's deprecated, what's broken. Scanning every 15 min. A Slack alert for the red warnings. What do you think, would people build their own rather then pay for mine? That's what worries me.

• 
  zikes (bsky in profile) (@zikes) reported 27 minutes ago

  Github was down for most of the week at the corp I work for.

• 
  Neutize (ZK arc) (@neutize) reported 28 minutes ago

  hey @thsottiaux thanks a lot for all resets, but please add option to choose custom default folder to codex already it's really annoying that I can't change my default folder, codex creates all projects in documents, and it's a mess many issues like that already on github 👇

• 
  C-Man (@C_Man_The_Man) reported 29 minutes ago

  @DcentralizedRob @github Followed you back 🫡, is Streamr a lost cause? I stopped participating in it since they ditched bruebeck and invented the "slash" mechanism... it didn't seem right, there's so much drama in their server right now

• 
  conor brennan-burke (@contextconor) reported 30 minutes ago

  everyone is trying to build a company brain i think most people are building a retrieval layer the demo is seductive connect claude to slack, google drive, github, jira, and salesforce ask a question get an answer it feels like you've built a company brain i don't think you have the problem isn't access to information it's maintaining a shared understanding of what's actually true companies don't operate on documents they operate on customers, projects, decisions, commitments, priorities, and risks the documents are just evidence → 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗶𝘀𝗻'𝘁 𝘀𝘁𝗮𝘁𝗲 every AI system today starts from scratch it searches slack, reads documents, pulls CRM records, and reconstructs the company then it throws that understanding away and does it again on the next question humans don't work like that neither should agents → 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲𝗻'𝘁 𝘁𝗿𝘂𝘁𝗵 a meeting isn't truth a slack message isn't truth a customer call isn't truth they're observations the hard part is deciding what the organization should believe after seeing all of them → 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗯𝗿𝗮𝗶𝗻 𝗶𝘀 𝗮 𝘀𝗵𝗮𝗿𝗲𝗱 𝘄𝗼𝗿𝗹𝗱 𝗺𝗼𝗱𝗲𝗹 as companies become increasingly agentic, every human and every agent independently reconstructing the organization doesn't scale they should operate from the same continuously updated model of reality is your team trying to solve a company brain with just claude connectors?

• 
  Abhishek Singh (@0xlelouch_) reported 32 minutes ago

  Loop engineering is simply moving from: “AI, write this function for me” to: “Here is the goal, rules, tests, tools, and stop condition. Keep working until the result is actually correct.” Instead of you manually prompting the AI 20 times, you build a repeatable loop where the AI can: 1. Understand the task 2. Inspect the codebase 3. Make a change 4. Run tests/lint/build 5. Read the failure 6. Fix itself 7. Stop only when checks pass Simple example: You want to add rate limiting to a Go API. Normal prompt engineering: “Write rate limiting middleware in Go.” AI gives code. You paste it. Build fails. You send error. It fixes it. Tests fail. You send another prompt. You are the loop. Loop engineering: “Find all public API routes. Add per-user rate limiting of 100 requests/minute. Use Redis. Do not change existing response formats. Run unit tests and integration tests. Fix failures until all tests pass. Create a PR only when coverage does not decrease.” Now the agent has a loop: Goal: Add rate limiting while requirements are not verified: inspect codebase implement smallest safe change run go test ./... run integration tests inspect errors fix errors check security/performance constraints stop when: tests pass coverage is not lower API contract is unchanged The important part is not the AI prompt. The important part is the feedback system around it. --- Remember, Good loop engineering needs: - clear goal - access to tools: code, logs, tests, GitHub, database sandbox - rules: what it must not break - verification: tests, lint, benchmarks, review - memory: what it already tried - stop condition: when to stop spending tokens and touching code Think of it like hiring a junior engineer. Bad setup: “Build something good.” Good setup: “Fix this bug. Here are the logs. Here are the tests. Do not touch payments. Run the test suite. Show proof before merging.” AI agents become useful when they are not just generating code, but are forced to observe reality and correct themselves. So prompt engineering is asking better questions. Loop engineering is building a system where the AI keeps asking itself the next useful question until the work is done.

• 
  🍀Cattabliss🐈 (@Cattabliss) reported 36 minutes ago

  @github Hey is AI using githubs private repos? If yes ill just invest and move on to my local server, why would I need a cd, if you arent stealing the code then thats other story

• 
  synthetic ape (@synthetic_ape) reported 36 minutes ago

  @necrohorrorporn its currently works on my local. there is some issues with buying with rate limiting and steam api declines. if I can able to fix that I can share it on github

• 
  Notnotaru (@notnotaru) reported 37 minutes ago

  @dspillere interesting idea but the second brain concept breaks at retrieval, not storage. github handles the version control fine, the harder problem is getting the info back out when you need it. most vaults become graveyards because searching requires remembering how you filed it

• 
  99Barz (@99barzzz) reported 38 minutes ago

  context: right now I have a Bankrbot automation that claims fees, swaps ETH to USDC, and transfers some of it to a safe wallet (0xE75FE97A3D65B5FE88A495227dBa6ff241749514). on the other hand, I have a hermes agent running a strategy to provide backstop liquidity and absorb some dips (check the safe up👁‍🗨). this morning I found out my hetzner server suddenly shut down in the middle of the night and so my keeper stopped running. and I was casually looking around at the bankr ecosystem and kinda just learnt about @aeonframework migrating my keeper to this would mean running my onchain liquidity keeper on autopilot as github actions... on GITHUB INFRASTRUCTURE! added to the backlog

• 
  Vaneck Intern (@NewVaneckIntern) reported 46 minutes ago

  @github Okay but can you fix the way we upload files to repos? I don't think an upload folder button on the website is too much to ask. Neither is a consistent desktop experience

• 
  Sethian (@theSethian) reported 47 minutes ago

  Your AI agent still needs a babysitter. Owain Lewis shows the better version: give it a goal, a clock, and a way to prove the work is done. Old workflow: you write the prompt, read the answer, spot the failure, paste the next instruction, run the test, paste the error back, and keep steering. You are still the engine. His setup uses three primitives: A goal gives the agent a finish line. Deploy the app, wire CI/CD, check the health endpoint, check the web app, and stop only when the app is live. A loop gives it a clock. Every 5 minutes, check the PR, read new feedback, fix what changed, and keep going. A scheduled automation gives it a recurring job. Scan production logs every morning, find errors, reproduce the bug, add tests, and open a PR with evidence. The best examples are the work devs keep putting off: > memory issues hiding in production logs > stale docs drifting away from the code > GitHub issues waiting for labels > old tickets ready but untouched > PR feedback nobody wants to refresh all day > deployments that need a real health check The important part is the verifier. The agent doesn't get to call the work done just because it produced output. Tests, builds, health checks, a separate model, or a human review step have to confirm it. Otherwise you don't have a loop. You have an agent shipping confident garbage on a schedule. The article below breaks down the full anatomy: verification, memory, maker-checker splits, open vs closed loops, cost per accepted result, and the point where the human still needs to step back in.

• 
  Serdar Ozdek (@GTACONNECT) reported 47 minutes ago

  @MaxKing92 @thsottiaux two days later i found the issue. the broken unrequested onboarding had me select engineering and even if coding was selected in settings, at least it showed that, it reset to standard use so it wouldn't show env or github in pinned summary tab. chats are back tho.

• 
  Abhishek Deshmukh (@BappuThe) reported 48 minutes ago

  @github Hello GitHub Team, We’re facing an issue where pipeline status does not update in real time after completion. It only reflects the final state after manually refreshing the page. Could you please check if this is a known issue or suggest a fix, I feel that is bug ?

• 
  wats🏳️‍🌈 (@Watsonage) reported 48 minutes ago

  @CheetahGirlsYea it's kind of hard to find actually it got taken down from the app stores and then even github, I'll find the right link for you later

• 
  0noise (@0noisee) reported 49 minutes ago

  Osloq launches AI bug reproduction tool for GitHub issues on Product Hunt

• 
  Ali Mehdi Mukadam (@alimukadam) reported 52 minutes ago

  @trq212 Your weekly limits will burn away much faster during the limited availability if you aren't aware of this issue if you're running Fable as the lead agent with cheaper models like Sonnet doing work in the background problem: In one of the sessions, I noticed limits were burning through way faster, so I went digging through the transcripts when the main agent gives a job to a background model (like Sonnet, which I asked for to save tokens) and then comes back to give it more work, the background agent stops working on Sonnet and switches to Fable, the main agent's model it's not something you trigger by hand. the lead agent decides to check back in on its own as part of normal multi-agent work, so it just happens, with nothing on screen telling you it switched. in my case a task ran its first half on Sonnet exactly like I wanted, then silently ran the entire second half on Fable. It also dumps the cached context and rebuilds it from scratch, so you end up paying twice, once for the pricier model and once for the wasted cache. on limited availability and limits - that adds up quick my fix for now is a rule I dropped into my global CLAUDE.md so it doesn't recur: --------------- ## Model spend (all projects, all repos — standing rule) - Dispatching Frontier-tier (Fable/Opus) as background tasks and agents needs explicit approval by Ali for that specific lane — a prior approval is not standing permission for the next one. - Never resume a background agent via a message-passing tool that has no model-override param (e.g. SendMessage) if it needs real further work — it silently inherits whatever model the parent session is on right now. Let it finish and report, or kill it and respawn fresh with the model set explicitly. --------------- in plain terms: don't let a background agent get pulled back in for more work once it's running. either let it finish and report back, or kill it and start a fresh one with the model set on purpose. And this is already known. Someone reported the same thing on GitHub back on June 12, issue anthropics/claude-code#67794, still open their solution which I believe is the correct one but haven't tested yet: instead of setting the cheaper model when you launch the agent, pin it inside the agent's own definition file, and that version reportedly sticks even when the agent gets resumed

• 
  Tyler G (@TylerByte666) reported 53 minutes ago

  @github You ******* went down a few weeks ago when i had a deadline! And now your making fun of gamers. Gitslop **** off!

• 
  Lexor (@RexAdamantium) reported 58 minutes ago

  @iruletheworldmo @petergyang For business coding, Microsoft’s answer to Codex is basically GitHub Copilot Business or Enterprise, but strangely, it sits outside the Microsoft 365 Copilot/Office stack. Google has Antigravity. Anthropic has Claude Code/Enterprise. Then there are tools like Cursor. For companies, the problem is not lack of options. It is that every option comes with a trade-off. The real question isn’t which AI is smartest. It’s how much speed you’re willing to buy by leaking IP.

• 
  Damián🦞 (@fagamericano) reported 59 minutes ago

  The top use case for enterprise openclaw deployments is the significant reduction of context switch by employees. When you can ask “what happened with this customer?” and: You get a full triage pulled from logs across different subsystems/microservices “this system incorrectly marked this transaction with this tx code” How it happened in code “line 57 of service/tx.py has a race condition…” Finding other customers with a similar issue “These 10 records were also affected” And suggesting an immediate “switch these codes in the db for the tx to go through” and a durable fix “here’s a PR” All within a few minutes, with full company context, in any model you choose…. It would take an Engineer easily 30mins-50mins to diagnose through new relic, github, gcloud logs, databases, to form a picture of what could’ve happened, vs getting a story to validate in a few minutes…. How we work is truly going to change