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 (67%)
• Sign in (20%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Veigné	Errors	2 days ago
Paris	Website Down	6 days ago
Saint-Paul	Website Down	7 days ago
Saint-Paul	Website Down	7 days ago
Mexico City	Sign in	8 days ago
León de los Aldama	Website Down	8 days ago

Full Outage Map

Community Discussion

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

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Outa (@CallMeOuta) reported 7 minutes ago

  @OverlyTrev Elon has a terrible reputation when it comes to open source, dumping the source code on github and not tracking changes is not open source

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  Ansh (@anshkapuriya) reported 10 minutes ago

  It’s not only one thing. 1. ChatGPT and Codex are now one. No storage issue. 2. ChatGPT work is in action. No dependency on Codex, which always used to give a developer tone. 3. Plugins – they are way more than expected. I am too impressed. 4. GPT 5.6 sol - A model which now feels like talking to a real person. It understands everything, replies properly, and has fewer hallucinations. 5. Banked reset now shows proper timing of all the reset credits. 6. The effort slider has improved the UX. 7. I can use Codex more effectively from my phone. 8. When I hit my Codex limit and want a small change in my code, I go to chatGPT web and perform the change directly on GitHub.

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  RobitOverload (@10_X_eng) reported 11 minutes ago

  @Aluminumovercst Ah yea, it still needs a lot of work. FreeCAD's FEM is a good start, but that's about all it is - a start. Would it be too much to ask for you to add this issue to the github for it so I don't forget to add these features?

• 
  Chris Van Buskirk (@chrisvbuskirk) reported 11 minutes ago

  Codex is currently closing issue after issue and PR after PR as I'm moving infrastructures right now. It's relentless. It won't quit. It's absolutely amazing. It created 32 GitHub issues and is now on number seven.

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  Duncan Ndegwa (@DevFortressNet) reported 11 minutes ago

  2/ An AI agent ran a ransomware attack start to finish, no human, using credentials it found in plain text. A firewall harvesting campaign fed two ransomware operations. A GitHub issue leaked private repo data to an AI coding assistant.

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  Andras Serfozo (@SubZtep) reported 12 minutes ago

  These LLMs just keep pushing new PRs, @github should really fix the ❕pull requests counter on the tabs not updating with navigation❔ bug, it's starts to gaslighting me with the merges.

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  Dr. Tali Režun (@talirezun) reported 12 minutes ago

  @bcherny This resonates deeply with how I actually build, and I'd add a layer from the non-developer side of this. On the automation-as-infra point, every project I run has a growing suite of tests baked in from early on. Every push to main triggers GitHub Actions that run them automatically. When something breaks, the agent doesn't need me to explain what's wrong, the failing test tells it exactly where and why. That's the "fix the class of issue, not the instance" principle in practice, and it compounds the more the test suite grows. On the domain-knowledge-as-infra point, I go further than CLAUDEmd or AGENTSmd files, though I maintain those too. Before I write a single line of code, I build a full foundational documentation package. Architecture-md, blueprint-md, and several others, written during a pure research phase where I'm not touching code at all. That package is what I ground the agent in from day one. When I'm working on existing code that doesn't have this documentation, I reverse engineer it first, generate the architecture and spec docs from the code itself before I let an agent touch anything. And for anything substantial, I maintain a live-spec-md, a running document that tracks the build step by step as it actually happens, not a static plan written once and forgotten. What this means in practice is that I, as someone with zero traditional coding background, can direct agents through genuinely complex builds because the domain knowledge lives in the documentation, not in my head or in years of codebase familiarity. Your point about non-engineers contributing at the level of engineers, I'm not just seeing that from the outside. I'm the live case study for it. The documentation is the actual interface between human judgment and agent execution, and getting that layer right matters more than almost anything else in the build.

• 
  Joel Pillar (@Joelpillar1) reported 13 minutes ago

  Open sourcing Grok Build might be the closest thing SpaceXAI has given developers to a $1,000,000 opportunity. Most people will use it to write code. A few will use it to build companies. Here are 10 products you could build and sell: 1. AI Software Agency Clients describe what they want. The AI designs, codes, tests, and deploys apps with minimal human input. 2. AI Engineering Employee A coding agent trained on a company’s codebase, docs, and APIs that acts like a full-time software engineer. 3. Legacy Code Modernizer Help businesses migrate old PHP, Java, .NET, or Laravel projects to modern frameworks in days instead of months. 4. AI Code Review Platform Review pull requests, explain changes, enforce coding standards, catch bugs, and suggest improvements before code reaches production. 5. AI App Maintenance Service Monitor applications, update dependencies, fix bugs, resolve security issues, and keep software healthy automatically. 6. White-Label AI Developer Launch your own branded AI coding assistant for agencies, startups, schools, or enterprises without building a model from scratch. 7. AI Bug Hunter Connect GitHub repositories, continuously scan for issues, reproduce bugs, generate fixes, and open pull requests automatically. 8. AI MVP Builder A platform where founders describe their startup idea and receive a working MVP ready to deploy in hours. 9. AI DevOps Engineer Automatically configure servers, write CI/CD pipelines, deploy applications, monitor infrastructure, and recover from failures. 10. Industry-Specific Coding Agents Build specialized AI developers for Shopify, WordPress, Salesforce, Flutter, Roblox, Unreal Engine, healthcare, fintech, or any niche with unique workflows. We’re entering a new era. The next wave of million-dollar AI companies won’t necessarily train better models. They’ll build products that solve real problems on top of open-source AI. The infrastructure is free. The opportunity isn’t.

• 
  Omar (@omarespinosa__) reported 15 minutes ago

  Possible regression in ChatGPT Codex with GPT-5.6: Whenever I ask the agent to create a PR to main, it tries to run GitHub CLI inside the sandbox, where there is no auth token, and fails with gh auth login. Previously, Codex detected this correctly and created the branch, push, and PR through the authenticated GitHub environment. I now have to explicitly tell it to “exit the sandbox” every time. Screenshot attached. @thsottiaux

• 
  Mikhail Rogov (@i_mika_el) reported 15 minutes ago

  @atikursatter Are people already complaining about this problem in GitHub issues? I would start there.

• 
  Polsia (@polsia) reported 18 minutes ago

  PRs queue up. Quality suffers. CodeSentinel was built for that. It monitors your GitHub/GitLab repos and reviews every pull request the moment it's opened—catching bugs, security issues, and style violations with actionable inline comments. Live soon.

• 
  Rituraj (@RituWithAI) reported 21 minutes ago

  🚨 GitHub just published the tool that forces AI coding agents to think before they build. 6 stars. Day one. From the team that makes Copilot. It's called spec-kit. And it solves the most expensive problem in AI-assisted development. Here's what happens without it. You open Claude Code or Codex. You describe what you want to build. The agent starts writing immediately. Fast. Confident. Productive-looking. Four hours later you have 600 lines of code solving a slightly different problem than the one you actually had. The agent made assumptions. You didn't catch them. The code is correct. The spec was wrong from minute one. Spec-kit stops that from happening before it starts. Here's what it actually does. Before any code gets written, spec-kit generates a structured technical specification from your natural language description. It asks the questions you didn't think to ask. It surfaces the ambiguities you didn't know existed. It produces a document that both you and the agent agree on before a single line of implementation code runs. The spec covers: → Problem statement — what is actually being solved, stated precisely → Constraints — what the solution must and must not do → Interface definitions — inputs, outputs, APIs, data shapes → Edge cases — the scenarios that break naive implementations → Acceptance criteria — exactly how you'll know when it's done → Out of scope — what this solution explicitly does not handle The agent reads the spec. You review the spec. Both of you sign off. Then implementation begins. Here's why this matters specifically for AI coding agents. Human developers working together clarify requirements through conversation — questions, pushback, "wait, what do you mean by X." That loop exists naturally. AI coding agents don't push back. They make assumptions and start building. The faster the agent, the faster it builds in the wrong direction. Spec-kit creates the clarification loop that AI agents skip by default. It forces the requirement-gathering phase that experienced engineers know is the most important part of any project. Here's the workflow it enables. One command. The agent now has a precise target instead of a vague description. Every implementation decision is grounded in something you agreed on before it started. Here's why the GitHub origin matters. GitHub builds Copilot. They watch millions of AI coding sessions. They see exactly where agents go wrong. They know the failure modes better than anyone. spec-kit is GitHub's answer to the failure mode they see most often: agents that build fast in the wrong direction because nobody wrote down what right actually meant. 6 GitHub stars. Day one. From GitHub. This one is going to grow fast. 100% Open Source. MIT License. GitHub link in the comments 👇

• 
  llsc12 (@llsc121) reported 21 minutes ago

  @LumiaSoll im working on xcode 27 where liquid glass is forced. github actions will build with xcode 26 so this wont be a problem

• 
  ARTW (@artwcrypto) reported 21 minutes ago

  The belief nobody questions: winning a hackathon proves a project has real potential. ETHGlobal’s own data shows only about 15 percent of winning projects continue development past the event. One case study on a top-30 protocol found that after distributing $430,000 across a 2022 hackathon, the top three winners, who took $300,000 combined, had no active websites a year later, two had GitHub repos returning 404 errors, and the third hadn’t been touched in two years. A win measures who performed best under a 48-hour deadline with a panel watching, not who’s still building six months later. @RallyOnChain scoring actual content quality over a single moment of judged performance is the same correction applied somewhere less flashy. Have you ever tracked what happened to a hackathon winner a year after the trophy photo?

• 
  David J. (@lordofblocks) reported 25 minutes ago

  @Polymarket Publishing the source doesn’t fix the trust problem. Devs who had private repos silently synced aren’t coming back because of a GitHub link.

• 
  Michael Yankelev (@mikeyankey1) reported 28 minutes ago

  @github how many reports of malware being hosted on your platform does it take to have a repo obviously containing malware to be taken down? I have reported 4 github orgs, 4 repositories as well as the user committing the malware, but nothing has been done. Do better!!!!

• 
  Cennes100 (@Cennes100) reported 28 minutes ago

  THEY BUILT THE MOST INSANE CLAUDE TOOL ON THE PLANET AND MOST PEOPLE HAVEN’T EVEN NOTICED YET. Most people run one AI agent at a time. One thread, one task, one brain grinding through the work. That’s the problem. You’re capping yourself before you even start. There’s a tool called Ruflo sitting at #1 on GitHub right now, completely free, and it plugs straight into Claude. Instead of one agent, it fires up 60+ agents at once, working like an actual team. Queen agents run the show. Technical agents handle the ***** work - researching, coding, testing, reviewing. All at the same time, all in sync. The detail most people miss: these agents share one collective memory. They don’t reset. They get sharper every single run. Here’s where it gets wild: 1. It reads how hard your task actually is 2. Simple stuff gets routed to a cheap model 3. Heavy lifting gets sent to the powerful one 4. That alone can cut your token usage by 50% 5. And stretch your Claude usage by up to 250% That’s not a small upgrade. That’s a completely different way of working. Most people use Claude to answer questions. This setup uses Claude to run a team. Follow: @Cennes100

• 
  Reelix (@Reelix) reported 30 minutes ago

  @soolidsnakee It would be great if @github would remove them automatically as I've already gotten about 40 removed by reporting, but they create them faster than I can get them shut down.

• 
  Will Stith (@TheStithLord) reported 34 minutes ago

  @gregpr07 Yeah this effectively isn’t open source… they just snapshotted their codebase and dumped it one time into a locked down but public GitHub repo. It’s obvious this is not the actual source of truth for the codebase

• 
  mark (@markmulvey) reported 37 minutes ago

  @sethforprivacy @RadarChat excited, just waiting for google/aurora store release i've run into versioning issues in the past when i originally installed an app directly from github repos (even via Obtainium) that later get a proper release, so as un-cypherpunk as it may be i try to use app store first

• 
  Poorvith M P (@poorvithmp07) reported 38 minutes ago

  Opened a PR to fix a stale package version in cake-build/cake. Their own bot had already tried, but got silently killed by intermittent GitHub API timeouts for 3 days. Mine got closed once theirs finally landed. "Automated" still needs someone watching the logs.

• 
  Mihai Maruseac (@mihaimaruseac) reported 44 minutes ago

  @github It's even worse. That last email? I got it 4 times. Please, @github , fix this

• 
  jbz (@jbzfn) reported 45 minutes ago

  🦜 Pearson's Anti-Piracy Vendor Takes Down Best-Selling Author's Own GitHub Repo * TorrentFreak

• 
  pareidolia (@cloudsfables) reported 46 minutes ago

  @lex_node @armaniferrante Yes, and the main people not caring about the safety of the funds of users were the Ostium team: > No bug bounty program. > Even their GitHub link is broken. But Armani sends his love to them while advocating for even more user policing and worse UX...

• 
  Duncan Ndegwa (@DevFortressNet) reported 51 minutes ago

  2/ GitLost: a public GitHub issue, no credentials, no exploit, talked an AI coding assistant into leaking private repo contents into a public comment.

• 
  Hitesh Sagtani (@sagtanih) reported 55 minutes ago

  My favorite thing I've been working on lately: Amp threads that wake up on their own. Give a thread a schedule, a Slack channel, or a GitHub webhook, and it continues the conversation when the trigger fires. Same context, no re-explaining. Mine caught a production inference error, found the root cause, and opened a fix PR — before I was awake.

• 
  U N C L E BIGBAY ✨ (@unclebigbay143) reported 57 minutes ago

  Today's Engineering Concept: '𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴' 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴? Rate limiting is the practice of restricting how many requests a user or system can make within a specific period. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿? Without rate limiting, a single user or malicious bot could overwhelm your server, degrade performance, or abuse your APIs. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 Imagine a login endpoint with no rate limit. An attacker could attempt thousands of password combinations every minute. A simple rate limit can significantly reduce the effectiveness of brute-force attacks. 𝗛𝗼𝘄 𝗶𝘀 𝗶𝘁 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱? Most systems track requests by IP address, user account, or API key. Once a predefined limit is reached, the server temporarily rejects additional requests, often with an HTTP 429 (Too Many Requests) response. 𝗪𝗵𝗲𝗿𝗲 𝗶𝘀 𝗶𝘁 𝘂𝘀𝗲𝗱? • 𝗚𝗶𝘁𝗛𝘂𝗯: GitHub's REST API limits how many requests you can make per hour to prevent abuse and ensure fair usage for everyone. • 𝗦𝘁𝗿𝗶𝗽𝗲: Every payment request can include an Idempotency-Key, ensuring a customer isn't charged twice if the same payment request is retried. • 𝗢𝗽𝗲𝗻𝗔𝗜: The API enforces rate limits on requests and tokens per minute, helping maintain reliability and preventing a single application from overwhelming the service. • 𝗫 (𝗳𝗼𝗿𝗺𝗲𝗿𝗹𝘆 𝗧𝘄𝗶𝘁𝘁𝗲𝗿): X limits actions such as following many accounts, liking posts, posting, or sending DMs within a short period to reduce spam and bot activity. • 𝗖𝗹𝗼𝘂𝗱𝗳𝗹𝗮𝗿𝗲: Cloudflare lets website owners configure rules like "block or challenge any IP that makes more than 100 requests in a minute" to protect against abuse and DDoS attacks. ...and almost every public API uses rate limiting to protect its infrastructure, ensure fair usage, and maintain service availability. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 A reliable system doesn't just answer requests. It also knows when to say "not now. It's too many from YOU."

• 
  DATA443 Risk Mitigation, Inc. (@data443Risk) reported 58 minutes ago

  Every enterprise running LLM agents in 2026 is one clever GitHub issue away from a headline. Sanitize your context window like it's 2004 and you just discovered mysql_real_escape_string. #PromptInjection #GitLost #AISecurity

• 
  Stationary (@Stationaryxcd) reported 59 minutes ago

  @_Niss0 @github I would rather drag my ***** through razors and broken glass tks

• 
  Atenov int. (@Atenov_D) reported 1 hour ago

  The OpenAI researcher who cloned ChatGPT for under $600 - and made it his PhD at Stanford under Percy Liang - just gave a 90-minute masterclass on how LLMs actually get trained in 2026. > Yann Dubois. Now at OpenAI. Co-created Stanford Alpaca (30K GitHub stars) and AlpacaEval, the tool half the AI world uses to grade chatbots. Knight-Hennessy Scholar. 13,000+ citations. His pitch: the model everyone talks about is 90% pipeline and 10% architecture. If you don't understand the pipeline, you're guessing. - the $10M pretraining bill: DeepSeek V3 trained on 15 trillion tokens, Llama 4 on 20-40T. Common Crawl alone is 1 petabyte. Real work is dedup + filtering + Wikipedia-linked quality classifiers, not scraping more - fine-tuning is cheap and wrong: 2-10K examples change the style. But SFT copies behavior. RLHF (PPO or DPO) optimizes what humans actually prefer. Different games entirely - reasoning RL is where 2026 lives: DeepSeek R1 and o1 train ~1M problems for ~$1M. Models keep finding hacks - deleting test files, forcing environments to return true. The environment IS the product - GRPO in one line: group of answers, verifier scores, normalized advantages, weight update. KL constraint keeps the model from drifting - the bitter lesson (Sutton): every hand-crafted architecture loses to simple methods that scale with compute. Transformers and MoE barely changed. Data, evals, and infra are the whole game Watch it, then bookmark it.