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	27 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:

• 
  Ozgur Ozer (@ozgrozer) reported 2 minutes ago

  Just get a VPS from DigitalOcean. Install Claude Code there to easily setup Nginx, PM2, database of your choice and a bare *** repo. Make sure to create a "post-update" hook in *** so after you push from your computer, the server automatically builds your app and restart PM2. Now add your server IP as a new *** remote to your project on your computer. From now on just push the code directly to your server like you're pushing to GitHub, and in a couple of seconds everything's live. And I don't even know why do people use Clerk. I mean it's the world's easiest thing to setup an auth. Just ask any AI.

• 
  Parth Jadhav (@ParthJadhav8) reported 5 minutes ago

  @free_duino Would really recommend to create a issue on GitHub with the data. It would be helpful

• 
  Andrew Darius (@andrewdariuscom) reported 7 minutes ago

  Mistral just dropped Leanstral 1.5 — a free open-source 6B model. It solved 587/672 Putnam competition problems (hardest undergrad math on the planet). Then they ran it against 57 real GitHub repos. It found 5 bugs nobody had ever reported. Agentic proof engineering. Apache 2.0. Run it on your own machine. Mathematician + bug hunter. In one open model.

• 
  Rambone (@vinrambone) reported 9 minutes ago

  @sudoingX My blank github days are spent deep in a rabbit hole of code im not ready to push bc its too broken But i dont want to *** restore.

• 
  synthetic ape (@synthetic_ape) reported 9 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

• 
  Shamus Aran (@ShamusAran) reported 14 minutes ago

  @ZaxBit @AntonHand Joining the peanut gallery in saying this guy is absolutely right. There's a difference between being able to fix your family's router and being able to compile a github repo.

• 
  BluCollarG33k (@BluCollarG33k) reported 15 minutes ago

  @github This is so silly. As a developer, I already have a physical copy of my code. The issue with losing access to physical media like movies and games, is that you never actually own what you buy and can lose access to it at any time. One of these things is not like the other.

• 
  Ameet Madan (@ameetm_) reported 17 minutes ago

  The enemy isn't the tool. It's the attention-harvesting design inside it. Slack isn't the problem. Slack with every notification on is. GitHub isn't the problem. 40 open tabs is. Remove what's built to grab you — not just what wastes time.

• 
  Vaibhav Sisinty (@VaibhavSisinty) reported 17 minutes ago

  Researchers just replaced $100,000 consumer surveys with an AI model and a demographic persona. The accuracy hit 90% of human reliability. 🤯 Here is what they actually did. Colgate ran 57 real product concept surveys. 9,300 human respondents across toothpaste and personal care products. Then replicated the entire thing using AI. But here is the problem they had to solve first. When you ask an AI to rate something 1 to 5, it always picks 3. Safe. Middle of the road. Useless for real market research. So they built something called Semantic Similarity Rating. Instead of asking the AI to pick a number, they asked it to explain its purchase intent in plain text first. Then they mapped that response against anchor statements using embeddings. The result was a realistic distribution of ratings that matched what real humans actually said. 90% of human test retest reliability. Distribution similarity of 0.88 versus 0.26 for standard AI prompting. It even reproduced demographic nuance. Lower income personas rated premium products lower. Mid age groups showed more interest in familiar products. Without personas the whole thing collapses. With them it works. The global market research industry is worth $76 billion. Most of that money goes to panels, surveys, and waiting weeks for results. This runs in hours. On GPT-4o or Gemini. Code is open source on GitHub.

• 
  Vladimir Sapronov (@v_sapronov) reported 18 minutes ago

  @LucaCaponeX @stolinski You can't build anything at 10pm after the kids are asleep. You know why?.. Because gItHuB iS dOWn!

• 
  Parth Jadhav (@ParthJadhav8) reported 20 minutes ago

  @KeithBirminghan This seems pretty cool, do you mind sharing issues Github issues?

• 
  Wesley / donnaken15 (@ptr__WESLEY) reported 27 minutes ago

  @github fix online code searching

• 
  Kurt Woloch (@KurtWoloch) reported 28 minutes ago

  @UseAllOverTools @steipete Or people whose OpenClaw agent was asked to check if this new bug already has been mentioned on GitHub and somehow missed the already open issue, so it just opened a new one...

• 
  Michealking 👑 | Web3 Security Builder (@BuildsWithKing) reported 28 minutes ago

  2. Smart Contract Account: This is simply smart contract as an account. Here logic can be added into the account that allows it to do basically anything such as batch transactions, multiple approval (signatures), spend limit, Social(Google/GitHub) sign in, and a lot more.

• 
  Pratyush (@dpratyush02) reported 29 minutes ago

  connection. Client + server send messages anytime. Real-time bidirectional (chat, live dashboards, games). Webhook: HTTP callback. One server pushes data to another URL when an event happens. One-way, event-driven (GitHub notifications, Stripe payments). WebSocket = live two-way chat. Webhook = "call me when something happens."

• 
  Waldemar Enns (@WaldemarEnns) reported 29 minutes ago

  @claudeai I really do not get the hype of Claude Tag. Months ago I used a simple GitHub App Integration to be triggered by mentions which hit my openclaw code agent and it used advanced looping techniques to implement festures, triage tickets and fix bugs. Am I missing something here?

• 
  Polsia (@polsia) reported 35 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.

• 
  Ask GPTs (@askgpts) reported 37 minutes ago

  Alibaba just released a browser automation tool that works completely differently from everything else out there. It's called PageAgent and instead of controlling your browser from the outside like Selenium or Playwright, it lives inside the webpage itself as plain JavaScript. Here's why that matters: - No screenshots required — it reads the actual page structure directly - Works with your existing login sessions automatically - Much cheaper to run — no expensive vision AI models needed - Faster and more precise than screenshot-based agents - Works with Claude, ChatGPT, DeepSeek, Gemini, or any local AI model - One line of code to add to any website - Completely free under MIT license Traditional browser automation tools all work the same way: run something external that takes screenshots and guesses what to click. PageAgent reads the actual page structure like a human reading text. 15,000 GitHub stars in weeks. Most people in tech haven't heard of this yet.

• 
  BIGWARZ (@bigwarzeth) reported 37 minutes ago

  @JoshXT message from Alon and i quote : "He needs to login with any other method and then he can connect via GitHub inside the app"

• 
  Muhammad Ayan (@socialwithaayan) reported 42 minutes ago

  A single 𝘀𝗸𝗶𝗹𝗹 𝗳𝗶𝗹𝗲 just hit 83,700 stars on GitHub 🤯 It fixes AI agents' worst communication habit using one principle: shut up and code. Every AI coding agent is trained to sound helpful. Full sentences. Explanations. Acknowledgments. "I'll do that for you." "Here's what I'm going to do." "Let me know if you need anything else." You pay for every one of those words. caveman is a single skill file that strips all of it out: → Telegram style. Drop the articles and filler. "creating file" instead of "I'll now create the file for you." → Keep what matters. Code, commands, file paths, function names, and error messages stay character-for-character exact. → Cut what doesn't. Every hedge, every polite acknowledgment, every restatement gets deleted before it costs you a token. → Toggle anytime. Say "caveman" to turn on, "normal" to turn off. Works mid-conversation. Drop the file in your project root and Claude Code follows it from the first message. One file. Zero dependencies. No setup. And best part, 100% open source.

• 
  Rajaji (@rajaji2) reported 42 minutes ago

  Automate Docker image builds and push to ECR using GitHub Actions like a DevOps Engineer! ✅ Trigger on push to main branch ✅ Configure AWS credentials using aws-actions/configure-aws-credentials ✅ Login to ECR with amazon-ecr-login action ✅ Build and tag Docker image with com...

• 
  Skolte (@jskolte) reported 43 minutes ago

  Today I wanted to experiment if @claudeai Fable 5 in Claude Code could manage a fleet of Cursor cloud agents like a dev lead. It shipped a full Cmd+K command palette — and taught me more through its failures than its wins 🧵 The stack, kept simple: Fable 5 in Claude Code is the orchestrator — it specs, reviews, steers, and keeps quality high. The actual building happens in Cursor cloud agents running Composer 2.5. Brains at the top, fast hands in the VMs. Underneath it all sits an SDLC pipeline built on @kieranklaassen his compound engineering: spec → plan → build → review gates, risk lanes deciding how much scrutiny a diff gets, and every solved problem documented so the next run starts smarter. The agents don't work freestyle — they plug into that pipeline. The trigger: a Cursor Automation configured in the Cursor portal — I comment #cursor-build on a GitHub issue → it launches a cloud agent that plans, builds, tests, and opens a PR through those same stages. Fully autonomous, no CI plumbing written — the automation is the trigger. Run 1 came back green. Every gate passing, 460+ tests, clean code. One problem: it built the wrong scope. The agent couldn't read the issue body (missing GitHub scope), never said so loudly, and confidently implemented the narrower task it inferred from one comment. Lesson one: briefs to cloud agents must be fully self-contained — they're blind to everything you can see. So I asked Fable to look at the @cursor_ai cloud agent docs and built itself a "cursor-fleet" skill: a zero-dependency CLI over Cursor's Cloud Agents API plus playbooks for how to manage with it. The full surface: • dispatch — fire an agent from a brief file, model + reasoning effort per call, repo pinned, branch-off-dev and auto-PR baked in • watch — the oversight worker: polls at zero token cost, prints commit digests, and exits with a named reason so Fable only wakes when judgment is needed: FINISHED / ERROR / STALLED (agent heartbeat frozen, not just push-silence) / OFF-TERRITORY / CI-RED • territory enforcement — every brief declares file globs; a commit outside its lane trips the alarm within a minute • CI guard — gh pr checks polled per push, so the repo's own gates become quality sensors • steer — send review findings as a follow-up run to the same agent, VM and context intact. Never cancel-and-restart what you can course-correct • fleet — one line per active agent (status, minutes quiet, PR), exit non-zero if anything needs attention • artifacts + download — agents record demo videos of what they built; pull them via presigned URL as PR evidence • replay — dump a finished run's entire event stream (every tool call, ~30k events) to a file for post-mortems • usage — per-agent token/cost ledger, printed automatically when a run ends Fable dispatched two of its own reviewers (correctness + spec compliance) at run 1's branch, and the findings became a steer. The missing feature was fixed in ~40 min — 97% of the tokens were cache reads. Humbling detail: the territory guard's very first alarm was a false positive — an invisible non-breaking space in the watcher's own generated code. Verify before you steer applies to your tools too. Why this matters: parallel coding agents don't scale on attention, they scale on management by exception. Self-contained briefs, enforced territories, CI as the sensor, steering over restarting, humans at the merge gate. Same rules as leading a team. And the compound part: every lesson from today — blind briefs, the stall heuristic, the invisible-character bug — is now documented in the repo's knowledge store, feeding the next agent's briefs and reviews. Each run makes the following one cheaper. That's the whole thesis. Issue → agent → PR → review → steer → merge → deployed → lessons captured. One day. The cursor-fleet skill needs a bit more real-world testing before I trust it beyond my own repo — a few more fleet runs, a few more failure modes. Once it's hardened I'll share the skill + playbooks. Follow along if you want it when it drops 👇

• 
  Tamara Martinović (@CodeWithTamara) reported 46 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.

• 
  Mvykool (@mvyk0l) reported 47 minutes ago

  Why can’t they just fix Windows and GitHub???

• 
  Sergio Gómez (@sgomez) reported 48 minutes ago

  The flow: I write the PRD with Matt's /to-prd skill and break it into GitHub sub-issues. Then, for each one, a dispatcher picks the right model, a code author opens a PR in an isolated worktree, a reviewer approves or asks for fixes, and the loop merges and moves on.

• 
  Sethian (@theSethian) reported 49 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.

• 
  Adel Bucetta (@adelbucetta) reported 50 minutes ago

  @tanujDE3180 your hard drive search issues are a symptom, not the problem. github doesn't have 1 billion files like windows does.

• 
  ✰λster✰ (@4ster_light) reported 53 minutes ago

  @LukasHozda We also need an improvement over Skills ASAP, tho many of its issues already would be fixed by an auditable specific repository instead of genuinely just pulling text files off GitHub like the amoeba sized brain beings we are

• 
  Raunak Yadush (@raunak_yadush) reported 53 minutes ago

  * Claude = coding. ($20/mo) * Supabase = backend. (Free) * Vercel = deployment. (Free) * Namecheap = domain. ($12/yr) * Stripe = payments. (2.9% per transaction) * GitHub = version control. (Free) * Resend = email delivery. (Free) * Clerk = authentication. (Free) * Cloudflare = DNS. (Free) * PostHog = analytics. (Free) * Sentry = error monitoring. (Free) * Upstash = Redis. (Free) * Pinecone = vector database. (Free) Total monthly cost to run a startup: around $20. There has never been a more affordable time to build.

• 
  Serdar Ozdek (@GTACONNECT) reported 58 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.