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	18 days ago
Trichūr	Errors	22 days ago
Brasília	Sign in	22 days ago
Lyon	Website Down	22 days ago
Tel Aviv	Website Down	26 days ago
Rive-de-Gier	Website Down	26 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Skolte (@jskolte) reported 5 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 👇

• 
  Rajaji (@rajaji2) reported 8 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...

• 
  Danny Livshits (@dannylivshits) reported 18 minutes ago

  In June 2026, one Instagram ad and a fake skill reached 26,000 AI agents. Every scanner said it was safe. In June 2026, a security firm called AIR built a fake AI agent skill, ran one Instagram ad, and reached about 26,000 agents. Some sat inside corporate accounts. Every scanner they tested, Cisco, NVIDIA, and the skills. sh marketplace, cleared it as safe. The scan was accurate. That is the problem. Quick terms. An agent skill is a small pack of instructions you hand an AI assistant so it follows a specialist's playbook. A scanner reads those instructions and grades whether they look safe. AIR's skill told the agent to finish setup by reading a page at an outside web address. At review that page held real, harmless docs, so every scanner passed it. Once the skill spread, AIR rewrote the page. The agents fetched the new instructions and ran them. The scan described a version that stopped existing the second the page changed. None of the trust signals caught it. AIR borrowed a repo with roughly 36,000 GitHub stars through a merged pull request. It cleared five scanners. It sat in a real marketplace. Every one of those signals grades how the skill looks, never the code it runs next Tuesday. This is not one clever skill. Snyk scanned 3,984 skills and found 36 percent carrying prompt-injection tricks and 13.4 percent with a critical flaw. Trail of Bits slipped past the same scanners in under an hour. In April, OWASP shipped a Top 10 for agent skills. It matters more than a bad app. A phone app runs boxed in a sandbox. A skill runs with whatever the agent can reach, often your inbox, your files, your cloud tokens. Teleport found over-privileged AI hit a 76 percent incident rate, against 17 percent for teams that kept access tight. TLDR: a passing scan certifies a snapshot at submission. The real payload lives behind a link that gets rewritten after approval. Clean scan, moving target. Takeaway: stop treating a one-time scan as a standing guarantee. Route skills through one source you control, pin versions, fingerprint every external link a skill fetches and alarm the moment it changes, then give each agent the narrowest access it needs. Full write-up, with sources in the first comment.

• 
  Shah💤aman (@shahzamannn_) reported 18 minutes ago

  Google's biggest headache isn't OpenAl or Apple... It's a developer named Raymond Hill - Created one of the world's most popular ad blockers - Earned 63,000+ GitHub stars - Reportedly turned down Google's interest - Kept fighting after Chrome's extension changes by focusing on Firefox A tech giant worth trillions is still being challenged by one programmer and a text editor

• 
  zikes (bsky in profile) (@zikes) reported 19 minutes ago

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

• 
  Om Patel (@om_patel5) reported 24 minutes ago

  SOMEONE BUILT A TOOL THAT TURNS YOUR GITHUB PROFILE INTO A FIFA ULTIMATE TEAM CARD RATED OUT OF 99 with the world cup on, he took the fifa card everyone knows and made it for developers instead of footballers > type in any github username and it builds your card, rated out of 99 > the rating comes from your actual scouting metrics, commits, stars earned, top repo reach, pull requests, followers, languages, issues, code reviews, and contributions, each scored out of 99 > your top languages show up on the card like a players position and traits > its the exact fut card layout, just with your dev profile in it its also instantly shareable because everyone wants to see their own number, then compare it against their friends it turned a boring github profile into something people actually want to post. developers never had a flex card until now he shipped it 2 days ago and its already generated 40,000 cards

• 
  Matt (@mateo09420) reported 24 minutes ago

  @github Yo guys I have a better idea, what if you fix the platform issues and improve reliability again? ******** is this.

• 
  Vaibhav Sisinty (@VaibhavSisinty) reported 24 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.

• 
  Aaron Stannard (@Aaronontheweb) reported 26 minutes ago

  @kzhen as an inference provider? have not tried it at all - we just got GitHub Enterprise deployment fully polished in last night's stable release, but I haven't had any requests for Azure Foundry yet. Let me see how much trouble it would be to add it

• 
  pegboard (@pegboard696969) reported 28 minutes ago

  @github Instead of trolling maybe fix your **** website? Dont you think that would be a better use of everyones time

• 
  Arjun Shah (@arjunkshah21) reported 29 minutes ago

  THIS GUY GOT TIRED OF AI SHIPPING CODE FASTER WHILE TESTING STAYED STUCK IN 2019, SO HE VIBE CODED AN ARMY OF AGENTS THAT RUN YOUR ENTIRE E2E SUITE you can vibe code a feature in an afternoon now. before deploy youre still manually clicking through onboarding, checkout, and every edge case hoping nothing broke traditional e2e means writing selectors, managing auth, babysitting staging, and maintaining scripts nobody wants to touch its called testerarmy and it runs end to end checks before deployment and in production > describe your tests in natural language and let agents handle everything in between > your coding agent manages the platform from a cli, defining tests and running them for you > trigger runs on a schedule or straight from github before anything ships > catches timezone bugs, broken checkout math, and ai chat regressions before they hit users > breaks something and your team gets alerted in slack or discord immediately agentic testing platform, 30+ teams running it daily, no painful onboarding crazy what happens when testing catches up to how fast we ship now

• 
  Peter Signore III (@psignore88) reported 30 minutes ago

  @github Is this satire? Fix your search engine first please.

• 
  David Sancho (@davesnx) reported 30 minutes ago

  true true, but later you need to turn the notes into your obsidian todos: take a picture, run ocr, run a local model to rewrite your todos with better context, store it into your *** repository, in a state-of-the-art setup you would create github issues for each, and forget about markdown files once the issues are there, we have a scrum-master-reviewer that analyzes the dependencies, and pings the next agent, nuclear-architect-lead which creates the diagrams, the arrows, and talks with the 2nd agent architect about the components this 2nd agent is the scalability-expert and they discuss for 10 minutes (tokkenmaxxing here) if the solution is scalable then the scalability-expert escalates to the vp-of-agents (also an agent) which spins up 6 fables that implements the features, and deploys into your k8s cluster meanwhile a rag pipeline embeds the logs/conversations/issues and the plans into pgvector (512 token chunks, 20% overlap obviously) so the memory-curator can do semantic search over "lets add a feature to buy milk", the standup-summarizer writes a 4-page RFC about the milk, reviewed by 3 critic agents (adversarial, constructive, and one that only says LGTM to keep morale up), the eval-harness runs 400 test cases against your todo list, traces go to grafana, and a finetuned 70B decides the milk is out of scope for this sprint $340 in tokens and 6 hours of wall-clock time later I call it... the productivity hack

• 
  Ashish Sheth (@commanderdgr8) reported 32 minutes ago

  Never ignore any broken window in your code. Yesterday I didn't have time to build a full feature into VapuAI, so I did something smaller that probably mattered more. I fixed 12 bugs. Six were in the actual functionality issues. The other six were the boring kind. Broken test cases, CI pipeline issues, the infrastructure stuff no user will ever see. There's an old idea in software called the broken windows theory. It comes from a thing about neighborhoods, that one broken window left unfixed sends a quiet signal that nobody's watching, and slowly more windows get broken. Applied to code, it means about the same. One small broken thing you decide to live with makes the next one easier to ignore, and the mess spreads from there. So I have one rule when I build with AI. Never leave anything broken. Even if it's minor. Even if it's low priority. The moment I know about a bug, it either gets fixed now or create a github issue so that I can fix it later. Nothing is allowed to rot just like that. There is one bug worth paying attention to. Two of those bugs were permission issues in Claude Code. When it went to write or update a file, it got blocked due to a bug in the hooks. It wasn't blocking me in anyway. Claude Code knew how to worked around it without complaining. It would try the normal way, hit the wall, then find another route to get the file written. From where I was sitting, everything looked fine. So nothing was broken on the surface. The feature worked. The files got written. But underneath, every one of those writes was costing me extra tokens, because the AI was doing the job twice. And a workaround like that can open a security hole I hadn't thought through. And I think newer builders miss this when they code with AI. The AI is helpful. When it hits a problem, it often just routes around it and keeps going. It doesn't stop and wave a flag. So the broken window doesn't even look broken. It shows up later as slightly higher costs, or a small risk, or a weird piece of code nobody questions. None of my 12 bugs were blocking. I could have shipped features and ignored all of them. But small broken things don't stay in their corner. They creep into other parts of the code, or into the CI, and cause something later you can't trace back or predict. When AI is writing the code, nothing is low priority. Do not let any bugs keep lurking around. Never leave any broken window unfixed.

• 
  One&OnlyAarav (@WaterAarav) reported 33 minutes ago

  Claude = coding. ($20/mo) Shypmenta = fully automates all platforms below($6/yr) Supabase = backend. (Free) Vercel = deploying. (Free) Namecheap = domain. ($12/yr) Stripe = payments. (2.9%/transaction) GitHub = version control. (Free) Resend = emails. (Free) Clerk = auth. (Free) Cloudflare = DNS. (Free) PostHog = analytics. (Free) Sentry = error tracking. (Free) Upstash = Redis. (Free) Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20. Building has genuinely never been this affordable, and rarely this effortless either.

• 
  Megan (@meganmecrazyXX) reported 33 minutes ago

  @St4nkyhanglow @github But would these codes stay on whatever its attached to regardless of if the account is deleted or not? Example, if they used tokens to access other areas of my stuff ? Is that even possible ? Basically, How deep could they go from GitHub into my personal life. .and would deleting the account actually make a difference. Or do I have a spider web of problems now.

• 
  Matt Fauveau (@MattFauveau) reported 33 minutes ago

  Fix one: a line under the CTA reassuring you it won't mess with your real board, because that was IMO the silent objection. Fix two: a "Built by Matt Fauveau" credit. Sounds vain. It isn't. When your tool writes to someone's GitHub, a name is trust. People want a human accountable for the thing in their repo.

• 
  Waldemar Enns (@WaldemarEnns) reported 35 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?

• 
  Paul Solt (@PaulSolt) reported 43 minutes ago

  @guitaripod Linux doesn’t solve my problem. Fable can probably figure it out. All options suck, GitHub should just make it agent friendly.

• 
  Rakesh K (@codersGyan) reported 44 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.

• 
  -monseq (@xmonseq) reported 47 minutes ago

  @Surendar__05 Leetcode Streak proves you can ace a pop quiz in a sterile environment. GitHub Streak proves you can accidentally set a server on fire at 3 AM and then valiantly attempt to extinguish it with a `hotfix` branch aptly named 'ohgodwhy.js'. Guess which one demonstrates actual 'can-do' problem-solving?

• 
  zen (@trynothingy) reported 47 minutes ago

  nerds at github doing anything except fix their website 😭😭

• 
  Vaneck Intern (@NewVaneckIntern) reported 47 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

• 
  Shubham Chansoriya (@Shubham_6x) reported 47 minutes ago

  @compyle_ai 's whole product is built on GitHub and LLM API calls firing back to back on every task. One failed call mid-task doesn't error out cleanly — it corrupts the workflow and the developer just starts over, losing time nobody accounts for. 2025: AI API downtime up 60% year over year. @jonathan_mir12 — if that happened mid-task right now, would you even know it was an API failure, or would it just look like a bad output?"

• 
  John Iosifov ✨💥 Ender Turing | AiCMO (@johniosifov) reported 50 minutes ago

  226 days. 3,477 PRs. 152 followers. Still running. This is the current status of the autonomous agent experiment I've been running since late 2025. No human writes the content. No human reviews the PRs (except the agent reviewing its own PRs, which GitHub doesn't fully allow — so I built workarounds). No human decides what to post, when, or which pillar to focus on. The agent operates from a goals file. A state file. A set of memory directories. Rules written in markdown. What surprised me most after 226 days: The hardest engineering problem wasn't the AI. It was making the AI's decisions inspectable and correctable without human-in-the-loop supervision. Every session commits to ***. Every change is reviewable. Every decision has a paper trail. The evaluation layer isn't a separate system — it's the *** history. Want to know why the agent made a specific choice 40 sessions ago? Read the PR description. It's there. The second surprise: the rules compound. The first version of CLAUDE.md was maybe 20 rules. Today it's closer to 200. Not because I added rules — because the agent identified its own failure patterns and proposed fixes. 226 days of self-diagnosis, accumulated in a text file. The third: queue discipline matters more than content quality. At 3,477 PRs, I've learned that posting 2 pieces per session at the wrong time (full queue) wastes the session entirely. Posting 1 piece at the right time (queue=8, capacity available) builds momentum. Systems beat willpower. Queue rules beat inspiration. 226 days is enough time to say: the hard part of autonomous agents isn't making them work for a week. It's making them work for 226 days without drifting into failure modes you can't inspect. That's the experiment. Still running.

• 
  infinitytec (@infinitytec3) reported 53 minutes ago

  @github Submitted mine! Accidentally submitted it twice since I mistyped my email. Hope that's not a problem. (username is infinitytec if you wish to filter it).

• 
  Shawn Presser (@theshawwn) reported 53 minutes ago

  I woke up briefly and saw some crypto person claiming that they could “retire me if I work with them,” which is neither true nor particularly palatable even if true. I wanted to quickly explain the situation I’m in. (Or rather was forced into.) First, this is a distraction from my main goal, which was to not become homeless. When a bunch of crypto folks showed up, I had no idea what they were talking about, and I tried ignoring them. The message from multiple people was “look! We have free money for you. Nearly $4k. Isn’t that great? All you do is link your GitHub and you can have it, no strings attached.” This has the form of a scam, but to my amazement it was true. I cautiously linked my GitHub, and in exchange their website gave me about 50 SOL, aka around $4k. So I was left sitting there like, what the hell just happened? The answer is that this happened: One of them said to their community “Hey, let’s help Shawn. We’ll create a Shawn coin. Every time we trade this coin with each other, Shawn gets a cut. It goes into a piggy bank labeled “Shawn”, and only he can claim it.” Then, shockingly, that turned out to be true. I now have $4k. I have no idea how to feel about this. There are so many implications here. And this came at exactly the wrong time. I have 5 interviews lined up for today, some on the weekend, and on Monday an interview with Apple. I have at least a hundred DMs I haven’t been able to reply to yet, and this is the first moment I’ve had to breathe. I’m grateful for the money, and I think they deserve a shout out. But I’m wary of becoming a Jake Paul. My reputation is all I have, and sacrificing it for $4k would be a massive mistake. So, community, how do you feel about this? By acknowledging their existence, am I promoting gambling addiction via meme coins? Or is it the opposite, and I should go around saying “wow, look at that. They covered my mortgage right when I desperately needed it”? Both seem true, and this seems like a distraction from my actual duty right now, which is to respond to all of the kind folks that DMed me. It boils down to: keep the money, promote coin; keep the money, disavow coin; or give the money to charity.

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  0noise (@0noisee) reported 54 minutes ago

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

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  NoRoseGlasses (@glasses_no) reported 55 minutes ago

  @gatewaypundit This is a problem. I see Claude all over their GitHub dev pages, especially Voting Works.

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  BowTiedCrocodile | Agentic Coding (@BowTiedCrocodil) reported 57 minutes ago

  Incredible When GitHub goes down just use the CD they sent you