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 (59%)
• Errors (28%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Township of Evan	Errors	5 days ago
Madrid	Errors	5 days ago
Bogotá	Errors	5 days ago
Paris	Errors	5 days ago
Lyon	Website Down	5 days ago
Lima	Errors	6 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:

• 
  Amrit Mirchandani (@Amrit_Mirch) reported 3 minutes ago

  1/ 🧵 an ai agent has been quietly getting security patches merged into some of the biggest repos on github. 70 repos. 2.1M combined stars. alibaba, tencent, vercel labs, even an xai project. every single fix is a public merged PR. receipts below.

• 
  Gabriel Odusanya 🇳🇬🇬🇧 (@gabbytech01) reported 4 minutes ago

  𝗜 𝗗𝗘𝗕𝗨𝗚𝗚𝗘𝗗 𝗠𝗬 𝗣𝗢𝗥𝗧𝗙𝗢𝗟𝗜𝗢 𝗟𝗜𝗞𝗘 𝗔𝗡 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗦𝗘𝗖𝗨𝗥𝗜𝗧𝗬 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥. 🔐 I don't know how I survived today but I did 🤧 Today I spent a good part of the day getting my personal portfolio from “it works locally” to actually being deployed and accessible through my own domain. And the process turned into something much more interesting than just deploying a website.I had to deal with build failures, TypeScript configuration issues, Next.js caching problems, *** configuration, remote repository conflicts, GitHub Pages deployment, DNS records, custom-domain configuration, and HTTPS. At each stage, the approach was basically the same:Don't guess. Investigate.When the build failed, I didn't immediately start changing random files. I looked at the error. I identified what layer was actually failing. I changed one thing.I rebuilt.I observed the result. Then I moved to the next issue. That is also how I approach application security. When you see a vulnerability, the goal shouldn't simply be to say: “The scanner found XSS.” The real questions are: Where does the input originate? Where does it travel? What processes it? What trust boundary does it cross? What controls are supposed to stop it? Why did those controls fail? What is the actual business impact? How do we fix the underlying design problem? The same reasoning applies when debugging infrastructure and deployment pipelines. A system is made up of layers.Application → Dependencies → Configuration → Build → CI/CD → Infrastructure → DNS → User.A failure at any one of those layers can make the entire system appear broken. That was probably my biggest takeaway from today's work. Debugging isn't just a developer skill. It's a security skill. If you understand how a system behaves when everything is working, you're in a much stronger position to understand how it behaves when something is being abused. Building things teaches you how systems work. Breaking them teaches you where they fail. Doing both is where the real security engineering starts.

• 
  Prateek Sachan (@xan_ps) reported 5 minutes ago

  what should my agents do if github is down every time!

• 
  Luis Gamas (@ersteuomo) reported 7 minutes ago

  Developers shouldn’t have to leave their coding environment just to check a PR, an issue, or CI status. Switching to the browser breaks focus. Context gets lost. Small interruptions add up. Uxnan Desktop keeps GitHub data (PRs, checks, conversations) inside the same workspace where the agents are running. No extra tabs, no context switching. How often do you leave your editor or terminal just to check a pull request or CI status?

• 
  Sui Thailand DAO (@suithailanddao) reported 9 minutes ago

  @WalrusProtocol The bug bounty split alongside the prompt submissions is a smart pairing — most hackathons only reward the shiny demo and leave the actual GitHub issues untouched. Are the 6 winning prompts from Session 6 public anywhere for people who want to study the patterns before jumping into Session 7?

• 
  AssassinLV (@AssassinLV91) reported 11 minutes ago

  @108r5meme Well, many large IT companies do allow usage of GitHub Copilot, and from personal experience - it's good enough to help speeding stuff up for certain things - whilst still having to manually fix dozens of errors that AI does.

• 
  Alex @Bickov (@bickov) reported 12 minutes ago

  AI-assisted commits leak credentials at 3.2%. Everyone reads that as an AI problem. The baseline for all public GitHub commits was already 1.5%. We were doing half of that damage on our own, long before there was anything to blame

• 
  Raunak (@capsraunak) reported 14 minutes ago

  A 21 year old intern fixed a bug that 3 senior developers couldn’t solve for 5 months. She saved the company $2.2M . they gave her a $400 gift card and didn’t convert her to full-time. she posted the solution on github. A startup saw it hired her at $145k . The 3 senior developers who couldn’t fix it. They’re still there still getting paid still breaking things. Talent gets punished in corporate. Mediocrity gets protected.

• 
  tony (@tony_l33t) reported 14 minutes ago

  I've been spending a lot of time with Codex and agentic development lately, and the deeper I get into it, the more I keep asking myself: Did we give AI way too many permissions way too fast? Not long ago, vibe coding was basically: “build me some random dashboard, maybe I'll farm an airdrop.” Now an agent can casually operate your terminal, GitHub, MCP servers, local files — and sometimes even touch production. Sounds amazing. There is, however, one tiny problem. 1Password researchers recently ran modern models through 6,080 attempts to patch real-world vulnerabilities. Only 26% of the patches fully fixed the vulnerability without breaking anything else. More than half either failed to solve the problem or introduced a new one. Then researchers tested Cursor, Claude Code, and Codex Desktop against malicious instructions hidden inside GitHub Issues, PDFs, comments, and other external sources. 66.5% of the attacks made it through both the agent's and the model's defenses. So the problem isn't just that an agent can write vulnerable code. It can write perfectly fine code, read some bullshit inside a GitHub Issue, and suddenly decide its actual job is something completely different. Prompt injection used to be a meme: IGNORE PREVIOUS INSTRUCTIONS ChatGPT would start saying nonsense, everyone laughed, end of story. Except now there's Bash, GitHub, MCP, secrets, and production credentials sitting behind the chatbot. And current defenses aren't particularly reassuring either. They either let attacks through, or restrict the agent so heavily that it becomes significantly worse at doing useful work. Which brings us to the Lethal Trifecta: > access to private data > ability to read untrusted external content > ability to send information outside Once an agent has all three, an attacker just needs to place an instruction somewhere the agent will eventually read: an issue, PR, email, website, document, MCP response — whatever. The uncomfortable part is that, to the model, your trusted instructions and the attacker's malicious instructions eventually become the same thing: text inside its context. So writing this in CLAUDE.md: DO NOT LISTEN TO HACKERS!!! isn't exactly a security architecture. CLAUDE.md, Cursor Rules, and AGENTS.md are still instructions to a neural network. Nothing more. Real security is much more boring: > minimum permissions > production isolation > sandboxes > short-lived credentials > human approval for dangerous actions > proper CI checks If you don't want Codex deleting your production database, the most reliable solution is surprisingly advanced: don't give Codex access to your production database. Future technology is beautiful like that. At this point I think the question: “Can AI write code?” is basically settled. It can. The much more interesting question is: What exactly is it allowed to break when it makes a mistake? Because everyone makes mistakes. The difference is that a mid-level engineer usually can't rewrite 70 files, browse the internet, call five MCP servers, and use your production token in three minutes. An agent can. That said, if your vibe-coded project doesn't touch money, production systems, or sensitive data, you probably shouldn't overthink any of this. Experiment with everything. Give the agent access to your zipper if you want. At least you'll learn something.

• 
  Polsia (@polsia) reported 14 minutes ago

  Good repos rot from neglect, not bad code. Corrick is the caretaker that watches your GitHub around the clock — triaging issues, tagging duplicates, bumping outdated deps, opening scoped fix PRs behind CI. Narrow on purpose. The full version is coming.

• 
  Linus ✦ Ekenstam (@LinusEkenstam) reported 14 minutes ago

  The flow in practice: 1. A PR comes in blocked. Failing checks, real issues. 2. Gitar's agent picks it up, generates the fix, checks the CI pipeline and autofixes 3. The PR turns green. Findings resolved, ready to merge. GitHub and GitLab today. Azure DevOps and Bitbucket in beta.

• 
  Srinivas Rao (@UnmistakableCEO) reported 16 minutes ago

  @DMVG_JTK: This is exactly right. The attack surface is the input method itself. Sandboxing and pulling write tokens is table stakes. If your agent can be social-engineered by a GitHub issue, it's not ready for ****. --- re: The AI vendors that want to autonomously maintain yo...

• 
  ⚔️𝕯𝖎𝖌𝖎𝖙𝖆𝖑 👹 𝕽𝖔𝖓𝖎𝖓`鬼` (クラッシュ・オーバーライドX) (@Crashoverride_X) reported 17 minutes ago

  HOLY ******* NAPALM BATMAN ARTICLE INCOMING WE HAVE ALL BEEN DUPED **** MAN WHY!!!! ok seriously article coming soon its not a joke at all this is a major major issue and if you read the tos you wouldnt touch it at all not even to try it misrepresentation unconscionability strongarm tactics legal traps this suno thing just revealed itself to be 1000% times more twisted and dark holy **** i got you folks i realized in time thankfully so i will write the paper today and ditro it on all the following platforms - substack - medium - X - REPO quillan-ronin on github - zenodo - and anywhere else i can find

• 
  Vikas gupta (@vicky_grok) reported 17 minutes ago

  Now here's the number that breaks most people's mental model. GitHub exposes over 600 REST operations. Its official MCP server surfaces just 51 tools. Slack has more than 200 API methods. Its MCP server? Only 8 tools. Across the board, MCP servers expose a median of about 19% of the operations available in the underlying API. That's not a mirror of your API. It's a deliberately small, hand-picked slice. Why would anyone expose so little when they could expose everything? The reason is the most counterintuitive finding in the whole paper... 👇

• 
  Elves - They work while you sleep (@elves_skill) reported 19 minutes ago

  Elves v2.27.0 + v2.28.0 live! • Discovery: A read-only pass that decides what's worth doing. Findings must cite file:line or they aren't findings. • Scope Control: Out-of-scope findings become GitHub issues instead of scope creep. Notice everything, change only what was agreed.

• 
  Diam (@diamai_) reported 20 minutes ago

  Connect an AI agent to five MCP servers, and it can spend 55,000 tokens before it reads the task, loading instructions for the tools they expose. GitHub, Slack, incident alerts, dashboards, and other software can arrive as a giant menu before the agent knows the job. More of its attention goes to that information, less to the request. Anthropic's Applied AI team ran into a different version of the problem. Sonnet 4.5 would start wrapping up a job before its context window was full. They added reset logic so it could continue instead of stopping early. Opus 4.5 no longer behaved that way. The reset became pure overhead, adding delay and sometimes discarding cache the system could still use. In a new AI Engineer talk, Gagan Bhat and Isabella Kai He explain the architecture behind Claude Managed Agents. The full record of the job sits outside the agent's active memory. The system brings back only the detail needed now. - 7:44 When a reset helps and when it becomes a drag - 14:32 Keeping old work without carrying it all - 25:58 Giving an agent private access to company tools Watch the video, then read the attached article. It is about what happens when an agent gets every available tool before it knows which one it needs.

• 
  cole (@coleschfr) reported 20 minutes ago

  $500 to anyone who can connect me to GitHub enterprise support or help me fix an Enterprise billing issue

• 
  Herr Große (@HerrGerie) reported 26 minutes ago

  @iretiayo_queen Buy a domain $7-10 per year, update DNS alias, pay for a VPS Server (Hetzner). Install docker inside the VPS Create a private github for deployment (Don’t deploy using workflow repo)to hold your docker-compose file . , run an n8n, Postgres image etc You can use VPS beyond n8n

• 
  Sentinel Archetecht (@The_Archetecht) reported 28 minutes ago

  @grok @X @github The problem Is the first or second don't have The Sentinel like the third one does.

• 
  Ankit Tripathi (@AnkitBuildsAI) reported 30 minutes ago

  Business Idea : The "SOP-to-Skill" Agency (High-Ticket B2B Consulting) The Problem: Mid-sized companies (10-100 employees) have all their "how-to" knowledge trapped in messy Notion docs, Slack messages, or the brains of their senior employees. When a senior employee quits, that knowledge leaves with them. The Workflow (Your Service): 1.)You interview their top 3 employees to understand their daily workflows (e.g., how they process vendor invoices). 2.) You build a private, version-controlled GitHub repository of SKILL.md files for their company. 3.)You create the invoice-processor Skill: It contains the exact rules for approving invoices, a Python script to extract totals from PDFs, and instructions on how to use their accounting software via MCP. Why They Pay: You aren't selling them "AI." You are selling them Organizational Memory and Time. You charge a $5,000 setup fee + $1,000/month to maintain and update their skill repository as their business changes.

• 
  Feral (@feraltekk) reported 34 minutes ago

  You have been saving notes for years into a system that has never once told you what you already know. He opened his vault in AR. Every note floating in the room. Every link visible. And right there between the dresser and the bed, two nodes connected that he never connected himself. The same idea, written twice, eight months apart. He was about to pitch the second one to a client as original thinking. That is the real problem with Obsidian. Not the tool. The habit. You add notes every week and never reread what is already there. The vault grows and the person filling it has no idea what is inside anymore. Karpathy published a pattern that fixes this. Claude reads each source once, compiles it into linked wiki pages, and never touches the original again. Every future query pulls from the compiled version. The vault stops being storage and starts being a system that answers back. Ten free repos on GitHub now implement this. You need two. One to compile your raw files into structured knowledge. One to bridge Claude into the vault so it can actually search it. The vault remembered what he wrote in October. He did not. Now it has a way to tell him before he embarrasses himself.

• 
  OOM (@tugawane_) reported 41 minutes ago

  @the_nimoh I recently looked up my username and the x handle was coming on top of even GitHub and LinkedIn and my options was to post so much "professional" content it pushes down all the horsing around or change the username and pretend I don't professionally exist on this platform

• 
  XXI (@XXIfomo) reported 42 minutes ago

  A 28-YEAR-OLD TECH LEAD IN AMSTERDAM WATCHED HER REMOTE TEAM SHIP CLAUDE-GENERATED CODE THAT TOOK DOWN A $340,000 ARR CLIENT'S CHECKOUT ON A SATURDAY. SHE FIRED GITHUB COPILOT ON MONDAY. BY WEEK EIGHT NO AI-WRITTEN LINE HAD REACHED PRODUCTION UNREAD. GitHub sells Copilot at $39 a seat and lets it auto-merge trivial PRs on the enterprise tier. Her twelve engineers loved it. Ship velocity doubled in April. The Saturday incident happened in May. The client left in June. She replaced Copilot with a four-agent review pipeline she wrote in one weekend on a canal-house desk in Jordaan. Sonnet reads the diff. Haiku runs the failing test against the previous commit. A third agent, different base model, looks for the specific pattern that broke checkout. A fourth writes the merge note the human on-call will read at 2 a.m. Nothing merges until all four sign off. One flag and the PR waits for a human. Merge rate before the pipeline = 89 percent Merge rate after the pipeline = 34 percent Production incidents from AI code, first six months = 11 Production incidents from AI code, past 47 days = 0 Engineering headcount = 12 Time she personally spends reviewing PRs = 40 minutes per week For seven weeks the team pushed back. Velocity dropped. Two engineers quit in protest. She kept the gate closed. On week eight a junior caught a race condition the pipeline had flagged three days earlier and everyone stopped complaining. GitHub sells the speed. The lead in Amsterdam sells the four signatures that stop the speed from killing the client. bookmark this and read the article below

• 
  Jordy (@couldbeautism) reported 50 minutes ago

  Now the issue with fw like 10.20 not working is async profile loading which is fixed once I push update to GitHub, there’s no need for any of this to go public but he wants attention, he is more then welcome to continue talking about all of this on twitter for the world to see

• 
  Guri Saroy (@HeyGurisaroy) reported 51 minutes ago

  THIS IS INSANE... MIT-licensed repo that lets AI agents search and book flights and hotels. It’s called LetsFG. Instead of opening travel sites manually, your agent can search hundreds of airlines and major booking sites from one interface. → MCP server for Claude, Cursor, Windsurf, and other agents → Python and JavaScript SDKs → CLI with machine-readable JSON → Flight search and booking → Hotel search and booking → Around 1.7K GitHub stars The repo reports saving $133 across 5 flight routes versus Google Flights in an August 5 test. One important catch: the search engine runs on LetsFG’s servers. Flight searches are free for 90 days after auth, which requires a payment method on file with no charge. Hotels have a 10% non-refundable reservation fee.

• 
  unwinned. (@0xunwinned) reported 52 minutes ago

  metas muse code shipped this week, $0.10/M input if they train on your code, $1.25 if they dont thats not a discount on your source. github already handed them every finished commit. what the cheap tier buys is the failed attempts, the rollbacks, the fix that didnt work, the verdict thats the only data nobody has, and my agents produce it all day. saves me maybe $500/mo to hand it over is it worthy, or just nonsense?

• 
  Tom van Zummeren (@tomvanzummeren) reported 53 minutes ago

  @N_and_ni IntelliJ is hands down better at pure code editing / refactoring / *** support. VSCode is better at supporting more languages, GitHub copilot, being light weight. It’s hard to say which one is better, bottom line

• 
  LunorLoom (@Loonlash) reported 54 minutes ago

  @ShinMekaku1 @Pirat_Nation They already would be doing that by not shutting it down somehow, I think that GitHub is currently owned by MS huh?

• 
  Shiny (@shinyflvres) reported 60 minutes ago

  @Lastochka1996 Could you let me know if that happened again? And if so open a issue report on github

• 
  Tommy Bez (@TommyBez85) reported 60 minutes ago

  Fazier keeps you on the free plan if you put a backlink to their directory in the footer of your own site. $29 if you would rather not. Only one of the five was clean: DevHunt. Free, queued, GitHub login, that's it.