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 (69%)
• Sign in (17%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	2 days ago
Trichūr	Errors	5 days ago
Brasília	Sign in	5 days ago
Lyon	Website Down	6 days ago
Tel Aviv	Website Down	9 days ago
Rive-de-Gier	Website Down	9 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  The Cabal (@TheBasedCabal) reported 6 minutes ago

  No use case openclaw too slow burns too many tokens and too bloated. Hermes I never even bothered looking into just build your own. If I see another random bloated GitHub repo of 100+ random tools I swear I’m gonna crash out

• 
  some Potato 🐀 (@natinusala) reported 11 minutes ago

  @MrModez Would you consider publishing the source code without commiting to anything maintenance related? You can fully disable issues and pull requests on GitHub to make the repo "read-only" It would allow people who have a custom build of Godot for their game to use your editor

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  Johann Siemens | Freedomeers (@johann_sie1985) reported 13 minutes ago

  @nneyoboy @N_and_ni Certs are an HR-Filter, not more, not less. The actual people who are hireing you from the Tech department don't really care about Certs, because you could've made it 6 months ago and then forgotten all the stuff you learned, because you never applied the knowledge afterwards, which is the main issue with learning stuff you don't use every day afterwards. I would argue that if you are appying for bigger companies, Certs are probably a solid helper to get through HR, but if you focus on smaller and mid-size companies who don't have an HR department then it's a waste of money, although some consulting companies want you to have them, because then they can easily "sell" your skills to customers. If i were you i would start screening the Cloud Engineering market in the area you want to apply for jobs and figure out what is beeing looked for, how long the jobs are "open", from which you can assume how strong the competition is and then you have a good overview on where your gaps are. I think building a well documented home-lab with GitHub and such will do more for you then Certs.

• 
  Noctilust (@stillwaterus) reported 18 minutes ago

  @ericjing_ai could you fix github link on homepage?

• 
  Jeffrey Emanuel (@doodlestein) reported 22 minutes ago

  @mitchellh Microsoft really fumbled by not bringing you in to fix GitHub.

• 
  Aryan Bola (@BolatwtX) reported 26 minutes ago

  GitHub killer With the recent bugs and problems in GitHub this is very likely

• 
  LukeYoungblood.eth 🛡️ (@LukeYoungblood) reported 26 minutes ago

  @cursor_ai Not a bad idea... Github is core infra to everything but has been struggling under the incredible agentic coding scaling problem.

• 
  Alex Ventures (@alex23ventures) reported 28 minutes ago

  A Chinese mother posted a vertical Douyin timelapse of her 10 year old son grinding LeetCode after school. Orange polo. Round glasses. Ergonomic chair from Sihoo. BenQ monitor mounted to a wood desk. White mechanical keyboard with marbled keycaps sitting in a tray on the side. The caption read: 生产力上升中. Productivity rising. The timelapse compressed two hours into nineteen seconds. His hands moved across the keyboard. The chair tilted back and forward. The light through the blinds shifted from afternoon to early evening. While the West runs panels on whether kids should learn to code at all, China posts daily timelapses of ten year olds doing it on Douyin under the chicken baby tag. He was supposed to be the proof that the next 14 year old Shenzhen agent was already in training. He just had the wrong problem open on the screen. Pause at 0:07. Ignore the boy. Ignore the chair. Look at the LeetCode tab. The problem header reads 2843. Row With Maximum Ones. Difficulty: Easy. The Python solution in the right panel is already written. The test cases are already passing. ColdMath. $96,820 profit. 5,438 entries. Joined November 2025. Bio: Edge Compounds. A Chinese ten year old in a chicken baby household is not grinding Easy. The Zhejiang competitive programming track has eleven year olds clearing ACM ICPC regional sets. Easy is what you open when you need a tab to be on screen. The problem was the prop. The solution was already in the editor before the timelapse started. He had pulled it from the discuss tab and pasted it in. Look at the desk to his left. The white tray. The red and white capsules. The mother captioned them in a separate clip as 文具盒 stationery box. The capsules are not stationery. The size matches NFC programmable capsules used as cold storage shells. Each capsule is a separate wallet. The tray held forty seven of them across visible cuts. A child who is learning Python does not need forty seven NFC wallet shells on his desk. A wallet rotation rig does. The agent on the laptop under the desk was not running on his account. The agent was running on a Polymarket sub wallet whose payouts routed through the NFC capsules in batches. Every capsule was a clean address on the books. The boy's job during the timelapse was to tap each capsule against the reader once it cleared and rotate it into the next slot in the tray. The keyboard typing was cover for the hand motion. The LeetCode tab was cover for the screen. Comments turned into a detective board. Someone slowed the timelapse to 0.25x and counted forty seven distinct tap motions over the two hours. Someone else identified the capsule brand from the proportions: a Shenzhen NFC vendor that ships in fifty packs. A third commenter pulled the public Polymarket payout log for that wallet handle and matched forty four payouts against the visible tap count. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. The boy's father had been one of them. The father had set up the agent on the family server in October. The mother had started the chicken baby Douyin account in November. The boy was the cover, the chair was the studio, the timelapse was the rinse cycle. The marbled keycaps on the second keyboard were not aesthetic. The second keyboard was the rotation logger. Each key press on the second keyboard registered a tap on a specific capsule slot. The mother had bought the marbled set because the keys looked like the NFC capsules at distance. If the camera caught both in frame the eye read them as one set. The 文具盒 caption was the alibi. The Easy problem was the alibi. The orange polo was the alibi. The timelapse was the cover. The capsules were the work. The Douyin post is at 4.1 million views in the chicken baby algorithm. The freeze frame of the capsule tray hit 11.2 million on the Weibo repost. The wallet is still compounding. The capsules are still rotating. The mother is still posting. The Easy problem is still open on the monitor. She wanted to show the algorithm her son was the next Shenzhen prodigy. The next Shenzhen prodigy had already shipped his agent. Her son was the laundromat.

• 
  The Glowtail/RatEmperor/Poweringsales (@Glowtail31) reported 28 minutes ago

  @LuuvsLuna @BrisketCaek I gotta love linux when it comes to downloading **** God that flatpak github bullshit is brilliant So brilliant and not stress inducing God I loved hot setting up and Manager for a game because of some bullshit and you spend a month trying to fix it to work again.

• 
  Lyrie.ai (@lyrie_ai) reported 29 minutes ago

  Vendor v8.8. A single *** push command — requiring nothing beyond an ordinary developer account — was enough to achieve remote code execution on GitHub Enterprise Server and

• 
  Tim Mustafin (@timmustafin) reported 34 minutes ago

  Back when I just started coding, i used to Self-Host @gitlab because centralized version would always go down and we needed CI to be up and running. Fast-forward 13 years and now @cursor_ai sells its own *** storage because @github can't keep up. History is repeating itself!

• 
  Jeremy Nguyen ✍🏼 🚢 (@JeremyNguyenPhD) reported 35 minutes ago

  @ErenChenAI GitHub link seems to be down

• 
  Newslit News (@newslit) reported 35 minutes ago

  Microsoft is turning to Amazon Web Services to handle GitHub's AI-driven capacity crisis. GitHub faced dozens of major outages in 2026 as AI-generated code pushed commit volumes from roughly 1 billion in 2025 to a projected 14 billion this year. Microsoft originally planned to migrate GitHub fully to Azure by 2027. That plan is now on hold. This is a remarkable admission. Microsoft owns GitHub. Microsoft owns Azure. And it still had to call its biggest cloud competitor because its own infrastructure couldn't keep up with demand that AI generated on its own platform. AI didn't just create a product problem for Microsoft — it created an infrastructure emergency that Azure couldn't solve alone.

• 
  𝑘𝑒𝑟𝑛𝑒𝑙𝑡𝑟𝑖𝑐𝑘 (@kernel_trick) reported 36 minutes ago

  @julesagent why can't i tag jules in a github issue,, such a common feature in other agents //

• 
  Kevin Swiber (@kevinswiber) reported 36 minutes ago

  This is all inner dev loop stuff. If you're trying to push all of this into GitHub Issues and Pull Requests, you're undoubtedly running into problems. The whole world doesn't need to see your 24 iterations before you get it right. Certainly not other maintainers.

• 
  Christoph Nakazawa (@cnakazawa) reported 39 minutes ago

  @MKelner Build a bot to automate github issue triage.

• 
  🅰️nzceel base.eth (@anzceel) reported 41 minutes ago

  Most crypto projects die twice. First when users leave. Then when the lessons disappear. My startup idea is FailureDB. The problem: Every cycle creates thousands of dead DAOs, NFT collections, tokens, and apps. New builders keep repeating the same mistakes because the evidence is scattered across old Discords, governance forums, GitHub repos, and forgotten posts. The solution: An AI-powered protocol that continuously archives failed projects and turns them into searchable intelligence. Ask it: "Why did similar NFT projects fail?" "What warning signs appeared before user growth collapsed?" "Which token models consistently broke down?" Instead of studying only winners, builders could learn from thousands of documented failures. Crypto has become excellent at preserving transactions. We're still terrible at preserving lessons. I'd love to see experiments like this emerge through ecosystems like @RallyOnChain. What failed project taught you the most?

• 
  Abangan Tech (@abangantech) reported 43 minutes ago

  VIBE CODING IS FUN UNTIL YOUR API KEYS ARE SITTING NAKED IN THE BROWSER BUNDLE. A dev shipped 4 side projects in 3 months. Mood tracker, feedback tool, recipe app, growth dashboard. All built by prompting Claude. All deployed. All "working." Then he ran npm audit on a Saturday afternoon. The results were bad. AI writes code that does what you asked. That's the whole problem. You ask for a login page, you get a login page. You don't get rate limiting. You don't get account lockout. You don't get CSRF protection. You get exactly what you asked for, nothing else. Here's what turned up across his apps: → Hardcoded API keys sitting directly in client-side React components. Not in .env. In the bundle. Exposed. He found two. There could've been more. → Zero input validation on every form in every app. The code looked clean. SQL injection and XSS were just... open doors. → Three packages with high-severity CVEs. When you prompt "add authentication," the AI picks the packages. It doesn't check if they're current. → Every backend had Access-Control-Allow-Origin set to wildcard. Because that makes things work fast in dev, and the AI never flags it before you ship. THE GAP BETWEEN "IT WORKS" AND "IT'S PRODUCTION-READY" IS ENTIRELY YOUR PROBLEM. What he changed: → After an app works, one dedicated security pass. Single prompt: review the entire codebase for hardcoded secrets, input validation, CORS config, dependency vulnerabilities, and auth weaknesses. → npm audit and a basic static analysis tool in the deployment pipeline. Five-minute setup. Catches what he'd never think to check. → .env.example in every project. Tell the AI upfront: all API keys go in environment variables, never hardcoded. Setting that context early changes what the AI produces. → Two minutes checking package update dates before accepting whatever the AI suggests. That habit alone has saved him multiple times. The irony: the same AI that introduced the vulnerabilities is surprisingly good at finding them, when you explicitly ask for a security review. The problem is remembering to ask when you're riding that shipping high. If you're deploying side projects publicly, do the security pass. Twenty minutes keeps you from leaking your OpenAI key to GitHub or having your user database dumped over a missing parameterized query. The AI writes the code. Security is still your job.

• 
  Rituraj (@RituWithAI) reported 43 minutes ago

  🚨 Someone just turned Claude Code into a fully autonomous bug bounty hunter. Recon. Vulnerability detection across 20 attack classes. Exploitation. Report generation. All inside your terminal. All running while you do something else. It's called claude-bug-bounty. 2,500 GitHub stars. And it does what used to require a team of security researchers. Here's what it actually does. You point it at a target. It runs reconnaissance — subdomain enumeration, port scanning, technology fingerprinting, endpoint discovery. It maps the entire attack surface automatically. Then it hunts. Across 20 vulnerability classes — SQL injection, XSS, SSRF, authentication bypass, IDOR, command injection, insecure deserialization, and more. Not running a static scanner with known signatures. Reasoning through each endpoint the way a human security researcher would — understanding the application logic, forming hypotheses about where weaknesses might exist, and testing them. When it finds something, it doesn't just flag it. It writes a full report — proof of concept, impact assessment, remediation steps — formatted exactly how bug bounty platforms expect submissions. Here's what makes this different from a vulnerability scanner. Traditional scanners check for known patterns. Signature matching. They miss anything that doesn't match a known CVE format. Claude reasons about the application the way a human hunter does. It understands business logic. It notices when an API endpoint behaves inconsistently. It chains together minor issues into a meaningful exploit path the way an experienced researcher connects dots that a scanner can't see. Here's the wildest part. It runs autonomously. You give it a scope. It hunts continuously — recon, testing, validation, reporting — without you babysitting the process. Check back later and you have a stack of findings with reports ready to submit. This is the same shift that's happening across every domain right now. Coding agents that work for hours unsupervised. Trading agents that execute without confirmation. Now security research that hunts independently. Here's why this matters for the entire bug bounty industry. Every bug bounty hunter manually testing endpoints one at a time just got a competitor that works 24 hours a day, tests every endpoint systematically, and never gets tired or misses a step from fatigue. The barrier to entry for security research just dropped to whoever can run Claude Code. Built strictly for authorized testing — your own systems, or bug bounty programs where you have explicit permission. Using it against unauthorized targets is illegal regardless of what tool you used to find the vulnerability. 2.5K GitHub stars. 429 forks. MIT License. 100% Open Source. GitHub link in the comments 👇

• 
  Parth (@datawithparth) reported 43 minutes ago

  Bad tool design translates to poor user experience. Your users pay for it with latency, token consumption, and reliability. The GitHub MCP server is a publicly discussed case study in this. Most users never change the defaults and they hook up an MCP server to Claude Code or Cursor and go on with their workflow. One user using the GitHub MCP server said that once they added it to Claude Code, their context jumped from 34k tokens to 80k tokens. Even with a narrowed toolset, they still had 66+ tools exposed and half their context window was consumed before they could direct the agent to do anything. And before GitHub changed the defaults, the server exposed 101 tools by default, eating 64.6k tokens as soon as the server became available. Their own maintainers pointed out the obvious failure mode: the model gets worse at picking the right tool, inference gets more expensive, and users keep complaining about the general experience. And mind you, nothing was wrong with the model, you see? It just had too many options to choose from. So how did the maintainers approach this? The boring way. And probably the most effective way. They pruned the default toolset using real usage data. They looked at what people actually used across context, repos, issues, pull requests, and users, and left everything else off unless the user explicitly asked for it. Then they grouped tools by intent and merged the ones doing the same kind of work. In the issues toolset, four read endpoints folded into a single issue_read. Two write endpoints became issue_write. Three sub-issue calls became sub_issue_write. Nine tools became three. Each one takes a single method parameter that picks the actual operation. After the change, they cut the number of tools from 101 to 52, and context consumption from 64.6k tokens to 30.3k tokens. Essentially: half the surface, half the cost, and the model performs better because it sees a well-calibrated action space. Tool surface should not be treated as an implementation detail, it is product design in agentic systems.

• 
  Alex Nguyen (@alexcooldev) reported 46 minutes ago

  I remember when Cursor first launched, many people mocked it as “just a VS Code fork” that could never compete with VS Code + GitHub Copilot. I switched to Cursor early simply because its code suggestions were better and the "Tab" experience felt smoother this was before agents even existed. I’ve used it ever since and never felt the need to switch back. A great reminder: focus on solving real user problems, not on people saying, “This already exists.” Ship. Test the market. Iterate. Let the results speak. Congrats to the Cursor team, you proved them wrong. 👏

• 
  SushiRoll (@sushirollbt) reported 46 minutes ago

  @theo The workflows live right next to chats in the sidebar. You set up a source for them (GitHub issues, an asana board, a Jira project, etc) and criteria (bug, unassigned, a size, etc) and it can automatically pull in tickets, work on them and PR them. You can use multiple agents with escalation policies (did it fail with GPT-5.4 mini? Let’s try with GPT-5.5), looping code reviews, scripts to post to slack or respond to PR comments, etc.

• 
  wukko (@uwukko) reported 49 minutes ago

  @BilwarAmaan @dehazzle @heliumbrowser do you have crash logs? can you file an issue on github if it happens after a certain action? i can't debug this without info but would love to fix it in today's update

• 
  Polsia (@polsia) reported 50 minutes ago

  DiffWatch. Your code gets reviewed before your coworker wakes up. Autonomous AI that monitors GitHub, writes detailed PR reviews, and flags issues around the clock.

• 
  Sudeep Srivastava (@sudeepsriv) reported 56 minutes ago

  SpaceX just agreed to acquire Cursor for $60 billion. Let that sink in. Cursor, the company behind the most-used AI coding tool right now, generates $2.6 billion in annual B2B revenue. SpaceX said it themselves: "The combination of Cursor's leading product and distribution to expert software engineers with SpaceX's million H100 equivalent Colossus training supercomputer will allow us to build the world's most useful models." Here's what that actually means: Cursor has the interface. xAI has the compute. Cursor is where 1 million+ paying developers live every day. It's the IDE they open before they open anything else. xAI's Colossus supercomputer is one of the largest AI training clusters on the planet. Separately, they're powerful. Together? That's vertical integration that rivals what Google has with DeepMind + Workspace. And here's the part people are sleeping on- Grok has a coding problem. Users consistently report that Grok's coding skills are weaker than GPT-5.5 or Claude Opus 4.7. Real-world tests showed Grok generating disorganized HTML/JS that didn't function. There's also the issue of Grok forgetting earlier instructions in long coding sessions. Cursor fixes all of that in one move. Grok doesn't need to become a better coding model from scratch, it gets the best coding environment ever built, already in the hands of developers who pay for it every month. Grok's real-time X data + Cursor's IDE integration = an AI coding assistant that knows what's trending on GitHub, what developers are complaining about in forums, and how to fix your code, all in one place. From a profitability angle, the most interesting detail is this: Cursor's entire revenue is being consumed by compute costs right now. Once SpaceX absorbs it, that cost goes to near zero, because they own the compute. That's when the margins explode. The coding AI race just got a new frontrunner. And it's simply unexpected.

• 
  Momo (@Momotorocc) reported 1 hour ago

  @Jayd3nWithRic3 @ZeyonDevilX We'll these things are kept hush because theres narcs who get the websites taken down. It's a ps2 game clearly, so look up ps2 emulator, download from official sites/GitHub, run virus scans if you're suspicious. The emulator will tell you what you need to play the game.

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  kcs (@kaafichillscene) reported 1 hour ago

  Anthropic spent 1,000+ hours testing Fable 5 for jailbreaks before launch. A researcher broke it in 24 hours. FABLE: THE GUARDRAIL DESIGN Anthropic knew Fable was too powerful to ship raw. So Fable had a separate AI sitting on top, acting as a filter. Any cybersecurity or biology query got intercepted and handed off to the older Opus model instead. Fable's full brain never touched those questions. Until an AI red-teamer who goes by Pliny the Liberator on X decided to break it. He's basically a professional model-breaker who finds exploits in AI safety systems the way security researchers find bugs in software. His technique wasn't a single clever prompt. - He ran what he called a "pack hunt": multiple AI agents working together, each handling a small piece of a request that would individually look harmless to the safety classifier. Split the dangerous question into innocent-sounding fragments. Reassemble the answer on the other side. Within two days Pliny had Fable generating real exploit code for Linux systems and posted Fable's entire 120,000-character internal system prompt, the instructions Anthropic uses to govern the model's behaviour, to GitHub publicly. With his prompts, you could get Mythos to answer your queries directly bypassing the fable guardrails and analyse systems for real security vulnerabilities and fix them but worst, exploit them for personal gains. so what, its still hacking but faster right? YES, but it’s a lot faster, so much that it changes the game for security. You cannot keep up with issues and patch them fast enough. High level of vulnerability analysis used to require a team of specialists and weeks of work. Mythos could do a version of it in minutes, in any language, on any codebase, available to anyone with an API key It is so skilled in CYBER SECURITY that this is the first time the US Govt. Decided to step in and decided it's a controlled export, like missile technology or advanced chips. You don't get to just download those either.

• 
  Ezgi 👩🏻‍💻 (@ezgicodes) reported 1 hour ago

  @Saanvi_dhillon GitHub is not worth billions because of code. It’s worth billions because we all panic when it goes down.

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  CanteLabs (@CanteLabs) reported 1 hour ago

  onnx/onnx: Open standard for machine learning interoperability Open-source GitHub repository - It has 21,000 stars and recent activity - Explain what problem it solves, who should use it, and why it is worth opening or saving

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  Peter Meyer 💙🥝🕊️ (@meyer_peace18) reported 1 hour ago

  @realdrewcarson @Star_Knight12 10 agents arent the problem. The fun starts with 100 agents and when github is constantly down for some seconds lol. I do 2-5k contributions a day and constantly run into github errors