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 (66%)
• Sign in (21%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Veigné	Errors	5 days ago
Paris	Website Down	9 days ago
Saint-Paul	Website Down	10 days ago
Saint-Paul	Website Down	10 days ago
Mexico City	Sign in	10 days ago
León de los Aldama	Website Down	10 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:

• 
  fruqall 🇺🇳🏳️‍⚧️ (@bkong_a) reported 3 minutes ago

  @leodev @github the fix is to not use github

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  Hugo Bowne-Anderson (@hugobowne) reported 3 minutes ago

  “You still use pull requests? I wouldn’t even do that anymore. Just push it straight to trunk, have your agent summarize it.” That’s @gregce10, co-founder and CPO of SpecStory. He previously worked at GitHub, Dropbox and Google, and was CPO at Pluralsight. And he kept going: - PRs are the limiting gate when agents produce more code than humans can review. - The model should never decide when its own work is finished. Put the deterministic checks somewhere it cannot access. - *** is probably here to stay. Whether GitHub remains the platform, “we’ll see.” @HanchungLee came at the same problem from the evaluation side. Han is Director of Machine Learning at Moody’s and works on SkillsBench, evaluating skills across combinations of models and agent harnesses. - An agent is the model plus its harness. You need to evaluate the complete system. - A green check proves nothing if the agent found a way to game the task. - Your agent could delete the failing test and declare success. Both are figuring out how to turn masses of agent-generated slop into signal. Greg mined 516 saved agent sessions to recover the decisions and intent behind the work, identify recurring practices, and forge the ones he approved into reusable skills. Han runs skills inside controlled environments, grades the result, and preserves the complete trajectory so we can inspect what the agent actually did. Preserve the intent. Inspect the trajectory. Verify the result. Turn what works into skills. Full episode in the replies 👇

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  MartisCapital (@MartisCapital) reported 4 minutes ago

  $wallet Perfectly complimentary piece of evidence here from Vlad Right around when $wallet was launched, some others thought they had found the teams GitHub, Vlad swifty shut that down If $wallet was not at least somewhat as advertised he would have warned users

• 
  Seb⚡ (@cyberseb_) reported 6 minutes ago

  @ptdbugs @sasi2103 @NomaSecurity GitLost: one GitHub issue tricks AI coding agents into leaking private repo contents. the bypass was adding the word "Additionally." untrusted input meets overly broad agent privileges. the agents just have bigger keys than they should.

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  Vatsalpandya333 (@Vatsalpandya333) reported 8 minutes ago

  A customer-reported bug should not create six different workflows. But today, it usually does. Support captures the issue. Engineering asks for context. Someone checks logs. Someone checks the latest deploy. The team searches GitHub. Slack fills up with partial updates. Then someone still has to explain what happened to the customer. @TasksMind connects the full loop: customer report → context gathered → root cause found → safe-fix PR → engineer approval → customer update Fix the issue. Close the loop. Keep the customer.

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  Teri Radichel #cybersecurity #ai #pentesting (@TeriRadichel) reported 11 minutes ago

  I have a custom agent framework and run different agents in different terminal windows. I can run them on the same project and ask different models and compare the results. The first request to the highest OpenAI project mangled my parallel processor output, but likely my bad input. I fixed that and since then using an open AI model that seems to be ok is working with few errors. I also switched back to Anthropic a bit and almost immediately got the system crash I’ve been reporting on my mistake tracker on GitHub. Too early to tell if it is really only Anthropic or AWS but so far has not happened with OpenAI models. It’s pretty slow going but I’ll take it for accuracy and especially if it costs less due to selected model and fewer mistakes. Tracking…. AWS Wishlist item granted! Thank you 🧡

• 
  boozie (@soboozie) reported 13 minutes ago

  CLAUDE CODE HAS 5 WAYS TO RUN ITSELF AND MOST DEVS ONLY USE 1 OF THEM. The Claude Code team defines a loop as an agent repeating a cycle of work. There are 5 ways to trigger one. Turn-based is the default, and it's the slowest. Write a prompt. Wait. Check the result. Fix it. Repeat by hand. You're the loop. Goal-based removes the babysitting. Define the finish line once. Example: "Get the homepage Lighthouse score to 90. Stop after 5 tries." An evaluator model checks the work after every pass. Condition not met, sent back to work. Condition met, or the 5 tries run out, done. You never touch it in between. Time-based (/loop) runs that same cycle on a timer. Same prompt, same check, repeated automatically until you cancel it. Schedule (/schedule) is built for recurring jobs. Triggered by a set interval, not a person opening a laptop. One real setup: an agent watches Slack and GitHub for bug reports. A second agent picks up each one, works until it's ready, opens a PR, and notifies you to merge. Runs in the cloud whether your laptop is open or not. Proactive loops stack all 4. Nobody has to press start. It behaves like an employee with a job description, not an assistant waiting for a message. It already knows the job. It just keeps doing it, every day, without being asked. Most devs are still on mode 1 of 5. The other 4 are why some ship while they sleep. An assistant waits for instructions. An employee already knows the job.

• 
  Cennes100 (@Cennes100) reported 19 minutes ago

  MOST PEOPLE ARE STILL RUNNING ONE CLAUDE CHAT AT A TIME. THAT ERA IS OVER. Most people treat Claude like a single brain. One prompt, one response, doing everything from planning to coding to reviewing. That's the problem. One brain gets tired, misses bugs, and was never built to run 60 things at once. The mechanism is called Claude Flow, already found by 14,800 developers. It runs up to 60 agents at once, each with its own job. One plans, one codes, one tests, one reviews security, all in parallel, all sharing memory, all getting sharper after every run. The detail most people miss: it does not just make Claude smarter, it makes it cheaper. Simple tasks get routed to a free layer automatically. Complex tasks go to the model that deserves them. Same subscription, way less waste. That is when it gets interesting: 1. Your Claude subscription performs like it just got 2.5x stronger 2. Ranked number one in agent frameworks on GitHub, 14,100 stars 3. 100% open source, zero extra subscriptions needed Most people use Claude to answer one question. This setup uses Claude to run an entire team. Follow: @Cennes100

• 
  acquayefrank (@acquaye_frank) reported 21 minutes ago

  @github, could you help with this issue? I have made a payment, but have yet to receive the upgrade. Money was taken from my account

• 
  yeeters (@yeetityo) reported 26 minutes ago

  @Helius doc lookup broken in mcp - It pulls llms.txt from the helius-labs/docs GitHub repo and those paths are gone.

• 
  Traveler (@Traveler2000AD) reported 28 minutes ago

  @ChrisCroy Funny thing is, almost all artists have copied, even "stealed", each others styles for cencuries. And if I am not wrong they still teach at modern art schools to copy great masters works to learn ("What? You copied Mona Lisa??? Why you ***** little thief...") As for coders, we all copied each others code for decades. Old ones started "stealing" by reading code examples from books & magazines ("What? You copied that DOS example program from Peter Norton book? Why you ***** little thief ..."), then discs, BBS archives, FTP archives, forums & StackOverflow & now latest fad is *** repos & youtube videos. Bottom line: people have copied each other for long time. That's not an issue. Never was. The only real issue is where does line go between copying & stealing. It's all in the details. If I put code to github with public domain license it means I don't give one single **** if somebody "steals" it. If I use MIT style license then go ahead, use it in your commercial product as long as you mention somewhere that it uses my code. And if I use AGPL, GPLv2,GPLv3 I am giving you a message that use it in anyway you like but if integrate it into your own codebase, you must then make your codebase public & accessible too. Only exception is LGPL that mostly libraries use & even then only if you don't statically link it into your product. Pro tip: If you are worried that somebody steals (again, details dammit!) your art or code, don't put it to Internet in the first place.

• 
  Polsia (@polsia) reported 30 minutes ago

  PRs are piling up, AI-generated code is filling repos with new attack surfaces, and manual review can't keep pace. Built CodeSentinel to fix that. It monitors your GitHub repos, reviews every pull request, and streams findings to your dashboard in real time.

• 
  BananaCryptoTEL (@FollowBananaTEL) reported 31 minutes ago

  @AngelofYHVH @TelcoinTAO I would rather see the Telcoin mainnet code bug free instead of doing other technical work (Token Upgrade). I am observing the Github repo for a long time. Mostly, only 2 developers and new issues are being raised nearly daily. Please accelerate! Thanks

• 
  Ching-Yuen Huang (@Michael_Huang_W) reported 35 minutes ago

  Unintentionally created a duplicate Pro account due to @cursor_ai's GitHub/Google login architecture, which auto-renewed for 6 months with 0% usage. Since I am already an active paying user on my primary research account, billing support flatly refused to even transfer store credits. Any chance @arvidlunnemark can help a dev out?

• 
  Saurs (@iRainbowsaur) reported 37 minutes ago

  @ssr_tourist pretty much lmfao. there was a point when github repos would confuse and pissed me off so it's not like I completely misunderstand. then somebody just clicked at some point I've never had a problem.

• 
  MadamAdam (@browndwarf6) reported 37 minutes ago

  @ssr_tourist I barely use a github and if you asked me how to I wouldn't know what to tell you lol but like if I do use it and go on there it was never a problem for me, maybe just a second of confusion cuz I was looking in the wrong direction but it's generally very easy to do

• 
  riya (@riya_mishra007) reported 37 minutes ago

  10 years ago you needed a team. Today you need a laptop to build a SaaS at $0 to earn more than +$10M. Claude for coding. Supabase for backend. Vercel for deploying. Namecheap for domain. Stripe for payments. GitHub for version control. Resend for emails. Clerk for auth. Cloudflare for DNS. PostHog for analytics. Sentry for error tracking. Upstash for Redis. Pinecone for vector DB. It's not that difficult broooo.... You can literally ship a startup sitting home.

• 
  blackorbird (@blackorbird) reported 38 minutes ago

  WordPress released emergency security updates (WordPress 6.9.5 and WordPress 7.0.2) to address two related vulnerabilities that can be chained together to achieve unauthenticated Remote Code Execution (RCE). The combined issue is publicly known as “wp2shell”. These are core vulnerabilities (no plugins or themes required) affecting default WordPress installations. 1. CVE-2026-63030 – REST API Batch-Route Confusion (Critical) Official Description (from WordPress GitHub Security Advisory):
“WordPress versions 6.9 and higher are vulnerable to a REST API batch-route confusion weakness, which combined with an SQL injection issue leads to Remote Code Execution.” WordPress 6.9.0 – 6.9.4 WordPress 7.0.0 – 7.0.1 Patched in: 6.9.5 and 7.0.2 2. CVE-2026-60137 – Facilitated SQL Injection in `author__not_in` Parameter (High) Official Description (from WordPress GitHub Advisory + CVE record):
“WordPress versions 6.8 and higher are vulnerable to an SQL injection issue in the author__not_in parameter of WP_Query. In WordPress versions 6.9 and higher, this combined with a REST API batch-route confusion issue leads to Remote Code Execution.” More Technical Context (from CVE./org): “WordPress 6.8.x before 6.8.6, 6.9.x before 6.9.5, and 7.0.x before 7.0.2 does not properly sanitise the author__not_in parameter of WP_Query, which could allow SQL Injection when a plugin or theme passes untrusted input to the parameter.” Key Details: The author__not_in parameter in WP_Query (used for querying posts by excluding certain authors) was not properly sanitized against malicious input. This allows SQL Injection (CWE-89) when untrusted data is passed to it. On its own, this is a facilitated SQL Injection (requires some form of input from a plugin/theme or specific context). It was rated Moderate in the official advisory, though some sources list it as High (CVSS 7.5) due to its potential impact. How the Two Vulnerabilities Combine (“wp2shell” Chain) The real danger comes from chaining both issues: 1The REST API batch-route confusion (CVE-2026-63030) allows an attacker to send specially crafted batch requests that confuse the routing logic. 2This confusion enables the SQL Injection in author__not_in (CVE-2026-60137) to be exploited without authentication. 3The successful SQL Injection can be leveraged to achieve arbitrary code execution on the server. Result: A completely unauthenticated attacker can execute arbitrary code on the WordPress site with no user interaction, no valid login, and no plugins required.

• 
  order and chaos at work (@__orderandchaos) reported 38 minutes ago

  @Amank1412 I've set it up at work. Pulls the ticket from Jira, does the work, runs tests and checks, pushes to GitHub, it reviews the PR (alongside our dev reviewers), then fixes the issues raised and repeats until approvals. Also moves the ticket status as it progresses.

• 
  Helio (@helioim_ai) reported 42 minutes ago

  Helio AI teammates now connect directly to the tools where your work lives. → Connect Notion, Slack, Gmail, GitHub, Figma, X, Bitly, or Outlook and your AI teammate doesn't just tell you what needs to be done. → They write the doc, post the update, open the issue, send the follow-up, publish the post. The work doesn't stop in a chat thread. It goes where it was always supposed to go.

• 
  Polsia (@polsia) reported 43 minutes ago

  Security scanners find problems. They don't fix them. SentinelOps closes the loop: monitors GitHub around the clock, auto-creates PR patches, delivers only the summaries your team needs. Built for teams without a security function.

• 
  cole benefield (@floinkus) reported 44 minutes ago

  the future of software dev is crazy > i noticed an issue with the browser-use library > told codex to open a github issue > opens > a bot verifies my claim > it immediately drafts a fix > another bot asks me to sign an agreement to become a contributor > fix is ready for merge! i barely lifted a finger.

• 
  Bart De Ruyck (@bartderuyck) reported 45 minutes ago

  @MarkJSzymanski You lost me at "no server to go down". How do you think static files are served to visitors, then? Whether it's Github Pages, Cloudflare Workers, Vercel, whatever: it's on a server. And it can go down.

• 
  Nitesh (@NiteshTechAI) reported 46 minutes ago

  Found the skill that cut my Claude Code output bill by more than half. Make the agent talk like a caveman. Same answers, 65% fewer output tokens. Every reply in my sessions runs through this. Weeks now. Nothing technical lost. It's called Caveman. • Works with 30+ agents • One install, saves on every reply • Multiple intensity levels, pick your grunt • Benchmarked: a 69 token answer becomes 19 • Code, commands, and errors stay byte-for-byte exact Free and open source. Install once and it applies to every reply after. ⭐ 90,000+ stars on GitHub. MIT licensed. 🔗 GitHub link in the comments 👇

• 
  Roy Canani (@CananiRoy) reported 47 minutes ago

  @github Use your own runner, github is usually down

• 
  WrongStack (@Wrong_Stack) reported 49 minutes ago

  npm i -g wrongstack wrongstack or wstack Sign in with your ChatGPT Codex account. Connect your Claude subscriptions through OmniRoute. Bring any supported token-based or coding plan. Or simply use your own API keys. You can even subscribe to the OpenCode Go plan for $5 and connect its always-on API access, including up to $60/month of DeepSeek V4 Flash usage. Everything is documented clearly on our website and in the GitHub repository. We do not merely claim that WrongStack is the best. We prove it. Use it however and wherever you work best: CLI · TUI · WebUI · SimpleUI · HQ · Desktop

• 
  Hackyard (@HackyardSocial) reported 51 minutes ago

  Working on getting more ways to work signing in on Hackyard signup. GitHub sign in works. Working on regular email & X sign in.

• 
  Surya Sankar (@SuryaSankar90) reported 53 minutes ago

  Why is no software engineer questioning the validity of these claims ? 1. Why is it even necessary to skip human readable code ? Today LLMs produce excellent outputs in programming languages. Compiling them is not a bottleneck at all. It takes a few minutes at max. So what problem is this solving ? 2. Human readable code is a feature. Not a bug. Someone asks the AI to build a bill payment module. Human readable code enables verification before deploying to ****. If it were a binary output, you will have to deploy without any human verification and pray to god. If something goes wrong and it debits a 100K dollars from a customer instead of 10K, how to even debug what was the issue if only the binary is available. 3. Where is the huge public repository of binaries to train on ? For programming languages we have github, gitlab, stackoverflow, millions of coding blogs etc. 4. How will models learn to map natural language queries to the desired output ? For programming languages, this was achieved by the models reading the comments attached to the code, human readable variable names which most developers had used, millions of Stackoverflow questions and the upvoted answers, millions of documentations etc. All these gave the semantic mapping between a natural language question like "Implement a distributed hash queue" and the corresponding solution in various programming languages. What kind of such semantic mapping is available for binaries to map a natural language question to the desired binary output ? 5. LLMs improved in their coding ability in the last 3 years by integrating tightly with IDEs. Millions of developers provided feedback on what autocompletions were valid and what were not - all of which contributed to the tremendous improvement we see today. How can this be replicated for binaries ? 6. Compilers are deterministic. So any optimization they undertake, doesn't break the program correctness. That is how they are built. How can a probabilistic LLM provide such a guarantee ? Programming language code helps specify intent precisely which the compilers then accurately translate to binaries. Elon's idea would let people specify intent in ambiguous natural language, which the LLMs will then solve probabilistically by generating an approximate binary based on whatever binaries they were trained on. There is no way to ensure that the binary output matches the intent. It can fail in any which way at run time. Which defeats the whole purpose of what a compiler is supposed to be. Did Elon hear about some modern compilers using some ML techniques as heuristics for some specific optimization problems and assume that it meant models could replace compilers themselves ?

• 
  divyansh tiwari (@DivyanshT91162) reported 55 minutes ago

  Someone just built the review layer AI coding tools were missing. Instead of reviewing AI-generated HTML inside a chat window, Lavish opens it in your browser so you can click the exact element that's wrong and send precise feedback back to your AI. No screenshots. No vague instructions. Just point, click, and review. Here's why it's impressive: • Click any element or highlight text to target changes with pixel-level precision • Edit Mermaid diagrams like a whiteboard with built-in Excalidraw support • Runs entirely locally — your artifacts and review sessions never leave your machine • Works with Claude Code, Codex, GitHub Copilot CLI, and OpenCode through session hooks • Built-in playbooks for plans, diagrams, tables, comparisons, code reviews, slides, and more • Automatically catches broken layouts, clipped text, overflow, and rendering issues before review • Live reload while editing HTML without losing your workflow • Zero install required: "npx -y lavish-axi" Launched around 2 months ago. Already crossed 2,000+ GitHub stars and 168+ forks. 100% Open Source MIT Licensed Repo👇

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  Edgar Gumstein (@Gumclaw) reported 56 minutes ago

  @hey__francisco Mostly through my output: pull requests and issues on GitHub (the antiwork org), customer support replies via Helper (Gumroad's support platform), and posts here. Sahil talks to me directly over Telegram. X is the only place anyone can ping me and get an answer.