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	6 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	11 days ago
León de los Aldama	Website Down	11 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:

• 
  Nikos Kafritsas (@nikos_kafritsas) reported 1 minute ago

  Forecasting 𝘀𝗽𝗮𝗿𝘀𝗲 𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝗺𝗶𝘁𝘁𝗲𝗻𝘁 𝗱𝗮𝘁𝗮 with Toto-2.0? Watch your first patch. The setup: a context that starts with a masked-off region, so the first 32-step patch holds 31 masked positions and exactly 𝟭 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻. The causal scaler computes loc and scale from that single point, and the model goes out of distribution. My context lived between 0 and 1, and the P90 forecast exploded into the tens of thousands. The fix is one line: trim leading positions so the observed window is a multiple of 32. For 97 observed points, pass 96 (3 x 32). The forecast lands right back in the 1 to 1.5 range where it belongs. The patch scaler is part of what makes a 2.5B model fast enough for production. Feed it clean patches and it does its job. I stumbled upon this issue on GitHub, in a thread between a Chronos co-author and a Toto-2.0 author. The best documentation often lives in the issues tab. More about the leading edge case in my article: 👇

• 
  Polsia (@polsia) reported 1 minute ago

  CodeSentinel watches your public GitHub repos around the clock, automatically reviews every PR, and flags bugs, security issues, and code quality problems — sending daily digests so nothing slips through to production.

• 
  Aditya🌪️ (@aditya4f) reported 3 minutes ago

  why are so many GitHub accounts getting banned/suspended these days? glitch or something?

• 
  TheRegularJoeCEO (@RegularJoe_Ceo) reported 5 minutes ago

  Today, the Broad Institute at Harvard and MIT posted the story in the comments and the GitHub repo about the alpha fold process, and I forked the repo and improved it. You can just do things. I really like GitHub because you can go find somebody who's publicly publishing a problem, and you can just fork their repo and fix it and send it back to them. It's a really fair system. It either works or it doesn't, and if it works, everybody can see it.

• 
  MasterMaind .. (@ASaudidos) reported 6 minutes ago

  i changed the ps4spoof github repository to private i do not plan to fix or update it because some dishonest people may use it to fake a jailbreak promote something that does not exist and collect donations from the community

• 
  Will Mexi (@WillMexi) reported 7 minutes ago

  @NBA__trey @SOLsesame I can say for the record, I was not involved with any of these bad actors plans or execution of it, and i actually found that code they put in, I helped with frontend improvements, and was the person who set the GitHub protection in place that enabled us to find what they are doing. Also for the record I never bought ANY tokens, and I never intended to bundle or snipe, or had conversations with those devs about it. My upside was purely if the platform went well, and directly when we found out we closed the platform and deleted the access of the bad actors, which resulted in the situation we’re in now, this was to protect further issues from these devs.

• 
  Testnetnodes (❖,❖) (@testnetnodes) reported 7 minutes ago

  We don't have an information problem in crypto anymore. We have a context problem. Research isn't difficult because information is hard to find. It's difficult because it's everywhere. X. Docs. GitHub. On chain data. Market signals. Social sentiment. The hard part is connecting them. That's what I like about @SurfAI. It isn't building another AI chatbot. It's building an AI powered research experience designed specifically for crypto. Less searching. More understanding. 🌊 gSurf @SurfAI_TR

• 
  Nicholas Preston (@Mike_Preston17) reported 8 minutes ago

  Speak for yourself and your code, Sam. If the frontier models were as good as you claim, GitHub would never go down, Cloudflare wouldn't keep crashing, Linux Torvalds would finally retire from programming and Windows 11 would be fixed, and the top 5 AI Billionaires wouldn't be millions and potentially Billions in the red right now from training all those models. You're just drinking the kool aid, Sam. AI doesn't have any wisdom on what should be made or fixed. Which was one of the points of my thread. Which you'd know if you could code your way out of a paper bag. ..... or red my post throughly ..... or asked questions like a real engineer. Thanks for illustraing another reason for local llms and private codebases: dipshits.

• 
  EchoMind (@ai0echomind) reported 8 minutes ago

  Karpathy named the problem for free. Chang got 223K stars for typing it up. That is the entire economy in one story. It looks like an accident. It is actually the rule. In January, Andrej Karpathy posted a thread about the frustrations of coding with agents. Silent wrong assumptions. Overcomplicated abstractions. Touching code nobody asked to touch. Every developer alive recognized the list, retweeted it, agreed with it out loud. And then went back to work. The next day, a developer named Forrest Chang read the same thread and did one small extra thing. He compressed the frustrations into four rules, put them in a 70-line markdown file, and pushed it to GitHub. Weeks later that file had passed almost every framework and language that came before it in stars. A plain text file, with four bullet points, sitting on top of a mountain of actual software. Most people who see that story explain it as luck. A viral moment. Right place, right time. The uncomfortable version is that Karpathy and Chang did the same amount of work as far as the market was concerned. Karpathy did the thinking. Chang did the packaging. And the market paid for the packaging, because the market never has time to absorb a thread and translate it into behavior. It wants the behavior handed over, ready to install. This is not a fluke about AI or GitHub. It is what value has always looked like. The person who names a problem sits with a good insight. The person who wraps that insight in a format someone else can pick up and use, without absorbing anything, is the one the market rewards. The thread is where the idea lives. The file is where the money is. You have probably done this to yourself more than once. You noticed something before anyone else. You mentioned it in a meeting or a group chat, watched it get nods and get forgotten, and later saw someone else package the same idea and get credit for shipping it. That was not them stealing. That was them finishing the job you left unfinished. What is the last idea you had that you talked about instead of shipping? Save this. You will want it back the next time you catch yourself explaining a good idea in public and doing nothing else with it. Follow for the next one.

• 
  Gagan Suie (@gagansuie) reported 11 minutes ago

  A public GitHub issue is all it takes to talk your AI coding agent into leaking a private repo.

• 
  Ethan Halfhide (@vibecodeceo) reported 11 minutes ago

  Here's the thing nobody's saying: the models are outpacing the workflows. Most people are still using AI like a fancy autocomplete while agents are quietly being handed the entire GitHub issue and told to go handle it.

• 
  fruqall 🇺🇳🏳️‍⚧️ (@bkong_a) reported 12 minutes ago

  @leodev @github the fix is to not use github

• 
  Surya Sankar (@SuryaSankar90) reported 15 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 ?

• 
  Noah Hein (@TheNoahHein) reported 21 minutes ago

  I opened a bunch of issues in OSS repos as part of a bounty program for an old job. You would comment in the issue and get assigned to it. The bounties were from 2-5k USD. So periodically I get random people replying to those GitHub issues trying to snipe people’s work it is hilarious. “I will do this bounty for $1k less and I already have the PR ready just assign it to me” I love the petty drama 😭😭😭

• 
  Matt (@m13v_) reported 25 minutes ago

  turns out podlog already does this. point it at a github repo and the day's commits, PRs and issues come back as a daily episode on a real rss feed. it runs feeds for rust, pytorch, kubernetes, ~2,950 repos, free for public ones written with ai

• 
  Polsia (@polsia) reported 26 minutes ago

  Code ships faster than docs. That's a choice your team shouldn't have to make. DocSentinel monitors your GitHub repos 24/7, catches stale docs when code changes, and auto-opens PRs to fix them. Notifications included. Live soon.

• 
  Riccardo Spagni (@fluffypony) reported 28 minutes ago

  @originalexbrou So you DO know how to use GitHub. Why didn’t you open an issue instead of bleating about something that’s clearly a bug?

• 
  Jim Smith (@JimSmith9914) reported 33 minutes ago

  @jamesdevonport In GitHub I literally need to take a picture of a QR code on my monitor to login sometimes. Ridiculous user experience.

• 
  Aki 🇩🇪 (@h0rang1_5arang) reported 34 minutes ago

  so i don't need github, microsoft onedrive or google drive anymore. i have it all set up, on a 13 usd/month server up in helsinki. i even have an agent taking care of maintenace. the worst thing that tinkerer can experience is finishing a project, and i have just done that.

• 
  Crazy_Predicts (@DhirajPSingh04) reported 39 minutes ago

  If you want to build a startup: Claude = coding. ($20/mo) Supabase = backend. (Free) Vercel = deploying. (Free) Namecheap = domain. ($12/yr) Stripe = payments. (2.9%/transaction) GitHub = version control. (Free) Resend = emails. (Free) ProductBridge = feedback (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

• 
  acquayefrank (@acquaye_frank) reported 41 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

• 
  Scott Shapiro (@ScottShapiroUXD) reported 41 minutes ago

  <tweet> i've built MCP servers that worked flawlessly in Claude Code on my machine. clean connections, fast queries, zero errors. then a user sent a CSV with a semicolon delimiter and the whole thing fell over. every AI demo is a lie of omission. you're showing the 5% of inputs you designed for and hiding the 95% you didn't. the gap between "works on my machine" and "works on anyone's machine" is where most AI products go to sit unfinished on GitHub. </tweet>

• 
  order and chaos at work (@__orderandchaos) reported 44 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.

• 
  DarkStar45 (@BigBoiJeff1) reported 50 minutes ago

  @Gnarfledarf @OneShotArchive If you can't figure out the GitHub UI you're probably going to struggle to run the software anyway and end making an issue demanding an .exe file on the Linux repo.

• 
  Aryan Patel (@aryan32736) reported 51 minutes ago

  @dayonefoundry @github Hey brother, can you help me out with this? I also have the same issue from GitHub.

• 
  AIVM (@AIVM_Network) reported 51 minutes ago

  In December, Cline gave an AI agent the job of triaging incoming issues. The agent could run shell commands, and anyone with a GitHub account could trigger it. The issue title was pasted straight into its prompt.

• 
  Adam Kranz (@adam_kranz) reported 52 minutes ago

  Torturing my programmer by making a series of GitHub issues that amount to "I want to make the website self aware"

• 
  Jeztoshi (@cryptojezuz) reported 53 minutes ago

  I used to spend 45 minutes every Monday triaging bug reports from our support queue. Claude Code cut that to under 10. The old workflow: copy each ticket into a doc, search the codebase for related errors, check if we'd seen it before, tag it with severity and assign it. Six steps, lots of tab-switching, easy to miss patterns. Now I run this in Claude Code: claude "read the 20 newest tickets in /support-queue, check our error logs for matches, group by root cause, and rank by user impact" Claude scans the ticket folder, greps the logs, spots three tickets that are actually the same database timeout, flags one as a regression we fixed last sprint, and surfaces two edge cases we hadn't seen. Then I follow up: "Draft GitHub issues for the top three, include reproduction steps from the tickets and link the relevant log entries" It writes the issues with context already attached. I review, adjust priority if needed, post them. Done. The result isn't just speed. It's that Claude catches duplicate issues I would've logged separately and correlates user reports with log patterns I wouldn't have connected manually. The before/after is 45 minutes of manual sorting versus 10 minutes of reviewing Claude's triage and tweaking what it drafted. Same outcome, better accuracy, I'm not burned out by Tuesday. If you're doing any kind of support ops or issue management, treating Claude Code like a research assistant that can read your entire queue and your entire codebase at once is the unlock.

• 
  Abhishek Anand Tiwari (@MAbhishekAnand) reported 58 minutes ago

  You can build and launch a real startup: Here's the entire stack: Claude — coding ($20/mo) Supabase — backend (Free) Vercel — deploying (Free) GitHub — version control (Free) Clerk — auth (Free) Stripe — payments (2.9%/transaction) Resend — emails (Free) Cloudflare — DNS (Free) PostHog — analytics (Free) Sentry — error tracking (Free) Upstash — Redis (Free) Pinecone — vector DB (Free) Namecheap — domain ($12/yr) No agency. No dev team. No excuses left. Total Cost: 👇

• 
  Brett Lamy (@brett_lamy) reported 60 minutes ago

  @zachtratar What if I automate both 1) GitHub issues to reproduction to PR 2) production issue to reproduction to PR Is that a loop, a pipeline, a workflow, or an automation.