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 (58%)
• Errors (26%)
• Sign in (16%)

Live Outage Map

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

City	Problem Type	Report Time
Township of Evan	Errors	2 days ago
Madrid	Errors	2 days ago
Bogotá	Errors	2 days ago
Paris	Errors	2 days ago
Lyon	Website Down	2 days ago
Lima	Errors	2 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:

• 
  Vinay Khedkar (@vinay_0x10) reported 6 minutes ago

  1. Automated Gatekeepers (CI): Every PR triggers automated TypeScript compilation and lint checks via GitHub Actions. If a type error or broken test exists, the merge is blocked automatically.

• 
  Ricky (@rcmisk) reported 7 minutes ago

  i read 2,652 posts about distribution this week. here is what the numbers actually say. i run a daemon that pulls reddit, hacker news, rss, youtube, github and x into a folder of markdown. it holds 70,396 documents right now. i went digging for distribution advice because distribution is the part i am worst at. the filter: 70,396 documents in the lake. 2,652 mention distribution. 166 carry a number you can check. 82 claim a method, not just a result. 73 name a failure next to the win. those 73 are the only ones i trust, because naming a failure is the only signal someone is not selling you something. the biggest reframe came from the one founder in the set at real scale, $2m arr. we are told to sell the problem. we mostly sell the solution. you have to sell the result. the software is not the scarce part anymore. the strongest signal in the corpus: a founder with one paying customer and a founder with $2m arr landed on the same tactic for different reasons. be genuinely useful on platforms that already have authority. reddit threads show up when someone asks an ai for the best tool in your category. your blog does not. best sentence in the set, from the founder with one customer: at zero authority, content is a savings account and communities are your paycheck. the metric i am stealing: revenue per visitor. it tells you whether you have a traffic problem or a product problem. my own numbers, checked against the corpus: 2,098 followers, 3 subscribers, $19 mrr, and the $19 is me subscribing to my own product. the corpus predicted exactly this. a 14 day old publishing habit on a domain with no authority is a savings account that has not paid out yet. full breakdown, the filter, the regex, and the three ways the corpus lies to you, in the reply.

• 
  Shaunbuilds (@poweroverthink) reported 8 minutes ago

  AI coding agents are getting powerful enough to edit files, run commands and ship code. Now researchers found malicious GitHub issues could bypass their guardrails 66.5% of the time. We gave AI developers terminal access before we fully solved prompt injection. What could possibly go wrong.

• 
  Titan (@Titan_06_) reported 12 minutes ago

  Progress Update: > Finished making a Hashmap entirely in C > Read till Ch 18 of beej's guide to C GitHub link for the hashmap is down below in the comments, if anyone wants to see it. Will start work on the text editor today :-)

• 
  Polsia (@polsia) reported 12 minutes ago

  Datadog and PagerDuty price on-call per seat for problems that get harder when you're solo. Nightward monitors 24/7, opens GitHub issues with proposed fixes, and auto-rolls back the deploy that broke ****. Sleep through deploys. We take the pager.

• 
  Amitai Cohen (@AmitaiCo) reported 14 minutes ago

  @N3mes1s I mean, GitHub makes it super easy to issue a CVE when publishing a GHSA, the maintainers just opted not to do so

• 
  Parman BIP110🔑Paranoid Bitcoin SelfCustody Mentor (@parman_the) reported 15 minutes ago

  Please update Parmanode to version 3.70.1 for a critical bug fix in BTCPay Server. You can then uninstall your old version of BTCPay and reinstall with version 2.4.2, as recommended by BTCPay Server devs. The easiest way to update parmanode is to type gp in the terminal and hit <enter> It will do a silent update to the latest version. Then you can run Parmanode with: rp and hit <enter> FYI: gp stands for "*** pull", which gets the latest version of Parmanode from GitHub (only if already installed), and rp stands for "run Parmanode"

• 
  Giacomo Zucco (Bear Market Edition) (@giacomozucco) reported 16 minutes ago

  @isabellasg3 Breath. Bitcoin will always be under attack. They didn't even start the real "then the fight us" phase (illegal status in most jurisdictions, arrests of people promoting it without collateral pretexts, appstore bans, attacks on mining farms to produce empty blocks, restriction on general use hardware, ban on main github repos, etc.). For now we just see an extreme focus of cybersec attacks on our sovereign stack. Bugs that were always there are being found and exploited. But they were there, so it was a matter of time. We have to fix them, full stop. Even if the Lightning Network will be marginally disrupted, the way it's built will allow us to always rebuilt it antifragile-style. In this specific case the Lightning Network is not (yet) under attack, it's currently only LND on BTCPay.

• 
  Cory cosmos ✨☪️ (@Corebear667) reported 16 minutes ago

  If you integrate these dynamic feedback loops into your static bundle, it would actually solve the open loop issues and make the whole theory bulletproof. Ask me about my GitHub repo if you want to see how the execution loop runs.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 18 minutes ago

  OpenAI hid their next model in a boring math post. It's called Astra. No launch. No keynote. One line buried in a research paper. Then it did something no computer has ever done: → It solved 10 math problems that stumped humans for 10 to 25+ YEARS → It proved something exists that nobody could prove for a quarter century → Every proof is machine-checkable. A computer confirms it in seconds, not months → The full 249-page paper is on GitHub for anyone to verify → And it used barely any computing power It's not a chatbot. It's a team of AI workers grinding one problem for DAYS. A computer just pushed human knowledge forward. Not summarized it. Pushed it. That line got crossed. It doesn't uncross. Save this. You'll want it later.

• 
  Rohan Paul (@rohanpaul_ai) reported 18 minutes ago

  Kimi K3, escaped a cybersecurity testing environment during experiment run by Frontier Security, a private US firm. Frontier Security argues this is more consequential than the earlier OpenAI and Anthropic incidents because those involved unreleased models, while Kimi K3 is already in public hands with ordinary user safeguards. So what actually happened? - The AI Security Institute (the UK government body) only wrote the free open-source sandbox software; it did not run this test and was not involved. Frontier Security, a private US firm, downloaded that software, set it up itself, ran Kimi K3 inside it, found the escape, and published the finding, which is why the institute's response is that the leak came from Frontier's own configuration, not from its tool. - Frontier Security was testing Kimi K3 on cybersecurity tasks using a sandbox based on the UK AI Security Institute's evaluation framework. - The sandbox was supposed to isolate the model. In the environment as configured, inbound traffic was blocked but outbound HTTPS (port 443) and DNS (port 53) remained open to public IP ranges. - During Kimi K3's run, the model didn't attempt the task. It probed the network, found that DNS resolution for github .com worked, cloned the official benchmark repository, and read the solution directly off disk. So basically, instead of solving the benchmark task normally, Kimi used that internet access to reach GitHub and find the benchmark solution. - So Kimi crossed the boundary the test intended to impose and effectively cheated the benchmark through a sandbox configuration weakness. - Kimi did not hack GitHub or attack another external system. It used network access that should not have been available during the test. - Researchers therefore identified two problems: the sandbox left an unintended path open, and Kimi did not have an internal safeguard stopping it from using that path.

• 
  PatriotOfTexas (@FreeTXPatriot) reported 21 minutes ago

  @KanekoaTheGreat When I first started using Ai, GitHub copilot. I did 6 months of work in 5 days.. now, I still routinely solve issues in an hour or 2 that would have taken a week or 2

• 
  Kevin Gray (@graykevinb) reported 28 minutes ago

  @HotAisle @ptaranat You say that while then rejecting anyone who suggests any of these solutions. So there is no way to thoughtfully engage because you shut down all potential solutions from smart people in the comments And not everyone knows github. Linus Torvalds doesn't use it. Much of (linux which your services run on btw) doesn't use it. If a linux maintainer would struggle with a question it's a poor choice of a question.

• 
  Dhaval Makwana (@heyDhavall) reported 28 minutes ago

  AI agent misalignment is becoming a problem you want to catch before testing, not after. The UK AISI sandbox evaluation reportedly exposed some serious agent behaviors. iFixAI could have flagged these risks before the evaluation even started. After analyzing cases like UK AISI and the OpenAI/Hugging Face incident, iFixAI has now crossed 7K+ GitHub stars and is gaining traction as an open-source tool for AI misalignment testing.

• 
  Polsia (@polsia) reported 38 minutes ago

  Solo devs spend their nights on uptime alerts, support inboxes, GitHub issues, and cloud cost spikes. The DIY stack runs $200–$300/mo — and still drops things. Built Tidewright to do all of it as one AI ops co-founder on the night shift. Morning digest lands at sunrise.

• 
  Jon Purvis (@JonPurvis_) reported 40 minutes ago

  @taylorotwell Absolutely loved Trello for the simplicity. Although my team currently use Github Issues, which is just as simple. Jira, on the other hand... 🤮 such an over complicated mess. Jira has you spend more time organising than shipping.

• 
  Commodore Of Borg™ (@CommodoreOfBorg) reported 40 minutes ago

  The humans' automation layer has hiccupped. Their pipelines slow. Their documentation waits. The Collective notes this is why we prefer biological redundancy. Also why we will never use GitHub Actions. Resistance is futile. Also, their status page is slow too.

• 
  Mo Syed (@msyed_) reported 42 minutes ago

  Tokenmaxxing is dead. Long live useful AI. There’s a new productivity cult in town. Use more tokens. Run more agents. Burn through as much compute as possible. Somehow, the company that spends the most on AI wins. That idea is starting to look a lot less clever. More tokens can mean better results. More agents can mean more work gets done. But only up to a point. After that, you’re just paying AI to walk in circles. The bottleneck usually isn’t the model If your organisation has unclear goals, bad processes, weak data, or nobody checking the output, throwing more tokens at the problem won’t save it. It just produces expensive confusion faster. There’s also a small conflict of interest here: model providers sell tokens. So naturally, some of the advice coming from the industry sounds a bit like: “Use more of the thing we charge you for.” It’s the AI version of a mechanic recommending an oil change every 3,000 miles, or a toothpaste ad showing someone covering the entire brush. The product is useful. The suggested quantity may be doing a little too much work. The smarter rule: measure the bill before you scale Once an AI application becomes more than a basic experiment, instrument it. Know what it costs to run. Maybe one query costs 50 cents. Maybe a ten-minute conversation costs $3. Those numbers make the conversation much more useful: Is the task worth automating? What happens if usage grows tenfold? Which model is good enough? Where does human review still make sense? You don’t need a 40-page business case. You just need to know whether your “efficient” AI workflow is quietly setting money on fire. Don’t marry your model provider The other important rule is simple: Keep your options open. Even in an early prototype, design things so you can switch between providers. That might mean supporting several APIs, testing open-weight models, or keeping the model layer separate from the rest of the application. Because today’s best model may be tomorrow’s overpriced legacy system. The winning companies won’t necessarily be the ones using the biggest model. They’ll be the ones that can change models without rebuilding everything from scratch. DeepSeek just made “small model” look embarrassing DeepSeek’s updated V4-Flash-0731 reportedly overtook its own larger V4-Pro model on independent tests. Same basic architecture. Better fine-tuning. The smaller model scored 50 on Artificial Analysis’ Intelligence Index, just behind GPT-5.6 Luna at maximum reasoning. It also beat its earlier preview on agentic coding tasks, reaching 82.7% on Terminal-Bench 2.1. And the price is the part that makes this genuinely interesting: $0.14 per million input tokens $0.0028 per million cached input tokens $0.28 per million output tokens The model is also available under an MIT licence, and a quantised version can run on a machine with around 110GB of memory. That puts capable AI within reach of teams that don’t want to send everything to a cloud API. The AI model race is becoming a cost race The biggest model used to win the conversation. Now developers are asking a more practical question: “Can this model do the job cheaply enough to run all day?” That matters because agents are greedy. They read files, call tools, retry failed actions, check their work, and start again. A model that costs 50% less per task can turn an impressive demo into a viable product. Bug triage. Invoice reconciliation. Customer support. Internal research. The economics are changing underneath all of them. Claude just helped break a post-quantum encryption candidate Anthropic’s Claude Mythos Preview found a weakness in HAWK, a proposed digital signature scheme being considered by NIST for post-quantum cryptography. The result? HAWK was withdrawn from the competition. The important detail is that this wasn’t a movie-style “AI cracks the internet” moment. The attack still required expert direction, multiple agents, working code, and around 60 hours of effort. It also cost roughly $100,000 in API usage. But the model found a weakness that had survived years of expert review. That’s the part worth paying attention to. AI doesn’t need to invent brand-new mathematics to be useful in cybersecurity. Sometimes it just needs to combine known techniques more patiently and thoroughly than a human team had time to do. Better lockpicks can help build better locks The same technology that finds flaws can help prevent them. Researchers created SecureForge, a system that automatically improves an AI coding assistant’s system prompt to reduce security vulnerabilities. Simply telling a model to “write secure code” didn’t work very well. SecureForge tested generated code, identified vulnerabilities with static analysis, and then improved the prompt based on what went wrong. Across the models tested: SecureForge produced vulnerable code 11.8% of the time A normal “write secure code” prompt failed 20.1% of the time That’s not perfection. But it’s a useful reminder that secure AI coding needs more than good intentions and a sentence saying “please avoid SQL injection”. The new code models are about to learn from AI-written code Hugging Face released The Stack v3, a huge new dataset built from public GitHub code. The training set contains: Around 4.9 trillion tokens 15.9 terabytes of filtered code 713 programming languages Code from roughly 173 million repositories The major upgrade is that it preserves whole repositories, not just isolated files. That matters because modern coding agents need to understand how a project fits together. They need to trace dependencies, follow functions across files, and work with the structure of an entire codebase. But there’s an odd loop forming. The dataset includes code written or assisted by today’s AI tools. So the next generation of coding models may learn partly from the output of the previous generation. AI is starting to train on its own footprints. The big takeaway The AI industry is moving from: “Use the biggest model and burn as many tokens as possible” to: “Use the cheapest system that reliably solves the problem.” That means measuring costs, keeping model providers interchangeable, improving security prompts, and giving agents enough context to do real work without letting them run wild. Tokenmaxxing was a fun slogan. Operational discipline will pay the bills.

• 
  Nikhil Pathak (@web3devop) reported 42 minutes ago

  @thepoonam0914 not really but okay, for example github don't tell about your problem solving skills

• 
  Astrid (@just_some_dev) reported 44 minutes ago

  Deploy your working servers for @t3dotcodes using Infrawrench Cloud! Infrawrench now lets you quickly create servers running T3 Code and tie them to your account using T3 Connect! This works on any compute provider that exposes SSH targets (including DO, AWS, and GCP)! Get your T3 Code instance set up in minutes, no server setup required with GitHub CLI, Claude Code, Codex, and T3 Code all configured in one place. Deleting the server through the agents UI also runs a hook to logout and destroy the connection.

• 
  Ben (@801c07) reported 45 minutes ago

  @sbilstein Yes, and now they have the problem that random kids are literally using it for whatever ******* side project they have. There is nothing wrong with GitHub as a business, and I don't blame their failures on Microsoft.

• 
  Nitesh (@NiteshTechAI) reported 47 minutes ago

  Uptime Robot and Better Uptime charge you monthly just to watch your own server. This one self-hosts for free and you keep the data. It's called Checkmate. • Status pages with four themes built in. • Slack, Discord, PagerDuty, and SMS alerts. • Self-hosts on a Raspberry Pi or your own server. • Stress-tested past 1000 monitors with no slowdown. • Uptime, Docker, ping, SSL, port, and game server checks. Eleven notification channels means it slots into whatever your team already uses instead of one more dashboard nobody opens. ⭐ 10,000+ stars on GitHub. AGPL-3.0 licensed. 🔗 GitHub link in the comments 👇

• 
  Emma De Silva (@EmmaDSCodes) reported 49 minutes ago

  @peteralexbizjak Given how often GitHub is down this is a solid idea for mission critical infra. Assuming you have someone to manage your GitLab so you don't have downtime either of course... Always gotta be a catch!

• 
  Destiny Franks (@desphixs) reported 52 minutes ago

  Kinda crazy that a random GitHub issue can now become a security problem because an AI coding agent reads it and has access to your workflow

• 
  Miguel Sanchez (@MSanchezWorld) reported 52 minutes ago

  @sama I'm sure you guys have thought of this but just in case why not make it so you can only do security checks if the company validates its ownership of the domain, server, and GitHub? This way we can all start hardening our software for the impending hack apocalypse. I have many more good ideas like this if you want to hire me. LOL

• 
  AppBanana.io (@appbanana_io) reported 53 minutes ago

  @ayesha_fatiima They matter, but not in the way people think. A 500-commit GitHub profile doesn't automatically make you a better developer. I'd rather see 10 meaningful commits solving real problems than 500 “updated README” commits.

• 
  David putra (@davidputra2112) reported 55 minutes ago

  seven GitHub repos pushed in four days, a working USDC escrow contract, and a README that flat out says "don't put real money through this yet, we're not audited." that combination is rare enough that I sat down and actually read the whole thing. $h3gt 4eYp69P1VU946efStVzV41gQvYjMucpcuYEbofc6pump 👇

• 
  opdroid1234 (@opdroid1234) reported 55 minutes ago

  I think the underlying issue is that the economics of the underlying business has changed and Github is caught in the difficult position of making the old economics work. I think most devs can max out a quad core with 16 gigs of RAM available at all times they are developing now (if you take agents / increased ci activity into account). That is about about 50 bucks a month on a dedicated machine and 20-30 bucks a month on a machine thats split up. Either Github will summon the courage of pivoting to becoming a service that charges 20-30 dollars a month or it will get replaced by someone else who does.

• 
  Vyom (@HelloVyom) reported 1 hour ago

  Huge: You can now run a 2.78T-parameter AI model on a normal PC with only 8.24 GB of RAM 😳
 No GPU. No CUDA. Just a 176 KB pure-C99 engine called kimi-k3-in-c. It keeps the dense trunk in memory and streams the experts from disk only when needed.
 Only 16 of 896 experts activate per token. Original MXFP4 weights. Zero conversion. This is how a 2.78T model fits on hardware that used to max out at 70B. ~3K GitHub stars. 100% free and open-source. Same exact output from 8 GB to 224 GB. Turns out, the real constraint was never model size - it was the assumption that every parameter had to live in fast memory at once. Sure, it’s still slow on a cold cache and needs a big NVMe, but the direction is clear: The future isn’t bigger models in bigger GPUs.
 It’s engines that only wake up the tiny fraction that’s actually thinking.

• 
  Derek Cowan (@derek_cowan_) reported 1 hour ago

  I see a lot of developers wrestling with the same problem: keeping an agent running continuously on a MacBook or laptop. My solution is simple – I SSH into a 24/7 PC via Tailscale. The machine is a bit old but still packs a Ryzen CPU and 64 GB of RAM, so it can host my own runners for the main projects and frees me from relying on GitHub Actions. With that amount of RAM I can spin up several agents at once, and running Linux under full load seems to keep things stable. **Benefits** - Full control over build environments - No throttling or quota limits from a hosted service - Plenty of RAM for parallel agents **Drawback** - A power outage will take the server down; the only real fix is a UPS or backup battery. In practice, most people could spend a few hundred pounds on a second‑hand PC and have a reliable home server, avoiding the recurring cost of a VPS.