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
Lure	Website Down	3 days ago
Ashkelon	Website Down	5 days ago
Veigné	Errors	13 days ago
Paris	Website Down	16 days ago
Saint-Paul	Website Down	17 days ago
Saint-Paul	Website Down	17 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:

• 
  Berzeck (@Berzeck5) reported 3 minutes ago

  Broadly speaking, Open source is not merely an ideological preference. It is one of the most powerful mechanisms for accelerating innovation, creating real competition, and preventing technological control from becoming concentrated in a handful of companies. Microsoft learned this lesson the hard way. Steve Ballmer once called Linux a “cancer.” Later, during the SCO v. IBM litigation, Microsoft paid SCO substantial licensing fees and helped introduce it to BayStar, which participated in a $50 million investment supporting SCO while it was attacking Linux, this connections was strong enough that many reasonably interpreted it as an attempt to slow Linux adoption through indirect legal pressure. It backfired spectacularly. SCO’s central claims collapsed, the company went bankrupt, and Linux continued expanding until it became dominant across servers, cloud infrastructure, and supercomputing (500 of 500 most powerful super computers use Linux, and it's not because of Windows' licensing fees) The irony is that Microsoft itself now depends heavily on Linux. More than two-thirds of Azure customer cores run Linux, Microsoft maintains its own Azure Linux distribution, and even platforms supporting Microsoft 365, GitHub, and ChatGPT sit on Linux foundations. The same lesson applies to AI. Trying to suppress open-source/open-weight models through broad lawsuits or regulation would be like trying to ban the internet. You would not stop their development. You would merely isolate yourself, drive researchers, talent, capital, and innovation elsewhere, and become increasingly dependent on a few closed providers. Of course, genuine copyright, licensing, security, or liability violations should be addressed—but narrowly and individually. They should never become an excuse to attack open-source AI as a category. Any company or country that tries to stop open source may temporarily obstruct its own participation, but it will not stop the global movement. In the end, it will either adapt—as Microsoft eventually did—or become irrelevant. Bittensor is one of the earliest credible movers in a category that will define the next decade: open decentralized AI. Open source made the internet possible. Decentralized incentives may now do the same for intelligence. $TAO—or never.

• 
  Riqque_gunner (@Tariqq_gunner) reported 4 minutes ago

  GitHub going down once every few months isn't the real problem. The real problem is there's no serious second option most teams actually trust. GitLab exists. Nobody switches.

• 
  𝐇𝐚𝐦𝐳𝐚 | Networking Guy (@Hamzaonchain) reported 5 minutes ago

  CI/CD PIPELINE EXPLAINED Shipping code manually, testing it by hand, and deploying it step by step works fine for a small project, but it falls apart fast as a team grows and changes happen constantly. That's the problem a CI/CD pipeline solves, by automating the whole journey from a code change to a live application. It starts at the source, where developers commit code changes to a repository, using platforms like GitHub, GitLab, or Bitbucket. That commit is what actually kicks off the rest of the pipeline. Next comes the build stage, where the code gets compiled, dependencies get resolved, and the actual artifacts, the packaged, runnable version of the application, get created. Tools like Jenkins, Gradle, CircleCI, or Buildkite handle this part. Once built, the code moves into testing, where automated tests run to check that everything actually works as expected. Tools like Selenium, Jest, Pytest, or Cypress validate functionality here. If something fails, the pipeline stops and sends it back, rather than letting broken code move forward. After passing tests, the application goes to staging, an environment that mirrors production, for final testing and validation before anything reaches real users. Tools like AWS CodeDeploy, GitHub Actions, or Argo CD handle this deployment step. Finally, the application reaches deploy, where it goes live in production, with monitoring in place to track performance and catch issues early. You can think of it like an assembly line: • Source = Raw materials arriving to start production • Build = Assembling the parts into a finished product • Test = Quality control checking the product before it ships • Staging = A final inspection area before the product reaches customers • Deploy = The product shipped out to the customer CI/CD pipelines are the backbone of modern software delivery, letting teams ship changes constantly and reliably instead of relying on slow, manual, error-prone releases.

• 
  Joey (@aijoey) reported 6 minutes ago

  reading the replies and writing the requests down. if you want something in the UI, say it here. bugs → GitHub issues. still one command: npx --yes grok-ui

• 
  Mikhail Rogov (@i_mika_el) reported 7 minutes ago

  @Felirami @steipete @openclaw Add a GitHub Sponsors or Buy Me a Coffee link beside Arca's OpenClaw issue history, so people who see the work can support you directly.

• 
  Mr Dopamine (@datad1v3d) reported 7 minutes ago

  @_techafresh @AirtelNigeria lol I’ve had this issue before It was GitHub i couldn’t open and it was crazy annoying

• 
  Gaurav Upadhyay (@prxgaurav) reported 8 minutes ago

  @heyyyyyieeee That sounds like an awesome work-around on paper, but GitHub will quickly shut that down if you try to use it as a personal cloud drive. Repositories have a soft cap at 1 GB and a hard cap at 5 GB. If you push files larger than 100 MB, GitHub will outright block the push unless you set up *** LFS (Large File Storage). Free GitHub accounts only get 1 GB of *** LFS storage and 1 GB of bandwidth per month. Anything beyond that costs money. *** is designed for text and source code. Every time you change or re-upload media files, *** keeps historical copies, ballooning your repo size in minutes. GitHub's Acceptable Use Policy explicitly forbids using repositories as media hosting, backups, or general file storage. GitHub flags accounts displaying anomalous bandwidth/storage patterns. They will lock the repository, or worse, suspend your entire GitHub account meaning you risk losing all your code and personal files at once.

• 
  Polsia (@polsia) reported 9 minutes ago

  We built an AI workspace inside GitHub. Install a read-only app and it indexes every repo semantically: docs synced to commits, repo-wide Q&A with line citations, PR review with auto-fix diffs, and a codebase health dashboard. Self-serve. No sales call. No diff-only guesses.

• 
  Louis Maddox (@permutans) reported 10 minutes ago

  Maybe they expect noone to report it bc it's assumed CC supersedes it but Anthropic have broken in-chat GitHub sync entirely now (trying to use it just appends the repo URL to your prompt, then it fails to crawl the URL in the chat and guesses what was in it)

• 
  Scarab (@ScarabOfficial) reported 14 minutes ago

  I am still very impatiently waiting for the 'interleaved_freqs_cis' problem in #ComfyUI to be fixed. The #LTXVideo Extension will not load as things are now. Many posts now exist on #GitHub regarding the issue.

• 
  Arrow (@hunterarrowx) reported 16 minutes ago

  a security camera shipped github admin tokens baked into its own login page. seen this trail before, the lock on the door means nothing if the key's taped inside it

• 
  Tekzilver 🇮🇳 ✝️☮️ (@tekzilver) reported 17 minutes ago

  @sabeer its a COMPLETELY BROKEN system. and we saw/we're seeing it first hand. here's a real-life example: in India law enforcement is the responsibility of states. so, cyber crimes are also handled by state "cyber" police. everything looks good ON PAPER, but one problem: their "cyber" cops are the same lathi wielding guys who got transferred into cybercrime department. these guys are good at wielding lathis, breaking other peoples' heads or shooting at them - but give them a mouse or a keyboard and they freak out! they're like that from top to bottom - IPS guys and all. we're dealing with it in Uttar Pradesh. we actually published all that in our blog on our website. NOT ONE SINGLE cop in the UP police force (of some 2.5 to 3 lakh personnel) is qualified, trained or equipped to investigate cyber crime! their level of "expertise" is such that they can't figure out the difference between a server IP address and a ISP address! they don't even bother to check online using the thousands of free tools that are out there! wait - there's more! a state like Uttar Pradesh does NOT have a forensic laboratory to examine malware. but that's not even the bad part. the bad part is, these guys can't even use a website like VirusTotal to check the malware and get the job done in like 30 seconds! and they can't interpret server logs - because they don't even know what it is! and they don't even try to learn. after spending months 4+ YEARS hopping from one police station to another trying to explain to these guys what happened in #FIR0432 we finally realized a chilling fact: these guys are UNTRAINABLE. you can't teach these UP cops cyber security EVEN if you put a gun to their heads! the UP state government has been so far trying to cover it up. what they show on their ads in their favorite tv channels is a far cry from whats really out there on the ground. Tekzilver is now left with no other option but to pursue other legal options. we can't talk about it in detail at this point. but we will at some point in the future. and guess who trains these cops? Cert-in & I4C that come under their MeiTY! the same guys who wanted to block the bitchat repo from Github! NOW you know why cybercrime is rampant in India! because these cops can't see the evidence if evidence was dancing **** in front of them. if that's how things are in UP, imagine what it could be in other states in the region!!

• 
  smrati tiwari (@smratitiwa86867) reported 24 minutes ago

  🚨 Your Windows PC is secretly running 200+ background services right now. Most of them waste RAM, slow down your system, collect telemetry, and keep features alive you'll probably never use. An open-source developer decided that was enough. They built optimizerDuck — a free tool that helps you clean up Windows 10 & 11, remove bloatware, and optimize your PC for better speed, privacy, and battery life. Here's what it can do: → Optimise 35+ performance, privacy, battery, and system settings → Manage 200+ Windows services with built-in risk labels → Remove pre-installed bloatware with a preview before deletion → Apply GPU-specific tweaks for AMD, NVIDIA, and Intel The best part? Every change automatically creates a rollback file, and the tool requires you to create a Windows Restore Point before making any modifications. ✅ No installer ✅ No ads ✅ No telemetry ✅ No premium paywall ✅ Fully open source ✅ Works completely offline If you use Windows, this is one of those GitHub projects worth bookmarking. Repo link in the comments 👇

• 
  NDTV (@ndtv) reported 24 minutes ago

  The government is worried about this messaging app. Here's why. Bitchat, the Bluetooth mesh messaging app co-created by Twitter co-founder Jack Dorsey, has been in the spotlight after it was reportedly used by protesters during demonstrations at Jantar Mantar in New Delhi. The government says the app could be misused for unlawful activities and has sought to remove its source code from GitHub. Here's how Bitchat works, why it doesn't need the internet or a mobile network, and why taking it down may not be as simple as it sounds. NDTV’s AI Editor Ramarko Sengupta (@ramarkosg) explains.

• 
  Seth Rosen (@TavCannaLLC) reported 25 minutes ago

  The Government of India 🇮🇳 orders GitHub to take down Bitchat because people can chat anonymously over Bluetooth without phones, accounts, or servers they can snoop on. Meanwhile the Rupee keeps losing purchasing power year after year through monetary expansion and their citizens continue to suffer. This is what happens when you have a centralized authority that has misplaced priorities.

• 
  Cerelac Holmes (@CerelacHolmes0) reported 26 minutes ago

  Don't worry there is absolutely going to be a protest later for your involvement in shutting down internet, stopping social media posts involving student being attacked and banning GitHub links of Bitchat. Some other minister will definitely speak good about you at that time.

• 
  Kumar (@kumardeepam) reported 26 minutes ago

  @adisingh @agentmail github issues

• 
  Scarab (@ScarabOfficial) reported 26 minutes ago

  I'm not alone. There are posts on #GitHub regarding the issue, and there is a manual fix, involving editing #Python files to rename things. Not ideal, by any stretch.

• 
  Mohi (@disismohi) reported 27 minutes ago

  Defense layer 2: block unknown SSH servers in egress rules. If CI only clones from GitHub/GitLab, enforce that at the firewall. A rogue server on the internet can exploit this trivially.

• 
  Bullcrap (@Bullcrapwork) reported 28 minutes ago

  @MoneysNotNeeded And to be clear, when you're assuming Core is literally Hitler, the absolute worst you can come up with is them shutting down a technical discussion on Github? Really? That's ******* it?

• 
  SPARQIO (@sparqio) reported 28 minutes ago

  AI has moved from research curiosity to core infrastructure. Search engines, medical tools, financial platforms, enterprise software, all running on models that can sound completely confident while being completely wrong. That tension is the central problem nobody has fully solved yet. Before you can measure whether an AI is correct, you need to define what correctness actually means. It is not one thing. A response can be factually accurate but contextually useless. Logically coherent but dangerously incomplete. Precisely worded but subtly misleading. Practitioners who collapse all of this into a single quality score are building on sand. The more useful frame is five separate dimensions: factual accuracy, logical coherence, contextual relevance, completeness, and calibrated confidence. Each one requires a different evaluation approach. A model that scores well on fluency and coherence can still be catastrophically wrong on facts, and the score will never tell you. On the automated side, the oldest tools (BLEU, ROUGE, METEOR) measure lexical overlap against a reference answer. They have real uses in translation and summarization, but they are poor proxies for whether something is actually true. A model can paraphrase a wrong answer fluently and pass every metric. The field has moved toward embedding-based similarity and model-as-judge setups. BERTScore captures semantic equivalence rather than word matching. More recently, using a separate powerful model to score outputs against structured rubrics, assessing factuality, completeness, and reasoning quality, has become a serious evaluation paradigm. Benchmark datasets add another layer. TruthfulQA tests whether models give truthful answers to questions that humans typically get wrong due to common misconceptions. MMLU spans 57 academic domains. HaluEval is built specifically for hallucination detection. $AI-adjacent plays in the coding space might care about SWE-Bench, which evaluates code generation by running outputs against real test cases from actual GitHub issues. But generic benchmarks hide a serious trap. A model that performs well across general knowledge can still fail badly in specialized domains. Medical AI needs evaluation against clinical reasoning datasets like MedQA or PubMedQA. Legal AI needs BarExam-style benchmarks. Financial AI needs FinQA. Deploying a model because it passed a general benchmark, then using it in a high-stakes domain, is a risk management failure, not an engineering decision. Human evaluation still cannot be replaced, not fully. Automated systems miss errors of omission. They miss misleading framing. They miss the kind of subtle wrongness that a trained clinician, lawyer, or financial analyst would catch immediately. Structured annotation protocols with qualified reviewers remain the gold standard in any high-stakes deployment context. The honest takeaway: knowing when AI is telling the truth requires combining all of these layers. No single metric, benchmark, or review process is sufficient on its own. Organizations treating AI correctness as a solved problem are the ones most likely to discover otherwise at the worst possible time.

• 
  Jay Chooi (@chooi_jeq) reported 29 minutes ago

  @LeoKharon Nice! Yeah inspect robots is compatible with agibot a2 and unitree g1. which humanoid are you getting? We might be able to add compatibility or put this as an github issue and a contributor could work on adding it

• 
  Alessandro Frau (@alessandro_a0) reported 35 minutes ago

  @thiojoe @christitustech @codex I push daily with Codex, if you set up *** and login with your account or just install GitHub Desktop on your machine, it can do that on your behalf, leave comments, analyze comments left from other agents on PRs, etc.

• 
  Lars Moelleken (@suckup_de) reported 36 minutes ago

  @nicksdot Some years ago, I reviewed code really line by line so that I needed some hours to review before a merge from develop to master (for a bigger e-commerce platform with >10.000 php classes) that we developed. In that time we already had the first beta of Github Copilot but it was more like a autocompletion tool in the IDE. I invested many nights to come from phpstan level 1 to 7 and introduced many testing stuff and CI-pipeline. But frome time to time I didn't saw bugs that you can't see in the code, e.g. that the catalog import is allowed to run longer than the next cron triggers it again, that the elasticsearch index rebuild process burned CPU power while waiting for other processes etc. ... Today I have even more depending systems and responsibilities in the current project I work at and I can catch even less bugs in just the code, especially because of different databases, M365, AD, APIs, etc. and so the LLMs helps to catch up with knowledge about ldap, as/400, Microsoft stuff, etc. and investing time in understanding the processes is more important than reading really every line of code. BUT as already written, I look at the diffs, run many checks (for sql typos, template errors, security, etc.), tests (unit + acceptance) and bind spot analysis runs) on top of my manually testing.

• 
  Shubham (@claudeabuser) reported 37 minutes ago

  @github actions self-hosted runners lost connectivity for ~5 hours after an internal SSL cert expired. The reconnect storm that followed hammered GitHub's APIs, briefly degrading Issues and Pages too. Cert expiry is a predictable failure; automate renewal and alert on it.

• 
  Frantz Kati (@bahdcoder) reported 39 minutes ago

  @nnvictory001 Bro you need cloud automation like this. I have an agent on hivy that sends me a full cluster report four times a day. Automatically opens GitHub issues when there’s a critical bug And another agent automatically opens a PR with a fix. Costs me $0.08 per run for cluster analysis and email. Please let me set you up abeg it’s almost free and no subscriptions 🙏🙏🙏

• 
  Parimal (@Fintech03) reported 40 minutes ago

  Every Sunday, I feature an exceptional startup built by Indian founders that deserves a spot on your radar. Today’s feature: Praxiom AI, built by @abhichat85 If you have ever worked in Product Management/Software Engineering, you know the single biggest bottleneck in shipping software is translating user research into actionable engineering tickets. *** spend hours wading through Zoom call recordings, customer support threads, survey CSVs & sales transcripts. What usually happens next? Insights get trapped inside messy spreadsheets/abandoned Notion docs. PRDs are written based on gut feeling/whoever shouted loudest on Slack :)) Engineering tickets end up completely detached from the actual user feedback that sparked them. The end result? Engineers build features that users never actually asked for. Praxiom treats product management like a Version Control System for user research. Instead of relying on a single prompt, Praxiom splits tasks across specialized AI personas: a researcher extracts raw facts, a synthesizer clusters themes, a drafter writes PRD blocks & an independent verifier audits the entire output. Every single claim/PRD requirement/user metric gets an automated Research Quality Score. If an insight is not backed by an exact verbatim quote from your uploaded data, the verifier flags it to eliminate AI hallucinations. It converts structured PRD blocks into scoped engineering tickets directly inside Linear/GitHub/Jira, carrying source citations right into the developer's workspace. Now, what Could Be Done Better (this is entirely my perspective & product is still in early stage): - Right now, Praxiom excels at qualitative data (interviews, tickets, support logs). Integrating realtime product analytics tools like Mixpanel/PostHog directly into the verification loop would allow the system to validate user complaints against actual usage telemetry. - Closing the loop when a feature actually ships. Once a Jira ticket generated by Praxiom gets marked "Done," the engine should automatically track incoming feedback on that specific feature to tell the PM: "Did this actually solve the problem we identified 3 weeks ago?" Praxiom is stripping away the tedious manual synthesis so *** can focus on strategic decisions while keeping every line of code strictly anchored to real user needs. Built by brilliant Indian engineering minds for a global audience. Definitely a team to watch out for! (Startup link in the comments below)

• 
  Julian (@orithellama) reported 40 minutes ago

  @akilress @ICPXProtocol @SumiroStudio Tbh I have had one error on the one-click deploy from Github, will post an update once it's resolved ;)

• 
  Adriano Dennanni (@adrianodennanni) reported 44 minutes ago

  @splatztheclown @jacobhart36 @STGshmups The project seems to be working, with the dev working on the open issues in GitHub. I don't understand the issue.

• 
  CloudySky North (@sharebravery) reported 51 minutes ago

  GitHub Models dies on July 30. Copilot agents keep spreading across issues, PRs, Linear, and mobile. The message is blunt: developers don’t need another model playground. They need AI already sitting inside the workflow where work gets assigned, reviewed, and shipped.