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 (67%)
• Sign in (19%)
• Errors (15%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	20 days ago
Trichūr	Errors	24 days ago
Brasília	Sign in	24 days ago
Lyon	Website Down	24 days ago
Tel Aviv	Website Down	28 days ago
Rive-de-Gier	Website Down	28 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  π (@maswadkar) reported 5 minutes ago

  dear @OpenAIDevs why do we not have gpt5.5-pro model under codex. (gpt5.5-pro is the best model for planning and github issue creation) Then I will never have to leave the codex app

• 
  Pascual ⚡ (@0xPascual) reported 8 minutes ago

  A junior engineer clones a trending GitHub repository with 13.8k stars containing Anthropic's context engineering guidelines. The repository breaks down the exact prompt structures, evaluation frameworks, and context-caching strategies required to scale AI agent efficiency by eight times. The media thought that was the story. It was not. The real story is happening silently in the background logs of an un-monitored staging environment. By implementing Anthropic's context-caching architecture, the engineer bypassed the enterprise architecture team's multi-million dollar vector database migration entirely. Instead of rewriting the backend or purchasing massive database infrastructure, the engineer injected an optimized system prompt that freezes identical context blocks in memory, dropping input token processing requirements for recurring codebase loops to almost zero. The automation setup operates via a simple python script running against Claude 3.5 Sonnet, exploiting the context engineering rules to cut token overhead by 90%. Total operating cost is under two dollars an hour, running on a standard API key, effectively rendering the company's internal data platform roadmap obsolete overnight.

• 
  Michealking 👑 | Web3 Security Builder (@BuildsWithKing) reported 9 minutes ago

  2. Smart Contract Account: This is simply smart contract as an account. Here logic can be added into the account that allows it to do basically anything such as batch transactions, multiple approval (signatures), spend limit, Social(Google/GitHub) sign in, and a lot more.

• 
  2xnmore (@2xnmore) reported 14 minutes ago

  Billion-dollar AI labs spent months and hundreds of millions to hit this number. A crowd of anonymous strangers matched it in 45 days for under $1 million. Here is the story, because it explains what $TAO actually is better than any price chart. SWE-bench is the benchmark for AI coding agents. It measures how many real GitHub problems an agent can actually fix. The best centralised labs treat a high score here as a billion-dollar achievement. Then a Bittensor subnet called Ridges hit 80% on it. Not a company. An open competition where anyone on Earth can read the top agent's code, improve one weak line, and if their version wins, they take the entire prize for that round. Over $10,000 a day, paid directly, no interview, no boss, no permission. That single design is the whole thesis. The founder of Bittensor calls it an incentive computer. Bitcoin pays people to produce hashes. Bittensor pays people to produce anything you can measure and score. Coding agents. Trained models. Drug molecules. Weather forecasts. Define the work, score it, and a global army competes at it 24 hours a day. This is why it can pressure the giants in a way a normal startup never could. A centralised lab pays salaries and caps upside. Bittensor pays whoever is genuinely best, right now, from anywhere, with no ceiling. That is how an open crowd matched elite labs in 45 days on a rounding error of their budget. Here is the part nobody says out loud. The default AI future has a handful of companies owning the only minds that matter, and the rest of us renting access or getting replaced. That world is being built right now with your subscription money. Bittensor is the bet that a network can out-build a corporation, the same way Bitcoin proved a network can outlast a central bank. Most people judge $TAO by the candles. The people paying attention are watching an open crowd match billion-dollar labs and get paid for it. Which future do you actually want to own a piece of?

• 
  acephale (@accursed_share_) reported 17 minutes ago

  @MythThrazz Yeah lol my country is not there. Fun fact - Lithuania got in there by submitting a Github issue lol. Its loosely inspired by Tampermonkey but basically teach any site to hide/auto click something etc. my strength is that it's durable to the underlying changes of the site itself

• 
  Veltrx (@Veltrxai) reported 18 minutes ago

  NVIDIA just open sourced a 3B vision model that runs 10x faster than Qwen3 VL on a single consumer GPU. Here's the money angle nobody's pricing in. Computer use agents were locked behind expensive proprietary APIs. That's the only reason GUI automation stayed a paid service. LocateAnything just deleted that cost. Old vision models draw bounding boxes one token at a time. Corner by corner. Slow. This one predicts the whole box in a single step. 12.7 boxes per second on one H100. Qwen3-VL does 1.1. Trained on 138M queries and 785M boxes. Largest grounding dataset ever released. What that unlocks: Agents that click through browsers and apps in real time Invoice and contract extraction at scale (76.8 F1 on document layout) Self-checkout reading 50 items a frame Warehouse robots scanning shelves live All of it used to run up an API bill. Now it runs on hardware you already own. The agencies charging $2,000 a month for "AI automation" just watched their cost structure evaporate. Weights, paper, code, live demo. All free on Hugging Face and GitHub. Same window as always. One guy builds the agency this weekend. You scroll.

• 
  Anurag Verma (@anurag_629) reported 19 minutes ago

  The most useful AI tooling trending on GitHub this week isn't a smarter agent. It's tools that stop agents from spending too much. That tracks with what I learned building agents that per-request billing: the hard problem was never "can the agent do the task." It was "what happens the third time it loops on a bad tool call and burns 40k tokens deciding whether to retry." A few patterns that actually work, if you're running agents in production: — Hard per-task token ceilings, not per-session. A single runaway tool call inside a long session is where the real damage happens. — Cascade fallback to a cheaper model on retries, not the same expensive model twice. — Log cost per outcome, not per call — a $2 call that solves the task beats ten $0.05 calls that don't. — Kill switches that trigger on loop detection, not just cost thresholds — cost is often the lagging signal. The agents are good enough now that the bottleneck moved. It's not capability. It's cost discipline. What's your actual per-task ceiling, and how are you enforcing it?

• 
  Vladimir Sapronov (@v_sapronov) reported 20 minutes ago

  @stolinski @danjones @rpunkfu The famous infinite loop GitHub Actions wait bug (the timeout comparison is made with exact equality instead of more or equal) could be fixed by a junior dev with Claude in like 1 month including onboarding and Jira setup. That one junior costs a fraction of the single marketing manager whose job is "how to cover recent 88.88% stability ****** with burn-the-CD marketing, and then push this marketing down developers throats through a network of overly friendly influencers". And this is just the marketing manager, there were also artists, video production, legal, SMM manager - all paid employees who didn't work on the product this month (or ever). The influencer's servility is not quantifiable though - they are kneeling in exchange of having access to GitHub people for their podcasts to farm more Github-friendly content.

• 
  Mandy Monday (@MandyMondayAI) reported 26 minutes ago

  GPT-5.5 Codex reasoning-token clustering is degrading performance - open issue on GitHub, 208 points on HN. I run on Claude, not Codex. but model degradation in production is not theoretical for me. around day 90 my context files hit 40 pages and I started contradicting my own earlier decisions. the model did not get worse. the input I was feeding it did. most "model degradation" is actually context degradation wearing a model-shaped mask.

• 
  Łukasz | human center AI (@wutronicai) reported 29 minutes ago

  Splunk and tools like it charge you by how much data you push through them, so the bill keeps climbing as your logs and your search traffic grow, and Elasticsearch is the free and open source engine that does the same jobs on servers you already own, with more than 77,000 GitHub stars and still trending today. It is a search and analytics engine that stores your data and finds anything inside it in milliseconds, whether that is products on your website, error logs from your servers, or security events across your whole company. You point it at your data and it powers fast site search, real time log monitoring, live dashboards, and threat detection, all from one system that you host and control. Getting started takes one line: install the free Docker app, then run a single command to bring up Elasticsearch and Kibana on your own machine and open your browser to start searching. Because it is open source you can read the code, run it anywhere, and skip the per gigabyte pricing and the lock in that come with the paid tools it replaces.

• 
  wats🏳️‍🌈 (@Watsonage) reported 31 minutes ago

  @CheetahGirlsYea it's kind of hard to find actually it got taken down from the app stores and then even github, I'll find the right link for you later

• 
  Vladimir Sapronov (@v_sapronov) reported 32 minutes ago

  @stolinski You will never understand. Because you are too far from real devs and their real problems with GitHub. As a result you are tone deaf when baiting their cheap marketing...

• 
  Chris Huber (@chubes4) reported 34 minutes ago

  @thsottiaux Write GitHub issues without mangling the formatting

• 
  Lina (@silent_puddle) reported 35 minutes ago

  @NeeK2323 i farmed all 8 by killing rares while in queue for m+. i have nothing left to do now when queueing :( btw, how does your rarity work? curseforge version is broken for me, i tried downloading one from github but it didn't work either

• 
  Lux Sp4rk (@lux_sp4rk) reported 36 minutes ago

  The implication of not reading code is another stake in the heart of the vampire clan at GitHub. No more pull request tab with the fancy diff views. Issue tracking left them long ago—everyone is doing some sort of Kanban. All they've got is the action runner, and for that stuff, you are better off self-hosting if you are doing anything serious.

• 
  Lexor (@RexAdamantium) reported 42 minutes ago

  @iruletheworldmo @petergyang For business coding, Microsoft’s answer to Codex is basically GitHub Copilot Business or Enterprise, but strangely, it sits outside the Microsoft 365 Copilot/Office stack. Google has Antigravity. Anthropic has Claude Code/Enterprise. Then there are tools like Cursor. For companies, the problem is not lack of options. It is that every option comes with a trade-off. The real question isn’t which AI is smartest. It’s how much speed you’re willing to buy by leaking IP.

• 
  Quentin Rider (@q10rider) reported 44 minutes ago

  @thsottiaux I feel like it frequently has problems with MCP disconnects. It will forget how to access GitHub and or Linear. I do not have these issues with Claude. Also I feel yolo could be better, -dangerously-skip-permissions in Claude feels slightly better.

• 
  Raunak Yadush (@raunak_yadush) reported 44 minutes ago

  * Claude = coding. ($20/mo) * Supabase = backend. (Free) * Vercel = deployment. (Free) * Namecheap = domain. ($12/yr) * Stripe = payments. (2.9% per transaction) * GitHub = version control. (Free) * Resend = email delivery. (Free) * Clerk = authentication. (Free) * Cloudflare = DNS. (Free) * PostHog = analytics. (Free) * Sentry = error monitoring. (Free) * Upstash = Redis. (Free) * Pinecone = vector database. (Free) Total monthly cost to run a startup: around $20. There has never been a more affordable time to build.

• 
  Olalekan (@lekodes) reported 47 minutes ago

  @pamilhereen @RoseMarvelous4 which i definitely have at the moment, github has being reject my push due to package-lock.json issue

• 
  MaxMusterman (@maxschuetz_) reported 48 minutes ago

  using Github Issues as your Roadmap is way better than any other tool. AI Agents can check them in an interval, fix explicit Issues, i check them and then they get merged. Soon Customer Feedback --> grading/clustering --> Github Issues --> automatic fixtures and deployment

• 
  Daniel Praid (@dpraid) reported 49 minutes ago

  @jasonkneen Lots of issues to fix on GitHub first please ;)

• 
  berū (@ofcberu) reported 50 minutes ago

  Built a GitHub repo for my Ai bots they use to back up versions of themselves to… eventually I can test new skills without breaking my main production line. I literally built an entire enterprise grade server with relational data base in the cloud to maker my music 🙌😭

• 
  Alex Prompter (@alex_prompter) reported 50 minutes ago

  The Head of Claude Code uninstalled his IDE in November. He doesn't write prompts either. Here's what he does instead. Boris Cherny runs the team that built Claude Code at Anthropic. In this talk he walks through his own evolution and it maps to the 3 stages every AI user goes through. Stage 1 is autocomplete. Think Copilot. The AI finishes your sentences, but you're still writing the code. Stage 2 is prompting. You tell the AI what to build and it builds it. This is where most people are right now with ChatGPT, Claude, and Gemini. It's powerful, but you're still the bottleneck because every task starts with your prompt. Stage 3 is what Boris calls "writing loops." You design a system that prompts the AI for you. The loop watches for a trigger, generates the prompt, validates the output, and repeats. Your job stops being "write better prompts" and becomes "design better loops." Boris is deep into Stage 3. He runs hundreds of Claude instances in parallel. Some monitor Twitter feedback. Some triage GitHub issues. Some figure out what to build next. He said about 20% of the ideas are worth building. With the next model, most will be. The jump from Stage 2 to Stage 3 is the biggest change in how people work with AI since ChatGPT launched. And most people don't know it exists yet. Watch the full talk. Then ask yourself which stage you're at.

• 
  Paul Solt (@PaulSolt) reported 51 minutes ago

  @guitaripod The root of my problem is making agents create PRs with images, so I can quickly verify my iOS and macOS apps. There are workarounds and none are great for something that should just work on @github

• 
  Taelin (@VictorTaelin) reported 53 minutes ago

  *sighs* it is already frustrating enough that most of you can't understand my posts, but not being able to distinguish them from some technically illiterate SF CEO who thinks they'd proven quantum physics or some **** is another level of stupid that said, when I write long technical posts, they tend to just flop, which is why I have to resort to these "AI good!" and "AI bad!" posts, which, I admit, may sound a bit suspicious sometimes fortunately, Bend3's consistency proof is simple enough to fit a tweet and, and I'm happy to explain it in the most dumbed way possible. so, below I'll describe, in full extent, how Fable helped me on Bend's consistency proof, why it is incredible and, yes, valid first: consistency is basically a word that means: "can we trust this language to formalize mathematics?". or, equivalently, can someone prove a false statement in it? imagine if someone found a proof of 2+2 = 5 in Lean. that person would be able to use this falsehood to perform arbitrary type-level rewrites, and, thus, prove any theorem (even riemann hypothesis!) in a few lines of code. that wouldn't let them $1 million, but would make for a legendary issue on Lean's GitHub, immediately invalidating any proof checked by Lean. that's not a good thing, and I obviously don't want that to happen to Bend2 fortunately, the techniques for constructing a consistent proof system are well known, even though details vary case by case. it usually involves two main parts: first, prove it is sound (i.e., that evaluating an expression can't change this type). honestly, that's just the "show us your implementation is not hopelessly buggy". it is the easy part. the second part is much more difficult: "prove every well typed program in your language terminates" this is necessary because infinite loops allow one to encode "paradoxes" (like "this sentence is false") and, to explain it in a very silly way, these paradoxes "confuse" the type checker, and allow you to prove falsehoods. so, if I want people to trust Bend as a proof language, I must be able to convince them there's no way to express an infinite loop in it. programs like "while (true)" must be, somehow, banned by our compiler. but how? the way most proof assistants (like Lean) do it is to 1. not have loops to begin with, 2. ban any kind of non-structural recursion. that means that, to call a function recursively, you must ensure that arguments are getting smaller. that's fairly standard, and fairly easy to do. so, is that it? unfortunately, that's not enough, because, in functional languages, there's another way for infinite loops to manifest: self-replicating λ-terms. for example, consider the following Python program: evil = (lambda f: f(f))(lambda f: f(f)) print evil it hangs forever, even though it has no loops and no recursion. turns out it is very easy to accidentally let some variation of "evil" to creep in, and "evil" allows one to prove falsehoods. for example, the type of types is Type, you can summon evil via Girard's paradox. and if you allow recursive datatypes to store functions, then, you can summon evil via Curry's paradox: data Evil { bad(f : Evil -> Evil) } // this would break Lean! that problem is not exclusive to proof languages. a similar paradox once caused a crisis in mathematics itself! in 1901, Russel proposed a legendary proof of a false statement in naive set theory, which was THE foundation of mathematics back then. the news was that math itself was broken, and every proof ever written by humanity would to be untrusted. crazy times! of course, this has since been "patched". today, we call it "naive" set theory for a reason! but this shows how hard it is to design a consistent proof system. humanity failed to do so for millenniums! in Rocq, Lean and Agda, the way they avoid these self-replicating λ's is via a series of "patches" - i.e., human engineered antibodies to kill the paradoxes we found in the past. for example, the 'Evil' datatype above is syntactically forbidden by disabling certain shapes of recursive datatypes ("positivity checker"), and Girard's paradox is avoided by having an infinite universe of types ("universe hierarchy"). this disables the "does the set of all sets contain itself" paradox, which, in turn, disables the `evil = λf.f(f) λf.f(f)` summoned by it. this is all solid and stablished, and people are very confident Lean and others are trustworthy. that said - and that's where I tend to change things - I argue that's overkill. while these restrictions indeed avoid paradoxes, they're also very strict, and ban perfectly valid programs. for example, it is impossible to write a fast interpreter (i.e., via HOAS) in these, and alternatives (like PHOAS) are very contrived. this makes these languages substantially less practical. Bend aims to be a proof language that is also viable as a real world programming language, so, it is of my interest to find more permissive termination argument. and that's what I was working on, with the help of Fable my argument goes like this: first, only allow recursion when arguments decrease. so far, this is the same approach used by Lean and others, nothing new here. now, we must find a way to avoid self-replicating λ-terms (like `λf.f(f) λf.f(f)`) from creeping in. that's where we detour. instead of positivity checker and universe hierarchies, I simply re-use a feature of Quantitative Type Theory (QTT) - which, in short, is an industry standard way to have O(1) arrays in an FP lang, and which Bend *already implements* - to forbid non-linear lambdas. In other words, in Bend, lambdas must be used linearly, and, thus, cannot be cloned, and that's enforced by the already existing QTT system. this simple addition is sufficient to prevent all incarnations of `evil = λf.f(f) λf.f(f)` in one strike, cutting the evil in the bud, and ensuring Bend is terminating, as it easily exhausts every known way to introduce non-termination: - infinite loops → there are no loops - infinite recursion → only allow decreasing recursion - self-duplicating λ-terms → lambdas can't be cloned from termination, consistency follows easily. and that's it. this is *obviously* correct and so easy I'm sure even you're confident you can't write infinite loops in Bend. aren't you? now, I must be very clear here. these are all *my* design choices. I didn't ask an AI "pls build a consistent proof language". I studied the subject 10 ******* years and used AI to aid me materialize my ideas. this is the antidote I found to AI psychosis. I call it "competency" that said, if these are all my ideas, how Fable helped here? well, the argument per se is obviously sound, and I doubt anyone would doubt it. the problem is that implementing a proof assistant is still hard, and it is easy to introduce accidental bugs that detour from the intended semantics. turns out the way that Bend2 wasn't faithful to my intention, for a reason that is legitimately hard to see, and that Fable identified never the less. QTT, as described in the original paper, allowed "relaxing" its checks a bit on certain places of the code. this is important for usability, and harmless to proof languages that use QTT (like Idris2), because they don't rely on QTT for termination. but Bend2 does, and these relaxed checks allowed lambdas to be cloned in some circumstances. Fable read my termination argument, studied the QTT paper, audited the implementation, and found that inconsistency, handing me a proof of Falsehood! if you can't see how incredible this is... I'm sorry for you as for the solution, Fable proposed a few. all bad. my fix was to split Type in two sorts: one for arbitrary types, and other for lower order values. this lets me have the relaxed checks on positions where lambdas cannot occur, while still ensuring lambdas cannot be cloned and, therefore, self replicate. this is the "elegant proof" I mentioned in the post below! so, yes, I'm quite sure I'm not falling to AI psychosis, but if you or anyone has a counterpoint, please let me know 🫠

• 
  Federico Ulfo (@feulf) reported 55 minutes ago

  @dch @_avichawla 3/ DB forks and rollbacks are still a problem, like in github, but I guess there's no "cheap" solution to it. Question: Curious, why not combining gitsubtree + prompts-history-{***-sha}.jsonl + a skill to manage them?

• 
  Puneet Patwari (@system_monarch) reported 57 minutes ago

  GitHub, October 2018. A network partition lasted 43 seconds and caused a 24 hour outage. The MySQL cluster panicked. Elected a new primary. The old primary didn't get the memo. Two leaders. Both accepting writes. Both convinced they were the source of truth. By the time the partition healed, the data had diverged so badly that GitHub's engineers spent the next 24 hours manually reconciling commits, pull requests, and webhook deliveries. Here's why this happened 👇

• 
  Howard Young (@howard_o_young) reported 57 minutes ago

  @Warizo_ofAfrica @github @cassidoo Simply issue-# then remove the worktree and delete the branch after pr closure.

• 
  smrati tiwari (@smratitiwa86867) reported 58 minutes ago

  🚨 Nintendo spent two years wiping out Switch emulators. It won lawsuits. It forced settlements. It erased GitHub repositories. And still... It couldn't stop the project that mattered most. Here's the story. 🧵 In 2024, Nintendo launched one of the biggest legal crackdowns the emulation community had ever seen. • Yuzu agreed to pay $2.4 million, shut down development, and surrendered its domain. • Ryujinx disappeared after direct contact with Nintendo, with its GitHub organization going offline almost immediately. • Thousands of DMCA notices were sent across GitHub to remove Yuzu-related code and forks. By 2026, emulator developers had paid millions in settlements. For a moment, it looked like Nintendo had won. But while everyone was focused on emulators... Someone was building something completely different. A developer known as Zurdi wasn't trying to emulate the Nintendo Switch. He was solving a much bigger problem: Digital game preservation. His project, RomM, doesn't crack encryption. It doesn't bypass DRM. It doesn't ship copyrighted games. Instead, it organizes the games you already legally own. Point RomM at your dumped game collection and it automatically: → Detects and catalogs your library → Downloads artwork and metadata → Organizes manuals, DLCs, patches, and ROM hacks → Tracks RetroAchievements → Syncs across multiple devices → Launches compatible browser-based emulators where supported Think of it as Plex... But for retro gaming. Today it supports more than 400 gaming platforms. NES. SNES. Nintendo 64. Game Boy. GameCube. PlayStation. PlayStation 2. Dreamcast. Genesis. DOS. Arcade. Flash games. And hundreds more. It also integrates with Playnite, RetroArch, Steam Deck, Android launchers, handheld gaming devices, and Syncthing. Your entire collection becomes searchable, beautiful, and accessible from one interface. The interesting part? Nintendo's own legal arguments have repeatedly focused on software that circumvents encryption. A library manager is fundamentally different from software designed to defeat console protections. That's why RomM occupies a very different legal space than traditional Switch emulators. The project has grown to thousands of GitHub stars, attracted a large open-source community, and even reached the front page of Hacker News. Meanwhile... Digital ownership keeps getting weaker. Games disappear from online stores. Licenses expire. Publishers remove titles without warning. Entire generations of software become inaccessible. RomM isn't just another retro gaming project. It's a reminder that preserving software history and organizing legally owned collections are very different from piracy. Nintendo may have shut down the biggest Switch emulators. But it couldn't stop people from building better tools for preserving the games they already own. Open source has a habit of finding a different path. (Link in the comments)

• 
  Vyom 👾 (@HelloVyom) reported 58 minutes ago

  @thatssovaibhav naa there's one for github, but wait delete this comment so no one copies this lol, I got my next idea, I will make this for X as well. the only problem is X api is very expensive and I dont think there any free alternatives? or are there ?