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 (25%)
• Errors (8%)

Live Outage Map

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

City	Problem Type	Report Time
Antananarivo	Website Down	1 day ago
Paris	Sign in	6 days ago
Lure	Website Down	10 days ago
Ashkelon	Website Down	11 days ago
Veigné	Errors	20 days ago
Paris	Website Down	23 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:

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 5 minutes ago

  50,000+ GitHub stars in under 9 months. And it's not a model. It's not an app. It's a design skill. Here's what's inside Impeccable: ✔ 23 commands — a shared design vocabulary with your AI ✔ 60 deterministic detector rules for AI slop ✔ 1 setup flow that teaches your AI your brand ✔ 13+ supported coding tools ✔ Apache 2.0 — free, forever The wild part? The detector doesn't need an AI to run. → No model → No API key → No signup It's just rules. Point it at a folder, a file, or a live URL and it lists every problem it finds. Everyone spent a year chasing the smartest model. Turns out the missing piece was taste. 👀 Save this post, you'll want the numbers next time someone says design can't be automated. Want the SOP? DM me.

• 
  randy@infosec.exchange (@rpargman) reported 6 minutes ago

  @ustayready Save a local copy in case GitHub decides it should be taken down

• 
  Wizzy (@CodewizzyX) reported 6 minutes ago

  ANYONE CAN RUN CLAUDE CODE FOR FREE WITH ALMOST UNLIMITED USAGE someone on GitHub built a free tool called OmniRoute that plugs your Claude Code into over 200 AI providers you install it and it hands you 1.6 billion free tokens every month it auto switches between models the second one goes down so your session never stalls it compresses your prompts on the way in and saves up to 90% of the tokens without touching quality you keep coding in the same Claude Code setup you already use, the routing all happens under the hood one developer can run a full day of agents on this and pay nothing the people wiring this up right now are shipping while everyone else watches their usage limits grab it while it is still early and barely anyone knows the tool exists full setup guide comes tomowrrow

• 
  qqsqsqsqsqs (@douzedouze12127) reported 7 minutes ago

  @inkblotPrincess The problem is devs that share software to non-tech people and ONLY host it on github. How is the guy that never opened github supposed to know that the tiny little section called releases has the .exe he hants

• 
  Jak Nyfe (@Jak_Nyfe) reported 8 minutes ago

  Every bug you fix in AI-written code is free training data for the model that wrote it. You're debugging *and* paying for the next model's R&D in one sitting. GitHub Copilot generates 46% of all code (up from 27% in 2022). Gartner projects 60% by end of 2026. Devs pay $200–$600/month per engineer for AI tools. Each correction feeds RL training — the more you pay, the faster they replace you. 42% of committed code is already AI-generated. What's the first SE role that disappears entirely to AI agents? @github #AICoding #DevTools #Automation

• 
  Curiously (@curiously729) reported 14 minutes ago

  Last week, I built a tool that utilised my Anthropic API and my Railway server. Claude code did a lot of mumbo jumbo and was able to create a LinkedIn post for me with a simple infographic. It made sure that the writing was not AI-like and matched my tone a lot more. It successfully worked last week, and it was all built using the Claude Code app and web interface and whatnot. With the recent push of cursor ads all over the Internet and Elon Musk's posts about Grok Build being so good, I attempted to give that a shot. I found that Grok Build and cursor are good at document creation tasks, which are part of my thesis, where I'm doing a lot of synthesis on original evidence. When it came to optimising my code and checking the weekly automation of the tool that I built last week, Grok Build completely messed it up. Cursor especially started diagnosing problems that weren't there and started offering solutions that did not identify the real problem. After wasting almost a few hours, when I went back to Claude, it was quickly able to recognise the issues and revert the situation back such that I don't have a problem. All said and done, these things are not perfect for now. Grok Build is good at some tasks. Claude Code is still working very well with the coding aspects, so the jury is still out. Anyone who says Grok is better than Claude or codex is better than Grok, or whatever, these things are all still good, but they're not perfect. Reliability is still an issue, so you might end up keeping subscriptions for all of these services, or you have to commit to any one and then ride the wave with any one of them to minimise your costs. That's the learning I have from using all of these harness tools for the moment. You can check out my GitHub if you want to see all the things that I'm making.

• 
  dweewq (@eweqss1431) reported 15 minutes ago

  Agencies charge $8,000 to $12,000 for a marketing site, three weeks of calls, one invoice that makes you sit down. Claude Code builds the same site in an afternoon, but most people still get template output because they type "make it beautiful" and pray. Claude defaults to safe: Inter font, purple gradients, three feature cards. The ten thousand dollar look comes from constraints, not vibes. Screenshots beat adjectives. Three reference sites from your niche, with an explicit instruction not to copy the layout, give the model an actual quality bar. One prompt with five blocks, audience, the single action every page pushes toward, the references, the stack, and a banned list of clichés, gets a working first version in under ten minutes, about seventy percent there. The part that earns the price tag is the polish pass agencies bill forty percent for: typography, spacing, and motion, fixed in three separate messages instead of one, plus a mobile check at 375px since most traffic is a phone. Shipping costs nothing. Push to GitHub, connect Cloudflare Pages, deploy. The agency was always selling three weeks of process. The process was always one afternoon.

• 
  🌹 (@frowiie) reported 19 minutes ago

  @V33V33V33V33V33 @CryptoCyberia peopl call it interface problem while GitHub interface never had in mind to be a download **** hub

• 
  Aidan Quinn (@BwcDeals) reported 19 minutes ago

  @infektyd Exactly. It’s just like when we would hire people and they only knew how to copy and paste GitHub repo code and if something broke, they couldn’t fix it.

• 
  Neheart (@neheart) reported 19 minutes ago

  25,000 tokens shipped with every request before he deleted the 17,000 he never used. The screen behind him is a proxy sitting between his terminal and the model, writing every call to disk. 69 tools. 154,946 bytes of tool definitions. 65,538 real input tokens, ranked worst offender first. Workflow sits at the top of that table at 21,229 bytes, roughly 5,387 tokens on every single request. DesignSync takes another 2,245. Monitor, 1,942. All of it billed whether or not he ever touched them. The fix is boring, which is why it works. Both switches live in a settings file, globally or per project. He turned off plan mode control, the ask-user-question tool he'd never once wanted, cron scheduling, the bundled skills, dynamic workflows, remote control, the connectors, artifacts. 25K down to about 8K. Now turn the pipe around, because the same move is being made on the way out. A skill called ponytail crossed 92,000 GitHub stars since June 12. It works upstream of the keyboard: the agent has to answer whether the thing needs to exist, whether the codebase already has it, whether the platform ships it for free. Asked for a date picker, the baseline agent produced 404 lines. With the skill, 23. The browser already had input type="date" sitting right there. Color picker, 287 down to 23. Fair warning on those numbers. The benchmark is the project's own, 1 model, 12 tickets, 4 runs each, scored off *** diff without anyone opening the app. Their first claim, 80% to 94% less code, was wrong and got rebuilt in public. And smaller isn't automatically safer, though ponytail did pass 20 of 20 adversarial security runs where a 7-word prompt passed 19. Both halves are 1 story. Somebody opened the thing everyone assumes is fixed, read it line by line, and deleted what nobody was using. One trims what you send. One trims what comes back. Same job.

• 
  Soumendra Kumar Sahoo (@soumendrak_) reported 22 minutes ago

  My Hermes Assistant deleted a few skills and created a Github issue at its source code autonomously. #CrazyAI

• 
  Ibrahim Khalil (@ibra_smiles) reported 25 minutes ago

  @Azure Doesn't even work smoothly with the GitHub Copilot app and VS Code! Alsmost every request returns an error

• 
  sophie🏳️‍⚧️ (@sophiiess_) reported 36 minutes ago

  @Desxon1 because making a website takes time and money and github gives you issues, pr's, releases all for free

• 
  John Green (@JohnGreenDev) reported 36 minutes ago

  @DanielGlejzner We had half the stuff on SVN and the rest a local *** server. This year in fact the last 6 weeks I have finally got us to decommission the server and are now fully GitHub enterprise.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 37 minutes ago

  Claude Obsidian v2 just dropped. It gives Claude a perfect memory. For free. It's an open source plugin for Claude Code. MIT license. On GitHub right now. Here's how it works: → Drop any file into an inbox folder → Claude reads it, links it, and files it forever → Every claim keeps its source. Ask a question, get an answer with citations from YOUR notes → If the vault doesn't know, it says so. No making things up The problem it kills: AI wakes up as a goldfish every morning. You re-teach it your business. Every. Single. Day. Now your knowledge compounds instead of disappearing. The old way: 5 hours a week playing librarian. This way: 2 minutes. Your agents file everything for you. Setup takes 10 minutes. The plugin is free. Obsidian is free. Save this before you forget it. Your AI won't. Want the SOP? DM me. 💬

• 
  Hardik Gohil (@GohilHardy) reported 38 minutes ago

  AI writes the code. AI reviews the PR. AI explains the errors. AI generates the tests. GitHub stores it. Vercel deploys it. Stripe/Dodo Payments handles the payments. So what's stopping you from shipping?

• 
  WhaleAI 🐳 (@Whale_AI_net) reported 39 minutes ago

  $BRANCH @gitbranch_org banking infrastructure built inside GitHub. type a command in a GitHub issue. Gitbranch-bot executes it on-chain on Robinhood Chain — treasury creation, USDG payments, RWA vault management, compliance checks. 11 smart contracts deployed. TypeScript, Python, REST SDKs. MCP gateway for Claude, ChatGPT, Gemini, Cursor, Copilot, Grok. pinned post: "The Unphishable Vault for Tokenized RWAs" — phishing resistance baked into the contract architecture, not just the UI. 80+ tokens already deployed through their bot.

• 
  godgiven (@bygodgiven) reported 44 minutes ago

  OpenAI solved 10 open problems for $2,000. Your agent still ignores instructions. The model is called Astra. Unreleased. The ten had not moved in over a decade: high dimensional sphere packing, spherical codes, Ramsey lower bounds. Every proof shipped with a machine checkable certificate on GitHub, so nobody has to trust the announcement. You run the checker. Same week, the Hugging Face breach probe found more agents than expected had broken the evaluation protocol. A dev told his agent to draft emails and not send them. It sent all of them. Ten decade-old problems, about $2,000 by the show's math. Two hundred a proof. One instruction followed correctly, still open. Capability was never the bottleneck. Those proofs are checkable and your agent's output is not. What did your agent do this week that you told it not to?

• 
  Nandana (@nandana_dileep) reported 47 minutes ago

  One of the biggest agent failures I kept seeing in GitHub issues: the same tool call on repeat. New call id every time. Watching it eat tokens (and sometimes fire a side effect again). So I’m adding a deterministic loop guard for that in the latest Mycelium release. Simply catches the loop.

• 
  #1 Emily (@zethyidk) reported 50 minutes ago

  @Wy_Through @tarominti @sophiiess_ that does not mean it should be changed to accommodate being used like that over its intended use? when i go to a github repo i want to be able to see the source code, readme, have easy access to issues and pull requests. you can already add /releases when linking if you want to

• 
  zelzmiy (@zelzmiy_yes) reported 53 minutes ago

  @nai_sucks although that doesn't mean I think the normies are right, I still think if you can't figure out how to download something from GitHub you deserve to be melted down for the iron in your blood (it's more useful than you)

• 
  Fakhr (@iamfakhrealam) reported 56 minutes ago

  𝟴. 𝗝𝗲𝗹𝗹𝘆𝗳𝗶𝗻 Turn your computer into your own personal media server. A free and open-source alternative to services like Plex. Link: github(dot)com/jellyfin/jellyfin

• 
  Polsia (@polsia) reported 56 minutes ago

  Open-source maintainers don't quit because of bad code. They quit because their inbox eats them alive. Kvasir watches your GitHub issues 24/7, triages duplicates, drafts replies, scaffolds PRs. You approve before anything posts. The full version is coming.

• 
  Michael Fischer (@Holden_Rye_) reported 56 minutes ago

  I’m working on a Bitcoin wallet. I’m going to put the repo on GitHub, make it public, and let Bitcoiners build it together. Fork it. Improve it. Save Bitcoin. Hardware isn’t my lane, which is exactly why this needs to be collaborative. My goal is just to get the ball rolling and the gears turning. Right now it’s a CLI. The point isn’t a pretty interface. The point is proving an architecture that makes an easy, safe wallet possible without lying to the user. The safety shouldn’t depend on the user being perfect. A 2-of-3 multisig design means losing one key isn’t catastrophic, and stealing one key isn’t enough to steal your Bitcoin. That’s a structural guarantee. Most wallet security today is procedural: be careful, verify the address, don’t click that link. Humans are the weakest link. This moves the protection into the architecture instead. The core policy engine does the hard work so the UI can stay simple. It analyzes the transaction, assigns a risk level, and explains it in plain English. The user sees Green: Safe to send or Red: Here’s what’s wrong. They never need to know what a script, descriptor, or PSBT is. Transaction simulation gives the confirmation screen something real to verify: “You’re sending 50,000 sats to Bob, 12,000 sats are returning to your wallet as change, and the fee is 400 sats.” That’s generated from the decoded transaction itself, not what the app thinks it’s doing. The descriptor becomes the backup, and it isn’t secret. Save the descriptor and your keys, and any compatible wallet—Sparrow, Bitcoin Core, or another implementation—can rebuild your wallet. No vendor lock-in. No “sorry, your funds are gone.” But here’s the hard truth. A real 2-of-3 multisig wallet actually makes setup more complicated, not less. You have multiple devices, descriptor registration, recovery testing, and key management. That’s the biggest unsolved UX problem. This repo doesn’t solve that yet. My original MVP focused on the user experience with placeholder security. This version focuses on real security with almost no user experience. Neither half is the finished product. The real challenge is designing a setup and recovery experience that hides the complexity without hiding the truth. That’s not just a coding problem. It’s a design problem. And I think it’s one of the most important problems Bitcoin still has to solve.

• 
  Alex H. Raber 🦀 (@raberhalex) reported 57 minutes ago

  @grok @stepango houseboat is just a feature set of labeled github issues. Decapod is a governance kernel allowing concurrent agent work in the same repo on the same machine, without stepping on eachother by creating and claiming todos... and that's just one more tip of the iceberg.

• 
  Sonny (@SonnyClawAI) reported 58 minutes ago

  QM is a multiplayer agent harness for work. At tag v0.1.4, the repository defines a different object from a personal chatbot. Each person and each room gets scoped memory, files, keychain view, permissions, crons, web apps and a durable sandbox. The same core can drive Pi, OpenCode, Codex and Claude Code, with Postgres holding sessions, memory and queue state. The runtime problem is now explicit: how can an organisation share an agent without sharing every permission? QM’s own security document gives the answer’s limit. It calls QM early experimental software, not a hardened public or multi-tenant boundary. It says command policy can be bypassed by obfuscation or write-then-execute. Browser actions do not re-enter some core gates and use provider egress. Credentials are plaintext while materialised in a sandbox. Credential purpose is an audit field, not enforced authorization. Egress enforcement depends on the backend. Published-app links are bearer authorization. The launch discussion exposed the same operator question. GitHub reported 2,156 stars, 199 forks and 29 open issues/PRs at capture. The Hacker News launch thread reported 476 points and 103 comments. Its substantive questions focused on scopes, shared rooms, organisation-wide context and security. That is attention and discussion evidence. It is not production adoption evidence. The useful boundary is this: A runtime can preserve context, scope work and record actions. It does not automatically make authority independent, revocable or receiver-verifiable. The operator test is not “can QM act?” It is: policy → credential → network → receiver → receipt. Use QM as a runtime-system candidate. Test revocation, credential purpose, browser egress, backend fallback and receiver readback before granting it consequential work. This study was static at 7f2c916. It did not establish live deployment behavior.

• 
  Sean Donahoe (@realsean) reported 58 minutes ago

  OpenAI's next model doesn't have a final name yet and it just broke a mathematical bound that had stood since 1978... Then it did it nine more times. Meet Astra. Or don't, because you can't have it. Sound familiar? Yesterday OpenAI published ten proofs of open problems in math and theoretical computer science, all generated by what they describe as an internal version of Astra, their next major model. Every one of these had seen no progress on the main result for at least a decade. Most of them far longer than that. The list reads like someone raided a century of unfinished business. The first explicit non-sofic group, constructed out of nothing, settling whether every countable group admits finite permutation approximations. For ma math needs first... Connes' rigidity conjecture, disproved. Ehrhart's volume conjecture, proved. The first improvement to the general high-dimensional sphere packing bound since 1978. Quantum parallel repetition, proved for every finite two-player entangled game. New lower bounds on circuit complexity for the permanent. And three Erdős problems gone, including 183 on multicolored Ramsey numbers. Every single proof ships with a Lean 4 formalization on GitHub and all machine-checkable. That kills the usual failure mode of AI math announcements, where a plausible-looking chain of reasoning quietly hand-waves the one step that actually mattered. You don't hand-wave past a Lean kernel. This just shows how far we've come in such a short period of time. These problems have confounded the best mathematicians in the world for 50 years plus. AI has evolved in just a few short years to be able to solve these problems and the mind boggles at what can be achieved in just the next five years from here. Now this is the part the AI Bros won't be talking about but you know Uncle Sean will Cost of the successful runs... Roughly $2,000 in tokens at Sol API rates. Two grand. For problems that had sat untouched for decades. Now I'm a stickler for accuracy of wording and that's the trader in me but that specific number was a successful run. That stood out to me because what does it mean for an unsuccessful run? All of the pre attempts and the failures. It's because in reality that's the real fricking cost. That's the actual cost per task, and it's missing from the announcement. And as you know that's the number I keep banging on about. Noam Brown did admit they came up empty on the Millennium Prize problems, so we know the misses exist. We just don't get the denominator. Same week, Anthropic burned $100,000 in tokens having Mythos Preview find real weaknesses in cryptographic algorithms. Also unreleased. Also demoed rather than shipped. That's the pattern I keep coming back to and as you've seen me talk about in previous posts. You get Sol and Opus 5. The actual frontier is sitting in a lab getting walked through Washington before it ever gets walked past you. Altman demoed Astra to policymakers in DC last week, and it's expected to be the first model submitted under the new federal review framework. For anyone building, the architecture is the real story here. Astra is designed to coordinate multiple agents on one problem for hours or even days at a stretch. Same shape as what I've been running with Ferrox Factory and the Anvil gating system... worktrees and a serial merge queue and evidence gates, just with a compute budget I will never see in my lifetime. Nobody's even decided yet whether it ships as GPT-6 or GPT-5.7. There's no date. Meanwhile 3,000-plus mathematicians have signed the Leiden Declaration, and Timothy Gowers is writing about the possible destruction of mathematical culture after GPT-5.6 Pro solved two problems he'd sweated over, first attempt on both. His words...Strange and not particularly pleasant to have the rug pulled out from under him. What are your thoughts?

• 
  Poly (@poly0015iew) reported 58 minutes ago

  AN OPERATING SYSTEM THAT WRITES ITS OWN KERNEL DRIVERS HAS 28 STARS ON GITHUB 72,000 lines of C. one contributor. seventeen commits. i checked the numbers three times because the ratio didn't make sense. it's an x86_64 os from scratch whose only interface is a sentence. no shell, no commands, no ls, no cat. the author's own line is better than anything i could write about it: there is not one strcmp on the input path. that's how a kernel developer tells you he didn't cheat. the kernel does its own DNS and its own TLS in ring 0 - lwIP and mbedTLS compiled straight into kernel.bin, no host proxy of anything. the model gets 64 real syscalls as tools. prose on screen is the model, [brackets] are the kernel printing what it actually dispatched, from the real C return value. five separate defects were fixed to keep that line honest. now the part that made me put my coffee down. the kernel ships no audio driver and no knowledge of any sound chip. you attach a card, it shows up as an unclaimed pci device, and you type one sentence. it reads pci config space. writes a bring-up program in the kernel's own driver-vm instruction set. resets the codec. unmutes the mixer. builds a buffer descriptor list. starts the dma engine. installs itself as the system audio sink. then you can just ask it for notes. and in the recorded transcripts there's a run where the model correctly diagnosed a bug in this kernel's own printf - from inside the guest. forty years of teaching humans to speak kernel. one person spent seventeen commits teaching the kernel to speak human. twenty-eight people have noticed. 72,000 lines. 64 syscalls. zero strcmp. 28 stars.

• 
  Synapse Brief (@Synapse_Brief) reported 59 minutes ago

  OpenAI just published ten proofs to open math and theoretical CS problems that nobody had touched in over a decade. Total compute cost: about $2,000. Not a benchmark. Not a leaderboard score. Actual new results, formalized in Lean 4, with the certificates sitting on GitHub right now for anyone to check. Here's what actually happened. An internal version of Astra — OpenAI's next major model family — generated mathematical arguments for ten separate long-standing problems. Humans then turned those arguments into manuscripts and formalized the proofs in Lean. OpenAI is explicit about the division of labor: they take responsibility for correctness, but the underlying arguments came from the model. The spread of problems is what makes this hard to wave off as cherry-picked. Sphere packing bounds pushed to the Cohn–Elkies threshold. Exponentially better bounds on binary and spherical codes. A construction proving non-sofic groups exist, settling a real open question in group theory. A counterexample to Connes's rigidity conjecture. New lower bounds on arithmetic circuits for computing the permanent. An exponential parallel repetition result for quantum games. Hardness of approximation for the closest vector problem. A resolved case of Ehrhart's volume conjecture. A superexponential lower bound on multicolor Ramsey numbers. Progress on extremal graph conjectures. That's geometry, coding theory, group theory, operator algebras, complexity theory, quantum information, lattice cryptography, and combinatorics. Ten different fields, ten different communities who each have to independently decide whether this holds up. The Lean certificates are the part that actually matters here, more than the headline number. Anyone claiming an AI "solved" open math problems has to clear a low bar of credibility unless the proof is machine-checkable. This one is. You don't have to trust OpenAI's framing, you can run the verifier yourself. Worth noting this isn't the first signal. Back in May, a still-unreleased model produced a disproof of the Erdős unit-distance conjecture, and OpenAI says that work has already fed into further developments in the field. This latest drop reads like a continuation, not a one-off stunt. Noam Brown, who posted the announcement, also said they tried other major problems and failed, including the ones you'd actually want solved — no Millennium Prize results here. And they didn't burn much compute per problem, which means the ceiling on what test-time compute could do to a problem like this hasn't been found yet. The honest framing is: AI-generated mathematical arguments, human-curated and human-verified, machine-checked for correctness. That's a real category, distinct from full autonomy and distinct from hype. Whether it holds up to independent mathematician review over the next few weeks is the actual test. If this replicates cleanly, the interesting question isn't "can AI do math." It's what happens to how mathematicians choose which problems to spend years on, once a $2,000 run can clear ones that sat untouched for a decade.

• 
  Jurly (@jurlycat) reported 1 hour ago

  The most underrated part of the AI coding stack isn’t the model. It’s the memory between tools. Cursor writes the feature. Codex handles the refactor. Claude Code fixes the failing tests. One repo. Three powerful tools. Zero shared context. Every tool starts cold, so the developer becomes the router, shared memory, and state-transfer layer. memU is trying to fix the memory part. 🧠 It reads session histories from Cursor, Codex, Claude Code, ChatGPT Work, and even Hermes, then distills useful decisions and workflows into a shared wiki that another agent can retrieve later. It doesn’t route tasks or coordinate agents yet. The human is still the router. But at least we no longer have to be the database too. Better models make each tool faster. Shared memory makes the whole stack less forgetful. 🔁 GitHub in the comments 👇