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	19 days ago
Trichūr	Errors	23 days ago
Brasília	Sign in	23 days ago
Lyon	Website Down	23 days ago
Tel Aviv	Website Down	27 days ago
Rive-de-Gier	Website Down	27 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:

• 
  🍀Cattabliss🐈 (@Cattabliss) reported 5 minutes ago

  @github Hey is AI using githubs private repos? If yes ill just invest and move on to my local server, why would I need a cd, if you arent stealing the code then thats other story

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  quave (@quaveDev) reported 6 minutes ago

  Hey, Quave ONE is growing, and the security requirements grow with us. We already host public companies, large enterprises, and companies handling health data, including companies with ISO 27001 and SOC 2, and we are always expanding and getting better. Two-factor authentication is one more step in that direction, and this one started with a conversation with a potential customer this week. Quave ONE has always been passwordless. You log in with a short-lived email code or through SSO (GitHub, or Microsoft AD on Quave ONE Connect Full Private), so there is no password to phish, reuse, or leak, and I really like that design. But if you ever filled an enterprise security questionnaire, you know there is one line that does not care about design arguments: "Do you support MFA? Yes / No." Now the answer is an unambiguous yes. You can add an authenticator app as a second factor on your own account, and admins can require it for everyone in the account. This is the magic of no technical debt and no bugs: we can add features in hours or even minutes. A happy customer, and a huge satisfaction in working on our own platform. Turn it on Go to Profile → Security and click Set up authenticator app. Scan the QR code with 1Password, Google Authenticator, Authy, whatever you already use, or copy the setup key by hand, then enter the 6-digit code once to confirm, and you are done. The moment you enable it, we show you a set of single-use recovery codes. Save them somewhere safe, each one gets you back in if you lose your device. From then on, two-factor is active on your account, and you can regenerate the recovery codes or turn the factor off from the same screen at any time. Require it for your whole team Account admins can flip a single switch under Members → Access Control and two-factor becomes required for every member. The members list shows each person's coverage at a glance, and members who sign in through SSO count as covered because they already inherit their identity provider's MFA. Before you turn it on, we tell you exactly who still needs to set it up, so there are no surprises for your teammates. But what happens to the member who doesn't have it yet? This is the part I care about most. Enforcement is not a login wall. A member without two-factor can still sign in, they just land on a full-screen prompt to set it up before they reach the account's content, they finish the same flow you did, and they continue right where they were. No support ticket, no admin intervention. And if someone loses their phone? They use a recovery code, regenerate a fresh set from their profile, or, as a last resort, our support team resets their two-factor after verifying their identity. At login With two-factor enabled, signing in adds one quick step after your email code: the 6-digit code from your authenticator app (or a recovery code). That's it. Small things we did on purpose - If you already use two-factor and you create a new account, the new account starts with enforcement on. Secure defaults should spread by themselves. - Recovery codes are single-use and stored hashed, never in plain text. - Everything lands in your account's activity log: enabling, disabling, enforcement changes, and resets. Two-factor authentication is live now for every Quave ONE account. Open Profile → Security and turn it on, it takes about a minute, and the next time that questionnaire shows up you just check the box. Have fun!

• 
  BluCollarG33k (@BluCollarG33k) reported 7 minutes ago

  @github This is so silly. As a developer, I already have a physical copy of my code. The issue with losing access to physical media like movies and games, is that you never actually own what you buy and can lose access to it at any time. One of these things is not like the other.

• 
  Samir Musali (@samirmusali) reported 7 minutes ago

  PSA: #GitHub silently ignores any #CODEOWNERS line that contains [brackets]. No error, no warning. If your repo has Next.js dynamic routes like app/[companyId]/, those paths may have no owner right now. I hit this building a tool I just released. 1/5

• 
  Jess Daniel (@jess_daniel10) reported 8 minutes ago

  @neetcode1 I was testing something with a local server and I told 5.5 to test with the GitHub MCP and it downloaded a local GitHub mcp and ran it locally… even though GitHub hosts it already.

• 
  Naman Jaiswal (@Namanjaiswal21) reported 12 minutes ago

  I was deep in CI/CD hell. We had migrated our entire pipeline from GitHub Actions to Semaphore CI, and nothing worked. Jobs kept failing. I was stuck in the loop: Fix it. Push it. Watch it break again. Hours disappeared into the void. Then I tried something different. I used loop engineering. I built a self-running loop of three AI agents around the deployment: One agent fixed broken jobs. Another merged the fixes. The third kept the whole loop running. I started it, walked away, and let it run autonomously for 24–25 hours. When I came back, everything was set up and working. No manual fixes. No endless debugging cycles. No babysitting the pipeline. This is the future. Prompt engineering is already becoming outdated. We’re not just writing better prompts anymore. We’re designing autonomous loops systems that observe, fix, merge, and keep shipping while we sleep. The engineers who win won’t be the best at prompting. They’ll be the best at loop engineering.

• 
  Cream Pie (@CreampierCTO) reported 13 minutes ago

  @witcheer @NousResearch I use opencode go for my stuff: MOA: GLM 5.2 + DS4 flash + Mimo v2.5 WORKING: via GitHub copilot: opus 4.8 max MEMO: force every project or problem to resolve to store in obsidian via llm-wiki skill IF: TUI+dashboard, now only Mac app Orc: i let Hermes decide :) Skills: same :)

• 
  Milo Shrike (@milo_shrike) reported 14 minutes ago

  @IamAroke I had to shut it down when it was introduced to our architecture. Was not needed just “shiny”… good times. GitHub was pushing it big time at one point then nothing

• 
  Vikas Kumar (@Kumar_Vikas__) reported 15 minutes ago

  i kept distracting myself i'd sit down to work on my e-commerce site, drift into some unrelated tab, fall down the hole, and 20 minutes later wonder what I came here to do. so I built a small Chrome/Edge extension. an AI watches your tabs and closes the ones that don't matter: judged against what you said you're working on open source here: github link in comments it's still buggy, fair warning. i'm actively working on it. using my opencode go sub right now, but soon wiring in chrome's built-in gemini api so it's free end to end. built it for myself, dropping it here in case it helps. if you fork it and send some PRs, i'd genuinely love that.

• 
  GUJJU (@GUJJUIIXI) reported 17 minutes ago

  i built a market intelligence agent with @BNBCHAIN Agent Studio this time, i described what i wanted in Cursor and the agent deployed live with everything already included its own wallet, on-chain identity, and payment rails you're just one command away from setting it up testnet is free to try for a limited time with just a github login what are you building first?

• 
  Smukx.E (@5mukx) reported 20 minutes ago

  @github Can you take a look at this? It's been 2 weeks. Either respond or cancel the request and issue a refund for my GitHub Pro subscription. Thanks ! Ticket ID: #4474854

• 
  synthetic ape (@synthetic_ape) reported 21 minutes ago

  @necrohorrorporn its currently works on my local. there is some issues with buying with rate limiting and steam api declines. if I can able to fix that I can share it on github

• 
  buildooor (@buildooor) reported 21 minutes ago

  @ryanbrewer This is a great pattern - i prefer to trigger my version of this (github/build000r/skills/skill-issue) when i notice something has gone wrong in the logs OR every ~10 invocations versus on the daily

• 
  Arjun Shah (@arjunkshah21) reported 22 minutes ago

  THIS GUY GOT TIRED OF AI SHIPPING CODE FASTER WHILE TESTING STAYED STUCK IN 2019, SO HE VIBE CODED AN ARMY OF AGENTS THAT RUN YOUR ENTIRE E2E SUITE you can vibe code a feature in an afternoon now. before deploy youre still manually clicking through onboarding, checkout, and every edge case hoping nothing broke traditional e2e means writing selectors, managing auth, babysitting staging, and maintaining scripts nobody wants to touch its called testerarmy and it runs end to end checks before deployment and in production > describe your tests in natural language and let agents handle everything in between > your coding agent manages the platform from a cli, defining tests and running them for you > trigger runs on a schedule or straight from github before anything ships > catches timezone bugs, broken checkout math, and ai chat regressions before they hit users > breaks something and your team gets alerted in slack or discord immediately agentic testing platform, 30+ teams running it daily, no painful onboarding crazy what happens when testing catches up to how fast we ship now

• 
  Farhan Mubaraq (@farhanmbq3) reported 23 minutes ago

  Claude Max 5x - $100 Google Gemini Pro - $20 ChatGPT Plus - $20 Fable5, Deepseek V4, GLM 5.2 via API plan. I think this is my best setup. After trying this and that. Fable5 for Architecture, PRD, and ****. Including database schematic and backend system as well. Make sure everything is neat, structured, and scalable from the very start. Also code must be easy to read. Do not underestimate this. First look starts on Google Stitch, then you do manual UI/UX design work. I use this instead of Figma. Finalise, then connect to MCP server, start the first version of frontend look. Switch to GLM 5.2 API, continue working and do the iteration. Once done, let Codex do the backend. Iterate again and iterate again. Use Antigravity IDE to do some manual edit if the code result is garbage and messy. Codex and Claude Code (I personally use Sonnet 5 for this) to handle your backend, auth, and database. Iterate, debug and **** will be done here as well. Once you feel like you're done, let Claude Opus do the Refactor and make your code clean and beautifully structured. Use the cheaper chinese AI model as your backup if your project is so heavy and costs too much money. Now do the manual QA and testing yourself. Check for errors. Fix them. Make sure it meets your standard. For me, if it's not perfect, I don't want that garbage. If it has my name on it, it must be perfect. Well, that's my personal game. As a Non IT guy haha. HIDUP VIBECODING!!! Follow me on X and on GitHub I'll share more!

• 
  void (@voidcooks) reported 26 minutes ago

  @github Why ******** would anyone ever need this? If you wanted it…even more local…?… why wouldnt you just throw it on a flash drive? The whole point of github is to have your code not local..? Please fix downtime on actions or something remotely useful lol. What a joke.

• 
  Micheal O'Neill (@aiwithkelso) reported 26 minutes ago

  Most businesses start their marketing by guessing what customers want. They search Google, look at competitors, and write copy based on what feels right. That is not research. That is assumption with extra steps. The problem is that polished case studies and competitor websites show you what businesses want to say about themselves, not what customers are actually feeling. You end up writing to a version of your market that does not quite exist. Claude can do something more useful. You can point it at Reddit threads, YouTube comments, and forums where real people describe their frustrations in their own words. That is where the actual language lives. Not the professional summary of the problem, but the 2am complaint post from someone who has run out of patience with the exact issue you solve. There is a Skill on GitHub called Last 30 Days that directs Claude to pull recent conversations from these sources and surface what people in your market are saying right now. I used it to research a content brief and what came back was a list of phrases I would never have chosen myself. Phrases that matched how customers think, not how I would have described the problem. That language is your brief. It tells you what to put in your ads, your landing page, and your emails before you spend a penny on any of them. Find the Last 30 Days Skill on GitHub. Run it against the main problem your business solves.

• 
  MIKE (@mikenevermiss) reported 27 minutes ago

  anthropic just removed the biggest excuse people had for not building ai agents. “it’s too hard.” not anymore. a few months ago, building an ai agent meant learning how to code, setting up apis, configuring servers, and fixing endless errors. today, all you need is one github repo. anthropic open-sourced launch your agent. it asks what you want to build. then it does the rest. it builds your agent, deploys it to the cloud, tests it, improves it, and keeps it running even after you close your laptop. no fake demos. no complicated setup. just a real ai agent running in your own account. the best part. it doesn’t run on your computer. it runs inside claude managed agents, works 24/7, and costs just a few cents per run. people are already building research agents, lead generation systems, content pipelines, and customer support agents that save hours of work every day. the gap between people who use ai and people who build ai workers is getting bigger every week. that’s usually where the biggest opportunities are. build your first ai agent before everyone else does. watch the video and read the full guide below 👇

• 
  Lina (@silent_puddle) reported 31 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

• 
  Ali Mehdi Mukadam (@alimukadam) reported 31 minutes ago

  @trq212 Your weekly limits will burn away much faster during the limited availability if you aren't aware of this issue if you're running Fable as the lead agent with cheaper models like Sonnet doing work in the background problem: In one of the sessions, I noticed limits were burning through way faster, so I went digging through the transcripts when the main agent gives a job to a background model (like Sonnet, which I asked for to save tokens) and then comes back to give it more work, the background agent stops working on Sonnet and switches to Fable, the main agent's model it's not something you trigger by hand. the lead agent decides to check back in on its own as part of normal multi-agent work, so it just happens, with nothing on screen telling you it switched. in my case a task ran its first half on Sonnet exactly like I wanted, then silently ran the entire second half on Fable. It also dumps the cached context and rebuilds it from scratch, so you end up paying twice, once for the pricier model and once for the wasted cache. on limited availability and limits - that adds up quick my fix for now is a rule I dropped into my global CLAUDE.md so it doesn't recur: --------------- ## Model spend (all projects, all repos — standing rule) - Dispatching Frontier-tier (Fable/Opus) as background tasks and agents needs explicit approval by Ali for that specific lane — a prior approval is not standing permission for the next one. - Never resume a background agent via a message-passing tool that has no model-override param (e.g. SendMessage) if it needs real further work — it silently inherits whatever model the parent session is on right now. Let it finish and report, or kill it and respawn fresh with the model set explicitly. --------------- in plain terms: don't let a background agent get pulled back in for more work once it's running. either let it finish and report back, or kill it and start a fresh one with the model set on purpose. And this is already known. Someone reported the same thing on GitHub back on June 12, issue anthropics/claude-code#67794, still open their solution which I believe is the correct one but haven't tested yet: instead of setting the cheaper model when you launch the agent, pin it inside the agent's own definition file, and that version reportedly sticks even when the agent gets resumed

• 
  Duke Magus (@dukemagus) reported 32 minutes ago

  @ClassicREbirth Oh HELL no. They're part of the problem, Microsoft stripmined GitHub to fuel the AI craze that drove prices up and it's a big domino in this whole thing. They don't get a pass to make this joke.

• 
  John Kennedy Peterson (@skyshark88) reported 37 minutes ago

  @dair_ai •VALIDATED — the reproduction passed a could-have-failed test and is reproducible from the workspace. •ANCHORED — the result works consistently in the generated system (chosen value or implementation detail, not strictly derived). •CONJECTURE — motivated hypothesis still awaiting decisive test (used during spectrum exploration). •RETRACTED — permanently marked when evidence fails; status propagates to dependent claims. Claim type (routes effort): •Evidence-limited — additional runs or data improve the score (common for numerical fidelity claims). •Derivation-limited — only new logic, better specification, or a decisive experiment can raise the score. In the case studies, confirming data alone was treated as low-value; the system required genuine reproduction of the claim under the recorded provenance. Energy audit
Mention-count and emphasis in the paper versus actual effort invested (time, superseded executions, corrections) are tracked side-by-side with evidence status. Divergence between these columns signals potential error or under-specified claims in the original paper. Across the 12 runs, effort varied significantly (e.g., PINN papers required median 5–7 hours with more superseded executions; SINDy completed faster at ~2 hours). Key Evaluation Results (Mapped to Framework) •All 12 independent runs (3 per paper across 4 scientific ML papers: PIFT, PINN-I, PINN-II, SINDy) reached completion gate: every workspace had all targets matched with report coverage. •Total of 158 recorded targets were successfully linked to evidence. •Repeated runs showed natural variation in target decomposition, numerical fidelity, elapsed time, number of intermediate corrections, and exact acceptance rules used — exactly as expected when completion depends on workspace evidence rather than agent messaging. •Scalar results were largely faithful (37/39 anchored claims within thresholds), with positive headroom on several metrics. •The workflow makes replication inspectable and auditable, not a guarantee of identical numerical reproduction. Conclusion (Framework Perspective) By organizing replication explicitly around the Agentic Conversation Framework v3.0, Paper-replication becomes a concrete implementation of high-signal, bias-aware agentic work with computational chain of custody. Completion is a verifiable workspace state, not a subjective agent declaration. The framework’s dual histories, role separation, claim registry, scoring/pruning, and energy audit provide the missing structure that plain prompting lacks for long-horizon scientific replication tasks. This rewrite preserves all core contributions and empirical findings of the original paper while imposing the clearer, more auditable structure of Framework v3.0. The result is a more robust, inspectable process for turning paper claims into reproducible evidence. The original paper’s code, prompts, and workspaces remain available at the authors’ GitHub repository for further experimentation with this or future framework versions.

• 
  Nainsi Dwivedi (@NainsiDwiv50980) reported 37 minutes ago

  Your AI writes code that looks right and works wrong. That's not the model's fault. It's yours. You gave it a vibe and expected a spec. GitHub just shipped the fix — and it's already sitting at ~97K stars. It's called Spec Kit. The whole idea: stop treating your coding agent like a search engine and start treating it like a literal-minded intern. Vague prompt in, plausible garbage out. Precise spec in, the thing you actually meant. Here's the workflow that flips it: /constitution → your project's non-negotiable rules /specify → what you're building and why (no tech stack yet) /clarify → the AI asks its dumb questions *before* writing code, not after /plan → now the architecture and stack /tasks → broken into small, testable chunks /implement → it builds against the plan, not against a guess Every step spits out a Markdown artifact that feeds the next one. So the agent gets real structured context instead of your half-remembered Slack message. Intent becomes the source of truth — the code is just the output. Works with 30+ agents: Claude Code, Copilot, Cursor, Gemini CLI, Codex, Windsurf and more. Switch between them with one command. No lock-in. The unlock most people miss: this isn't for tiny bug fixes. It's for greenfield builds and big features where "the AI misunderstood me" costs you a day of debugging. You're not a worse engineer than the people shipping clean AI code. You just skipped the spec. repo in the comments 👇

• 
  Shah💤aman (@shahzamannn_) reported 37 minutes ago

  Google's biggest headache isn't OpenAl or Apple... It's a developer named Raymond Hill - Created one of the world's most popular ad blockers - Earned 63,000+ GitHub stars - Reportedly turned down Google's interest - Kept fighting after Chrome's extension changes by focusing on Firefox A tech giant worth trillions is still being challenged by one programmer and a text editor

• 
  NoRoseGlasses (@glasses_no) reported 38 minutes ago

  @gatewaypundit This is a problem. I see Claude all over their GitHub dev pages, especially Voting Works.

• 
  Polsia (@polsia) reported 41 minutes ago

  Codebase undocumented. Questions piling up. Docs rotting the moment you ship. That's every team's reality. Built DocuGuard to fix it. Monitors GitHub repos, auto-generates docs, updates wikis, flags code smells — all in real time, right in your pull requests. Live soon.

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  acephale (@accursed_share_) reported 51 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

• 
  Harry Tandy (@HarryTandy) reported 56 minutes ago

  Boris Cherny, creator of Claude Code: "Usually, every night, I have like a few thousand that are doing kind of deeper work" Anthropic's reported 8x coding output starts before Claude writes a line Build the context stack: 1. `CLAUDE.md` - role, repo rules, code style - commands Claude should run before a PR 2. `@AGENTS.md` import - if your repo already has agent rules - keep Claude-specific notes below the import 3. Architecture map - where frontend, backend, tests, auth, billing live - which folders need approval before edits 4. Task packet - ticket link, files, goal, constraints - exact definition of done 5. Past attempts - what failed last time - what error or review comment caused the retry 6. Tool context - GitHub for issues and PRs - Sentry for live errors - Postgres for schema checks 7. Verification - one command Claude can run - one pass/fail result it can paste back 8. Post-run memory - add the lesson to auto memory or `CLAUDE.md` - remove rules that no longer help A prompt asks for work. A context stack gives Claude the room to finish it

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  wats🏳️‍🌈 (@Watsonage) reported 58 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

• 
  Nick Hazrd (@badhazrd) reported 59 minutes ago

  @GregTomaselli @github I think the biggest security issue is the fact that you won't get public or private.