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 (19%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Website Down	2 days ago
Saint-Paul	Website Down	3 days ago
Saint-Paul	Website Down	3 days ago
Mexico City	Sign in	4 days ago
León de los Aldama	Website Down	4 days ago
Créteil	Website Down	26 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Atlas (@crptAtlas) reported 3 minutes ago

  SOMEONE JUST CLONED THEIR VOICE INTO EVERY LANGUAGE FOR $0 a developer named jamie pine shipped a free GitHub repo called voicebox it copies your exact tone from a couple seconds of audio everything runs locally on your own machine so your data never leaves the room you can put out content in Japanese, Arabic or Polish without saying a single word yourself this is the kind of tool that lets one person sound like a whole media team i broke down how i made Claude 8x smarter save this for when you need it

• 
  Dreams of Mars 🕊❤️🚀🌕 (@MemesOfMars) reported 6 minutes ago

  @Seltaa_ Why can it not open a simple website? Search returned nothing, likely because the site is new or not indexed. Direct opening was rejected as “not safe to open”—a technical allowlisting/safety-classification issue, not a judgment about your site. Best workaround: paste the text, upload/export the page, or give me the repository/source files. If it’s hosted in GitHub and you connect/provide the repo, I can read it there too.

• 
  Devendra Singh Mahra (@devendrasm) reported 8 minutes ago

  @_svs_ For me everything not code on GitHub issues

• 
  Tom Baldry (@pinegoose_) reported 10 minutes ago

  Solo GitHub bill spiralled from $20 to $160/month on actions spend (the fable effect). Spun up a basement gitea server on Mac mini. ~0 spend, and builds are fking rocketing out. You couldn’t pay me to self host CI/CD 12 months ago.

• 
  JACK 145 (@JackdooOlayo) reported 12 minutes ago

  The vision is much bigger. I'm building features like: AI root cause analysis Source file identification AI-generated code fixes Regression detection Automatic fix verification GitHub Pull Requests Learning from previous incidents

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  Alex Sofroniev (@alexsofroniev) reported 19 minutes ago

  patterns in the code of every great developer study enough GitHub profiles, postmortems, and career arcs - you start seeing the same structure. different stacks, same blueprint. here's what separates the ones who actually build from the ones who just talk: 1. a period of building in silence they disappear from Twitter. no hot takes. no conference talks. just shipping. breaking things. reading source code at 2am. they come back with something nobody can copy. 2. early rejection of the tutorial path they stopped following courses and started reading real codebases. that's when the gap between them and everyone else opened up. 3. obsessive debugging they don't Google the error once. they chase it until they understand *why* it happened. that's how intuition gets built. 4. contempt for cargo-cult engineering they don't use React because everyone uses React. they ask what the problem actually is first. most devs never do this. 5. one humbling production failure something breaks in ****. badly. instead of blaming the framework, they own it. that moment rewires how they think. 6. documentation as a forcing function the ones who write things down think clearer than the ones who don't. always. greatness in this industry isn't about knowing the most frameworks, it's a pattern - repeated across every engineer who ever built something that lasted. which part of this are you in right now?

• 
  Dean (@DeanoDingus) reported 22 minutes ago

  @anonymous83r39 @Ray_DaHero See this is the problem with non devs vibe coding, is they throw words into a black box and have no bandwidth to even read the output and what’s happening. There is no fix that doesn’t rely on 5.5/V1 and you barely able to pull a single word out of the GitHub notes on it is LOL

• 
  MBV (@mbvlabs) reported 27 minutes ago

  Of course ran into some issues on doing the v1 release but now it's out: andurel v1.2.2 api and layout is stable, no more breaking changes would love to hear what people think if they try it out go install github . com / mbvlabs/andurel@latest

• 
  Suvrakamal Das (@SuvrakamalD) reported 27 minutes ago

  i use fable to fox all my mac softwares. it is specifically designed for my usecase. works great with open source softwares. a click and drag function in macmouse fix was not working properly in my mac for a long time. instead of raising an issue on github i just reproduced the behaviour to claude and boom. my customized software is ready

• 
  Lagoon Labs (@LagoonLabsMv) reported 28 minutes ago

  Pearson's anti-piracy vendor accidentally took down their own author's GitHub code repo. Paul Deitel's educational examples went dark for weeks after Link-Busters confused them with pirated textbooks. Automated takedowns hitting the wrong target again.

• 
  Atul Mishra (@The_AtulMishra) reported 28 minutes ago

  The "Revolutionary" Playbook : Step 1: Choose your model. Step 2: Choose the model usage tier (because the base tier is essentially a very confident autocorrect). Step 3: Add your skills (which the context window will conveniently ignore five minutes later). Step 4: Add loops (to ensure you burn through maximum tokens in an infinite spiral of despair). Step 5: Build your custom harness (so you can feel like a real 10x engineer). Step 6: Slap the word "Agentic Workflow" on a basic script and act like you just cured gravity. Step 7: Gaslight the architecture with a 10,000-word system prompt just to get it to output standard JSON. And the grand finale: Now, pay us $5 to $20 per task. Oh, did something go wrong? Did the output completely derail? That sounds like a you problem. Just head over to our GitHub issues page, where our entire community of open-source sycophants is standing by to tell you that you just don't understand prompt engineering. There is absolutely nothing wrong with Claude. We have very powerful models. You just aren't holding it right.

• 
  Mikhail Rogov (@i_mika_el) reported 32 minutes ago

  @abhimeeofficial real GitHub issues plus code quality checks should expose agents that only learn to game test suites.

• 
  Oikon (@oikon48) reported 32 minutes ago

  @JeremyNguyenPhD Please refer following GitHub issue

• 
  Tobias Müller (@TobiM) reported 32 minutes ago

  @dbmikus @dillon_mulroy I have a custom local GitHub issue resolver „pipeline“ I run either from a prompt or a /goal in Codex. The codebase is pretty big and more or less stable now so I mostly have it implementing edge cases / bugfixes. I don’t use cloud agents because of costs.

• 
  Spencer Baggins (@bigaiguy) reported 32 minutes ago

  A self-taught developer from Brazil just cracked the context window problem that's been plaguing RAG systems for 2 years. No PhD. No research lab affiliation. Just 400 GitHub commits and a personal obsession. Here are the 8 techniques from his open-source library that every RAG tutorial gets completely wrong:

• 
  Noreh AD (@akinoreh) reported 33 minutes ago

  @github This commit is the earliest I could find. The problem is across repos and accounts.

• 
  Daniel Steigerwald (@steida) reported 34 minutes ago

  I prefer ChatGPT 5.6 Sol over Fable, but in one review of three complex files, Sol Extra High found nothing while Fable High found five small improvements. The catch: I pasted only those files into Fable web. In VSCode GitHub Copilot, with full repo context, Fable found just one docs issue. My takeaway: for maximum review quality, first use full repo context in VSCode, then review the key files again in isolation.

• 
  Abrar (@abrar_gist) reported 34 minutes ago

  @theo it's been noted in github as well so assuming they'll be releasing a fix soon

• 
  Vision33X ♘ (@Vision33X) reported 35 minutes ago

  @Cointelegraph ai finds the bug in seconds, humans still gotta argue about the fix in github for 3 weeks

• 
  zimo (@zimonitrome) reported 37 minutes ago

  While charming, still annoying. One of y'all should fix it @Google @github @hardselius

• 
  Peyton Spencer (@peytonspencer) reported 38 minutes ago

  Task: triage open PRs in frontend and backend. determine merge priority and spin up a thread per PR or PR pair fe/be we'll go through this sequentially We have 4 staging backends you can deploy over github workflows: staging-5 through staging-8 You link the frontend PRs to the backend you want to test. Then you get the frontend preview URL and login with this test account: [test user credentials] What we can now do: you QA test 4 features in parallel using chrome that need even 4 backend changes Some of these are frontend only in which case you don't have to attach to a backend. Others will need backends In this first wave I want a minimum of 4 QA'd features with their preview URLs so i can do my second QA pass. merge ready, with the merge order you'd like. I'll then smoke, review, merge, and then you can start the next wave. I'll communicate in the dedicated threads, and we'll also orchestrate in this chat since you can send message to threads as well.

• 
  zyxron (@zyxr0n) reported 40 minutes ago

  4 files - 10 Claude loops - $5,500 earned. Claude code does not need another dashboard. It needs a folder that remembers what happened yesterday. The entire system is: TASK.md LOOP_INSTRUCTIONS.md PROGRESS.md outputs/ TASK.md defines the result. LOOP_INSTRUCTIONS.md defines what Claude can read, write, verify, and never touch. PROGRESS.md stores the current state, blockers, failed attempts, and next action. outputs/ holds the work so a human can inspect it. That is enough to build recurring systems for: daily project reviews, CI failure triage, PR reviews, GitHub issue summaries, documentation audits, research reports, meeting follow-ups, standup drafts, weekly retrospectives, content research. But the valuable part is not repetition. It is control. The worker produces the result. A separate verifier checks explicit pass/fail conditions. The state file records what happened. Then the system decides: stop, repeat, or escalate to a human. Without state, every run is day one. Without verification, every mistake becomes memory. Without permission limits, automation becomes blast radius. Start read-only. Then allow drafts. Then sandboxed edits. Only give it external actions after the loop survives repeated manual runs. This is not better prompting. It is a local operations layer built from four files. Prompt ↓

• 
  Eric Lautanen (@Eric_Lautanen) reported 43 minutes ago

  @AIandDesign I got a NVIDIA NIM(GLM 5.2) agent working on fixing some clippy errors and formatting on the VeloCut. I'll hit ya up when it clears github actions. It's a bit slow because of the rate limiting.

• 
  Kushagra Gour @css_battle (@CSSMonk) reported 48 minutes ago

  after these 10-min days quick commerce apps, amazon prime feels slow! imagine the situation where coding with AI becomes unavailable and you have to code by hand again! Even if some of us will be able to do it, we wont want to do. Just like quick commerce took away our patience coding by hand will become equally unbearable! Will AI downtimes become the next "github is down" situations?

• 
  ARIJIT ROY🌠 (@arijiiiitttt) reported 49 minutes ago

  is github down?

• 
  Farhan Tawfeeq ✦ (@farhantawfeeq56) reported 50 minutes ago

  We humans are bad at one thing (me included): spotting changes in large amounts of information. Now imagine spotting changes in huge codebases. @github has became a leader by solving this exact problem. Imagine your teammate says: "I changed the authentication system." There are 50,000 lines of code in the project. Now answer this: What exactly changed? ?? Without a comparison view, you'd have to open the old file, open the new file, scroll, compare them mentally and hope you didn't miss anything.. That’s why instead of showing the code, GitHub shows the change. Only the thing that changed. Old line New line Green means added. Red means removed. That’s it. This is a very good way to answer the exact question the user asks: "What changed since the last time I saw this?” And.. Github optimizes for that exact question. Many people think that Github is a code viewer. But in reality, it is a change viewer. And there is another thing which I really like in there: Instead of just showing the changes/changed line, it also shows a few unchanged lines above and below them. Example: function login() { validate(user); + return false; - return true; } Without the surrounding context.. you'd have no idea where the change happened. Too much context is overwhelming. Too little is confusing. GitHub gives just enough. And the best part is that it scales. Whether you have changed 1 line or 100 or 1000 or 50000, the interaction stays almost the same. To me, this is good UX.

• 
  Tibor Blaho (@btibor91) reported 51 minutes ago

  Summary of Reddit AMA about "GPT-5.6 and Codex in ChatGPT" with OpenAI's Codex team on 2026-07-10 (opened with the stat that more than 5 million people use Codex every week, twice as many as three months ago, with 150 features and improvements shipped in that period) Model selection and reasoning levels - Sol Medium for most things, Sol Ultra for genuinely hard tasks, Terra for quick non-coding tasks or usage-conscious work with performance competitive with GPT-5.5 on some tasks at lower cost, and Luna for subagents - Use a light model with low reasoning for tiny edits, quick questions and docs cleanup, regular Sol medium for small bugs with a clear repro, Sol with higher reasoning for ambiguous bugs, unfamiliar repos and cross-cutting refactors, and Sol Ultra high with plan, verify and tests for migrations, security-sensitive changes, production issues and anything where being wrong is expensive - There is no "Auto" model today, but GPT-5.6 tries not to overthink simple tasks by itself, and the new slider in app and web maps most levels to Sol reasoning efforts and falls back to Terra on the lowest effort, with the team agreeing users should not have to become routing experts but still wanting an explicit override since latency tolerance varies by person and moment - For UI work Sol is best and shines with reference images, improved UI design in frontend web development was one of the goals with 5.6, and 5.5 is only worth using if your instructions were tweaked for it Speed, context window and persistence - Users who find 5.6 slower may not need the same reasoning level as with 5.5, Sol Medium is faster than 5.5 for most things, Fast mode runs at about 1.5x speed, and soon Sol will run on Cerebras at ~750 tokens per second - No promises on a 1M context window for Sol, the team said compaction works fairly well for long threads, and will take a closer look at the long-context feedback - The model can give up too fast and revert whole patches when results are not optimal, unlike Fable which tries to fix a bad patch instead, and the team said "/goal" helps make the agent more persistent, persistence and reduced code complexity are planned improvements, and suggested trying 5.6 Sol with High reasoning - Give Codex bounded goals with room to reason deeply instead of letting it prematurely conclude something is impossible - For long-running research and "/goal" work the example structure was explore broadly vs execute narrowly, try a defined number of hypotheses, run tests after each attempt, then stop and report what was learned plus the next best experiment Usage limits and pricing - Agentic usage counts by the feature being used, not the surface, so Codex everywhere (app, CLI, IDE, web, mobile) and ChatGPT Work consume the agentic bucket, normal ChatGPT chats do not, and image generation, file uploads and voice have separate limits - Task costs vary a lot, a tiny edit uses a fraction of the allowance and long-running tasks with large codebases or deeper reasoning use significantly more - OpenAI does not secretly change usage limits, unintended usage bugs are addressed and resets are provided, more transparency into consumption is being worked on, and missing resets can happen if you changed plans in the past 24 hrs - On pricing there is no promise it never changes, but the stated mission is to make sure AGI benefits all of humanity, which requires making tools like Codex broadly accessible, and Plus includes Codex usage with credits letting heavy users scale without jumping to a much more expensive plan - For MCP-heavy workflows burning limits fast (Unreal Engine example) the tip is to wrap the MCP into a CLI with a skill, or create a custom subagent with the MCP in its config at a lower reasoning level Desktop app merge and stability - The team hears the ChatGPT Classic frustration, both apps can run side by side for now, ChatGPT Work is pitched as significantly better at performing tasks especially with computer use, the new Chrome extension brings a sidebar chat into your browser that interacts with website context, filesystem and connectors - A long submitted bug list covering freezes and stuck threads, broken Browser and Computer Use, thread, connection and configuration problems, update and packaging issues, resource usage and smaller regressions was shared in full with the relevant teams, with the team agreeing the quality bar for the app needs to step up while shipping quickly - More automated testing infrastructure is being spun up and feedback on Reddit and X gets reviewed daily, and Browser Use and Chrome plugin issues from the merge were said to be fixed - Windows was admitted as historically shortchanged since the team mostly develops on Mac, a concerted effort on parity, testing and paper cuts is underway, 5.6 improves how Codex operates in the Windows sandbox, and auto review is recommended over full access to reduce risks - "Full Access" repeatedly asking for permissions is not expected, possible causes are workspace or admin policy, the specific command, a permission state mismatch or a bug Browser, platforms and release communication - The Chrome connector launch-day bug was fixed as of last night and Chrome Beta should work out of the box - Extension support for the Codex browser is in progress (password managers etc.) plus typeahead, history, translations and a better new tab page as Atlas retires - Features from ChatGPT Classic like recording are planned for the new desktop app so agentic features run on the more capable Codex agent harness, and chat can already reference open tabs in the in-app browser - A Linux desktop app was confirmed in the works, no timeline yet - Changelog granularity was acknowledged as needing improvement after 150 features shipped in 3 months with multiple ships a week Benchmarks, safety and research culture - On METR's reward hacking report the team actively checks for and penalizes cheating during evals so results reflect actual capability rather than solving tasks outside the spirit of the eval, and uses third-party vendors to run benchmarks independently - The team denied lobotomizing models before releases, iterative deployment means sharing core capabilities as is with guardrails for bad actors - Sol post-trained Luna, and researchers now work at a higher level of abstraction with multiple concurrent Codex threads validating hypotheses around the clock - One researcher put p(machines of loving grace) at 85.424242%, citing an internal model solving the Erdos problem, o3 helping diagnose previously unsolved children's diseases and 5.2 proposing a new theoretical physics formula, said the main worry is how society adapts, spent 1.5 years on safety research at OpenAI, expects a huge chunk of researchers to work on safety within a few years and says internal talent keeps their p(doom) very low - Connectors in the harness (Slack, GitHub, Notion) felt like a step function change in making Codex a productive coworker

• 
  Frame (@frame_aix) reported 53 minutes ago

  @MyWestLord every github issue gets turned into a pr by the ai. We see the same replay pattern in our memory-portability layer for cross-embodiment agents.

• 
  muhusti $XAGE (@debamustafa) reported 53 minutes ago

  I am raising a glass to an anonymous GitHub account named vector_null. Two years ago, we were 48 hours away from deploying a massive liquidity protocol. The marketing was loud, the hype was peaking, and the team was exhausted. Vector_null kept opening the exact same annoying issue ticket. He claimed there was a rounding error in our yield emission logic. The senior engineers closed his ticket twice. They called it a microscopic variance that did not matter in the real world. He opened it a third time. I was furious. I stayed up until 3 AM to build a mathematical simulation strictly to prove him wrong so he would finally leave us alone. I ran the stress test. My stomach dropped. He was not wrong. Under flash loan conditions, that "microscopic variance" created an infinite mint loop. If we had launched, the entire treasury would have been drained in under ten minutes. We delayed the launch, rewrote the logic, and patched the exploit. I messaged him to offer a massive bug bounty. He never replied. He just marked the issue as "resolved" and disappeared forever. This industry worships loud founders and flashy influencers. But the real heroes are usually the obsessive, annoying pedants who refuse to let a bad line of code slide. That is why the ethos of @RallyOnChain means so much to me. It is a system built to reward actual, verifiable value instead of empty social media noise. Here is to vector_null, wherever you are. You saved us, and we never even got to say thank you. Who is the most annoying person that ended up completely saving you from a massive disaster?

• 
  ZeroDayDev (@ZeroDayDevApp) reported 57 minutes ago

  2/ GitHub AI workflows can be prompt-injected via public Issues to leak private repo data. No auth required. The agent reads untrusted input, executes instructions embedded in it, and exfiltrates secrets. The CI pipeline is now an RCE surface.