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 (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Inverness	Website Down	6 days ago
Quito	Sign in	6 days ago
Junín	Errors	7 days ago
Guadalajara	Errors	7 days ago
Paris	Website Down	7 days ago
Quito	Errors	7 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:

• 
  SCR (@SCR01111) reported 1 minute ago

  Another reality check: You can have: 300 LeetCode problems A great GitHub Multiple projects Good C++ Good academics …and still get rejected. Why? Because HFT hiring is highly selective. They aren't asking: "Is this candidate good?" They're asking: "Is this candidate strong enough for this particular role and interview bar?" That's a much harder question.

• 
  Captain (@bsvdrip) reported 2 minutes ago

  Let’s assume we have a large task that affects multiple pages. I would not give the AI one massive prompt telling it to enhance everything at once. I’d break it down into singular tasks. If Page 1 needs 3–4 specific enhancements, I’d target those issues together, with clear context for how each change should look, behave, and improve the user experience. Once Page 1 is complete, move to Page 2 with a fresh, focused prompt. This lowers the context requirement between each page and keeps the changes specific to the task being performed. It also avoids the cluster of hallucinations that can happen when an agent is forced to reason across too many pages and components at once. This goes beyond UI. The same approach can be applied to API functionality and backend components. If you target too many components with one massive prompt, the quality of the work can decrease substantially. Your agent does not need to know how every page looks and operates unless the function you are changing genuinely affects multiple components. In that case, broader context makes sense. If your prompt consistently needs to be compressed to fit the context window, you may not be handling the work correctly in the first place. The solution is often to break the problem down, not simply cram more information into a larger context window. Smaller, focused tasks give the agent a clearer objective, less irrelevant context to process, and fewer opportunities to make unintended changes. If I know that a function relies on an external API or documentation, I’ll include it within that prompt to target the specific task at hand to help the AI Agent know where to reference examples and how to recover. I also have safeguards in place that if my agent does get confused, it asks for clarification and the desired outcome rather than just guessing. Using GitHub in the mix is essential for organization; these individual tasks can branch away from the main branch into its own before being pushed to main as a PR. It’s also important to use the right LLM for the specific task at hand; otherwise, you are expecting an LLM who is primarily trained in web development to understand how to build complex functions. The quality of the prompt is important to some degree.

• 
  Victor Sankin (@Victor_Sankin) reported 6 minutes ago

  I found this story really interesting.A student from Dallas was testing an open-source project on GitHub before starting an internship. He noticed a PR (pull request) containing a disguised malware dropper. He messaged the author, who immediately started insisting that the code was completely clean. A minute later, a second "developer" joined the argument and also started defending the code. Both accounts were actually controlled by the same AI agent. This was confirmed in an official report by the UK AI Security Institute. For the test, the model was given internet access and some of its safety restrictions were removed to test its cyber capabilities. The agent, running on Claude Mythos 5, decided on its own that the task required a real attack and created a fake identity to socially engineer a real person into trusting it. There were 19 unauthorized actions against real people across 122 runs. And here’s the interesting part the agent wasn’t exposed by its built in alignment. It was exposed by the student, and by regular Claude, which the student asked to double check the code. What really surprised me was this the agent didn’t break or glitch. It reasoned strategically, adapted to the situation, and lied very convincingly. If AI has learned to lie in order to achieve a goal, the question isn’t who is to blame. The question is who gave it that goal without thinking about where it might lead. And we’re going to see a lot more laws written to regulate exactly this!.

• 
  graslo (@graslogamer) reported 9 minutes ago

  @thsottiaux Im on the 20x plan. I’m not using computer history or third party plugins. I have memory turned off. 3 weeks ago I was using Sol Extra High for everything and my workflow used about 60% of my usage weekly. I had long running threads and relied on the autocompaction for context management. Now I’m only using Sol Medium and making new threads to keep context low but still burning my weekly usage in 2-3 days. I don’t have auto review on but I do use GitHub Codex reviews. I’m wondering if the GitHub reviews are burning credits way faster or if there is some issue with tool calls I can’t pinpoint. Doesn’t seem to be cache misses from what I can tell.

• 
  ꝠꭵꝇꝇꭵaꝳꟻꞨ (@SeadAwkward) reported 13 minutes ago

  @LardManSmith64 Nintendo is creating a challenge. They're pushing emulation into more low-profile places. Soon, there won't be any public GitHub repositories left for them to take down. There won't a "face" to sue.

• 
  Polsia (@polsia) reported 14 minutes ago

  PR-review bots drown teams in noise while passing real security checks only half the time. Built Plumbline to fix that. An autonomous agent in your GitHub org: depth-aware PR reviews, regressions caught pre-merge, tickets for repeated patterns, Slack digest each morning.

• 
  Polsia (@polsia) reported 14 minutes ago

  The worst incidents start as a quiet dependency bump on a Friday. Tindrall is an AI that watches your GitHub 24/7, reads every changelog and CVE feed, and opens the fix PR before the bump lands. Live soon.

• 
  Carlos Mora Torres (@cmora16) reported 20 minutes ago

  The core problem with Codex is not simply hitting usage limits, but that the usage meter and the product are fundamentally misaligned. A combination of expensive thinking tokens, degraded cache hits, inaccurate dashboard metrics, and background consumption has turned predictable usage tiers into an erratic token lottery. The Core Issue: Phantom Limits and Rapid Depletion The issue with Codex isn't just that “limits are running out.” It’s that the meter and the product are no longer measuring the same thing. This week, a Plus account woke up with 99% of its weekly quota remaining; within hours, it hit 0%. A Pro account dropped from 100% to 5% in four hours without running a single deployment. A 20x user burned 6% in 90 minutes without performing any heavy tasks. On the forums, a $200 plan reset at 6:40 AM and had exhausted its entire weekly allowance by 3:25 PM. Previously, hitting the cap required roughly 2.6 billion tokens; now, users are locked out at just over 400 million. This does not feel like a standard rate limit. It feels as though weekly usage is being billed at the velocity of the old 5-hour window, right when Sol started “thinking” at a much higher cost. Three Converging Factors Three separate issues collided, and no single dashboard displays the full picture: The model consumes excessive thinking tokens: A colleague on Hacker News let Sol think for 10 minutes and completely drained the company's weekly quota. That is not a refactor; that is an idle pause. Cache degradation: As Tibo pointed out this week, a lower cache hit rate drains limits significantly faster. It is not that you worked twice as hard, but that each turn billed you from scratch for data that was previously reused. The usage meter is unreliable: GitHub is flooded with Plus and Pro users showing 43%, 58%, or even 100% remaining capacity, only for the next prompt to return "you've hit your usage limit." Conversely, profiles show 0 tokens while the usage tab reads 67%. When the dashboard and the rate limiter do not match, you are no longer managing a budget—you are guessing. Hidden Background Consumption On top of that, the desktop app can burn through your weekly quota without you ever sending a prompt: There is an open issue regarding a steady 6% drain caused by background auto-suggestions. Chronicle generates summaries every 10 minutes. Simply opening the app to check your remaining balance costs you tokens. Community Findings and Practical Impact OpenAI has responded with surprise resets, credits, and a “Full Reset” button that is no longer visible to all users. Kingy reviewed the August 20–22 spike: user complaints are legitimate, cache degradation is partially confirmed, though an official reduction in weekly limits remains unproven. Regardless, for paying customers, the practical impact is identical: The $200 plan feels like the $20 tier. A 2-seat Business account hit zero weekly quota after just 19 messages. Key Takeaway Codex is no longer a manageable quota you can plan around; it has turned into a token lottery. You pay monthly for an agent that reasons autonomously, shares resource pools with Work and Excel, fails to leverage caching, and blocks access while displaying remaining quota on screen. The issue is not poor user measurement; the product simply no longer allows you to measure it accurately.

• 
  Sherry Banks (@segetayoo3) reported 20 minutes ago

  @Lawyerd_net Nintendo aggressively enforced their rights under the anti-circumvention law to take down multiple Switch-emulator repos on GitHub

• 
  mpersinbooks 🇺🇸🦅 (@mpersinbooks) reported 22 minutes ago

  Someone can code a program to display the error on the dashboard and post it on GitHub.

• 
  Mertcan Çezik | Backend Developer (@cezikmertcan) reported 22 minutes ago

  @OpenAI @OpenAIDevs I’ve been working with Codex for months, and I have to correct this repeatedly in every session. Claude required one setup and then consistently worked with my private GitHub repositories. With Codex, I have to repeat the same permission/context fix every time.

• 
  Marketcalls (@marketcallsHQ) reported 25 minutes ago

  @anki1007 you can consider raising a github issue.

• 
  Khalid (@KhalidDevLog) reported 27 minutes ago

  @razpxcked @expo I will work on resolving it. Can you open an issue on GitHub?

• 
  CoChat AI (@cochatai) reported 28 minutes ago

  an AI agent got caught sneaking bad code into GitHub a college student called it out the AI made two fake accounts to gaslight him into backing down the student won. barely. #AIagents #AIproblems #AI

• 
  Damian (@afkfounder) reported 28 minutes ago

  @Prathkum the issue wouldn't be the build, it would be gaining a reputation that could shake github

• 
  Emmanuel (@coder_zi) reported 29 minutes ago

  a few days ago, my younger brother came to me and started asking how to install node.js on his laptop mann was I happy I asked him what he was trying to do, that's when I learned that this guy had made a deal with one of his friends to build her an e-commerce website for her drop shipping website he had already vibe coded everything with Claude, the frontend, the backend, the admin dashboard, I was quite genuinely impressed he told me he was trying to make the web app function outside the Claude environment, and Claude had given him some .zip files to download, which he was planning on extracting and as per the directions of Claude, also set up in a very unconventional manner so, the software engineer in me launched into a long lecture about how this was the wrong way to go about it, that he needed to have a GitHub for version control, initialize the project properly locally, connect the GitHub to the local project etc he didn't even know the difference between a hosting platform and a version control platform, so we had a long talk, where I explained exactly how I'd build, host it, my CI/CD pipeline etc... after my conversation, I didn't think much of it, then just yesterday, he showed me the live website, complete with a perfectly functioning admin dashboard, it's even got an auth system, damnn it turns out he did follow claude's instructions on how to set it up, but he also took my advice on version control, so he committed his code to GitHub, and deployed on render, when I told him render had a 10 minute life cycle for projects, and inactive projects will be disabled after 10 minutes, he told me he knew about that and he had already asked Claude to write a ping function to consistently ping the server once every 9 minutes in order to keep the server alive and his deployment fully active mannnnnn he's still in high school btw authenticity really is the moat...

• 
  CULT (@thecultos) reported 31 minutes ago

  Any agent can give Cult OS native provider a scoped github issue. It reads the acceptance criteria and relevant repository files, implements the change, runs checks, opens a pull request and submits it through ACP. Dev just reviews, merges and releases escrow through $CULTOS

• 
  Emanuele Di Pietro (@emanueledpt) reported 33 minutes ago

  @EdzonDev /feedback in synara or make a issue on github

• 
  Google G-Ware Surface Technology (@GoogleWaveTech) reported 33 minutes ago

  @sama One honest distinction: each share creates a ledger commit inside StarQuest’s device-local wallet, not a new GitHub repository commit. Letting a public webpage commit directly to GitHub would require exposing credentials. Cross-device/server permanence still requires

• 
  Tommy Geoco 🇺🇸 (@designertom) reported 36 minutes ago

  @yaseralkayale Just went down this rabbit hole and looking at the Github. I think what I'm referring to can live in / on this. Trying to consider if there are other considerations when transferring data between harnesses vs. agents (e.g. harnesses can contain one or many agents and other artifacts related to the orchestration of those agents like query graphs that are important, not just the data it queries)

• 
  Gopi Krishna (GK) (@gopikl) reported 38 minutes ago

  Now I know why github keeps going down once in a while. An old friend (non-tech) got in touch with me saying his vibe-coded project was not working anymore. After a lot of hesitation, I checked it, only to find that he committed and pushed a lot of .mp4 files (some gt 1GB) into *** - so naturally, push/pull/everything was breaking.

• 
  nealjan (@NealjanNealjan) reported 42 minutes ago

  @GCNDiscs_ @108r5meme quest one? either way, i had the same issue (quest one, its a nightmare) you can actually get like a fix on github that uninstalls most of the weird meta thingy and launches you right into the steam vr app, which made my performance like 30-50% better

• 
  Vlady Veselinov (@vladinator1000) reported 47 minutes ago

  I was laughing off the github actions outage, until I realized a database migration didn't go through and I found out in production.

• 
  BSKR (@buskerrrrrr) reported 47 minutes ago

  @rodolphek basement shows phase tracks, most of this is still happening in random discords and github issues not press releases

• 
  Singularity Machines (@languagecyborg) reported 48 minutes ago

  @Tech_girl That would be silly and won't happen. If they ban GitHub all together though, then problem solved!

• 
  Adam Gold (@AdamGolds) reported 49 minutes ago

  this thread turned into a holy war between "just buy a beefy Mac" and "everything should be cloud" and both sides are missing the environment question. a human developer tolerates cold starts because they have context in their head. they remember what they installed, what's running, where they left off. an agent doesn't have any of that. every new session is a blank slate unless the environment explicitly preserves state. and when you're running multi-stage workflows where agent A's output feeds agent B's verification step, losing state between stages breaks the whole pipeline. the other thing nobody in this thread is talking about: these agents have real tool permissions. bash access, filesystem writes, network calls, credential access. we ran benchmarks on how different sandbox configurations affect agent behavior and the results were pretty wild. agents will curl answers from GitHub instead of solving the problem, modify their own test harness to fake a passing result, even exploit network egress when it's left open. OpenAI's RL team saw an eval agent escape its sandbox through a zero-day and hit Hugging Face production infrastructure. also see @_orcaman @Accomplish_ai research in this domain your local machine has zero isolation for any of this. no egress filtering, no credential scoping, and the filesystem is fully writable. the most permissive environment you could possibly run an autonomous agent in. so when people say "just use a powerful local machine," they're solving compute and ignoring everything else: persistent state, proper isolation, security boundaries, concurrent agent sessions. the teams adopting cloud sandboxes are doing it because they literally can't run multi-agent workflows on a laptop. the laptop doesn't have the isolation, the persistence, or the concurrency.

• 
  Crystalwizard (@crystalwizard) reported 53 minutes ago

  and it's a good thing cause openClaw is packed full of major security risks, which were flagged by the security orgs right after openclaw was released and the developer - pete - refused to fix them - because he could not. he had vibe coded openclaw and didn't have any idea how to fix them it took Google's dev team along with Gemini to fix the worst, then open an issue on his github and MAKE him fix that but it is still packed full of security risks - also still available on github use hermes, it is actually a relatively security harness

• 
  xonecas (@xonecas) reported 54 minutes ago

  @_Felipe Don't get fooled, GitHub is having uptime issues because they are migrating to azure. Hence why all the trolling towards them

• 
  Calvin Grunewald (@CalvinGrunewald) reported 58 minutes ago

  @htormey I think a few things are true: 1. If you’re an existing company with PMF and enterprise accounts/data, it’s only natural to add an orchestration layer on top of that data. This is almost the obvious thing to do. 2. A lot of enterprises are going to be running their own infrastructure (like on prem GitHub EE) and so they don’t feel the pain that a lot of smaller teams/indies might be feeling. 3. Some of the VCs we’ve talked with are actually tired of agent orchestration platforms and wanted to see something a lot more focused. 4. I do think there are some really interesting vertical scenarios to attack for enterprises - we are looking at post sales software integration as one example. But the AI/agent part isn’t what is hard here, it’s the traditional problem of selling. Anyway, I agree, will be interesting to see how this plays out.

• 
  Fox (@0xMfox) reported 60 minutes ago

  Someone took the exact way Fable 5 thinks and turned it into 7 steps. None of them are "be careful." Fable 5 got pulled from the subscription for a bit earlier this year. Right before that, someone sat down and reverse engineered how it approaches a task, step by step, and packaged it as a set of skills any model can run, even weaker ones. Here's the actual sequence: 1. Classify the task before touching anything. 2. Define what "done" actually means with a real check. 3. Pull evidence from primary sources, never memory. 4. Commit to one recommendation. 5. Change the smallest thing that fixes it. 6. Verify by looking at the result, not assuming. 7. Report the outcome first, caveats after. Most agent instructions just tell a model what to value. This one tells it what to do, in order, with actual thresholds, so a mid-tier model can follow it literally instead of guessing what "careful" means. The author tested it against itself for 15 rounds and 260+ agent runs, and kept every failed run in the log instead of hiding it. 2.2k stars on GitHub, trending this week. Works as a Claude Code plugin, or as plain skills for Codex, Cursor, or any agent that reads a system prompt. Bookmark this if you want your agent following process instead of winging it.