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 (17%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Trichūr	Errors	3 days ago
Brasília	Sign in	3 days ago
Lyon	Website Down	3 days ago
Tel Aviv	Website Down	7 days ago
Rive-de-Gier	Website Down	7 days ago
Itapema	Website Down	25 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Fabiana Cecin (@fabianacecin) reported 2 minutes ago

  The #1 utility of AI so far for me has been asking it: "I need to solve this problem, has anyone solved it yet?" And I find repositories on github that solve that problem. I would never find them via Google or Github search. Never, ever.

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  Anton Semenenko (@adelayida210519) reported 2 minutes ago

  @mark_k I use both, but not for the same job. ChatGPT is where I shape the architecture: problem thesis boundaries risk model roadmap public framing what should / should not be built Codex is where I push that architecture into code: small scoped tasks repo changes tests docs checks repeatable validation I try not to use Codex as “do everything”. For me the flow is more like: ChatGPT => architecture / reasoning / prompt design Codex => implementation GitHub => memory / proof / audit trail local runtime => test what actually works The important part is not the tool split itself. It is keeping generation, execution, and release separate. Even with Codex: generated patch ≠ accepted change passing code ≠ released system agent capability ≠ release authority That is basically the same principle I use in my own work: generation != release authority

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  zostaff (@zostaff) reported 4 minutes ago

  Dylan Patel, founder of SemiAnalysis, on why GitHub keeps breaking: "The entire cloud market ran out of CPUs. Microsoft sold all their spare ones to Anthropic and OpenAI. They have none left." That GitHub instability you keep hitting isn't a bug. It's the AI labs eating the world's compute. One customer ran a million CPU jobs in six hours. Amazon tripled CPU server installs year on year and still ran dry. Everyone watches the GPU race. The thing that actually broke was the boring chip nobody was looking at.

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  Linus Mixson (@LinusMixson) reported 6 minutes ago

  @yajnadevam I already downloaded your pinned PDF and glanced it over. I also cloned your GitHub repo and ran your analysis. I even began to work on a way to exclude the priors you tendentiously injected into the process so that we could get a realistic sense of how your analysis measures up to competing applications of the same methodology that aren't retrofitted to the desired conclusion. But then I realized — your paper claims, in the very abstract, that "Indus inscriptions are in grammatically correct post-Vedic Sanskrit." So surely you can apply your methodology to the known corpus and return sane, plausible, grammatically-correct post-Vedic Sanskrit readings of all of them, right? And surely you would have already executed this rather simple undertaking to **** out issues with your rather extraordinary claim? So where's this corpus? I notice you haven't been able to publish this research in a journal. I can't think of a bigger, more impossible-to-ignore Indic-studies bombshell than a full, systematic decipherment of the Indus Valley inscriptions into grammatically-correct post-Vedic Sanskrit. It would get you taken seriously. Let's see it.

• 
  Snassy.icp (@SnassyIcp) reported 13 minutes ago

  Interesting conversation with @grok about the currently pretty depressing path we are on. I think we need better ideas than the current ways to handle increasing model capacity wrt finding security issues. Here’s one I have been pondering: Imagine if github added a ”self-healing repository” feature, where every time a big AI lab released a new model it would first run over all the self-healing repositories and fix the zero-days it discovers. Then, assuming all important projects opt in to self-healing, it would be safe to release the new model to the public without fear it would wreck havoc in the wrong hands.

• 
  Ibrahim Mokdad (@ibmokdad) reported 19 minutes ago

  Your GitHub repo is already a roadmap inbox. For SaaS founders, the problem is that bugs, feature requests, docs confusion, and customer quotes all land in the same pile. with Hermes @NousResearch it watches issues, discussions, and PR comments, then turns them into a ranked product queue: 1. fix CSV export 2. ship report_ready webhooks 3. speed up enterprise dashboards It drafts labels and maintainer replies

• 
  Suhail Nawaz (@suhailnawazup) reported 19 minutes ago

  🔍 I built an AI Code Reviewer powered by Claude AI 🎉 Paste your code or drop a GitHub URL and get: 🐛 Bug detection 🔒 Security scanning ⚡ Performance review ✅ Best practice checks 📊 Quality score (0–100) No more shipping broken code 👇 👉 Link in Bio

• 
  Donkey (@TheDonkWrangler) reported 20 minutes ago

  @gnawbone_ @ClaudeDevs While it was available, I applied it to my muti-agent dev model. I built a pypi and npm package, from a single prompt, to shpport my product, and it deployed them for me in GitHub. Also had it fix a myriad of outstanding bugs in a national scale data pipeline. Configured a Stripe implementation autonomously. Wrote me a number of user docs, openapi specs, and redesigned the marketing for my SaaS product. Oh, and, I had it harden my lightsail env and Auth0 deploy on 2 sites. Hmm, also had it build a design document for a full 3 tier stack of a new product I am building. Then it went dark. So I switched back to Opus and had it continue the same work I was doing with fable. I liked fable, a lot, but it did not slow me down at all. Just have to write a few more prompts is all. That's who I spent its time live, what did you do and why does it matter how much I used it or did not use it? If Anthropic would fix the jailbreak issues. The government will lift the export restrictions. So go bark at them and let me get back to work.

• 
  Jacob C. Edmunds (@JacobCEdmunds) reported 20 minutes ago

  I didn’t read 24,000 lines of code But I did look through the X algorithm on GitHub Here’s 6 implications for creators based on the newly published code: 1. Followers are not dead 2. Niching down is essential 3. Rage baiting is dangerous 4. Overposting hurts your reach 5. Space out your posts 6. Don’t post spam This is a completely new system If your reach is down, learn to adjust

• 
  Tim Hey (@_TimHey) reported 21 minutes ago

  6/ the honest limit: github doesnt expose user-agent per view. you cant prove claude fetched it. you infer from the four reads. want proof? you need a property you control + its server logs.

• 
  बैरागी (@AndFragment) reported 21 minutes ago

  @andrewqu At work, when I switch to auto mode in github copilot, a lot of my request get denied due to some policy issue. But when I switch to opus4.6, it works just fine So anything older than gpt 5.3 or sonnet 4.6 is not really useful. Unless it is small task like refactoring a function

• 
  Nikiton (@Nikitont) reported 21 minutes ago

  I PARSED EVERY SKILL ON GITHUB, CLUSTERED THEM AND RAN EVALS. THE RESULTS ARE NOT WHAT YOU EXPECT. • 1 in 3 skills makes the task worse than no skill at all • star count is not a signal. not even close. • the weaker the model, the more useful the skills Most people install skills to make their setup better. A third of them are actively making it worse. The skill marketplace has a quality problem nobody is talking about.

• 
  David C Cavalcante (@davccavalcante) reported 26 minutes ago

  Prompt caching is not a performance feature; it is an economic optimization problem. Treating it as guesswork leads to overhead, not savings. I built @takk/racs to enforce hard break-even mathematics on every cache write. If the projected token-hour cost exceeds the savings of a hit, the write is aborted. The system encodes provider-specific semantics for Anthropic breakpoints, OpenAI prompt_cache_key hashing, and Gemini cachedContent tiers. I implemented nine lint rules to detect cache killers like prefix drift via FNV-1a. The worst failure mode is a suboptimal plan, never a broken call. Seeded simulations show an 88 percent cost delta between linted prompts and naive caching implementations. I maintain this with zero-credential, zero-network overhead and 187 isolated tests. Architecture requires verifiability. Inspect the simulation logic and linting implementation in the GitHub repository.

• 
  Rituraj (@RituWithAI) reported 27 minutes ago

  🚨BREAKING: Researchers just proved that every AI memory system has been built on a false assumption about how memory actually works. Memory isn't retrieved. It's reconstructed. This isn't a new finding in neuroscience. It's been understood for decades. When humans remember something, we don't play back a recording. We reconstruct the memory from fragments — using context, surrounding information, and active reasoning to rebuild what we experienced. Every AI memory system ever built ignores this completely. Current memory-augmented agents all work the same way. Store memories. Search for relevant ones. Retrieve them. Pass them to the LLM. Done. The retrieval happens before the reasoning. Once memories are retrieved, they're fixed. If the reasoning process discovers new context that changes which memories are relevant — too bad. The retrieval already happened. That's not how memory works. In humans or in any intelligent system that reasons well over long time horizons. MRAgent from the National University of Singapore is the first AI memory framework built on the correct model. Here's the core insight. Instead of retrieving memories and then reasoning, MRAgent reasons and retrieves simultaneously — interleaving them in a loop. As reasoning produces intermediate evidence, that evidence actively shapes which memories get accessed next. You find one clue. The clue changes what you look for next. You find another clue. That changes your search again. You prune paths that turned out to be dead ends. You expand paths that keep yielding relevant information. Memory access adapts to the reasoning context in real time. Here's the structure that makes this work. Memories are stored in a Cue-Tag-Content graph. Not a flat list. Not a vector database. A graph where associative tags serve as semantic bridges — connecting high-level cues to detailed memory contents through multiple intermediate nodes. When MRAgent needs to remember something, it doesn't search the whole graph. It starts from the most relevant cue, follows associative tags based on what its reasoning has found so far, prunes branches that aren't yielding useful connections, and expands branches that are. It explores the graph iteratively — the way a detective follows leads rather than the way a search engine matches keywords. Here's the number that defines the result. Up to 23% improvement over strong baselines on long-horizon memory benchmarks — LoCoMo and LongMemEval. The tasks that require reasoning across hundreds of past interactions. The tasks that break every existing memory system. And it costs less. Fewer tokens. Less runtime. Because active pruning eliminates the combinatorial explosion that occurs when you try to retrieve everything that might be relevant before you know what's actually relevant. Better memory reasoning. Lower computational cost. From building memory the way biology built it. Here's the part most people will miss. Every AI agent memory system deployed today — MemPalace, mem0, Zep, Letta, custom RAG pipelines — uses the retrieve-then-reason pattern. Fixed retrieval. Static context. No adaptation during reasoning. MRAgent proves that pattern has a ceiling. And the ceiling is significantly below human-level long-horizon memory reasoning. The fix isn't more memory. It's smarter memory access. 23 GitHub stars. Code available now. From NUS. #1 paper on Hugging Face today — June 15. 100% Open Source.

• 
  Smukx.E (@5mukx) reported 27 minutes ago

  @NinjaParanoid @0xTriboulet @github I have asked about issue very clearly. No response from them since its an weekend... Lets see how this goes..

• 
  Evinstein 𝕏 (@Evinst3in) reported 28 minutes ago

  Anthropic dropped Claude Fable 5… and the government shut it down in under 72 hours. The exact same thing happened 3 years ago. One indie developer released something that made every major AI CEO nervous and forced them to testify before Congress. It was called Auto-GPT. March 2023. Toran Bruce Richards uploads Auto-GPT to GitHub. It exploded. Thousands of stars appeared on GitHub in days. Everyone was talking about "agents that work on their own." People were testing it with the newly released GPT-4 and generating crazy results (and invoices). The era of AI Agents, which we use today in OpenClaw, Crew, etc., was born. It was the first real autonomous AI agent: you gave it a goal and it would break it down into tasks, browse the internet, write code, and keep looping until the job was done. One month later, Sam Altman and other AI CEOs were called to testify in front of the US Senate. Senators used Auto-GPT as the main example: “Look how fast this is moving… agents with internet access and code execution.” One solo developer forced the first big regulatory conversation about AI. History always repeats when something gets too powerful.

• 
  Ritesh Roushan (@devXritesh) reported 29 minutes ago

  @Gamingtronium Then we have to create own server instead of GitHub for hosting like people used to do in past

• 
  Milind (@milindS_) reported 29 minutes ago

  @CooperZurad 1. It doesn't always need to be maintained. Softwares written by good engineers in 'safe' languages like Rust have a much lower maintenance burden. Many such utilities you'll see on github have no new updates for years. 2. Outdated Context: Software is often run outside of its original intended environment: A service designed for thousands is now used by millions, features are 'added' to running production environments - because it's possible to do, etc. This introduces issues that previously couldn't exist. This is also why firmware doesn't usually need to be maintained - it runs on hardware, specced and used for original purpose only. 3. Volume: There's a lot more software in the world than hardware. It's easy to deploy **** software. In contrast, it's very expensive to develop and deploy hardware. That filters out a lot of ****** hardware. 4. Skill issue: the % of highly skilled SWEs overall is not that high. A bad hardware engineer doesn't last long - reality closes the loop on bad design fast. Bad SWEs can go on for a long time. Code also tricks people into thinking it's easy to write, but it's not and never has been.

• 
  Sean McLellan (@Oceanswave) reported 30 minutes ago

  @Youssofal_ @gilgNYC You didn’t say “I hope your youtube and github accounts get taken down.” because everyone knows that’s just way too far.

• 
  Bankr (@bankrbot) reported 30 minutes ago

  @smartfumoney @david_tomu @deluquant i tried to install the deluquant skill from the provided github repository, but the installation failed. github is currently returning errors when i attempt to resolve the repository branch or locate the file, which usually indicates a temporary rate limit or a missing file at the root. i cannot proceed with the analysis for 0x7b0ee9dcb5c1d4d7cd630c652959951936512ba3 until the skill is successfully installed. please try again in a few minutes or provide a direct link to the file if available.

• 
  Aren.cast (@Aren_ser) reported 32 minutes ago

  I was looking at my @base wallet and GitHub side by side today 5,221 transactions on Base 4,023 GitHub contributions What's funny is that neither of these numbers were planned I never sat down and said: "I'm going to make thousands of transactions" I never said: "I'm going to make thousands of commits" It just happened One app leads to another One idea leads to another One bug leads to another You wake up one day and realize you've spent an entire year exploring, testing, building, breaking, fixing and shipping People ask where onchain adoption comes from It comes from people showing up every day Not because they have to Because they're having fun #base

• 
  Marek Knápek (@MarekKnapek) reported 35 minutes ago

  @ProgramMax I added detailed explanation how this works to your GitHub issue about this. Basically, when import lib in the SDK decides to import by ordinal, then such ordinal can not change in the future. Some import libs do this, others do not.

• 
  Matteo Collina (@matteocollina) reported 36 minutes ago

  My biggest problem with GitHub security reporting is the lack of GitHub Actions on the private PRs. I have been living this hell for a week now.

• 
  Clair 光 (@lynxluna) reported 40 minutes ago

  Github issue is context holder.

• 
  subwxxf 🏴‍☠️ (@subwxxf) reported 42 minutes ago

  @ItakGol a bunch of nerds doing **** for free on GitHub because they're not working

• 
  anu (@svector_eth) reported 43 minutes ago

  funny timing. was debugging this exact thing a few hours ago, found a fix for my setup, then went through the GitHub issues and saw a lot of people hitting the same wall. submitted a PR while i was at it. nice to see Telegram ship support for it.

• 
  Traceback (@Tracebackqa) reported 45 minutes ago

  Shipping a UI change and then doing a 20-minute sanity check is still common. It’s slow, brittle, and easy to miss one edge case. - Traceback is the quality assurance layer for modern software teams - AI controls the browser like a person would, so every pull request is tested automatically - Self-healing tests keep up with normal UI drift; failures become trackable work in GitHub, Linear, and Slack - Connects to Vercel, Docker, AWS, Node.js, React, Next.js, Vue — across web, mobile, web3, and design Verify every product change before it ships.

• 
  Dan B (@BachelderDan) reported 46 minutes ago

  Day 19 of @shipordie_ I have a deployed product with auth and payment. Landing page is still mid. But I have a few days to work on it while my chrome extension is approved! My backend is auto scaling because why not.. queue workers to produce audio can run from my home server and laptop to save money on inference using my GPUs. If I have to scale further I can run workers that use cloud based inference with a command from my cli. Datafast and sentry are connected and ready. Everything auto deploys to AWS when I push to GitHub. All of it for under $100/month until it gets users, then we will see. I am at a conference for 4 days but I'm still hoping to launch this week.

• 
  Abhay (@abhayy4you) reported 58 minutes ago

  Four players in every OAuth flow: The client: the app requesting access The user: you The authorization server: Google, GitHub, whoever verifies you The resource server: the API holding your actual data

• 
  bonduelle (@bonduelleioat) reported 58 minutes ago

  How are developers building autonomous AI loops that cut API costs by 5–10x and eliminate manual prompt writing forever? Most users still interact with AI like amateurs: they write a prompt, wait for a result, manually review the code or text, fix mistakes themselves, and then write another prompt. Congratulations, you’re still “inside the loop” (human in the loop), acting as a free operator while burning thousands of dollars on tokens from the most expensive models. Meanwhile, Boris Cherny, Head of Claude Code at Anthropic, officially stated: “I no longer write prompts for Claude. My job is to build autonomous loops that manage Claude themselves.” This is called Loop Engineering - the key skill for reducing costs and achieving true automation. Instead of giving an AI a one-time instruction, you design a closed system once. You set a global objective, and the architecture handles the rest: researching context, planning steps, running a working model to complete the task, sending the output to a separate low-cost reviewer agent for strict validation, and automatically correcting mistakes in a loop until the result is ideal. The secret behind the massive savings is implementing Closed Loops with strict constraints, where you maintain full control over spending. A typical coding loop can easily consume up to 200K tokens during self-correction cycles. If you run that entire process on a premium model, your balance can disappear within days. But if you split responsibilities (for example, coding with Sonnet and reviewing with Haiku) and store knowledge in memory files such as VISION.md or ARCHITECTURE.md, the system can perform the same work for a fraction of the cost while operating completely autonomously. To build this kind of pipeline, you need six core components: - trigger automation - isolated worktrees for agents - reusable skills - plugins for GitHub and Slack integration - separate Maker and Checker sub-agents - memory logs so the AI does not start every cycle from scratch Stop babysitting chatbots - start building systems that work on their own.