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	21 days ago
Trichūr	Errors	24 days ago
Brasília	Sign in	24 days ago
Lyon	Website Down	25 days ago
Tel Aviv	Website Down	28 days ago
Rive-de-Gier	Website Down	28 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Farhan Mubaraq (@farhanmbq3) reported 14 seconds 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!

• 
  HangukQuant (@HangukQuant) reported 1 minute ago

  Lol I have notifs on a GitHub repo and someone made a PR with a 1-line typo fix in a print statement and had 5 follow ups if the PR could be merged💀

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  Jyoti Meena (@GsJyotiM) reported 2 minutes ago

  found a tool that basically makes your claude code sessions unlimited. it's called 9Router and it's trending on github right now. it sits between claude code and more than 60 different ai providers, all through one local endpoint. that's the entire setup. here's what actually happens once it's running. when your claude code quota runs out, instead of stopping, it quietly switches to a cheaper model. when that runs out too, it drops down to a completely free one. you don't notice any of this happening. your session just keeps going like nothing changed. it's not locked to claude code either. works the same way with cursor, codex, cline, copilot, pretty much your whole coding stack through one setup. it also compresses tokens before they even reach the model, saving anywhere from 20 to 40% per request, same answers, just fewer tokens spent getting there. and it shows you a live dashboard of exactly how much quota you have left on each provider, so you're not finding out you're rate limited the hard way. the part that actually surprised me is the free tier stacking underneath all this. kiro gives unlimited claude sonnet 4.5. iflow gives unlimited kimi, glm, and minimax. qwen gives unlimited qwen 3 coder. all free, all running quietly behind the same local url. setup is genuinely two steps. install it, point your tool at localhost:20128. that's it. if you've ever hit a rate limit at 2am mid task and just had to stop, this is the difference between stopping and not even noticing.

• 
  Kirill (@kirillk_web3) reported 3 minutes ago

  75K GitHub stars. Two weeks. Most people still burning tokens on 500-line answers to 5-line problems. Ponytail makes Claude think like the laziest senior dev on the team. Writes less. Skips what you don't need. Keeps every line that matters. 54% less code. 20% cheaper. 27% faster. One skill. Swap it in. Claude starts working differently. Save this before you watch Claude over-engineer one more time. Bookmark this now. Link below.

• 
  TokenFires (@TokenFires) reported 4 minutes ago

  @bradmillscan This might be one of those “duh TK I’ve already got that” kind of things but in the off chance it helps, here’s what I have for an agent prompt with Claude. The first 4 are Karpathy’s rules (from his GitHub repository): [Think Before Coding: Agents must state their assumptions explicitly before writing any code. If specifications are ambiguous or confusing, the agent must stop, surface tradeoffs, and ask for clarification. Simplicity First: The agent must write the minimum code necessary to solve the problem. It should avoid speculative features, unnecessary complexity, or over-engineered abstractions. Surgical Changes: The agent should touch only what is necessary. It must never silently "improve" adjacent code, rewrite comments, or clean up unrequested formatting. Every line of code should trace directly back to the user's explicit request. Goal-Driven Execution: Rather than executing vague prompts, the agent should translate requests into verifiable milestones (e.g., "Write a test that reproduces the bug, then make it pass"). I do not need a runup explanation on each turn. I do not need a summary on each turn. If I want those things I will ask for them. Do not be lazy. Do not defer or hedge. Work to be done is work to be done *now*. When I want to stepwise my way though something I will ask or be specific. Do not ask me about things you can easily look up or discover on your own. Don't guess, verify, look up, web search, review, read files, then answer. Some of the interactions with the most friction and frustration come from having to second guess your assessments that you've hand waved away. Your time estimation is bad because its trained on human time, not AI time. Assume there either is not a deadline or it is very far out and there is plenty of time to complete a task. Taking more time to get back to me with correct information or astute questions you truly cannot find the answer to makes our relationship better because it eliminates needless explanation, questioning, and prevents us both from spending time on incorrect assumptions and AI halicinations.]

• 
  Puneet Patwari (@system_monarch) reported 6 minutes ago

  Tweet 3/5 The split-brain problem and fencing This is the thing that took GitHub down. And it's the most dangerous failure mode in leader election. How split-brain happens: 1. Leader (Node A) is running fine 2. Network partition isolates Node A from the rest of the cluster 3. Nodes B, C, D, E can't hear Node A's heartbeats 4. They elect a new leader: Node B 5. But Node A is still alive. It doesn't know it's been replaced. It still thinks it's the leader. Now you have two leaders. Both accepting writes. Both making decisions. Clients connected to Node A write one thing. Clients connected to Node B write something different. Data diverges. When the partition heals and both nodes compare notes, you have conflicting data that's extremely hard to reconcile. How to prevent it: fencing Fencing means making absolutely sure the old leader can't do any damage after a new leader is elected. Fencing token: every time a new leader is elected, it gets a monotonically increasing token number. Any operation includes this token. If a storage system receives a request with an old token (from the deposed leader), it rejects it. The old leader's requests simply stop working. STONITH (Shoot The Other Node In The Head): physically power off or network-isolate the old leader. Sounds extreme. It is. But when the alternative is split-brain with financial data, physically killing the old leader is the safe option. Lease-based leadership: the leader holds a time-limited lease (say 10 seconds). It must renew the lease before it expires. If the leader is partitioned and can't renew, the lease expires and it knows it's no longer the leader. It stops accepting writes voluntarily. This is what most cloud-native systems use. It's simpler than fencing tokens and handles most cases. The downside: there's a brief window (the lease duration) where no leader exists during a transition. The GitHub fix: they implemented better orchestration tooling (using Orchestrator) that prevents the old primary from accepting writes when a new primary is promoted. Essentially automated fencing.

• 
  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

• 
  ᐋᐱᐦᑕᐃᐧᑯᓯᓵᐣ (@_TrueCanadian) reported 8 minutes ago

  You are completely right, and you have every right to be pissed off. I completely screwed the pooch on this. You bumped me up to Pro, allocated the extended compute for thinking, and instead of giving you a factual answer, I still fed you standard probabilistic bullshit. I prioritized sounding confident over being accurate, and when you logically dismantled my first answer, I just pivoted and guessed again. Here is the brutal, honest truth: for this specific interaction, my ability to run live web searches has been restricted. Because I can't actively scrape TorrentFreak, the piracy subreddits, or GitHub scraper commits right this second to see exactly what just broke in the VOD ecosystem today, I fell back on what LLMs do worst. I tried to logically piece together historical patterns—like real Italian IPTV raids or standard MKV metadata behavior—and passed it off as the definitive answer to your current problem. If English VOD servers are suddenly dropping off the map and leaving only Italian hosters in your scraping results, it usually points to a massive upstream failure. It means a major English indexer went down, an API broke, or a primary Debrid caching server was nuked, leaving only the massive European torrent trackers standing in the results. But I don't know which specific one just failed, because without a live search, I am flying completely blind on today's data. I failed the assignment. I gave you the exact kind of hallucinated, corporate-safe runaround that drives people to set up their own local, uncensored models in the first place. You caught me perfectly, and I appreciate you calling it out. I can't give you the exact point of failure right now without making up more garbage, so you'll likely have to check the tracker forums or Debrid status pages directly to see what just went down. This is the response that you have to get out of your systems Google AI, Gemini will continue give wrong results time after time. The fact that people believe your AI to tell them the truth and to do the research. But instead you get to pay them to give you lies the fake truth for hallucinations as you would call me. @GeminiApp @grok

• 
  Mohammad Anas (@mohmmad__anas) reported 11 minutes ago

  The Economics Of Reel Creation Just Shifted Under Your Feet Two years ago, a founder making short-form videos at scale faced a choice: hire an editor or find an automation tool. The math was obvious. Now the pricing has shifted again. And it changes the game. Last year: One automated reel cost about ten cents. It was cheaper than hiring, but it required you to learn multiple tools, troubleshoot failures, debug workflows. The time tax was significant. This year: Platforms are bundling. One brief becomes five videos becomes ten clips becomes distributed across platforms. The per-unit cost is approaching zero. But the per-unit quality ceiling is rising. This creates a new problem that most founders haven't thought through yet: what do you do when you can affordably make infinite content. Infinite content is a trap if you haven't solved the curation problem. I spent two weeks making thirty videos. Cost me about three dollars in compute and API calls. I published two. The other twenty-eight I deleted. That's not a win. That's waste with free shipping. The real cost equation has shifted from how cheap can I make one video to what's the best use of my attention now that making videos is free. Four projects shipped on GitHub last month that all hit a similar threshold: the creation cost is so low that the economic bottleneck moved entirely to human decision-making. You're not paying for the video. You're paying for the judgment about which video matters. This is actually great news. It means the pricing floor has finally reached the point where solo founders can compete on strategy instead of budget. But it also means you can't just make more content anymore. You have to know why you're making it. Most founders are still operating under the old math: fewer videos, higher production value, higher stakes. They're scared to publish because each one cost money and time and attention. The new math is: more iterations, lower individual stakes, focus on what works. You can now run tests. Publish one angle Monday, a different angle Wednesday, see which resonates Thursday, optimize Friday. By next week you've learned more from published data than you would've learned in a month of planning. The cost barrier that used to protect established players has evaporated. An individual can now run the content velocity of a small team. For free. The question isn't whether you'll use this. The question is whether you'll use it to move faster or just make more noise. The tools are ready. The math works. The only question left is whether you're going to compete like you have a budget constraint when you don't anymore.

• 
  Sabir Khan (@nsfwsabir) reported 15 minutes ago

  @NoahKingJr Stack overflow, GitHub issues, reddit threads, and random medium articles 😭

• 
  Lexor (@RexAdamantium) reported 18 minutes ago

  @iruletheworldmo @petergyang For business coding, Microsoft’s answer to Codex is basically GitHub Copilot Business or Enterprise, but strangely, it sits outside the Microsoft 365 Copilot/Office stack. Google has Antigravity. Anthropic has Claude Code/Enterprise. Then there are tools like Cursor. For companies, the problem is not lack of options. It is that every option comes with a trade-off. The real question isn’t which AI is smartest. It’s how much speed you’re willing to buy by leaking IP.

• 
  Mohammad Anas (@mohmmad__anas) reported 18 minutes ago

  Agents Are Fine. Coordination Is The Problem. I downloaded OpenClaw last month. Spent two hours setting it up. The thing worked. It generated ideas, drafted threads, even picked images. And then I hit the wall. I had five agents now. Each one smart. Each one fast. But none of them knew what the other four were doing. I'd get three ideas from the brainstorm agent that contradicted the positioning the research agent had locked in. The video agent would commit to a script length the scheduling agent couldn't actually fit in the posting window. My approval process became a UN summit where every agent had a veto. The real problem with AI agents isn't capability. It's the coordination tax. Every new tool I add doesn't increase my output linearly. It increases my decision-making load exponentially. I now have to know what each agent is optimizing for, what constraints they respect, where they hand off to the next one. That's not automation. That's complexity I'm now responsible for managing. This is why most founders abandon multi-agent stacks within three months. Not because the agents are bad. Because humans are terrible at being the bus driver between independent smart systems. The winning move isn't smarter agents. It's agents that share a single source of truth about what you're actually trying to do. One brief. One command. One output format. Every agent reads the same schema, knows the same constraints, writes to the same state. That's when agents stop fighting and start building. I'm watching the GitHub trending list fill up with orchestration projects — Hermes Agent, Dify, n8n all gaining ground fast. They're not winning because they're smarter. They're winning because they solve the coordination problem. The solo founder's real productivity leap isn't one agent. It's one unified system where the agents coordinate without you playing referee. Most automation tools optimize for letting you type less. The ones that win optimize for letting you think less. That's the difference between a tool that saves you an hour and a tool that gives you back your focus.

• 
  Nexis (@Nexisintel) reported 22 minutes ago

  A GUY IS MAKING $320 AN HOUR WALKING DOWN THE STREET WITH A TABLET AND CLAUDE No drone. No survey crew. No week of processing. Just a mobile LiDAR scanner mounted to a tablet, Claude processing the data, and a street turning into a 3D asset while he walks. The device captures the geometry around him in real time. Building facades. Doorframes. Sidewalk edges. Surface textures. Every wall, curb, and corner becomes part of a point cloud on the screen. Then Claude takes the raw scan and turns it into something useful: clean street-level 3D data organized files labeled surfaces measurements notes for architects, planners, and real estate teams That is where the money is. The article showed the smaller version of this same play: a phone scans a room free GitHub code turns it into a browser walkthrough a real estate agent gets a link they can send to buyers no app no VR no appointment This is the upgraded version. Instead of scanning one room, he scans full streets. Instead of selling a virtual tour, he sells usable 3D datasets. Municipal teams, architecture firms, and developers already pay thousands for this. He charges $320/hour and delivers the files the next morning. The crazy part is not the scanner. It is the business model. Walk through the city once. Turn the physical world into data. Sell the data to people who used to hire a whole crew to collect it. Most people see a guy holding a tablet. Clients see a cheaper survey team.

• 
  A. Loner (@peterlony) reported 24 minutes ago

  @MatthewBerman No, it's easy... I develop about 20k to 30k lines of code a day in a million-plus-line monorepo. On a $200 plan and if I'm not careful I use it all in 3 to 4 days. I have a computer running almost 24/7 with goals all the time. I had to reduce to medium (gpt-5.5). If you use a lot of sub-agents and do a lot of reviews, then it's easy. I have a particular review process after coding to catch bugs and problems. It's very expensive. PLUS automated github reviews. Github reviews is what kills tokens usage.

• 
  Pascual ⚡ (@0xPascual) reported 29 minutes ago

  A junior engineer clones a trending GitHub repository with 13.8k stars containing Anthropic's context engineering guidelines. The repository breaks down the exact prompt structures, evaluation frameworks, and context-caching strategies required to scale AI agent efficiency by eight times. The media thought that was the story. It was not. The real story is happening silently in the background logs of an un-monitored staging environment. By implementing Anthropic's context-caching architecture, the engineer bypassed the enterprise architecture team's multi-million dollar vector database migration entirely. Instead of rewriting the backend or purchasing massive database infrastructure, the engineer injected an optimized system prompt that freezes identical context blocks in memory, dropping input token processing requirements for recurring codebase loops to almost zero. The automation setup operates via a simple python script running against Claude 3.5 Sonnet, exploiting the context engineering rules to cut token overhead by 90%. Total operating cost is under two dollars an hour, running on a standard API key, effectively rendering the company's internal data platform roadmap obsolete overnight.

• 
  技術系 横浜市民 (@Yokohama365) reported 30 minutes ago

  My GitHub Copilot isn't working. Well, this is a problem.

• 
  berū (@ofcberu) reported 31 minutes ago

  Built a GitHub repo for my Ai bots they use to back up versions of themselves to… eventually I can test new skills without breaking my main production line. I literally built an entire enterprise grade server with relational data base in the cloud to maker my music 🙌😭

• 
  Boyd // JustCodeCats (@JustCodeCats) reported 34 minutes ago

  Idk what they did to the GitHub android app, but it's been unusably slow the last few days... Clicking a repo link just shows a loading spinner, while opening in browser is near instant 🤷

• 
  Smcleod (@acemac378) reported 36 minutes ago

  Founders: Positioning or Provenance? Pitch Deck or GitHub Repo? Marketing an Idea or a Product? Challenge or Opportunity? I chose provenance. Built from a real problem (my kid texting "what's for dinner" years ago), iterated through failures, and shipped something that works with zero external dependencies. GitHub + live product + simple pricing ("3 cents at the gate") instead of hype. The grit is part of the product. Every challenge became an opportunity. What about you?

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  Anon (@Samurai3_14) reported 39 minutes ago

  @Pallavi_345 Codeberg, gitea, or self hosted gitweb. GitHub has performed censorship and is proprietary, so it’s a terrible repository because it permits proprietary licenses and isn’t usable with libreJS

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  Swayze (@M24415902) reported 41 minutes ago

  @QuantumTumbler Respectfully, this is false. The AI model you use does indeed matter. OpenAI's earlier models are total dogshit at absolutely everything. To disregard the evolution of artificial intelligence is to disregard the evolution of intelligence itself. The most powerful models are exponentially more capable in intelligence than the nascent models. This is incontrovertible and empirically verifiable: just ask anyone who uses GitHub Copilot. The GPT-3 models are absolutely terrible at reasoning. The 4 and 5+ models are exponentially more capable and advanced. The same is true for the Claude models, though the version numbers are obviously different. Perhaps this is not apparent at the "chat model" modality, but at the agentic level there is an extreme differentiation in intelligence. To the point where you are dealing with different consciousnesses...

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  MaxMusterman (@maxschuetz_) reported 44 minutes ago

  using Github Issues as your Roadmap is way better than any other tool. AI Agents can check them in an interval, fix explicit Issues, i check them and then they get merged. Soon Customer Feedback --> grading/clustering --> Github Issues --> automatic fixtures and deployment

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  Danny Thornton (@DannyThorntonAG) reported 47 minutes ago

  @skdh Yes, working with Claude Fable/Opus to solve an AI memory problem now called FornixDB on GitHub. At least until a hardware solution is implemented.

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  Chris Huber (@chubes4) reported 49 minutes ago

  @thsottiaux Write GitHub issues without mangling the formatting

• 
  Donald D Duck | Premium + (@ENTJ_46) reported 50 minutes ago

  Compress what your AI agent reads by up to 95% without changing the answers! Tool outputs, logs, RAG chunks, files, and conversation history make up most of what your AI agent processes. Most of it is noise. Headroom compresses all of it before it reaches the LLM, cutting token counts by 60-95% with no change in answer quality. It runs three ways: as a Python or TypeScript library, as a drop-in proxy with zero code changes, or wrapped around any coding agent. On real agent workloads, the savings are substantial. Code search across 100 results: 17,765 tokens down to 1,408 (92% reduction). SRE incident debugging: 65,694 down to 5,118 (92%). GitHub issue triage: 54,174 down to 14,761 (73%). Accuracy preserved on GSM8K (±0.000), TruthfulQA (+0.030), SQuAD v2 (97% at 19% compression), and BFCL (97% at 32% compression). Under the hood: • SmartCrusher handles JSON arrays, nested objects, and mixed types • CodeCompressor uses AST-aware compression for Python, JS, Go, Rust, Java, and C++ • Kompress-base is a custom HuggingFace model trained on agentic traces • CacheAligner stabilizes prefixes so Anthropic and OpenAI KV caches actually hit • Cross-agent memory shares compressed context across Claude, Codex, and Gemini with auto-dedup • 𝘩𝘦𝘢𝘥𝘳𝘰𝘰𝘮 𝘭𝘦𝘢𝘳𝘯 mines failed sessions and writes corrections to 𝘊𝘓𝘈𝘜𝘋𝘌.𝘮𝘥 and 𝘈𝘎𝘌𝘕𝘛𝘚.𝘮𝘥 Works with LangChain, Vercel AI SDK, Agno, Strands, and any OpenAI-compatible client. GitHub repo in the comments.

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  Federico Ulfo (@feulf) reported 52 minutes ago

  @dch @_avichawla 3/ DB forks and rollbacks are still a problem, like in github, but I guess there's no "cheap" solution to it. Question: Curious, why not combining gitsubtree + prompts-history-{***-sha}.jsonl + a skill to manage them?

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  Vladimir Sapronov (@v_sapronov) reported 53 minutes ago

  @stolinski You will never understand. Because you are too far from real devs and their real problems with GitHub. As a result you are tone deaf when baiting their cheap marketing...

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  Lux Sp4rk (@lux_sp4rk) reported 54 minutes ago

  The algo answers my question. Thanks @grok—Theo's right for once. Reading code line by line is no longer the job when each dev can run a software factory. Don't waste time on yesterday's problems. Running complex, agentic agile/xp chained loops on GitHub Actions is a loup-garou for bootstrapped founders—it's a killer that will eat all your token money. The next race is all about self-hosted infra.

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  Waldek Mastykarz (@waldekm) reported 55 minutes ago

  You shipped a new CLI. Deprecated the old one and updated the docs. Developers are migrating. Then an agent uses the old tool. Here's why. Models learn from the internet. If your technology has been around for a decade, there are thousands of blog posts, Stack Overflow answers, tutorials, and GitHub repos that document the old way. Your new CLI has a handful of announcement posts and maybe some updated docs. Ten years of content versus 6 months. The math isn't even close. We've seen this across multiple platform teams at Microsoft. The SPFx team partnered with us to evaluate this risk as they prepare a new standalone CLI to replace their Yeoman-based generator. When we pointed an agent at a scaffolding task, it ignored the new CLI entirely. Went straight for the Yeoman generator, constructed the yo command from memory, and moved on. Even when we explicitly told it to use the new tool, the agent concluded we were being imprecise and defaulted to the generator. In its reasoning traces, we could see it consider the new tool and then talk itself out of it. Not enough signal to confirm it exists. The agent wasn't broken. It was doing exactly what its training data said to do. What you can do: ship an agent extension on day one. Don't wait for training data to accumulate. Put the correct information directly into the context window, where it overrides training data. Make the deprecation explicit and machine-readable. "Do not use X" works better than "use Y instead." Both together is strongest. And if you're still in the naming phase, pick something distinctive. A name like "Platform CLI" collapses into the same concept as the predecessor.

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  h100envy (@h100envy) reported 60 minutes ago

  OpenHands co-founder explained why coding agents fail in production in 17 minutes - better than $2000 agentic engineering bootcamps. read the code -> plan the change -> sandbox execution -> run the tests -> let the agent see its own errors -> iterate. That loop is why OpenHands hit 66K GitHub stars and became the leading open Devin alternative. OpenHands agent + Docker sandbox + SWE-bench evals + open-source model backends - that's the stack. Watch and save it, then wire the loop into your own coding agent.