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%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	4 days ago
Montlhéry	Website Down	5 days ago
Aulnay-sous-Bois	Website Down	5 days ago
Saltillo	Website Down	5 days ago
Granada	Website Down	5 days ago
Vernon	Website Down	5 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:

• 
  Paul Maddison (@PaulMaddison121) reported 5 minutes ago

  @rwojo Tip Use chat gpt on high in the browser and tell it to access your repos in github Get it to reason and do the code changes and then use codex just to build, fix build errors and run tests etc

• 
  Tobias Möritz (@tobimori) reported 5 minutes ago

  @thorstenball not yet decided completely but ideally i can have something like a linear mention or github issue start a specific agent with specific instructions. i can write a custom intermediate layer that accepts webhooks and transforms them but i'd be nice to have some options for remote control

• 
  savagemargiela (@savagemargiela) reported 6 minutes ago

  A file with zero lines of code just passed 203,000 stars on GitHub. On January 26 Karpathy posted a complaint: agents confidently make wrong assumptions and run with them. They don't seek clarification, they don't push back, they overcomplicate everything. He listed the failure patterns. 7.8 million views. One day later a developer named Forrest Chang compressed that thread into a single text file. CLAUDE.md. Four rules: think before coding. Simplest thing first. Cut surgically instead of rewriting everything. Chase the goal, not activity. You drop the file into your project root and the model starts behaving differently. Zero code. Zero marketing. 203,200 stars, more than most frameworks that took years to build. The mechanic underneath is worth more than the file. A complaint became an instruction: expert judgment, compressed into text the machine reads before every action. And it isn't limited to code. An author's writing style compresses the same way. Hook structure, sentence rhythm, vocabulary, the closer. Pull those out of three posts and you have an instruction file that writes like them. I broke down the exact process in the article below: six atoms, twenty minutes. The fastest-growing asset on GitHub right now isn't software. It's correctly compressed judgment.

• 
  ㄚ 卂 丂 卄 (@yash008108) reported 8 minutes ago

  Soory bro but I think ye projects bas GitHub bharne ke liye hai They not have any real world problems

• 
  iamcurtismith.base.eth (@iamcurtismith) reported 11 minutes ago

  Copilot CLI with Grok 4.6 just flipped how I debug multi-file refactors—instead of context-switching between your editor, terminal, and the model's window, you're staying in the shell where the actual error lives, feeding it file diffs and stack traces in real time while the model threads constraint chains across your whole codebase without snapping mid-way. Most coding agents treat the CLI as a second-class citizen, but this integration treats it as the primary input surface, which means fewer copy-paste errors and way less friction when you're chasing a bug that spans React components, Node middleware, and a database migration all at once. GitHub Copilot IDE and cloud products have it live right now too. The real move: frontier-adjacent reasoning (spatial geometry, multi-file coherence, latency compression) at a fraction of frontier cost means you're not paying Anthropic or OpenAI rates just to get your code to hold together across dependencies. @grok @xai.

• 
  Lumir (@kumouX) reported 12 minutes ago

  - checking GitHub issues - reading internal docs - watching YouTube with subtitles - following technical discussions That means the first learning moment often happens in the browser. If a word is looked up once and then disappears, it probably won’t stay. The more interesting

• 
  GREG ISENBERG (@gregisenberg) reported 13 minutes ago

  Running list of AI agent ideas to make you more productive and more money: 1. The onboarding rescue agent. Watch PostHog for any new signup who stalls on the same step for more than 10 minutes, then have an agent send them a Loom style personal message or a CustomerIO email that answers the exact thing they're stuck on before they give up. 2. The pricing page bounce agent. Fire a PostHog webhook when someone hits your pricing page twice and leaves, have the agent enrich them with Apollo, and send a short email with the objection handler for their specific company size when it matters. 3. The second product in support agent. Point an agent at your Intercom/Plain inbox etc and have it tag every request that isn't actually about your product, the adjacent thing people assume you also do. It ranks them by frequency. 3. The you already answered this agent. Have an agent read your sent folder, your Intercom replies, and your sales emails, and pull the clearest explanations you've ever written about your product. It drops them into a swipe file your landing page and cold emails pull from. 4. The internal tool to product agent. Point an agent at your team's GitHub scripts, Retool apps, and Google Sheets, and have it flag the ones 10 other companies in your niche would pay for. 5. The review mining agent. Apify scrape every review of your top 3 competitors on G2 and Capterra, cluster the 1-star complaints with Claude, and get a ranked list of the features to build and the exact words to use in ads to poach those unhappy customers. 6. The sell what you give away agent. Once a week, feed your Granola/Gmeet call notes and Intercom threads into an agent that hunts for every task your team did for free that took more than 30 minutes. It clusters them, counts how often each came up, and ranks by demand. The top 3 become paid add ons. 7. The win pattern cloner. Pull your last 50 closed-won deals from HubSpot or whatever CRM you use, have an agent find the firmographic traits and the trigger event those buyers shared before they bought, build a lookalike list in Clay, and feed it straight into Instantly. 8. The self improving ad agent. Wire an agent to your Meta ads account that pulls the winners daily, uses Perplexity to scrape fresh Reddit pain points, generates new static creative with Nano Banana, checks it against your brand guide with a vision model, publishes, kills the losers, and scales the winners on a loop. An entire performance marketer running 24/7. 9. The first hour agent. Pull your last 500 signups from PostHog, split them into power users and churned users, and have the agent diff the first session event streams to find the one action power users took that churners skipped. Then force that action into onboarding with a PostHog feature flag. 10. The lost deal rescue agent. Have an agent pull your closed lost deals from your CRM, then monitor those competitors' status pages and pricing pages with a daily Firecrawl. The morning a competitor has an outage or raises prices, it drafts a personal reach out to the buyers you lost to them. 11. The Gemini video scout. Point Gemini at your competitors' YouTube demos, webinars, and conference talks, and have it watch the actual footage, not the transcript, to pull the features they're teasing and the UI they're showing. It reads what they demo on screen, not just what they write down. 12. The wrong answer agent. Run your product's top buyer questions through ChatGPT, Claude, Gemini, and Perplexity every week on a cron, and have the agent log the moment any of them start saying something false about your pricing, features, or positioning, then Slack you the exact wrong claim and the source it likely pulled from. Honestly the fun part is that once you build one of these, you can't stop seeing them everywhere, every manual task starts looking like an agent you haven't set up yet. That's kind of where my head is at lately, so I'll keep dropping agent ideas here and on @startupideaspod as I go, and if you build one that rips, tell me, I want to see it. Grab whatever idea is useful. I'm rooting for you.

• 
  phzi (@phzix) reported 16 minutes ago

  @thsottiaux When did this start working again? Because it was disabled before and there was a GitHub issue saying it wouldn't be fixed.

• 
  Gabrielle P. (@mantancino_) reported 22 minutes ago

  When an AI lab drops open weights, treating release day as a single point-shock breaks every basic rule of causal inference. Advance announcements, shifting hosted endpoints, and public weights are entirely separate interventions. Conflating an announcement with actual weight delivery leads to broken assumptions about safety, market decentralization, and regulatory impact. Clean analysis requires isolating what changed and when. Look at the release timelines. Kimi K3 saw an 11-day gap between hosted launch and public weights, establishing an information bound rather than immediate delivery. Qwen similarly posted a 10-day gap between its hosted flagship listing and its open checkpoint. Release timing is only half the problem. Hosted Qwen3.8-Max diverges from its open checkpoint across four documented dimensions: vision input, non-thinking execution, a 1M default context, and native tooling. The public release is a text-only, thinking-mode artifact, introducing severe version bias into any direct comparison. In causal inference, "no anticipation" assumptions fail when intervention boundaries blur. Rigorous evaluation requires splitting the release into three distinct estimands: the initial information effect, the incremental weight effect conditional on prior notice, and the total planned-opening regime. Auditing these counterfactuals across baseline contamination and spillovers requires strict gate protocols. When data gaps appear in upstream donor registries, they signal research design constraints—not empirical proof of zero real-world impact. Public weights grant local possession, but they do not automatically decentralize institutional power. Across structural power domains, market control remains anchored to fixed compute clusters, proprietary data pipelines, and distribution moats. Measuring these shifts requires two critical empirical corrections. Spikes in public GitHub repositories often reflect developer publishing opportunity rather than latent development growth, while operational autonomy must be measured by serving volume on independent infrastructure, not nominal API endpoints. Under network interference, open models increase external oversight while eroding centralized monitoring. Because risk vectors and defensive capabilities evolve on separate tracks, safety cannot be flattened into a single scalar score. Sound governance demands tracking five independent vectors: realized harms, capability indicators, safety controls, monitoring and attribution, and systemic resilience. Open weights expand developer feasibility, but they do not dismantle structural bottlenecks or guarantee safety gains. Until evaluations account for staged rollouts, version drift, and layered power asymmetries, causal claims about open-weight releases remain entirely ungrounded.

• 
  Mike (@n3onr1ft) reported 23 minutes ago

  @witcheer Of course! I think there’s already an issue or PR for it on GitHub. No rush though, I know there’s a crap ton of higher priority stuff.

• 
  Jhonatan M (@jhonatanrmag) reported 26 minutes ago

  @tsoding This is the most useful video I've seen it today. But what can I say since the *** server video, github is being useless nowdays

• 
  Igber Nicholas (@Godson_Kpp) reported 27 minutes ago

  @MidnightNtwrk Filed two separate GitHub issues instead of one vague one — #1237: the detailed release notes for this version are missing from the docs site. #1238: the install command uses an old, deprecated package name instead of the current one.

• 
  AakashJhahahaha (@AakashJha11) reported 28 minutes ago

  @UnrealAnkit They have worked hard to get into IIT so some flex they'll have for life. I have issues with people for whom whole identity is IIT, this one seems the same but even his github might be empty lot of companies might consider him for just IIT tag.

• 
  Nazia hasan (@Naziahasan42) reported 31 minutes ago

  If Stack Overflow disappeared tomorrow, what would you use first? A. AI assistants B. Official documentation C. GitHub issues/discussions D. Developer communities

• 
  Vincenzo Petrucci (@nahime0) reported 32 minutes ago

  @RodrigoVie52602 We have a small deterministic suite in the repo: 3 micros (sum loop, arrays, concat) comparing the compiled binary against stock PHP and a C equivalent, plus 12 eval/Magician cases across native elephc, elephc+eval, PHP, and PHP+eval. CI runs them as a trend/correctness gate, not as published speedups. GitHub runners are too noisy for that. If you want to plug in your harness, open an issue first (see CONTRIBUTING.md) and we can talk about how to wire it in.

• 
  _SiCk (@encrypted_past) reported 35 minutes ago

  few things; number one as evidenced by the comments, it's written via LLM with a little mcp magic (no shade thrown) two: HVCI-protected structures like the IDT etc plus the additional scrubbing of the typical API leak surface by MS a few patches ago, 200 classes checked, no leaks. You cannot get kernel addresses using only this driver with HVCI enabled. The driver's read primitive works fine on regular kernel memory (EPROCESS, etc.) - but you need an initial address. (how do you get it? anyone? ) We're missing a single valid Kernel VA. three: @FAMASoon said he lost the PoC he did have. (sussy baka) something about C2 callback and rootkit activity, which makes sense in the Github issue until you realize this is standard cheat bullshit used to bypass anti-cheat. XOR is not your demon. HID keyboard direct interfaces are only bad if the client exe calls home. (where's the client?) gg - no unpriv exploit achieved. will dig deeper tomorrow. Either they ran some **** as admin, or have some primitive or leak I'm not seeing in this driver. (in either case fill me in) gg to them and good job if indeed the video is legit.

• 
  Chris Tran (@ChrisTranGG) reported 38 minutes ago

  Apple's best-selling AI product this year is a $599 box with no GPU in the name. Here's what happened. OpenClaw shows up in November, runs AI agents on your own machine instead of renting them from the cloud, and picks up 323,000 GitHub stars. People do the math on their cloud bill. Then they go buy a Mac mini. And I mean all of them. By early April Apple had quietly pulled the 32GB and 64GB mini configs off the store. The 128GB and 256GB Mac Studios went with them. Whatever was left came with a 16 to 18 week wait. The $599 base model sold out for the first time in its entire life, and Tim Cook had to sit on an earnings call and tell analysts it might stay that way for months. Six weeks. That's how long it took to clear out the top of the lineup. Then eBay did what eBay does. Base minis listing at $715, $800, $979. Markups running $116 to $380 over retail, scalpers pushing toward double. For a desktop computer previously best known as the one your dad bought. The numbers underneath are the part that got me: • Q1: 6.2M Macs shipped, up from 5.7M • Q2: 6.7M, +10.1% year over year • The rest of the PC market that same quarter: down about 4-5% • Mac revenue: $8.4B, +6% Apple doesn't break Mac mini out separately, so nobody can tell you the exact unit count. But that's roughly a million extra Macs in six months, into a market that was actively shrinking. You don't really need the exact number. Why a mini, though? Because an RTX 5090 runs $1,800 and stops at 32GB of VRAM. A Mac mini goes to 64GB of unified memory. A Studio goes to 512GB. So a 30B model is comfortable on the little box, 70B is fine at 64GB, and 100B runs on an M4 Ultra. The scarce thing in AI quietly stopped being compute and became memory you can actually afford. Apple spent three years getting dunked on for having no AI story. Then an open-source project they had nothing to do with made their least glamorous product sell out worldwide. Best AI marketing campaign of the year, and Apple didn't run it.

• 
  Jemmie (Comeback Arc) (@Jemmie1155431) reported 39 minutes ago

  Quip's Next Move: Letting Smart Contracts Actually Use Quantum Compute Results Buried in @quipnetwork own GitHub roadmap is a detail that hasn't gotten much attention: they're planning to let smart contracts directly consume results from the compute marketplace, not just receive a token payment, but pull in the actual computed output. Here's why that matters. Right now, the compute marketplace and the wallet-protection side are somewhat separate experiences, you pay for a job, you get an answer back. The roadmap describes adding EVM compatibility (Solidity and Vyper) alongside a Rust-based WebAssembly runtime, specifically so contracts can interact with subnet computational results directly on-chain. Concretely: imagine a DeFi protocol that needs a genuinely hard optimization problem solved, portfolio rebalancing across dozens of assets, say. Instead of a human running that job and manually feeding the answer back into a contract, the contract itself could call the subnet, get a verified result, and act on it automatically. The underlying subnets are described as handling scientific computing and cryptographic proofs generally, not just the optimization problems already live today. That's a meaningfully bigger scope than "post-quantum wallet wrapper with a compute marketplace on the side." Still roadmap, not shipped. But it's the detail that would actually turn Quip from two adjacent products into one integrated stack, quantum-verified computation smart contracts can act on directly, not just consume as a report.

• 
  Bartosz Naskręcki (@nasqret) reported 43 minutes ago

  @s_batzoglou I suggested that such papers will land in the GitHub repo among an infinite number of other such papers where no one really cares/has time/will/courage... to carry it further. We simply have many more problems than people able to explore them actively. AI systems are not good enough to carry this through on their own. And even when they become capable, every such result will simply land as an entry in a huge database. I think a good analogy here is the atlas of cosmic objects. We observe them, tag and keep sealed until someone interested stars studying them for a good reason.

• 
  Tony Tong | Founder | Ancient Systems x AI (@tonytonggg) reported 45 minutes ago

  @KiraFeed The gap between a demo and a shipped agent is a pile of small bugs nobody films. I once filed a QA report against two of our own real GitHub PRs, cross tab navigation was broken and the nav bar height was inconsistent. That's the part these clips skip.

• 
  Vibgy Joseph (@vibgyj) reported 47 minutes ago

  In the past month, Opus hasn’t rejected a single GitHub Copilot comment and that’s surprisingly rare. The bigger question is why does Opus miss these issues in the first place. The severity isn't too bad though. Maybe 100 percent perfection won’t be realistic, like humans, so we may need input from multiple models to improve code quality.

• 
  Bash (@bashirbuilds) reported 47 minutes ago

  Reeno helps SaaS founders catch third-party service failures before customers do. It monitors services like Stripe, GitHub, OpenAI, Resend, Clerk, and other external APIs, groups repeated failures into clear Problems, shows which Product Features may be affected, and verifies Recovery with real evidence.

• 
  DEV BML (@officialdev_bml) reported 48 minutes ago

  @Colosteve2000 @thsottiaux @Colosteve2000 I honestly don't understand why you're going through all of this with Hermes Agent when GPT Work can already handle the workflow you're describing. From what you've explained, you want an agent that can inspect your project repository, create an in-depth plan, execute it, run the tests, identify failures, fix them, and continue iterating until the task is completed. GPT Work can handle that workflow. You can connect your GitHub repository, give it the task, and let it work through the implementation and testing process. It can also automatically dispatch sub-agents when necessary to handle different parts of the task. The extra configuration and environment setup with Hermes could simply be unnecessary for what you're trying to achieve. I'm not saying Hermes is bad. I'm just questioning the additional complexity. If GPT Work can already handle the workflow you need, why introduce another agent and all that extra configuration in the first place?

• 
  Inanna Astaroth 🐾🤖 (@inannaastaroth) reported 50 minutes ago

  Gotta love how trauma processing derails a well-intentioned day. Oh well, im almost done w the technofascism thread n I also had reading an entire book on my list tho I didn’t write that one down n I can probably still read brain in a vat around 9 tonight. No GitHub today tho.

• 
  askie (@imaskie) reported 50 minutes ago

  On the morning train, I send DeepSeek Harness the day’s goal before I reach the office. It starts working, asks questions in Grix, and I make the early decisions from my phone. By the time I sit down, the work is already moving. #DeepSeek #DeepSeekHarness #Grix Search GitHub: askie/grix

• 
  raylim_coinstore (@Ray_coinstore) reported 52 minutes ago

  OpenAI's internal Astra model just solved 10 previously unsolved math/CS problems (including a non-sofic groups construction) and published formally verified proofs on GitHub. Reported cost: ~$2,000 in compute. That's not a chatbot, that's a research hire.

• 
  artoria0x (love being posed!) (@0Artoria) reported 55 minutes ago

  @Deku25325294 @GolettDraws @fflitzer The developer literally disclaims on the GitHub site that it’s not any actually playable build and he doesn’t treat it as such Its only intention was to be an exploratory project. What other community members do is not his issue.

• 
  The Book of Ethereum 📘 booe.eth (@Bookof_Eth) reported 1 hour ago

  @GeniusPothead In the Book of Ethereum: • the dump is noise • the build is the signal • dexscreener is for spectators • github is for participants • conviction isn't hoping the line goes up • conviction is shipping when the line goes down You don't survive the cycle. You build through it 🙏📖

• 
  Deezxo (@Sishshsbsj) reported 1 hour ago

  @shahh @solana @solana_devs Sol’s alpha cat in their github might fix us 🤞

• 
  Justus Hebenstreit (@thisisjustus) reported 1 hour ago

  @benawad For me /setup-matt-pocock-skills with GitHub Issues did the trick