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	19 hours ago
Montlhéry	Website Down	2 days ago
Aulnay-sous-Bois	Website Down	2 days ago
Saltillo	Website Down	2 days ago
Granada	Website Down	2 days ago
Vernon	Website Down	2 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  sid riff (@sidriff) reported 5 minutes ago

  Some notes on what didn't go so well, possibly some feedback for SpaceXAI. 1 - Context window management. As far as I can tell, there isn't any in the app builder. When the project gets more complicated, the agent tends to truncate or wipe files. You can nudge it to split large files which tends to help. 2 - Saving progress is generally cumbersome. Pushing to Github is clunky and sometimes it fails. That makes issue #1 extra terrifying. So far I've not encountered a state I couldn't recover from. 3 - All of these app builders always spring for React/R3F and in my experience it always gets in the way. There's some obvious stutter with the faster moving pieces, even on my 240hz display. I'm guessing once I rebuild it in straight three.js, that will go away. 4 - Sound. It's always the hardest part. I have some workflow solutions that involve existing libraries/elevenlabs but I was trying to stick with app builder and I wasn't willing to paste my API key in the chat! Disappointed that Build mode doesn't have access to the Grok Voice API (but it can get to Grok Imagine). All in all, I'll probably stick with Grok Build in the terminal. But build mode makes it so easy to get up and running with an idea.

• 
  Cubby (@rorshockbtc) reported 6 minutes ago

  Bad news: I decided to push a bug update to GitHub and this triggered DNS things with Namecheap, so Marigold is down. Good news, I've run through the main things I want to hit in the demo in about 30 minutes and we should be good, I just have to run the site locally.

• 
  Phil | Rentier Digital Automation (@rentierdigital) reported 7 minutes ago

  expensive encryption defeated by asking nicely. turns out the vault just needed a weaker key a team found that smaller models from the same family can read the encrypted reasoning of bigger ones without breaking a sweat. feed Opus's encrypted thoughts into Haiku and it transcribes them in plain text. no jailbreak needed, the strong model never gets touched they pulled 6,708 real sessions from GitHub and HuggingFace. 315,320 blocks of reasoning decoded. 182 credentials fell out: API keys, passwords, access tokens, private keys if you've ever shared a Claude Code or Codex session publicly there's probably personal data hiding inside the reasoning, not the visible chat the real problem isn't that one model broke. it's that the security of a model family is capped by its weakest member and nobody threat-modeled the weak one bc it looked harmless that's a structural gap, not an accident i build and ship daily. Claude Code, Codex, whatever ships fastest. SaaS, tools, automations. ⭐ if AI can build it, i've probably broken it first. what works → link in bio

• 
  im dem! im deer! im M☆D (@demdemdemdem__) reported 9 minutes ago

  @dbubble46 In general : their GitHub repo, I search for pattern that would indicate an AI (like non commented code, really neat and no errors in the text at all, weird things) Or inspecting element in general

• 
  MaMFlux (@MaMFLux) reported 10 minutes ago

  @startupideaspod Once again, I was misled by this profile’s framing. I opened an article titled “How to build an AI-Native Company in 2026” expecting an actual explanation of how the system was built. Instead, I found beginner-level advice—document your goals, provide context, keep approval gates and use smaller models—wrapped in a story about 34 agents. An org chart with agents named Simon, Phoebe and Toby is not an architecture. “Do smart things” is not an agent recipe. Saying that the agents can access Gmail, Calendar, Notion, Stripe, Supabase and GitHub does not explain how any of it works. To genuinely fulfill the title, the article would need to provide: - The agent harness and orchestration platform - The complete system and deployment architecture - How agents are defined, instantiated, versioned and isolated - Their system prompts, roles, tools, permissions and boundaries - How tasks are created, scheduled, delegated and routed - How proactive behavior is triggered - How agents communicate without loops, duplication or conflicting actions - How priorities, dependencies and escalations are handled - How Gmail, Calendar, Slack, Notion, Stripe, Supabase and GitHub are actually integrated - The authentication, authorization and secrets-management model - How personal, business and customer data are isolated and audited - The context-retrieval architecture and source-ranking rules - How context is refreshed and stale or conflicting information is resolved - The working-memory and persistent-memory design - How the personal wiki is structured, indexed and governed - How human approval gates are technically enforced - Which actions agents may execute and which they may only propose - Timeout, retry, rollback and failure-recovery mechanisms - Defenses against prompt injection and unsafe tool use - Logging, tracing, alerts, cost controls and the claimed mission-control interface - Evaluations and acceptance criteria used to verify agent output - Model-selection, fallback and escalation policies - Actual token, model, infrastructure, integration, maintenance and supervision costs - Measured productivity before and after introducing the agents - Evidence supporting “every hire costs close to zero” - Evidence supporting the “top 1%” or “top 0.5%” claims - At least one complete workflow with prompts, configuration, inputs, outputs and failure cases - A repository, template or other reproducible implementation The “software factory” is equally vague. We are told that login, payments, social sharing and newsletters were handled, but we are shown no stack, components, pipeline, code, deployment process or operating model. The uncomfortable truth is that “Do smart things” can work only after someone has solved all the difficult problems involving context, memory, orchestration, permissions, evaluation and governance. Those missing details are precisely what an article promising to teach us how to build an AI-native company should explain. This does not teach readers how to build the described system. It makes them spend time reading basic guidance for beginners while borrowing technical credibility from an unexplained “34-agent workforce.” A more honest title would be: “Basic management suggestions for people beginning to experiment with AI agents.” Just another clickbait.

• 
  Umair Ali (@buildwithumair) reported 12 minutes ago

  Solo Startup Founders Pack - Codex = coding. ($20/mo) - Supabase/Convex = backend. (Free) - Vercel = deploying. (Free) - Polar = payments. (3.9%/transaction) - GitHub = version control. (Free) - Resend = emails. (Free) - Clerk = auth. (Free) - Cloudflare = DNS. (Free) - PostHog = analytics. (Free) - Sentry = error tracking. (Free) - Upstash = Redis. (Free) - ShipClaw[.]io= ai agents ($14/wk) Total monthly cost to run a startup: ~$50 There has never been a cheaper time to build .

• 
  avery (@codeshaunted) reported 14 minutes ago

  @0xblacklight github down?

• 
  Artem Zakharchenko (@kettanaito) reported 18 minutes ago

  @oleg008 @threepointone I suppose you're right. It did have big impact on companies, but in a completely negative way. Smaller companies were confused and utterly incompetent even before AI so we can disregard them in the context of this discussion. Medium-size companies keep shoving AI into everything without any vision or reason. And large companies have been hit the worst. They've been played *****. Failed investments, huge reputation impact, and, the saddest thing, the quality of software has nosedived significantly. From Windows to GitHub to macOS/iOS—I've never seen software being in a more sorry state than today. They might be shipping ten times more things (they aren't), but none of that matters if everything is broken.

• 
  Bárbaro Javier (@cubancodepath) reported 22 minutes ago

  GitHub SSH is down, one more time. Another day, the world is getting worse since the IA.

• 
  🇹🇼 Phillip Haydon 🇹🇼 (@philliphaydon) reported 23 minutes ago

  @sameenkarim @github Nice, adding features instead of fixing problems. Go fix the bugs in stacked prs instead of working on this ****.

• 
  Yumzlef (@Yumzlef) reported 23 minutes ago

  4.3 MILLION AI REPOS ON GITHUB. THE ONE THAT COSTS YOU MONEY ISN'T THE ONE YOU FORKED - IT'S THE ONE YOU FINE-TUNED most repo lists rank by stars. stars tell you what's popular. they don't tell you what you're allowed to ship, what runs without a GPU, or where a pull request actually gets merged. so the catalogue got rebuilt around four questions instead 40 repos out of 4.3 million, sorted by what they cost you: > primitives - micrograd, ~100 lines, zero dependencies, no GPU. delete the backward pass and rewrite it. reading it teaches you almost nothing; rebuilding it teaches you everything. > local runtime - point base_url at localhost:11434 and the same client code keeps working. a 4-line diff kills the API bill. but running is not serving: vLLM wants a GPU, llama.cpp doesn't. > contribution lane - the review queue on a 23k-star main branch is the bottleneck, not your code. torch_geometric.contrib has lighter review and the same reviewers. > licence class - this is the one that bites. here's the part nobody checks until legal does. AGPL-3.0 has a network clause: serving a model over an API counts as distribution. and Ultralytics states it plainly - trained and fine-tuned models fall under AGPL-3.0 by default. your weights are not your weights. they inherit the licence. it goes further than most people assume. internal-only use still triggers it. a private parking-lot detector, never sold, never published, still requires either an enterprise licence or open-sourcing the entire surrounding project - scripts, configs, backend, and the weights themselves. the fix isn't to avoid the ecosystem. it's to build against the permissive interface and swap the detector underneath, so the AGPL part stays replaceable instead of load-bearing. star count is a popularity metric. licence class is a business decision, and it's sitting in a file most people never open. bookmark this before your prototype turns into a product. which licence have you actually read end to end - or is it still the file you scroll past? i take apart the repos people build on and show what they cost before you commit.

• 
  Mark yu (@Markymarco34) reported 23 minutes ago

  Yes, the network is currently running and the signer set is healthy. But current operational health does not mean the Aug. 12 stall has been permanently resolved. The related GitHub fix is still open and unmerged.

• 
  Andy Hawkes (@namboozle) reported 29 minutes ago

  Is GitHub down again 😬

• 
  Brown Geeky kid (@shigma_male) reported 32 minutes ago

  GitHub dropped a release candidate for Enterprise Server 3.22. It’s all about enterprise features these days—meanwhile, devs are probably just waiting for Copilot to write these changelogs itself.

• 
  🎱 (@aldea_trading) reported 36 minutes ago

  @zocomputer Like popular login pages does not have Google login page ?? Like wtf most of **** is already logged in through Google or github account

• 
  𝑨𝑯𝒂𝒑𝒑𝒚𝑺𝒐𝒖𝒍 (@IExist__Still) reported 37 minutes ago

  5/10 — The Vanishing Coding Projects ******************************************** Sushant was openly coding algorithms, working on mixed-reality (VR/AR) systems and developing AI-driven projects. Where are those hard drives? Where are the GitHub repositories, local server backups and proprietary algorithms he worked on in his home lab? 74Months Injustice To SSR #JusticeForSushantSinghRajput𓃵 #ArrestRheaChakraborty.

• 
  Shyamprasad reddy (@Shyam_JSP_) reported 38 minutes ago

  @DattuClay @github Last month issue start ayina 4 hours ki update chesaadu andaru vachi meedha padtharu too many issues from last 6 months

• 
  Chris | The DeFi Professor 🇵🇭🇺🇸 🐊 (@csoriano) reported 39 minutes ago

  *sigh* Another phishing attempt. Get me on a legit Google Meet, hear about what I do with my content and my story in this space, then introduce their content but ask me to clone the GitHub repo and run the app. Yeah, don't do that. @tarsprotocol you've got people out there. Fix it. @SolanaFndn

• 
  Aakash Gupta (@aakashgupta) reported 40 minutes ago

  There is an entire genre of content about how to write the perfect CLAUDE .md . Front matter debates. Line count debates. What to put in, what to strip out. Oji Udezue's scaffolding skill generates one per project, as an output. Not a template you adapt. A CLAUDE .md written for this specific codebase, that knows how to hunt bugs in it, follows the folder patterns it just created, and follows the prototype patterns it just set up. Then it does the part people miss. It writes the folder structure into that same file. Where documents go. Where milestones go. Where prototypes go. For every folder, what belongs in it, addressed to both you and Claude at the same time. That makes the repo self-organizing. The instructions for maintaining the structure live inside the structure. Nobody has to remember the convention, because the convention is loaded into context every session. Same pass sets up the rest of the scaffolding most vibe coders skip entirely. A test folder. Continuous integration, so every check-in runs the tests. Security, including finding your secrets and getting them gitignored before you ever push to GitHub. Then a bug classification system that catalogs failures over time and learns from them. That last one is the sleeper. Everyone treats bugs as things to fix and forget. Cataloging them turns your own failure history into context the model can read on the next project. Worth saying what he did not claim about any of this. On the market research the same skill produces, he was direct: you can get 60 to 70 percent of the way with LLMs before you go find real sources. He said the danger in the whole workflow is taking it as gospel, and that he does not fully trust LLMs 100 percent, so he reads every output. A skill that produces this much artifact this fast only works if someone is still reading it. The CI templates and the CLAUDE .md pattern are the pieces this offloads best, because they are the pieces most people either get wrong or never do at all. Your CLAUDE .md was never supposed to be a thing you author once and defend. It is infrastructure, and infrastructure should be generated.

• 
  pseudo 🇺🇦 (@pseudotheos) reported 41 minutes ago

  GitHub goes down more than solana at this point

• 
  Fernando (@Franc0Fernand0) reported 43 minutes ago

  Renaming an API field feels harmless from the inside. From the outside it takes down every dashboard that expected the old name. Every API change falls into one of two buckets, and the bucket decides everything. If you do breaking changes, you have to bump the version: removing or renaming fields, adding required parameters, or changing what an endpoint fundamentally does. Safe changes can ship: adding optional fields, new endpoints, or making things faster. Most of the pain comes from the two opposite mistakes. Version nothing and every release becomes a gamble for your users. Version everything and you end up maintaining five versions while developers can’t tell which one to use. A few simple habits keep the balance: - Put the version where people can see it (/v1/ in the URL, the way Stripe and GitHub do). - Use semantic versioning so a major version bump is the signal that client code needs changes. - When you retire a version, say it clearly in the response (Sunset header) and give people 6–12 months to migrate. An API version is a promise about what won’t change. Break it rarely and loudly, never silently. Which mistake have you seen more often?

• 
  Petri Kuittinen (@KuittinenPetri) reported 45 minutes ago

  Today I am removing 5 tool calls from Ainiux agent mode, based on actual analysis of how tool calls are used in real life applications. I could have removed even more if I would not later plan for Windows compatibility. Deleting is always both mentally hard and scary. My personality is to be like own a library of everything and have a backup plan (and back of a backup plan). I hate to give up a book, even if I never read it. And if you worked hard for something, it is not easy to just cut it out. But just like good film makers, you must do it. Cut the excess, which is not needed to make a better product in the end. Ainiux code indexer had at one point a full call tree - a bit like tree-sitter but self made. The code for that was very complex. But I noticed that updating the comprehensive index was slowing things down, not just in compute, but it added 50-100% tokens to many agentic tasks - a huge bloat! That is why I decided to make code index fully optional. If you don't want it, Ainiux then doesn't use it and if indexing is on, you probably don't notice it. Now the code indexer is super fast, fuzzy, but doesn't make a full call tree. In other words it doesn't know what other parts of code call a certain symbol (function, method, class constructor). Even such "dumb" indexing is sometimes useful: Let's say you have a large existing code base. Million lines of code (LoC) or more. Feeding that to most cpdomg agents will be costly and probably take a while. Ainiux just uses its own indexer in C++ and scans in a second or couple of seconds. It builds an outline for the LLM to see it. Not much any tokens spent and the model already has a good image, so it needs less read file, glob and grep style calls. End result is that I can give Ainiux a very large code base and ask multiple deep questions about it and I get the answer to all those questions in less than 10 minutes and the number of fresh tokens spend it ridiculously low, 100k or something, even though the code base itself would be 10+ million of token. And the cost with Deepseek models is also close to zero. Want to learn what a code base has inside it? Steal its best ideas? Find the vulnerabilities? Find the dead code? Find what could be optimized? Just install Ainiux and /index-code and start talking to the agent to get the answer you want from the code base. You might be surprised how fast it is. Compacting the context in Ainiux can also happen in an instant depending what compact strategy you use. This screenshot is not a fake. It really took zero milliseconds, as times lower than 0.5 ms are reported: 0 ms. Press enter, and the thing is done, as optimized C++ can be so fast. PS. New version of Ainiux coming to Github later today. Less tool calls, but smarter tools. Less fat, more lean.

• 
  nadavdas (@sadvadan) reported 45 minutes ago

  memstruct won’t get a github release soon. tests are complete & no changes planned, but real-world use in an ongoing project will potentially surface nuanced edge cases that need fixing first. after all it's a framework so errors, even if minute, can be cascading.

• 
  The obonigwe (@ogbonigwe1) reported 48 minutes ago

  blacksmith and github are down again. i don tire

• 
  Simon Davies (@sidavies_says) reported 50 minutes ago

  @maxktz Downtime? Non existent issue with github. I'm interested in origin though as repos likely need a rethink based on how we're all working now

• 
  Abi (@abh3i3) reported 51 minutes ago

  While trying to fix it, I checked old GitHub workflow actions from a year ago. Seeing those 1-year-old actions actually made me smile—it’s officially been a year! But I was still stuck trying every way to solve the issue without a clue what was wrong. (3/5)

• 
  Liberation Here Now (@RayThisLife02) reported 52 minutes ago

  Someone AI-checked GitHub lately? Ongoing state-level attacks, and GitHub is owned by Microsoft. It's a step-by-step approach, a slow death of privacy and decentralization...

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  Ràv3n... (@Rav3nlaud3) reported 52 minutes ago

  PHASE 2: I couldn't build as an engineer without proper version control, and I learned quickly that setting this up correctly is non-negotiable. Installed ***: I used pkg install *** to grab the industry standard for managing my project history. Secured My Keys: Instead of fighting with passwords, I generated a GitHub Personal Access Token (PAT). Think of it as a high-tech digital key that keeps your account safe. Stayed Logged In: I ran *** config --global credential.helper store. It cached my login so I only had to enter that token once.

• 
  athas.dev (@athasdev) reported 54 minutes ago

  @Coobyk_ Yeah 100% I'm using GitHub a lot for Athas. Tags, releases, actions, discussions, issues, PRs, etc. I can't do all of them on the CLI. Even if I do, it's hard to interact there. I need a GUI better than their current web app :( I can maybe add some of these to Athas, idk.

• 
  Pere Presh (@Pere_presh) reported 60 minutes ago

  One of the things I've learned while building a deployment platform is that "deploying an app" is actually a collection of engineering problems. The GitHub integration is probably the easy part. A real deployment needs to answer much harder questions. What happens when the build fails? How do you isolate builds from each other? How do you handle environment variables and secrets? How do you know an application is actually healthy after deployment? What happens when a process crashes? How do you stream build and runtime logs without turning the logging layer into a bottleneck? How do you provision PostgreSQL and Redis without making the developer think about the underlying infrastructure? How do you enforce CPU, memory and storage limits? What happens during a failed deployment? Can you roll back safely? What happens when the machine running the workload becomes unavailable? Then there is observability. You need to know what the application is doing before the customer tells you something is wrong. That is the part I'm enjoying most about building ProStack Deploy. The product looks simple from the outside: Connect GitHub → deploy. Underneath that button is an entire systems engineering problem. We're still building it, and there are plenty of things I want to improve before calling the platform production-ready at scale. But getting a real project to deploy on infrastructure I've built myself is a very different feeling from simply writing the deployment code. It makes the architecture real.