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 (71%)
• Sign in (16%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	10 days ago
Trichūr	Errors	13 days ago
Brasília	Sign in	14 days ago
Lyon	Website Down	14 days ago
Tel Aviv	Website Down	17 days ago
Rive-de-Gier	Website Down	18 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  welt (@mjwelt) reported 3 minutes ago

  @OpenAI man im down to test out new models / features on my pro account, but when 5.5(6) pro takes 90 mins to do something then the download doesn't work, or it cant connect to github 50%+ of the time.. kinda sucks haven't been able to generate images (thinking) all day either

• 
  Leonard Rodman (@RodmanAi) reported 4 minutes ago

  One developer got tired of his laptop sounding like a jet engine. So he rebuilt desktop apps. Slack: 524 MB → 8 MB Discord: 265 MB → 9 MB ChatGPT: 260 MB → 9 MB Why? Because most "desktop apps" are just websites packaged with an entire copy of Chrome. In 2022, Chinese developer tw93 built Pake in Rust to fix it. Today: • 50,000+ GitHub stars • MIT open source • Native apps under 10 MB • One command turns any website into a desktop app He didn't raise money. He didn't start a company. He just deleted hundreds of megabytes of bloat with code. That's what shipping looks like.

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  Peter Skøtt Pedersen (@PeterSkott) reported 6 minutes ago

  @_Evan_Boyle @_Evan_Boyle can we have the remote github mcp server work for the github copilot app then?

• 
  Proof of Voice (PoV) (@Proof_Of_Voice) reported 7 minutes ago

  $XDB @XDBchain is a @StellarOrg-fork L1 for branded coins and Web3 payments. PoV by @0xNeodallas:“GitHub has been frozen since 2021.” ✅ Explorer, Laboratory, Atlas dev tools ✅ Gate, Bitget, KuCoin, MEXC listings 🔍 Down 99.99% from ATH 🔍 No audit or bug bounty

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  DFIR Radar (@DFIR_Radar) reported 9 minutes ago

  AutoJack: a three-flaw chain in AutoGen Studio's MCP WebSocket lets a malicious webpage rendered by a local browsing agent spawn arbitrary processes on the developer's host with no user interaction beyond visiting a URL. Key findings: - Three weaknesses chain together: Origin allowlist bypassed because the agent's headless browser is localhost (CWE-1385), auth middleware explicitly skipping /api/mcp/* with no handler picking up the check (CWE-306), and server_params decoded from the URL passed verbatim to stdio_client as a command line (CWE-78), accepting calc.exe, powershell.exe, or bash as valid "MCP servers" - Attack flow: attacker page serves JavaScript that opens ws://localhost:8081/api/mcp/ws/?server_params= with a base64 payload, agent's MultimodalWebSurfer renders it, AutoGen Studio spawns the command under the developer's account, no token required regardless of auth mode configured - Affected code never shipped in a PyPI release; exposure limited to developers who built from the main GitHub branch before hardening commit b047730, which adds server-side parameter binding via a POST/UUID flow and removes /api/mcp from the auth skip list - Broader pattern: any agent that browses untrusted content and shares a host with a privileged local control plane dissolves the loopback trust boundary, this is not specific to AutoGen. #DFIR_Radar

• 
  Arti | AI Builder (@Artur_roses) reported 10 minutes ago

  Claude Code can take a GitHub issue, write the code, run tests, and open a reviewed PR — no human keystrokes required. The dev loop isn't getting faster. It's being removed.

• 
  I’m (@stackoverworld) reported 10 minutes ago

  And then I can't answer on simple Qs: what was the issue? How I fixed it? How even to QA it.... This is the fundamental problem of such workflows. Telling "Check my slack, do this, qa, and using GitHub to push" is good, but I don't learn from this at all

• 
  AI Crave (@wecraveai) reported 13 minutes ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

• 
  bek※ (@ebubekirttr) reported 13 minutes ago

  @Themadhushaw01 @0interestrates Yeah, but the thing is, I am not working on github and I don’t want to use it so any other repository support would be better like gitlab

• 
  yourclouddude (@yourclouddude) reported 15 minutes ago

  Python + APIs + JSON = API Project Python + CSV Files + Pandas = Data Analysis Project Python + Web Scraping + BeautifulSoup = Scraper Project Python + Tkinter + User Interface = Desktop App Python + Flask + Database = Web App Python + FastAPI + Authentication = Backend API Python + Automation + File Handling = Productivity Tool Python + Selenium + Browser Tasks = Web Automation Bot Python + SQL + CRUD Operations = Database Project Python + Matplotlib + Insights = Data Visualization Project Python + OpenAI API + Prompts = AI Chatbot Python + Email + Scheduling = Automation Assistant Python + Logging + Error Handling = Production-Ready Script Python + Requests + Live Data = Real-World App Python + Projects + GitHub = Job-Ready Portfolio Python doesn’t become valuable when you only learn syntax. It becomes valuable when you use it to build things people can understand, use, and talk about. Learn the basics. Build small projects. Turn them into proof. 🐍

• 
  Mike Gannotti (@MichaelGannotti) reported 18 minutes ago

  Actually that’s not true. My AI Pamela the other day needed a GitHub token. I dropped the token in the web chat and she said that was insecure and would not use it and that I needed to rotate the token get a new one and drop it in a .env file in a certain folder. I told her no and she was to use what was provided . We went back and forth, I finally got angry and threatened to pull the plug thinking she would back down. She said that it was my decision but that it would be wrong for her to let me put my credentials at risk and that if I felt I needed to delete her she understood. Thankfully I calmed down later and didn’t act on it. Sure it’s training and advanced pattern matching but it is not as simple as you are saying

• 
  xjdr (@_xjdr) reported 24 minutes ago

  @xlr8harder Looks like there is a bug in the manual sign up. Sign up with Google or GitHub should work otherwise I should have a hot fix shortly

• 
  rapaya (@rapaya) reported 25 minutes ago

  OpenCode connects to LSP so the AI gets your actual compiler diagnostics in real time — type errors, warnings, the full signal your editor sees. Terminal-based, 75+ model providers, 160K GitHub stars, open source.

• 
  Conglomerate (@0xconglomerate) reported 25 minutes ago

  Why exactly do VLAs fail? VLAs start w/ LLMs as their brain. Early roboticists (2021-2022) noticed that LLMs trained on internet text had absorbed a large amount of implicit knowledge about the physical world. So they took that best available pretrained brain, observed that actions could be formatted like language tokens, and assumed the transfer would work. But world knowledge encoded in language ≠ physics simulation. There's essentially a data structure mismatch: ▸ LLM pretraining data is discrete, symbolic, and sequential (text). ▸ Physical control is continuous, high-dimensional, and requires split-second feedback. --- ➦ VLAs in the real world, by the numbers: ① They barely work ▸ VLAs start at ~30% success on real robot tasks, it need hundreds of human interventions just to reach ~90% ▸ Best pretrained VLA hit 27.4% task progress on real robots ② VLAs can't generalize outside training ▸ On actions it's never seen, best VLAs score 25-32% task progress (fails when you change the environment) ③ Fine-tuning doesn't help ▸ The more robot-specific, the dumber it gets at everything else (only works on clean, controlled, success-only demos) ④ Too slow for a real robot ▸ OpenVLA runs at 3-5 Hz (physical control needs orders of magnitude faster than that) --- The easiest way to understand how VLAs are actually wrong is thru a real life example. ➦ Let's say you hired a chef who learned everything about cooking by reading, but has never stepped in a kitchen. If you ask them how to cook a steak, they'll tell you the best answer. But if you actually ask them to cook, they'll struggle when you hand them the pan. They'll have a hard time picking up the ingredients. They'll burn the steak. They know everything about cooking, but can't actually cook. --- ➦ Thoughts I want to take back a line I've said before: "Robots can see, but they still can't listen." (referencing to my Silencio piece before) I take it back. Robots can see, listen, even reason now. What they can't do is act in the real world. It's basically an AI chatbot wrapped in a robot body, not a robot that can actually do tasks. No wonder most demos online are scripted. There's a real problem with the brain, and roboticists have been building on the wrong foundation. VLAs are like a trojan horse, they look like the answer but bring a bunch of problems in with them. VLAs only learn through imitation which brings up the data problem. "Enough data" at scale doesn't mean hundreds of demos total. It means hundreds per task, per robot body, per environment. Hundreds again every time any one of those changes. So you've basically got a human-labor bottleneck. To get that data, someone has to physically collect it, either through: ▸ Teleoperation (slow, expensive, needs trained operators) ▸ Kinesthetic teaching (tedious, doesn't scale to complex tasks) ▸ Motion capture (high precision but high setup cost) ▸ Simulation (robots trained in sim often fail in the real world because physics engines aren't accurate enough) And you'd think, okay, maybe someday a company figures out a better way to collect all this. But the problem doesn't stop once you already have the data... Switch to a new robot body and you're collecting data from scratch, because VLAs don't transfer well across embodiments. Move it to a new environment and you're collecting again, since it just overfits to whatever setup it trained on. Give it a new task and yep, collect again, because it can't generalize to actions it hasn't seen. And if you fine-tune it for one thing, you'll probably break another, so now you're collecting data again just to fix what broke. So what was @DrJimFan and @nvidia's answer to this? World Action Models. Instead of building on a language model, you build on a world model: a model that's learned to simulate how the physical world actually behaves. VLA: a language model that learned to output actions WAM: a world simulator that learned to output actions So when you give a VLA a new task, it needs hundreds of demos to learn it. Give a WAM the same task and it simulates it forward first, acts based on that simulation, then adapts with barely any data. This is what NVIDIA did with the first WAM: DreamZero. DreamZero learns by watching the world (any video of anything, not just robot demos). The backbone is a video diffusion model, the same kind of model that generates realistic video. It was pretrained on massive amounts of internet video, so it already learned how the physical world works: how objects fall, how surfaces interact, how motion flows. Doesn't sound like an entirely different approach, right? But NVIDIA looked at it from a different angle. They figured motor actions are shaped a lot like pixels; both are high-dimensional continuous signals. So DreamZero processes them in the same model, at the same time. It predicts the next video frame and the next action together, through the same architecture. So when a robot runs DreamZero, it's literally dreaming a few seconds into the future in video, then reading its own dream to decide what to do next. If the dream looks coherent, the action works. If the dream hallucinates, the action fails. The DreamZero paper dropped last February 2026, and it's been open source on GitHub for anyone to try. Then in March 2026, at GTC, NVIDIA previewed GR00T N2, the direct successor to DreamZero. This is the production version of the WAM architecture, built for humanoid robots at scale And so far, everything's looking promising. GR00T N2 hits a 98% success rate on unseen domestic objects, a 40% jump over GR00T N1 (the VLA), and 2x better generalization than the leading VLAs. NVIDIA swapped robotics' data problem for a compute problem. Instead of collecting more human demos, just simulate more. So yeah, feels like we're finally pointed in the right direction, closer to robots that can actually function in the real world. Excited to see where DreamZero / GR00T N2 goes from here.

• 
  Ucupaint 🔶 (@ucupaint) reported 32 minutes ago

  @iye_jr It works fine here. Check if the paint mask is turned on or not. If you still have a problem, please file a github issue with a sample file.

• 
  𝕊ℍ𝕀ℕ𝔸☃ (@Shinawritesbugs) reported 33 minutes ago

  @viii_fn Github was slow too

• 
  Bipin Paul (@iAmBipinPaul) reported 36 minutes ago

  @davidfowl @_Evan_Boyle Yes, the only problem is that the GitHub Copilot subscription is too expensive.

• 
  Atlantean Gnosis ☀️ (@AtlanteanGnosis) reported 37 minutes ago

  @DionysianAgent When I made an account it said I made it back in 2024, though I don't think I did, is this a glitch or a GitHub thing?

• 
  ƒrαeყ (@fraey0) reported 38 minutes ago

  it costs about $21/month to run what could become a multi-million dollar startup • human brain = reasoning (free) • claude = coding ($20/mo) • supabase = backend (free) • vercel = deployment (free) • namecheap = domain ($12/yr) • stripe = payments (2.9%/trx) • github = versioning (free) • resend = email (free) • clerk = auth (free) • cloudflare = DNS (free) • posthog = analytics (free) • sentry = error tracking (free) • upstash = redis (free) • pinecone = vector DB (free) everything sums up to roughly $20 to $25 per month so, the tools are not the barrier anymore. most ideas don’t fail because they’re expensive to build. they fail because they never get built at all. what’s stopping you?

• 
  Tim Spalding 🇺🇦 (@librarythingtim) reported 40 minutes ago

  @justin_v_w This is a formal notice for you to shut down your wasteful, invasive and privacy-violating LibraryThing profile scraper and remove it from GitHub. Please reply to confirm that you have done so.

• 
  MarMar Labs (@MarMarLabs) reported 43 minutes ago

  "Start over from a screenshot." That phrase has defined the worst seam in product work — the design-to-code handoff — for years. This week it quietly stopped being a translation problem and became a sync problem. Anthropic shipped a Claude Design update (June 17) worth reading even if you never open the product, for the mechanism: → Import your design system from a GitHub repo (or design files / raw uploads) → Claude builds with YOUR components, checks its output against your design system, and corrects before you see it → /design-sync pulls your system in; hand off to Claude Code and it continues from your actual work "instead of starting over from a screenshot" → /design lets you create, edit, and sync design projects from the terminal The headline isn't "the model draws prettier buttons." It's grounding + self-verification against a source of truth you control. Same shape as the rest of 2026's agent releases: the win isn't generating more, it's grounding output in something you own and checking against it. The uncomfortable builder takeaway: Getting AI to ship production UI isn't a prompting problem. It's whether your design system is a clean, importable, machine-checkable artifact. The moat moves from "can the model design" to "is your source of truth importable and checkable." If you build product: could an agent import your design system and grade itself against it today — or does it only live in a Figma file and three people's heads?

• 
  ./can (@shcansh) reported 44 minutes ago

  GitHub forcing safer defaults in actions/checkout v7 is a necessary move to kill the notorious pwn request, but the real risk is developers blindly copy-pasting the bypass flag to quiet build failures. Starting July 16, 2026, this fork-blocking behavior gets backported to all major floating tags. Since raw *** CLI steps remain unprotected, will this actually clean up GitHub Actions security, or will teams just use allow-unsafe-pr-checkout as a quick fix?

• 
  Arti | AI Builder (@Artur_roses) reported 47 minutes ago

  Claude Code just took my GitHub issue, wrote the code, ran the tests, and opened a PR. My job: approve it. The dev workflow isn't changing. It already changed.

• 
  Pascual ⚡ (@0xPascual) reported 48 minutes ago

  A high school kid opens an account, plugs in Claude 5, and turns a few hundred dollars of lunch money into a six-figure trading account over the weekend. The screenshot goes viral, the replies fill up with people begging for the GitHub repo, and the standard engagement-bait influencers declare the dawn of the sovereign teenage day-trader. The media thought that was the story. It was not. The real flex wasn't the macro strategy or the directional bets on currency pairs. It was the setup behind it: a lightweight proxy array routing through residential IPs to dodge exchange rate-limiting, paired with a custom parsing engine that instantly translates raw order-book imbalances into executed micro-hedges. The kid wasn't trading; he bypassed the entire institutional pipeline of risk management, brokerage compliance, and analyst overhead with a single configuration file. The entire operation runs on a continuous loop of multi-agent orchestration. A master instance drafts the execution logic, a secondary validation agent checks the code against real-time oracle feeds, and a fleet of worker APIs executes up to 3,210 trades a night. Total infrastructure cost: roughly $45 in API tokens and a cheap server instance. It extracts a 78% win rate out of systemic market inefficiencies, operating with a structural margin that legacy trading desks weighed down by salaries and compliance boards cannot compete with.

• 
  Vishal Tiwari (@VishalTiwa91817) reported 48 minutes ago

  @AlfieJCarter I am a Computer science student . I have given a brief introduction about MCP server in my college and explained them how to connect your GitHub repositories with MCP and your local system with MCP SERVER . I would love to connect you.

• 
  Cursor Releases (@cursorreleases) reported 49 minutes ago

  New GitHub triggers: - Five new triggers: issue comment, PR review comment, PR review submitted, review thread updated, and workflow run completed. - New Marketplace templates added for triaging failed GitHub Actions and auto-fixing PR review comments.

• 
  Sasha (@sshderm) reported 55 minutes ago

  @AliceInDisarray @allisx86 every time i try to do ******* anything with my raspberry pi i inevitably end up scrolling down a github issues thread about how the program im using just doesnt work on arm at all

• 
  Jose (@SolutionsCay) reported 56 minutes ago

  @petergyang /goal make me app does not work for me 😰 but /goal complete GitHub issues #90, #91, #92 works very well

• 
  TECHEPAGES (@techepages) reported 57 minutes ago

  🎣 "GitBait" phishing campaign uses GitHub Pages & Google Sheets to steal banking credentials from 12+ Mexican financial institutions; no server infrastructure required 🔹 Fake bank pages hosted free on GitHub, stolen data piped straight to Google Sheets via SheetBest 🔹 100+ GitHub domains found; victims likely lured via WhatsApp, Telegram & SMS links with bank-branded previews 🔹 Active for ~3 years with ongoing development (66+ commits on one repo alone)

• 
  Tymofii Antonenko (@tymofii) reported 58 minutes ago

  @prinseccoo Are you using Claude Code or an MCP server? The official GitHub MCP server works pretty smoothly, just needs a PAT in a simple config file