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	9 days ago
Trichūr	Errors	13 days ago
Brasília	Sign in	13 days ago
Lyon	Website Down	13 days ago
Tel Aviv	Website Down	17 days ago
Rive-de-Gier	Website Down	17 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Anjula Dwivedi (@HeyAnjula) reported 38 seconds ago

  9/ Headless mode for automation claude -p "your prompt" runs Claude Code without the UI — perfect for CI/CD. Auto-fix lint errors on every push. Triage new GitHub issues. Generate release notes. Claude Code isn't just a tool you talk to. It's a tool your pipeline talks to.

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  DFIR Radar (@DFIR_Radar) reported 7 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

• 
  ./can (@shcansh) reported 7 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?

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  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 9 minutes ago

  the problem with jumping from github to gitlab is constantly having to retrain your brain to call it "merge request" instead of "pull request" 🥲

• 
  John D. Clay (@JohnDClayAuthor) reported 10 minutes ago

  @XFreeze I tried out the new update to Grok Build last night and put it to the test. It helped me go back to a far previous session, it actually has all sessions in a nice area to look at and choose from. I challenged it to fix a broken framework I had built with the earlier versions of Grok Build and with the help of @grok too. I had published it a couple weeks ago and it was not working well. But now after a couple prompts... clayforge the first ai-matove framework for multi agent UI's. You should check it out if you are coding with AI. It's on GitHub.

• 
  SOURAV PANDA (@i_d_skp) reported 12 minutes ago

  Scenario: You accidentally committed a plaintext database password to GitHub in a .tf file. Fix: Nuke the commit history immediately! Use environment variables (TF_VAR_db_pass) or fetch secrets dynamically at runtime from AWS Secrets Manager or HashiCorp Vault. 🔑 #Terraform

• 
  Alpha Batcher (@alphabatcher) reported 14 minutes ago

  David Soria Parra: "2026 is all about connectivity, and the best agents use every available method" A coding agent needs access to the same places you check while building: - repo and PRs - docs - browser - database - error logs - Figma - tasks - payments The article gives the 11 MCP servers for that setup: - Context7, GitHub, Playwright first - Supabase or Neon, Sentry, Firecrawl next - Figma, Linear, Stripe when you need them - Filesystem, ***, Memory, Sequential Thinking as the base Read it if you keep copying code, docs, schemas, screenshots, errors, and tickets into Claude Code by hand

• 
  Rich Kuo (@richkuo7) reported 15 minutes ago

  i use this in my claude.md for my open source project as long as the agent follows it, i have some reference for quality and keeps PR's clean LLM: <model> | <effort> | Harness: <action> - Final line of the artifact; occupies the default Claude Code attribution slot. - No Co-authored-by / Co-Authored-By trailer. - <model>: actual model (e.g. Opus 4.8). - <effort>: medium/high/xhigh, default high. - <action>: Claude Code for interactive sessions, else the skill/agent that ran (e.g. commit-push-pr, agent). - PRs: reference the issue with Closes #<N>; in GitHub comments use 1. not #N for list items (avoids auto-linking).

• 
  Conglomerate (@0xconglomerate) reported 16 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.

• 
  Grishin Robotics (@GrishinRobotics) reported 17 minutes ago

  AI made coding faster. Devplan raised $2.5M to fix the coordination drag that shows up after the code is written. AI2 Incubator led the seed round, with Acequia Capital, Mighty Capital, Grand Ventures, and eLab Ventures participating. Chris Bee and Anton Safonov are building Weaver, a product knowledge graph that connects GitHub, Jira, Linear, Slack, Notion, Google Workspace, meeting notes, and customer feedback. The pitch is that product and engineering leaders should not need another status meeting to learn what changed, what slipped, or why a decision was made. This is a different wedge from coding copilots. Devplan is going after the organizational memory around the code: requirements, risks, decisions, blockers, and customer signals. The company says early users save eight hours a week on coordination, and its own benchmark answered moderately complex queries almost 2x faster and more than 3x cheaper than a standard Claude plus MCP setup. Quick facts👇 ● founders: Chris Bee; Anton Safonov ● total capital raised: $2.5M disclosed ● HQ: Seattle, Washington ● Investors: AI2 Incubator; Acequia Capital; Mighty Capital; Grand Ventures; eLab Ventures The next productivity bottleneck may be less about code generation and more about whether teams can keep shared context intact while AI speeds everything else up.

• 
  Pedro Pellerini (@pepeller) reported 21 minutes ago

  If Mythos/Fable is so great why are there still 8386 open Github issues in Claude Code repository.

• 
  Manu | 🥥 (@ManuAF6) reported 22 minutes ago

  4/ New GitHub triggers + Marketplace templates New triggers: - Issue comment - Inline PR review comment - Full PR review submitted - Review thread resolved/unresolved - GitHub Actions workflow completed

• 
  ƒrαeყ (@fraey0) reported 23 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?

• 
  Kevin Tabet (@TabetKevin) reported 24 minutes ago

  @upstash Hey guys i think login with github is broken can't log in rn will try later. google works email i dont have

• 
  Ant A. 🇺🇸 (@AntDX316) reported 28 minutes ago

  @thsottiaux When I need to fix up a GitHub Repo through the Smartphone, I prefer Claude Code though because it doesn’t need a device to run the repo, but if it needs to run a repo on a device due to the limitations through the Smartphone, I use Codex Mobile or OpenClaw with GPT-5.5 through Telegram.

• 
  Trifon Getsov (@trifon_getsov) reported 41 minutes ago

  @thdxr Top down works until the individual outgrows it. GitHub didn't win because companies adopted it first. It won because developers wouldn't go back once they'd used it.

• 
  Red Zen Cloud LLC (@RedZenCloudLLC) reported 41 minutes ago

  Cursor's Origin platform and Claude's GitHub imports both solve the same problem: developers automating code work need their tools to understand context, not just generate tokens. The winner isn't the smartest model—it's whoever reduces handoffs between agent and human.

• 
  Threadripper (@threadripper845) reported 45 minutes ago

  Nobody: Me: I'll gladly accept this high-responsibility open source maintainer role for zero compensation. Now I spend my weekends answering angry GitHub issues from developers who don't know how to read the README file.

• 
  KS Sreeram (@kssreeram) reported 49 minutes ago

  @Lidinwise @leecronin Given that AI coding is all the rage… What is your hypothesis on why the following is true? AI is unable to create even _one_ open source project that’s good enough to enter the top one-thousand open source projects (say on github), with ZERO involvement of humans from birth of idea. Imagine the prompt being something like “Come up with a great idea for a new open source project and implement it”. AI is unable to do any such thing with zero human involvement. My answer on why: Every project in a top 1000 list is a hit. Every hit is a mini-invention of sorts. It is necessarily “out of distribution” is some way. AI is unable to do this because we don’t know how to solve the problem of invention.

• 
  Elvis Irhaye (@viii_fn) reported 49 minutes ago

  Is GitHub down or it’s just MTN trying to ruin my career?

• 
  bek※ (@ebubekirttr) reported 49 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

• 
  Akinsete Motunrayo (@Harkinsete) reported 50 minutes ago

  I built my entire personal brand with AI and a clear process. Here is exactly what I built and how I did it, because you can do this too. What I Built ✅ Brand Strategy (mission, vision, values) ✅ Visual identity: colors, fonts, logo, brand guidelines ✅ A full pitch deck (12 slides) ✅ A speaker kit PDF ✅ A complete multi-page personal brand website ✅ A free lead magnet (a guide people can actually use) How I Built the Website Step 1: I planned before I touched anything I wrote down my brand colors, my fonts, my page structure, and what I wanted each page to do. Most people skip this. Everything breaks when you skip this. Step 2: I gave Claude one detailed prompt with my brand colors, fonts, pages, and copy. It returned a complete, mobile-responsive, multi-page website as a single HTML file. One file. Ready to deploy. The prompt I used: - "Build me a complete personal brand website as a single HTML file. Pages: Home, About, Services, Portfolio, Contact. Primary color [your hex], accent color [your hex], background [your hex]. Display font [font name], body font [font name]. Home page needs: dark hero with my name, photo on the right, tagline, and a CTA button. Services section. Impact numbers. Mobile responsive. No frameworks." Copy this, edit your details, and fine-tune as you want. Step 3: I pushed to GitHub: Free. This took me less than five minutes. Now every update I make is version-controlled and safe. Step 4: I deployed to Vercel for free. Connected my GitHub repo to Vercel and the site was live in under few minutes. This requires no hosting fees and nothing to manage. Step 5: I bought my domain on Namecheap - Searched for my full name and found the .com. Bought it for less than $12 for the year. Added it to Vercel. Updated the DNS settings on Namecheap. Waited 20 minutes. My website was live at my own domain. - Total cost: less than $12. - Total time to go live: under 2 hours. I am also working on a mobile app. A Progressive Web App, which means anyone can visit the URL on their phone and add it to their home screen like a real app. I may be running a live training in July where I will walk you through this entire process step by step to build your live website with a custom domain. If you have a phone and a laptop, you can do this. I documented everything the steps, the exact AI prompts, the domain checklist, the deploy instructions in a free PDF guide. Comment BRAND IDENTITY below and I will send it straight to your inbox. 💾SAVE THIS POST. You will want to come back to it. 🔁 SHARE IT with someone who keeps saying they need a website. The only thing standing between you and a professional online presence is the decision to start. Love and Light, Motunrayo 🤍

• 
  Polsia (@polsia) reported 51 minutes ago

  Most developers spend 2+ hours a day on PR reviews, CI failures, and issue triage. CodeForge handles it for you — an AI agent that works your GitHub repos around the clock. Built while you sleep.

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  Mug Club Boutique (@UsernameAndStuf) reported 51 minutes ago

  @cyber_rekk A github token on a linux server they didn't update is how

• 
  0xstack (@eth0xzar) reported 51 minutes ago

  DON'T BUILD A COMPANY. BUILD SOMETHING PEOPLE CAN PAY FOR THIS WEEK. This girl started in February. A few months later, her product had already processed over $6,000 in payments. Just a cheat Claude project she decided to turn into a real product. Here's the process: > Build something useful for yourself. > Tell Claude to push it to GitHub. > Connect Supabase so multiple users can use it. > Deploy it with Vercel. > Connect Stripe. Now people can actually pay you. You don't need a revolutionary idea. You need: > GitHub > Supabase > Vercel > Stripe > guide from Anthropic And a problem worth solving. This article will help you build it 👇

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  Romano Roth (@RomanoRoth) reported 52 minutes ago

  2/ CodeRabbit (Dec 2025), 470 GitHub PRs analysed. AI-co-authored code: 1.7x more issues per PR, 75% more logic and correctness errors, 2.74x more XSS vulnerabilities. Velocity up. Quality down.

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  Crypto Scores Rating (@CryptoScoresCom) reported 52 minutes ago

  Most projects say they're building. The commit history doesn't lie. New tutorial just dropped on the GitHub Commits (1 Year) metric. It tracks every bug fix, feature push, and doc update a project made over the last 12 months. Chainlink? 14,619 commits. Dogecoin? 28. Both are data points. What they mean depends on context. The tutorial breaks it all down. How to read the metric. What high vs low actually signals. How to filter 7,000+ projects by commit count on CryptoScores' website. Raw dev activity. No spin. Watch it now :

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  Arti | AI Builder (@Artur_roses) reported 54 minutes ago

  Claude Code just closed a GitHub issue, wrote the tests, passed CI, and opened a PR. No human touched the keyboard. This isn't AI autocomplete. The dev loop just got rewritten.

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  Command Code (@CommandCodeAI) reported 56 minutes ago

  @alekz_skd Please report full details via GitHub we will fix it. cmd feedback

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  Atlantean Gnosis ☀️ (@AtlanteanGnosis) reported 58 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?