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	11 days ago
Trichūr	Errors	15 days ago
Brasília	Sign in	15 days ago
Lyon	Website Down	15 days ago
Tel Aviv	Website Down	19 days ago
Rive-de-Gier	Website Down	19 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  xjdr (@_xjdr) reported 5 minutes ago

  @tolly_xyz @xlr8harder Sorry about that. I'll take a look. Looking with GitHub or Gmail should work but track this down and fix it asap

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  Blake (@devwithblake) reported 5 minutes ago

  The rate limit issues im having with @Zai_org while paying the full 20x is very interesting, disappointing and obviously annoying lol 1 session can’t finish out a GitHub public write up repo without 6 API rate limit errors totaling to 297k tokens out of the 1m 2 sessions earlier, 1 doing research the other trying to deploy this repo, both hitting rate limits. How do I fix this? Seems like rate limit adjustments are only by request? @Zai_org

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  Domi (@domirosari0) reported 5 minutes ago

  @ajayyy_k @hqmank If you got Github it would be no issue for you

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  Solomon Neas (@solomonneas) reported 7 minutes ago

  There's a fair number of downloads for Brigade and related repos. I'm dogfooding it everyday but not getting any feedback from users or github issues. I'm doing plenty of tests for how a new user would experience it but I could use more real time feedback. Lmk, I want to improve

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  David O. Ehibor 🇦🇷 (@grayontop_) reported 7 minutes ago

  GitHub Copilot didn't make developers faster It made slow developers more confident about writing bad code quickly 😭

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  Conglomerate (@0xconglomerate) reported 10 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.

• 
  Red Zen Cloud LLC (@RedZenCloudLLC) reported 13 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.

• 
  Crystalwizard (@crystalwizard) reported 14 minutes ago

  how about you now fix the false positive triggers - i put in an issue about this on github yesterday, and discovered there were already a number of other identical issues - from other people, that had been opened for a while now and that are being 100% ignored

• 
  Crypto Scores Rating (@CryptoScoresCom) reported 18 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 :

• 
  welt (@mjwelt) reported 20 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

• 
  Gustavo Alessandri (@webgus) reported 28 minutes ago

  If you find an error, have an idea, or want to propose an improvement, just open an issue or fork it on Codeberg or GitHub. Contributions are welcome. That’s exactly the point.

• 
  Teknium 🪽 (@Teknium) reported 29 minutes ago

  @majoragv Haven't heard of this issue. Do you have an issue on github?

• 
  severe engineer (@severeengineer) reported 31 minutes ago

  since github copilot onward leetcodes have become even more disconnected from how we all write code every day problem is any kind of standardized replacement probably ends up looking basically the same lol

• 
  SOURAV PANDA (@i_d_skp) reported 32 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

• 
  Gitbank (@Gitbank_io) reported 34 minutes ago

  Community update — GitStock delay + what we have been building First, we owe you an honest update. We promised GitStock would ship earlier and we went quiet. That was on us. No excuses, we were heads down in the contracts and infrastructure and did not communicate well. That changes today. Here is what actually took time. We refused to ship GitStock on top of third-party APIs or borrowed infrastructure. Everything you see in Gitbank; the vault, the relayer, the swap engine, the RWA layer runs on smart contracts we wrote, audited ourselves, and deployed. The GitVault contract is verified on Basescan. The GitStockFactory is verified on Basescan. You can read every line. No black box. No external custody API holding your assets behind the scenes. That decision slowed us down. We think it was the right one. On security specifically. Your funds sit in a soul-bound smart contract vault anchored to your GitHub ID. Transfers are disabled at the contract level — not by a rule in a database, by the EVM itself. We also built private transaction routing directly inside GitVault on Base. No Tornado, no third-party mixer, no privacy-as-a-service API. The privacy logic lives in our own contract. You can verify it. The relayer signs and submits transactions on your behalf so you never pay gas, but the keys to your vault are yours. We hold nothing. If you want to verify any of this: check our contracts on Basescan, check our GitHub, check the bytecode. We are open source. The code is the proof. GitStock ships tomorrow.

• 
  Andrew (@openmarmot) reported 36 minutes ago

  @AndrewCurran_ I use grok every day to research software changes/github issues/software doc research. It is very good at real time data search. Might be SOTA in this niche. Hardly a failure. Meanwhile LeCun only surfaces to let out more hot air. A very forgettable person.

• 
  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 40 minutes ago

  @TheEthanDing distributed systems at github scale make five nines almost impossible. the skill issue crowd has never run anything millions of people hit in the same second

• 
  Mike Muturi (@_muturimike) reported 42 minutes ago

  Hello @github on 2FA, SMS setup kenya 🇰🇪 is not in the list of countries, is it an error or deliberate omission? Kindly fix it @github @GithubProjects

• 
  Grishin Robotics (@GrishinRobotics) reported 42 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.

• 
  Bipin Paul (@iAmBipinPaul) reported 43 minutes ago

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

• 
  𝐇𝐚𝐦𝐳𝐚 | Network Engineer (Aspiring) (@Hamzaonchain) reported 46 minutes ago

  Here's a summary of what happened in case you didn't hear about it... A cyber extortion group called FulcrumSec (active since late 2025) hit Novo Nordisk the company behind blockbuster drugs like Ozempic and Wegovy pretty hard. They snuck in back in March 2026 through a compromised GitHub access token, roamed around for over two months, and walked out with roughly 1.3 TB of data across 700,000+ files. Now they've started leaking a 264 GB sample publicly. Inside? Source code, proprietary formulas for pipeline drugs like Amycretin (their next big obesity hope), clinical trial records, employee and patient data, manufacturing details, and even private internal AI models for drug discovery. The hackers straight-up roasted Novo's security, pointing out laughably weak hardcoded passwords like "novo123" and "p_assw0rd" in critical systems. After Novo reportedly turned down a $25 million ransom, the group decided to start dumping samples and shopping the rest around privately. Novo confirmed a limited breach in early June involving some pseudonymized patient data from trials. They say there was no major operational disruption and that they're working with experts but this feels like a massive wake-up call for the entire pharma industry on basic security hygiene. Crazy how a simple token slip-up can expose billions in IP and sensitive health data. What's your take — do you think this will finally force better security practices, or is it just another headline that'll be forgotten in a few weeks?

• 
  Digita (@digitaworld1) reported 46 minutes ago

  how well a model can fix real bugs in real open-source codebases. It is harder to game than older benchmarks because it uses actual GitHub issues, not synthetic problems. M3 scored 59.0% on SWE-Bench Pro, edging out GPT-5.5 at 58.6% and Google Gemini 3.1 Pro, while sitting just

• 
  Rafal Wachol 💙 (@RafalWachol) reported 46 minutes ago

  @itometeam @tsuyoshi_chujo I was playing with it and started creating issues on GitHub when I noticed something.

• 
  Harry Tandy (@HarryTandy) reported 47 minutes ago

  Andrej Karpathy: "Neural networks are not just another classifier. They are Software 2.0" 8-step MCP setup for vibe coders: 1. Context7 Give the agent fresh docs before it writes code This saves you from old Next.js, Supabase, Stripe, and Vercel patterns 2. GitHub MCP Let it read the repo, issues, PRs, branches, and CI logs The task should start from real project context 3. Playwright MCP Make the agent open the app after it edits code Click the flow. Fill the form. Check the screenshot 4. Supabase or Neon MCP Connect the database layer The agent should inspect schema before inventing table names 5. Sentry MCP Use production errors as input Stack traces beat “the app is broken” every time 6. Firecrawl MCP Let the agent read current web pages as clean markdown Docs, changelogs, competitors, pricing pages 7. Figma MCP Give it the actual design Spacing, copy, layout, components 8. Linear MCP Turn the work into tickets Tasks, comments, follow-ups, PR links The rule: If you paste the same context twice, wire it into MCP That is how vibe coding becomes a build loop instead of a long chat

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  Alexey Samoylov (@metalagman_dev) reported 47 minutes ago

  @geminicli Antigravity CLI is a trash, closed source, full of bugs. They don't even read issues on the github.

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  Sasha (@sshderm) reported 48 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

• 
  Denis (@nuculabs) reported 51 minutes ago

  Worst part of OpenCode is that they only allow login via GitHub or Google

• 
  Max Petrusenko (@petrusenko_max) reported 52 minutes ago

  A GitHub repo called Microsoft Activation Scripts has 178,783 stars and has run for six years without Microsoft taking it down. It activates Windows 7, 8, 10, and 11 plus Office 2010–2024 and related products for free, using four methods, including one for permanent Windows activation. Meanwhile, Microsoft licenses for these start at $139 and go up yearly for 365 bundles. The repo costs zero, requires one command, and remains active with recent commits under GPL-3.0. Do not install it. via @heynavtoor

• 
  Blum (@Blum_OG) reported 1 hour ago

  Andrej Karpathy on MCP: "it's a protocol of speaking directly to agents as this new consumer and manipulator of digital information." that is the cleanest way to think about MCP your coding agent is becoming a second worker inside the product it needs the same context you use: repo, docs, browser, database, errors, designs, tickets, payments if you keep pasting those things into chat by hand you are doing integration work manually the best MCP stack for vibe coding: 1. Context7 give the agent current docs this saves you from stale Next.js patterns, old Supabase calls, wrong Stripe webhook shapes, and Vercel config from 2 versions ago 2. GitHub MCP give it the repo, issues, PRs, branches, workflow runs, and review context half of real work lives outside the file you currently have open 3. Playwright MCP give it a browser the agent should click the thing it built, fill the form, check the mobile view, and catch the button that compiles but does nothing 4. Firecrawl MCP give it clean web research use this before building around a third-party API, writing a comparison page, reading changelogs, or checking pricing claims 5. Supabase or Neon MCP give it the database context that matches your stack start read-only. add writes only when you trust the permissions 6. Sentry MCP give it production evidence real stack traces beat "it crashes sometimes" every single time 7. Figma MCP give it design context when the interface matters spacing, layout, copy, components, and screen structure should come from the file, not from a screenshot and hope 8. Linear MCP give it the task queue bugs, feature work, release notes, follow-ups, and PR links belong somewhere more durable than yesterday's chat 9. Stripe MCP give it official payment context checkout, subscriptions, webhooks, billing, and test mode deserve docs close by and human review close behind 10. Filesystem, ***, Memory, Sequential Thinking give it the base layer files, diffs, history, decisions, and longer plans make the agent act like it is working inside a real project recommended install order: 1. Context7, GitHub, Playwright 2. Supabase or Neon, Sentry, Firecrawl 3. Figma, Linear, Stripe when the product needs them 4. Filesystem, ***, Memory, Sequential Thinking as the base

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  bek※ (@ebubekirttr) reported 1 hour 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