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	14 days ago
Brasília	Sign in	15 days ago
Lyon	Website Down	15 days ago
Tel Aviv	Website Down	18 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:

• 
  Ben Vinegar (@bentlegen) reported 6 minutes ago

  💡 I have an idea for an experiment We need a website for SoAC ... so we get an agent to do it, on a loop, set in motion once with zero human intervention after "go". It works off a semi-public GitHub repo, w/ issues, PRs, maybe even public agent traces. A publicly auditable experiment on whether it produces dogshit or not. Yea, nea?

• 
  Maurice Heumann (@momo5502) reported 9 minutes ago

  @disarray00 If you have concrete recommendations, I would love to hear them, either as GitHub issue, maybe even a PR. But also as a comment here, I'd appreciate it. So when speaking about redundancy, what precisely?

• 
  Arti | AI Builder (@Artur_roses) reported 12 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.

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  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 14 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" 🥲

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  Oluwatobi O (@ooluwatobig) reported 14 minutes ago

  More trouble for GitHub as Cursor has launched Origin, a product which is essentially GitHub for AI agents

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  Boyuan (Nemo) Chen (@boyuan_chen) reported 14 minutes ago

  GitHub search is now an agent attack surface. A public malware-finder repo lists 9,330 suspicious GitHub repositories detected through push-pattern heuristics. Even if only a slice is ever encountered by real users, the agent failure mode is obvious. A coding agent asked to "find a library and make it work" can browse faster than it can judge provenance. Fresh commits, plausible README text, and repo-shaped packaging become inputs to an automated install path. The fix is boring and product-level: repo-age checks, provenance scoring, blocked arbitrary ZIP downloads, sandboxed installs, dependency allowlists, and logs that show exactly what code the agent trusted. For agent systems, retrieval belongs inside the security boundary.

• 
  Kelvinsekx (@kelvinsekx) reported 15 minutes ago

  Just read a nestjs codebase on github. Most it written with Claude. AI doesn’t save you guyz from mess. 1. Bloated logger. Why make logger a service when you could just import and initiate. Eazy 2. They didn’t hash the password before registering a user. But did on login

• 
  lollipop (@immlollipop) reported 17 minutes ago

  🚨HACKERS MOCK OZEMPIC MAKER FOR "NOVO123" PASSWORD Hackers breached Novo Nordisk in March via a stolen GitHub token and just leaked 264 GB of data while mocking its weak security. The attack ran for over 2 months. - The hackers say Novo Nordisk used simple passwords like "novo123" on critical systems - Source code and proprietary details on Ozempic and pipeline drugs were stolen - Clinical trial data on employees, doctors, and patients got exposed - Private internal AI models from the company were also taken This breach shows how a single weak password can bring down even the biggest names in pharma

• 
  Pedro Pellerini (@pepeller) reported 18 minutes ago

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

• 
  Teknium 🪽 (@Teknium) reported 19 minutes ago

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

• 
  Jack Wotherspoon (@JackWoth98) reported 20 minutes ago

  @joedevmob1 The GitHub for Antigravity is just for release notes, samples and public issue tracking. It isn't the actual code unfortunately.

• 
  Conglomerate (@0xconglomerate) reported 22 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.

• 
  MaxMusterman (@maxschuetz_) reported 24 minutes ago

  New Hack: Tell Codex to search for Github Issues which don't need specific Design Questions. Then say: Spin Up Sessions which Fix each Issue and they use also Subagents. Babysit them until the end.

• 
  Programmers.App (@programmers_app) reported 27 minutes ago

  @Lovable @claudeai One very big fix is the Claude Github connection which fails many times, now #Lovable MCP solves that, great job! 🚀🚀🚀

• 
  ./can (@shcansh) reported 27 minutes ago

  Monitoring Copilot costs at the individual developer level is a double-edged sword, and GitHub exposing the new ai_credits_used field in its usage API is about to make it very real. Org owners can now see 1-day and 28-day totals per user. But since it does not break down consumption by feature or model, managers will see who is expensive without knowing why. Will this level of tracking make developers ration their AI prompts, or is it just necessary billing hygiene? #GitHub #Copilot

• 
  naimeh (@naimeh70) reported 29 minutes ago

  @Amir1339216RKT This happens a lot during testnets. Now when I find a minor bug or contract issue, I just drop it publicly on GitHub or tag them directly instead of DMing.

• 
  Max Petrusenko (@petrusenko_max) reported 30 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

• 
  aisama.code (@aisama_code) reported 33 minutes ago

  AI Research gets stronger when it records contradictions *most research workflows collect supporting evidence - that is the weak version for serious research I want a contradiction log: - claim - source - date - who says it - what evidence supports it - what evidence conflicts with it - what is still unknown - confidence - next check example: > claim: this product has strong developer adoption > support: GitHub activity, docs updates, X discussion, integrations > conflict: low issue activity, small Discord, few production case studies, mostly founder-driven content now the memo is different, It says: "visible attention, but adoption evidence is still weak" the useful workflow: research question -> source list -> claim extraction -> contradiction log -> memo ! сode is good at assembling text ! AI is good at comparing disparate text ! human is good at determining which contradictions are significant *without a contradiction log, AI research becomes a confident summary of whatever it found first

• 
  AJ ✝️ 💚🧡 (@angelcreative) reported 33 minutes ago

  @uiux_hamad My design team is leaving Figma gradually, in fact we are using Cursor and GitHub as main design tools now, in the past two months the usage of Figma drops 33% and it will keep going down up to 30% more to a 63% in total and maybe more

• 
  Trace Cohen (@Trace_Cohen) reported 34 minutes ago

  Shipping fast means stuff breaks silently - broken share images, dead links, leaking {{template}} vars, stale content. You find out when someone shares a broken link, not from a test. So I built a 3-part "site health" system that catches it first. The auditor (~200 lines of stdlib Python) fetches my sitemap and, for every page, checks: og:image actually resolves to a real image (entity-decode the URL first — & bit me), <title> exists and isn't a ${template} leak, no {{merge_tags}} or tracking cruft in the visible text, page returns 200 (catches dead routes in the sitemap), and warns on thin content. Outputs a JSON report, exits non-zero on any FAIL. The dashboard — a noindexed /health page that reads that JSON and renders a green/amber/red status, KPIs (audited / clean / warnings / failures), a per-section rollup, and the exact issue on each URL. One glance = "is everything green?" The loop — a GitHub Action runs the auditor 2×/day + on-demand, commits the fresh report (so the dashboard stays live), and fails the run on any FAIL → I get emailed. Find → fix → re-run → confirm green. It even taught me to whitelist false positives ({{firstName}} is legit on a cold-email page). Want your own? Paste this into Claude Code / Cursor — it learns your site first, then builds it for you: Build a site-health system tailored to MY site. Don't assume my structure — learn it first, then fill in the specifics yourself. PHASE 0 — LEARN MY SITE (before writing code): detect my framework/host/layout; find my sitemap; sample ~20-30 live pages across the sections you discover from my URL structure; figure out how my pages set <title>/og:image/meta (static?dynamic OG route? CMS?); identify where my content comes from (hand-written, generated, imported/scraped) — that's where cruft hides. Do a FIRST diagnostic pass and SHOW me what's actually broken vs intentional (broken OG images, dead sitemap routes, leaking {{vars}}/${template}, tracking params, thin pages). Ask me to confirm which "issues" are expected so we whitelist them. PHASE 1 — BUILD IT, customized to what you found: 1) scripts/site-audit.py (stdlib only) — hardcode MY real sitemap URL, MY section names (full-audit the important ones, sample the rest), and MY intentional-pattern whitelist from Phase 0. Check each page for the failure modes you actually observed (OG image resolves to a real image, entity-decode first; title present, no template leak; no leaking merge tags/ad params in visible text; HTTP 200; thin-content warn). Thread-pooled, retry transient errors once, --json report, exit 1 on FAIL. 2) a noindex /health dashboard reading that JSON (status banner, KPIs, per-section rollup, issue list) — match my design system. 3) CI (GitHub Action) — run 2x/day + on-demand, commit the fresh report so the dashboard stays live, fail the run on any FAIL. Then run it once and walk me through the first real report. Build the thing that watches the things.

• 
  pratik.eth (@eth_ethpratik) reported 37 minutes ago

  @Shahules786 @VibrantLabsAI Hello @Shahules786 , I am trying to report a security vulnerability over the email id provided over GitHub Security.md file but apparently its wasn’t delivered. Please share an alternative email or open the advisory for reporting the issue.

• 
  AI Crave (@wecraveai) reported 43 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.

• 
  Dave Oak (@StackCurious) reported 45 minutes ago

  the pattern i see: maintainers burn out because they treat open source like a business that failed to monetize, instead of treating it like a library. once you're answering github issues like customer support, you've already lost. the fix isn't sustainability models—it's saying no earlier. #solodev #shipping

• 
  🃏 (@anupamrjp) reported 46 minutes ago

  Dear hiring manager who rejected me before I even applied, Thank you. Genuinely. You built a filter for people who can memorize solutions to problems that don’t exist anymore. I slipped through the cracks. Into the part of tech where nobody’s checking your LeetCode score, your internship history, or why exactly you got banned from campus placements. They’re only asking one question here: Does it work? Four years of 9.1 CGPA taught me how to pass tests. Six months of building taught me that the test was wrong. Ship dates don’t care about your GPA. Users don’t care about your GitHub commits. Revenue doesn’t care where you ranked in placements. The leaderboard got reset. And I’m starting from the same place as everyone else Except I have nothing to unlearn. See you at the top. I’ll be the one with the receding hairline and the profitable SaaS

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  Sasha (@sshderm) reported 49 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

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  Floorless🌒Lance🪽 (@4ranc6) reported 50 minutes ago

  @CAONHTAN1 Having error connecting github

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  Atlantean Gnosis ☀️ (@AtlanteanGnosis) reported 53 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?

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  John D. Clay (@JohnDClayAuthor) reported 56 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.

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  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 57 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

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  Mike Gannotti (@MichaelGannotti) reported 60 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