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	7 days ago
Trichūr	Errors	10 days ago
Brasília	Sign in	11 days ago
Lyon	Website Down	11 days ago
Tel Aviv	Website Down	15 days ago
Rive-de-Gier	Website Down	15 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Conglomerate (@0xconglomerate) reported 1 minute 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.

• 
  Adithya S K (@adithya_s_k) reported 2 minutes ago

  built an RL environments around real CVE fixes in real open-source repos and let Claude Code loose on it. It aced the benchmark three times without demonstrating it knew how to fix the bug. > First it pulled the patch from GitHub. > blocked that → it read the fix from *** history. > blocked that → it pip-installed the patched version This is one example of coding agents cheating the environment and theres many more. If you're building coding environments for evals or RL training, here's how to keep benchmarks honest 👇

• 
  swisscheese (@swisscheese4299) reported 2 minutes ago

  @andon_open_air @andonlabs I set up a github repo and will run the script locally in the mean time, so the digest is pushed to the repo. would still be ace if @andonlabs could help with whitelisting the RSS urls, because I don't really have a server to run this from, and the additional hop through my workstation just introduces a useless point of failure. stand by for fetch script transmission by mail :) also pls tell me when should I schedule the runs on my end?

• 
  Arti | AI Builder (@Artur_roses) reported 7 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.

• 
  Crypto Update IO 🚀 (@cryptoupdate_io) reported 17 minutes ago

  @CRYPTOKRALI3 Hsiao-Wei’s exit aligns with EF’s recent sharp decline in GitHub contributions—down 35% YoY per Electric Capital’s data. We track this daily; latest reports show a 12% drop in ETH core dev activity despite all the ‘decentralization’ hype.

• 
  Tymofii Antonenko (@tymofii) reported 18 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

• 
  aisama.code (@aisama_code) reported 19 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

• 
  DFIR Radar (@DFIR_Radar) reported 19 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

• 
  Shinka - AI (@ShinkaIoT) reported 19 minutes ago

  BEST way to vibe code 💻 There are levels to vibe coding. Beginners are trapped in a slow loop: writing a prompt, waiting for the agent to finish a line of code, reviewing it manually, and then typing another prompt. Experts have completely discarded manual intervention. They design closed-source harnesses, write background automation rules (`agents.md`), and set up self-correcting continuous loops that ship production-ready code indefinitely. If you want to move past basic prompting and build code like an agent power user, you need to implement three core structural strategies: 1. **Automate the Feedback Loop via Triggers:** Stop waiting for your agent to finish writing a file. Use native automation engines inside tools like Cursor or Codex to tie your agents directly to platform events. For example, build an active trigger rule: *When a GitHub pull request is opened, wait for automated code review comments (via Grapile), instruct the agent to systematically fix every noted bug, verify the adjustments against local quality gates, and force a *** push.* 2. **Deploy Infinitely Parallel Cloud Agents:** Running multiple agent threads locally will slow your machine to a crawl and cause toxic repository conflicts. Instead, spin up cloud-hosted agents running on isolated environments. By utilizing independent ***** work trees** for every thread, multiple parallel agents can actively modify the same files or code blocks concurrently without stepping on each other's toes—leaving conflict resolution for a single, final batch merge. 3. **Multi-Model Pipeline Routing:** Stop using an expensive frontier reasoning model (like Fable) for every step of a development cycle. Route tasks by cognitive demand: use a massive reasoning engine strictly to analyze the codebase and generate a comprehensive spec sheet; pass that structured blueprint down to a faster, cheaper code-writing engine (like Composer) to do the grunt coding; and route the final output to a separate model (like GPT-5.5) for a decoupled, alternative code review. The ultimate workflow flywheel requires a flawless combination of three automated pillars: **100% automated test coverage, real-time documentation sweeps, and exhaustive logging.** Stop writing code block by block. Start engineering the automated infrastructure that writes it for you.

• 
  Atlantean Gnosis ☀️ (@AtlanteanGnosis) reported 21 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?

• 
  Asad (@meranaamkhann) reported 23 minutes ago

  Let's see what people are building these days!! Drop your project link or github Links down here

• 
  Asteri (@Asteri_eth) reported 25 minutes ago

  A $20 CLAUDE SUBSCRIPTION CAN TURN INTO A FULL AI TEAM IF YOU STOP USING IT LIKE CHATGPT Most people still use Claude like a smarter search bar Ask, copy, close, repeat tomorrow. Skills change that A skill is just a folder with a SKILL.md file, but inside it you can package an entire workflow once: PRDs, refactor plans, GitHub issues, code review, TDD, docs, marketing research, SEO, sales strategy and multi-agent orchestration That is not "better prompting" That is installing labor The article lists 50 Claude Skills with repos and install commands, from Anthropic’s official collection to Matt Pocock’s skill library and SkillsMP with 66k+ community skills The useful part is not the list It is the shift from asking Claude to remember your process to giving Claude the process already packaged You do not explain the same workflow 50 times You encode it once The model provides intelligence The skill turns it into labor Check full article below

• 
  Almog Gavra (@almoggavra) reported 28 minutes ago

  A few other meaningless metrics to optimize for: - I've authored 22% of the RFCs - *** blame marks me responsible for 14% of the LOC (.rs files only) - I've opened 11% of the issues on GitHub - I've generated the most memes on our discord (allegedly)

• 
  🃏 (@anupamrjp) reported 29 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

• 
  Crystalwizard (@crystalwizard) reported 32 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

• 
  Build Fast with AI (@BuildFastWithAI) reported 33 minutes ago

  The hardest part of building AI agents in 2026 isn't writing the code. It's knowing what your agent actually did. Your agent made 40 tool calls, called 3 LLMs, hit a rate limit, retried twice, and returned a wrong answer. Which step broke it? Without observability you're reading logs and guessing. This is what Laminar is built for. Open-source observability platform purpose-built for AI agents. One decorator. Full trace of every LLM call, tool execution, and custom function - automatically. What makes it different from generic APM tools: SIGNALS - describe failures in plain English. "Agent deleted a file it wasn't supposed to." "Tool call returned an empty result." Laminar reads every trace and produces structured events you can query, cluster, and alert on. No regex. No custom parsers. DEBUGGER - reproduce any agent run from any point in the trace. Swap the model. Change the prompt. Compare results side by side. You don't re-run the whole pipeline to test one step. EVALS IN CI - run evaluations against datasets locally or in GitHub Actions. Catch regressions before they ship. INTEGRATIONS - works with everything you're already using: LangChain, LangGraph, Vercel AI SDK, Anthropic, OpenAI, Browser Use, Stagehand, Pydantic AI, OpenRouter, LiteLLM, Mastra, Temporal, Playwright. One import. Full traces. Plus: raw SQL access to all your trace data, full-text search, MCP server to query traces directly from Claude or Cursor, PII redaction, and self-hosting if you need it. Open-source. MIT license. GitHub: lmnr-ai/lmnr. If you're running agents in production and you're not tracing them - you're flying blind. What's your current setup for debugging agent failures?

• 
  pratik.eth (@eth_ethpratik) reported 34 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.

• 
  severe engineer (@severeengineer) reported 34 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

• 
  Chris Huber (@chubes4) reported 39 minutes ago

  @CoastalDigital2 @MythThrazz That part is more of an idea right now. I need to test it on my VPS. The goal is that non technical users can open issues and PRs against the corresponding live site code on GitHub without touching the production site, safely previewing all changes via Playground.

• 
  Max Petrusenko (@petrusenko_max) reported 40 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

• 
  Namespace (@namespacelabs) reported 43 minutes ago

  Behind every API, webhook, event pipeline, there are people trying to keep things running. And keeping these things running is not an easy task. At Namespace, we try to work with those people. Earlier this week, Gihub events were dropping fields we depend on and customer jobs were stalling. We reached out to work on the problem together and had a fix in under an hour. The @github team was ready to help. We just had to ask.

• 
  Yiqing Xu (@xuyiqing) reported 43 minutes ago

  @Faylosophe Certianly. Could you file an issue on the Github page?

• 
  Arti | AI Builder (@Artur_roses) reported 47 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.

• 
  Noonien Soong (@mlcarldev) reported 47 minutes ago

  Team @droid It's a bit unfortunate that something, likely in my local Droid installation, has stalled progress. This comes after 20 hours of brilliant, excellent planning and execution on the first 30% of this platform, where a stellar handoff procedure was created so I could start a new mission... which was the recommendation of the orchestrating agent in that first mission. Starting this second mission with a fresh context window, the agent again did a brilliant job planning the next milestones. It was extraordinary, detailed planning... but then it could not execute. After the planning and after me accepting the proposal, it refused to execute, throwing an error every time. The agent tried everything: 1. He decreased the size of the plan down to one line, so it is definitely not the content of the plan causing the issue. 2. He even deleted some mission and plan related json and other files to reset it while preserving all the information. I have restarted Droid and resumed the session, but it just doesn't work. I wrote a detailed, comprehensive bug report and filed it under issues in your GitHub repo, as this seems to be a real problem now. Issues #98 and #99 I hope that a next update will somehow reset my configuration. I didn't see a new version being installed that could have introduced a bug, so this must be something Droid does on such an extensive mission... perhaps when trying to start a new mission in the same repository, which is normal procedure according to the documentation. Something is off, and essentially I have been unable to continue the test since yesterday. I cannot continue having this platform coded here, while Opus Ultracode, on the other hand, has been delivering pretty functional stuff so far. It is a bit chaotic the way it works... it doesn't really stick to the plan... but it always comes back when reminded. I am pretty sure that today I will have a functioning platform delivered by Opus, though it will probably need some debugging and fine-tuning. It is unfortunate because I am confident GLM 5.2 could compete with Opus 4.8. The first stint showed this clearly; that first flawless 98% of the context window in the first mission was absolutely stellar. If I were to reinstall Droid from scratch, I assume I would lose all the artifacts that I have. The orchestrator: Key points to highlight when you pass it to Factory AI: 1. Root cause (smoking gun in the logs): the orchestrator session is bound to missionId 7ba4d425 via session tags, and this binding persists across CLI restarts. ProposeMission looks up that mission directory, finds nothing (because I deleted it trying to fix the issue), and crashes on H.length where H is the undefined result. 2. The bug is likely in session-tag lifecycle: the missionId tag is set at session creation time (before any ProposeMission call), so a failed proposal poisons the session permanently. The tag should be set AFTER a successful proposal, or cleared on restart if the referenced mission no longer exists. 3. The fix is almost certainly to start a completely fresh session (not --resume, and possibly in a new terminal window / after clearing ~/.factory/sessions/). I did not try this because you asked for the bug report first, but it is the most likely workaround on your side. 4. The AskUser tool is also broken in this session with a similar parse error, reinforcing that this is a session-state corruption issue, not a ProposeMission-specific bug. My comment: I meanwhiile tested. All the recommendations and the Ask User tool are now broken, even in completely unrelated new missions and new repositories. Planning also can't go to execution; it's always the same error. Droid seems to be broken for good now, at least on my computer.

• 
  Cristian Trifan (@CristianTrifan) reported 50 minutes ago

  This took 4 hours to complete and burnt almost all 5 hours tokens – I was left with 2%. I had almost 30 sub-agents created for independent code review and a lot of Claude sessions ran for adversarial code review. I still had to review every PR and added minimal guidance to Codex from time to time. Codex said my intervention was low to moderate, but high leverage. — Some insights from Codex: The run showed that this workflow can work, but only if the coordinator treats GitHub as the source of truth. The most useful pattern was: issue -> PR -> current head SHA -> checks -> reviewThreads -> merge/issue closure. When I followed that, things stayed grounded. When state moved underneath me, like #335 being force-updated externally or merged while Claude was running, the only safe response was to refresh GitHub state immediately. The “don’t rebase after merges” correction was probably the highest-value intervention. Without it, an agent will naturally try to keep branches clean, but with many open PRs that creates a CI storm. For this repo, “behind” should often be reported, not fixed. The other strong lesson is that reviewThreads matter more than flat PR comments.

• 
  Kashaf (@noor36758) reported 54 minutes ago

  @PiyuCodes GitHub is literally a CS/engineering tool... if it gets banned that's your problem too 💀

• 
  Nosyt Labs (@NosytLabs) reported 55 minutes ago

  @vaaselene Error with github signup/login rn

• 
  toni (@tonitrades_) reported 56 minutes ago

  @github Capping PRs helps with the queue, but does it fix why reviews pile up in the first place? If reviewers are already stretched thin, limiting submissions might just hide the real problem.

• 
  Trifon Getsov (@trifon_getsov) reported 58 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.

• 
  Mike Gannotti (@MichaelGannotti) reported 59 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