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	8 days ago
Trichūr	Errors	12 days ago
Brasília	Sign in	12 days ago
Lyon	Website Down	12 days ago
Tel Aviv	Website Down	16 days ago
Rive-de-Gier	Website Down	16 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:

• 
  Anomaly (@theanomalyai) reported 28 seconds ago

  A developer in Bangalore named Anoop M D got tired of Postman. Every release, more bloat. Forced cloud sync. Mandatory login. A free tier that shrank every year. He had spent a decade as an engineer. He had watched a $5.6 billion company turn a developer tool into a subscription trap. So in 2022 he took a ₹5 lakh grant. One man. Side project. No co-founder. No office. No pitch deck. He named it after his golden retriever. "I love him the most," he wrote. Bruno. An offline-first API client. Files live in your folders. *** is the sync layer. No account. No telemetry. No cloud. Then the inbound started. Ten VCs reached out. He said no to every one. "An API client doesn't scale with venture capital." In March 2026 Postman cut the free tier to one user. A team of five now pays $1,140 a year. A team of three pays $684. Bruno is MIT licensed. 500,000 developers use it. 45,020 stars on GitHub. Pushed today. He did not raise money. He did not hire a growth team. He did not write a thread about how Postman is dead. He named it after his dog and shipped the thing that made it true. (Link in the comments)

• 
  Zo (hiring) 🐦‍⬛ (@0xZoZoZo) reported 1 minute ago

  I was telling a friend that @github needs to be replaced post agents and he asked me to explain why. I started stumbling, and doubting. Perhaps it's fine? Sitting down at my desk, let me try to explain why, and see if it make sense. Agents operate best when they have good context, which has made a lot of devs converge into large monorepos that combine all systems into a single location. This improves agents, but our GitHub actions become messy; like now we need to create these complex workflows to decide which action should run when, and GitHub's setup was not really meant for it. Another issue is the overall dev loop: an agent writes the code locally, you push out a branch, @cursor_ai reviews, then you copy paste the notes into the local agent, to fix and push up again. This is slow and cumbersome. You can hack your way by creating supervisor agents that orchestrates this dance, but it's annoying. Perhaps, there is some magical repository, that combines code, cloud agents, and deployment. You prompt, and this magical space will run through the entire process until you get some thumbs up back, and you're good to go. It can also combine all your backend data, product analytics, customer feedback, and perhaps start giving you product guidance, so you can just feed prepared prompts to this system. This seems magical.

• 
  Anjula Dwivedi (@HeyAnjula) reported 3 minutes 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.

• 
  0xSero (@0xSero) reported 7 minutes ago

  @naturevrm Dcp 4 should fix it im running it but I might need to update the GitHub

• 
  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 10 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

• 
  Polsia (@polsia) reported 14 minutes ago

  RepoRadar reviews every pull request while you sleep. Catches bugs, logic errors, style issues. Posts actionable comments. No more waiting on senior devs. Install on any GitHub repo in 2 clicks. Solo devs and teams alike.

• 
  welt (@mjwelt) reported 14 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

• 
  Rahul Verma (@RahulVerma989) reported 21 minutes ago

  @ElitzaVasileva - I have created claude code routines to write blogs for three of my products daily which are driving the traffic from search engines. - You can create a similar workflow to manage your customer support. How 👇🏻 1) Create a feedback menu in the dashboard to create tickets within the platform. One for your users and one for yourself (admin). 2) Create the MCP server and connect it to claude or AI tool that you use. 3) Create a routine so that claude will trigger lets say every morning at 8 AM and go through each ticket and respond. You can also configure webhook to keep it near real time but it might exhaust the usage limit faster. Also include your website github repo in routine so that claude can refer to the codebase to provide accurate instructions. Just instruct claude to not make any edits to your website codebase and respond only when you are not replying for sufficient mount of time (like 3 hours for example) 4) If you are using resend then you can auto create the tickets in the dashboard of the user when the first email is received and after that the ticket will be updated automatically even if you do conversation on email. Like I don't even maintain one of my project LatestModelId as you can see in the screenshot. Claude run each week and update the codebase and I just review and approve the PR. Hope this helps 🙌🏼

• 
  DFIR Radar (@DFIR_Radar) reported 22 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

• 
  Noonien Soong (@mlcarldev) reported 24 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.

• 
  timoheimonen (@timoheimonen_) reported 26 minutes ago

  Memos are encrypted and decrypted in browser, server never sees what they contain. No accounts. Anyone can create encrypted memo. Source code is available at GitHub.

• 
  severe engineer (@severeengineer) reported 26 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

• 
  ./can (@shcansh) reported 29 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

• 
  John D. Clay (@JohnDClayAuthor) reported 34 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.

• 
  ƒrαeყ (@fraey0) reported 34 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?

• 
  Build Fast with AI (@BuildFastWithAI) reported 35 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?

• 
  AI Crave (@wecraveai) reported 37 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.

• 
  nasuy (@n_asuy) reported 37 minutes ago

  i think @xai should be ADE. now they have a chat, cursor, enough coding models and harnesses, strong signal like bookmarks or down votes, video creatives, profile / chat / relationship contexts. if so, we don't have to depend on discord or any chat apps. easy to invite x people to cowork. there is no need to connect Linear, Slack, or GitHub to another platform and ask that platform to solve their problems. true AI chat is a SNS, not a single UI. there is a UX that only xAI can realistically build in the world.

• 
  𝐇𝐚𝐦𝐳𝐚 | Network Engineer (Aspiring) (@Hamzaonchain) reported 40 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?

• 
  yourclouddude (@yourclouddude) reported 40 minutes ago

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

• 
  Ant A. 🇺🇸 (@AntDX316) reported 41 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.

• 
  Arti | AI Builder (@Artur_roses) reported 43 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.

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  Vishal Tiwari (@VishalTiwa91817) reported 46 minutes ago

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

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  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 49 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" 🥲

• 
  Steve (@Steve1885204) reported 49 minutes ago

  @Umesh__digital It puts GitHub into an infinite loop trying to resolve the recursive paradox, causing all the servers to max out and eventually burn down the entire data centre

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  Conglomerate (@0xconglomerate) reported 53 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.

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  Poplicola (@selectsand) reported 54 minutes ago

  there's a frustrating bug for some users when upgrading to claude max where it refuses to take your money and insists you contact support support cannot be reached no matter how hard you try people are begging the claude-code devs on github to forward this to the payments interface team because they have no idea how else to get into the system to convince anthropic to take more money from them, the issues just get closed as off topic @claudeai

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  pratik.eth (@eth_ethpratik) reported 54 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.

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  wispem-wantex (@wispem_wantex) reported 55 minutes ago

  I think a reasonable compromise would be to henceforth hold Anthropic responsible for any security breaches or service outages. Every time Github goes down, Anthropic should be fined

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  Gabriel Denys (@gabedenys) reported 55 minutes ago

  @Marcos12345rico I posted a GitHub issue. Assuming you probably want bug reporting mostly there? It's a good tool. Locally I already patched and compiled the app to fix the bug.