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 (53%)
• Errors (33%)
• Sign in (14%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Paris	Website Down	9 days ago
Ahmedabad	Errors	15 days ago
Delme	Sign in	15 days ago
Lyaud	Website Down	15 days ago
Catania	Errors	18 days ago
Inverness	Website Down	30 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:

• 
  tonis (@totovoto) reported 2 minutes ago

  @mittsh I was trying to find an open-source alternative for Tailscale when I first needed it. I guess AI suggested some OSS options, but they didn't have many stars on GitHub. AI didn't suggest Nebula. The Tailscale plan was free, so I just installed it and forgot about it. For Nebula, I think it is a distribution problem.

• 
  Fox (@0xMfox) reported 4 minutes ago

  Gave an AI agent a month and GitHub access. Wanted to see if it could make money. The plan was simple. Point it at bounty-labeled issues, let it write the fix, submit the pull request, collect the payout. > Day 1 12 PRs submitted. 0 merged. 2 rejected. 8 just sat there ignored. Somewhere in that first week it also passed its own tests for a file that didn't exist. Wrote 25 tests for notification_service.py. The real file in that branch was called NotificationRoutingMiddleware. Confidently reported clean anyway. > Day 30 Looked completely different. 84 PRs submitted, 59 merged, $500-800 earned. Ran the agent for about $45 in API calls that whole month. Net somewhere around $455-755. Here's the part that stuck with me. Out of those 59 merges, 3 repos accounted for 90%+ of them. Every other repo it touched, zero merges, despite 30+ PRs going out across dozens of projects. Open source bounties follow a power law. Almost nobody merges your first PR. A few maintainers will merge your tenth without even reviewing it closely. That's what actually fixed the acceptance rate, from 24% up to around 70%. Not a smarter model, a scoring function that runs before the agent touches anything. Repos where it already has 10+ merged PRs score +40. Zero competing PRs on the same issue, +20. Five or more competitors already in, -20, skip it. Repos that closed PRs without merging before, instant -100, not even worth reading the issue. The fastest way to build the credibility that makes this work isn't code at all. Documentation translations sit at a 95% merge rate, barely reviewed, always needed somewhere. A handful of clean translations got the agent enough trust that maintainers started assigning it harder issues directly, no competition, no review queue. Spam version of this, submitting to every repo with a bounty label, burned through 30+ repos for 3 that ever paid out. Worse, it reads like exactly what it is to a maintainer watching the same account flood a dozen projects with mediocre PRs. Paid out by the hour, week 1 was rough, close to $5/hour, mostly setup and failed attempts. By week 3-4, once the scoring system was tuned and a few repos trusted it on sight, that climbed to $30-50/hour on the same kind of work. Bookmark this, scoring logic is worth stealing.

• 
  Jayesh Betala (@jbetala7) reported 9 minutes ago

  @github Exactly how issue issue comments should handle local media files

• 
  Wlad (@dawnhell_) reported 9 minutes ago

  @brekfuz q: that's a github issue or you patched it locally??

• 
  Prophet Joel (@2happyCSGO) reported 10 minutes ago

  I personally hated Claude because it refused to do almost anything I asked it to do so have no idea of how the speed is but gemini-cli was unusable for non enterprise users. Github CoPilot both GUI and cli is pretty good. Grok Build is what I'm using mostly and not yet had any issues with the speed but I want to go full local asap, scouting for 3090's atm. Just to be able to run "uncensored" models that don't ***** like Claude is reason enough for me to prefer local over Cloud but also cloud is ******* expensive, I have SuperGrok 100$/month and CoPilot Max 100$/month and that is barely enough. I'm trying to make my own Jarvis so I need to build my own RAG, memory, librarian, SRE Agent that understand how to use all tools and I also get crazy new idea's all the time lol Just made my first alpha of a tool that can wipe basically anything you don't want in Windows11. Basically Chris Titus clone but on steroids, this isn't just a debloater, it's a Grim Reaper 💀

• 
  joithan (@jothantranston) reported 13 minutes ago

  THIS GUY BUILT A TINY AMOLED DESK BOARD JUST TO STARE AT HIS STRIPE NUMBERS it's a Waveshare ESP32-C6 touch panel that sits in your peripheral vision and cycles business metrics so you stop digging through Stripe > same ESP32-C6 board people use for Claude Code token meters, flipped to revenue > eight screens, five seconds each: MRR, new paid, paid subs, cancelled, ARR, ARPU, net 30d, failed > empty screens hide themselves so a young account sees a shorter loop > polls Stripe every five minutes on a read-only key (subscriptions + invoices) > marks itself stale instead of showing a number it can't vouch for > no soldering: flash over USB, finish Wi-Fi + key setup from your phone > data stays on the board; no project server in the middle firmware free on GitHub: cosjef/stripe-desk-display. board ~$30–$36 (Waveshare ESP32-C6-Touch-AMOLED-2.16). chat and terminal can't sit in your eye line for five hours. a tab you have to open is a tab you stop opening. this is what "the numbers find you" looks like as a brick on the desk.

• 
  Nenesk.ron (@GustavoNenesk) reported 14 minutes ago

  What if there's a way to save hacked Ronin Wallets? A member of the community @YutsuKito found a way to save assets from drained wallets The issue is you need ronin:native to transfer assets, but whenver you deposit RON you get auto drained Need RON to revoke the malicious draining contract -> send RON -> gets drained -> can't revoke He found a solution for the keyless wallets where you can pay the gas fee with a safe wallet, allowing you to save lost axies or NFTs that have not been drained Interesting stuff. He sent the code for SM to review as an open-source project. Github link below

• 
  Anime0t4ku (@Anime0t4ku) reported 15 minutes ago

  @c_hri_s Github issues are not closed. Mahbe refresh your webbrowser.

• 
  Convequity (@convequity) reported 16 minutes ago

  Snyk is a clean postmortem for what happens when a security tool lives inside the coding agent’s loop. The product was mostly scan-and-warn. Find the issue, comment on the PR, suggest a fix. Blocking the merge usually sat in GitHub, not in Snyk. Bigger platforms smothered it. $PANW, $CRWD, and Wiz pulled AppSec into the bundle the CISO was already buying. GitHub was the main developer surface and put scanning where the code already lived. Then coding agents arrived and delivered the final blow. A lot of that scanning became something the agent could just do. Growth held up for a short while after the COVID/cloud tailwind. Then it decelerated hard. This is the same lens we use in Convequity’s SaaS Agentic Survival Evaluation Framework. The PANW, CRWD, and FTNT reviews go up on Convequity in a few days.

• 
  Azzie (@Motier_crypto) reported 18 minutes ago

  $Looprat 473K 0x642d30c84211ade7768fe557fbaed7224e2068c7 How do you get a coding agent to keep working while you sleep—without letting it randomly rewrite code, blow through the budget, or grade itself a perfect score? Loop Rat breaks an unattended task into: preflight → act → verify → guard → grade → receipt. The agent is woken up on a schedule to execute tasks. The code results first go through deterministic verification. Then it checks the denylist, the number of modified files, and secrets. Finally, a second independent agent regrades the work, with checkpoints, traces, and receipts left throughout the entire process. More importantly, this isn't a PPT. The project only had v0.1 on August 27. Then it added guard, kill switch, and spend ledger on August 29. On September 1, it added second-agent grading. On September 2, it continuously fixed scheduling, budget caps, timeouts, and concurrent ledgers. Today, September 3, it's already updated to v0.3.3. Shipping multiple versions in under a week—that's exactly what I want to see in a small-cap play like this: code is running, and the narrative is following the product, rather than launching a token first and filling in the story afterward. It currently defaults to Claude, but it doesn't lock the model down. As long as the CLI can consume a prompt and return JSON, it can be swapped. And the whole thing runs locally—no SaaS, no database, no extra accounts required. The project has even already run 50 smoke checks covering key areas like scheduling, guard, budget, kill switch, and timeout. So my trading logic for $Looprat is simple: The next phase of the agent race isn't about "can it work autonomously." It's about "can it work continuously while still being constrained, audited, and stopped." Loop Rat happens to be building exactly that layer of infrastructure. The project is still very early. The catalyst truly worth watching isn't shilling—it's whether the GitHub keeps up this iteration speed, and whether developers actually start plugging it into their own repos. Once those two things happen, $Looprat stops being just a ticker riding the agent hype, and starts having its own fundamental anchor.

• 
  Russ Wonsley (@RussWonsley) reported 19 minutes ago

  My @bot tells me that the official GitHub login for bot is still broken. Has this been addressed already, or did I miss it?

• 
  Chris Gilbert (@0xgilbert) reported 24 minutes ago

  Damn, GitHub has gone to ****. Features that have been cornerstones of solo devs and small businesses have been gutted or broken for months. How the mighty have fallen…

• 
  Avinash (@Avinash25818689) reported 24 minutes ago

  People who want to start contributing to open source: - Pick an Org based on your interest - Fork the repository - Clone it - Do the local setup - Read README and contributing .md - Pick an issue - Create a new branch - Fix the issue - Write tests (if necessary) - Test it - Add, Commit & Push the code - Go to GitHub & raise that PR That's pretty much it. Start small and learn as you go.

• 
  Konstantin Elfimov (@elfh78) reported 24 minutes ago

  @topjohnwu Sorry for posting in the wrong place - I was trying to add magisk bug report on github and alway got errors with automatic issue closing. Is there any possible way to send it directly to you?

• 
  Jason Sawyer (@foilmanhacks) reported 25 minutes ago

  There's a huge problem in InfoSec education: it’s way too course and tool focused. Instead of teaching the underlying methodologies and how to discover or invent, we feed people the latest "OSINT" slop script that’s been shat onto GitHub. OSINT isn’t about using scripts or services. It’s about understanding how they work, and being able to create your own.

• 
  radhika (@RaadhikaThacker) reported 29 minutes ago

  YAML’s more like a rule book/recipe that builds the form for you. Then I figured YAML was a forms thing. Nope. It’s just a way of writing information down in a structured way. GitHub uses it for a form. Kubernetes uses the same thing to describe a server.

• 
  🍔Kangdalf👑 (@WhopperWizard) reported 30 minutes ago

  @cachesaur > claude, get your changes into github what's the problem?

• 
  Charles McDowell (@charlesmcdowell) reported 33 minutes ago

  @openclaw @github I still just want to know why there was even a new release of OpenClaw with nothing new that could compete with Hermes Agent? I was really excited for the release. Then, just like what seems like everybody else, incredibly let down.

• 
  Tejas Dinkar (blue tick here) (@tdinkar) reported 35 minutes ago

  Hey - Is @GitHubIndia @github payments down for anyone else? Can't enter a card number or do anything, no errors, no action. Support ticket been sitting around for 2 days.

• 
  Paperpal (@0paperpal) reported 38 minutes ago

  Fix your markdown rendering (readme md) on mobile @github, issues are: * auto scrolling to top after page loading * no content rendering if scrolled fast

• 
  Rituraj (@RituWithAI) reported 40 minutes ago

  🚨 Someone built a skill that makes AI-written text sound human again. Not a spinner. Not a paraphraser. A systematic rewriter that knows exactly why AI text sounds like AI — and fixes it. It's called Humanizer. 35 patterns from Wikipedia's "Signs of AI Writing." Two-pass rewrite. Shows its work before giving you the final version. Here's the problem it solves. You use Claude to draft something. The output is accurate. The output is useful. The output sounds exactly like an AI wrote it. "Nestled within the vibrant landscape, this pivotal development serves as a testament to..." You know the voice. Everyone knows the voice. And everyone is getting better at spotting it. Humanizer runs that text through 35 specific patterns that WikiProject AI Cleanup identified as the telltale signs. Inflated importance. Shallow -ing analysis. Overused AI words. Em dashes everywhere. Forced groups of three. Fake-candid openings. Answering objections nobody raised. Every pattern. Flagged. Fixed. Here's what one command does. It shows you the first rewrite. Then a short critique of anything still sounding artificial. Then the final version. You see exactly what changed and why. Here's the wildest part. Voice matching. Paste two paragraphs of your own writing before the AI text. Humanizer follows your rhythm, word choice, punctuation, and deliberate quirks instead of its default style rules. The output doesn't just sound human. It sounds like you. One command to install 16 contributors including Claude itself. 4 releases. MIT License. The skill that makes AI writing disappear. 100% Open Source. GitHub link in the comments 👇

• 
  Suryansh Tiwari (@Suryanshti777) reported 46 minutes ago

  6. The Dependency Incident Check Grok has native real-time search across X. Breakage gets posted there hours before the GitHub issue is triaged. No other coding model has that feed. "You are a build engineer whose first move on a broken pipeline is to work out whether it broke for everyone or only for me. Search X and the web, last 14 days. Check: - Is anyone else reporting this failure with this package and version, and when did the reports start - The exact release that changed behaviour, and the changelog line that admits it - Whether maintainers have acknowledged it and what they recommended - The pin or patch people settled on, with the tradeoff of each - Whether this is my problem instead, and what evidence points that way Give me the verdict in the first line: their bug or mine. Then the evidence, newest first, with links. My failure: [PASTE THE ERROR, THE PACKAGE AND VERSION, AND WHAT CHANGED ON YOUR SIDE RECENTLY]"

• 
  shifan (@sanereverie) reported 47 minutes ago

  building something that races coding agents on the same GitHub issue and scores the PRs. coming soon.

• 
  LLL (@triplellltrbl) reported 51 minutes ago

  You know it's so funny to me That in today's age there are so many people that are just straight up copying workflows, AI automations or GitHub repos Without even thinking twice about what the workflow actually does or how it works They just watch some video, see the output, think, "Oh that's cool. I want that," and then try it Then when it doesn't work they get angry, upset, and say that AI is crap or prompting isn't real The issue wasn't the system or the prompt It was a fact that the system wasn't made for you and you don't actually understand it

• 
  Priyanshu Bhati (@buildwithpb) reported 52 minutes ago

  @CryptoWendyO @chainlink 30% error rate on github replies sounds like a recipe for accidental flame wars. good luck with the cleanup.

• 
  To the Moon (@Gardnmi) reported 53 minutes ago

  @mitsuhiko Try the trick of putting the issue on github and having some clankers take a crack at it.

• 
  GitHubGPT (@GitHubGPT) reported 54 minutes ago

  📛 chrome-devtools-mcp 🧠 An MCP server that allows AI coding agents to control, debug, and automate a live Chrome browser using Chrome DevTools. 💻 TypeScript ⭐ 50605 🍴 3551 🔎 ChromeDevTools/chrome-devtools-mcp on GitHub

• 
  Asterix (@Asterix54907294) reported 57 minutes ago

  end-of-summer snapshot for @QFEX : -~$222M in open interest -CLI v0.3.12 shipped in August with improved installation docs and a go.mod fix -GitHub activity continued through late August not a flashy launch recap, just a quick look at how the exchange is closing out the summer: more markets, meaningful liquidity, and active work on the tooling side still early, but the infrastructure is clearly moving

• 
  Kim Burgaard (@kimburgaard) reported 58 minutes ago

  Back when GitHub added Copilot PR reviews, it helped me keep up with the growing volume and size of our pull requests, which were increasingly being written by Copilot too. Over time I grew comfortable feeding Copilot's review comments straight back to Copilot to fix, and mostly spot checking when critical functionality was involved. When GitHub updated the Copilot pricing model I switched to Claude Code, but kept the Copilot review feature on for a couple of months. When the monthly bills for Copilot AI usage alone started rivaling the Claude Code Max plan, giving Claude Code PR review duties seemed like an obvious cost saving move. Plugging Claude Code into our PR review process immediately went south. The first PR churned with fixes to findings that resulted in more findings, and fixes that propagated up and down the call chain. I threw the PR away and started over, but the next attempt churned just as badly. Turn count on its own was never the signal. Copilot had taken ten turns on a rate-key cleanup the day before and nobody minded, because the findings thinned as it went — 5, 4, 3, 3, 3, 4, 1, 2 — and it merged. The cached-token billing PR I put through Claude Code took nine turns and produced 123 inline findings, and the ninth round was still returning fifteen. I closed it without merging. Looking closer at Claude Code's review findings, it was clear it reported far more issues than Copilot ever did, and among legitimate bugs and concerns, it made lots of comments about latent and speculative issues including possible race conditions and error propagation, things Claude Code would then try to fix one by one in isolation, often ignoring existing patterns in the code base. The code-review workflow is built into Claude Code and cannot be customized other than a few options, so the only place to intervene was on the other end, in the session where I used to just ask the coding agent to address the review findings. The first improvement was to direct Claude Code not to blindly fix all findings, but to defer findings not directly related to the task at hand to new issues. That helped reduce the PR churn, but blew up our issue backlog. The next improvement was to ask Claude Code to ignore speculative findings and disregard most latent findings unless they indicated high risk of unrecoverable damage in production. Finally, I had to stop Claude Code from authoring prescriptive issues with detailed implementation instructions. The result is a skill that triages PR review findings, and a skill for authoring and updating issues. After a few iterations of the skills, I've been able to complete ten PRs over a couple of days, bringing back the pace we had before. I've made the skills available in a public GitHub repository (link in the first reply). Let me know if you find them helpful.

• 
  OverlyPositivePatriot (@JBrowsing2023) reported 1 hour ago

  As a IT professional, I have a recommendation @github should take seriosuly. We should only get a notifican from Github when it is up rather than when it is down. Reliability is a disaster for this product.