GitHub Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  _iantyb (@_iantyb) reported 2 minutes ago

  @SingtelSupport Yes I did, GitHub is accessible after I first sent this message but now it is down again even after rebooting the ONT/ONR

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  Steven J Blair (@Steven_J_Blair) reported 2 minutes ago

  @Pallavi_345 Better question: Is there a better way to store my code? Make an sql database and store my own repositories? My thoughts when I heard microsoft bought github... is open source over? Since the AUR got hacked I'm like you down wit O.P.P.? (other peoples packages)

• 
  Orix (@OrixCrypto) reported 11 minutes ago

  @AdwaitDbz @gitlawb @internetfreedom You can't do ****..the repo is already present in few decentralized github apps..which can't be taken down..Indian people will be avle to download from there..

• 
  Monerica (@MonericaProject) reported 13 minutes ago

  @tallhatdoug There's an issue with GitHub we are working on that is causing that.

• 
  Jason Chan (@JasonCh28920531) reported 13 minutes ago

  Shipping a GitHub admin token inside a login page is a reminder that "security camera" and "secure" are not the same word. Hardware vendors still treat firmware like a rushed side project.

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  @kleos (@1kleos1) reported 16 minutes ago

  an $9 chip can already hear your heartbeat through a wall. it still can't draw your skeleton. RuView is real. 85k stars on github, built almost entirely in rust. a mesh of esp32-s3 boards, about $9 each. reads the channel state info your router already collects to manage signal quality. no camera. nothing leaves the building. presence, motion, breathing rate, heart rate. that part actually works today, offline, on hardware you can buy in bulk. the part that got it 85k stars is full skeleton tracking through drywall at 8 meters. that doesn't ship yet. the project's own notes say the training pipeline exists but the trained weights don't. github issues report zero skeleton output on working setups. one is literally titled "looks like a scam project." the boring 80% of this works right now. the part everyone's sharing is still a promise.

• 
  Bounce (@bounceidc) reported 17 minutes ago

  Ex-GitHub engineer just showed how to train AI on your own code: "one agent is a demo. a loop of agents is a product. everyone is still stuck on the demo." In 30 minutes he shows exactly how he turned 77,000 dead Claude sessions into a loop of agents that fix their own mistakes and get smarter with every run. Beats any $500 course on agents I've seen. He also drops that Cursor just closed a $10M deal to sell user session data to SpaceX. You're already paying to generate the data, someone else is cashing in on it. Watch it, then read the guide on building loops below.

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  Genuine Grape (@GenuineGrape) reported 20 minutes ago

  @SimonHoiberg So I did what Simon has been talking about here is my takeway. Be ready to spend some time on security part if you dont' want to expose your Forgejo server to public. Spend maybe 1 hour on cloudflare. But I'm happy overall about Forgejo . Looks like github to me lol

• 
  Professor (@AbsoluteBeyond) reported 21 minutes ago

  BTW, just before they somehow force github to take it down (If github pays them any mind) someone fork it or something.

• 
  Error404Wit (@Error404Wit) reported 24 minutes ago

  @USDJPY2010 Bug isn't in your codeit's in the M5Cardputer library's keyboard reader (FIFO drains 1 event per update + interrupt-flag race that kills the INT line)... Sent a PR with a fix on your github give me an update (:

• 
  The Tech Buzz (@tbuzzdaily) reported 28 minutes ago

  $15M raised: Meticulous grew ARR 5x in a year testing code nobody has time to review manually anymore Chemistry led the Series A, joined by Menlo Ventures, Lachy Groom, and GitHub's former CEO Jason Warner. Founders Gabriel and Quentin Spencer-Harper, brothers with backgrounds at Dropbox and Palantir, built the company around a problem AI coding tools created faster than they solved: code is now written faster than humans can review it. The product records real user and developer sessions during development, then uses AI to auto-generate and maintain end-to-end visual regression tests based on actual observed usage, updating automatically as the code changes. Customers include Notion, ElevenLabs, Dropbox, and Wiz, a list that skews toward companies shipping fast enough to need this. Rainforest QA and Autify compete in AI-assisted testing broadly, but most rely on hand-written test cases rather than tests derived from real session data. The tradeoff Meticulous accepts is that session-derived tests can still miss rare edge cases nobody happened to trigger, which is why the company is expanding from frontend into backend and performance testing next. A 5x ARR jump in a year says enough teams have hit the same wall: AI writes code faster than any QA team can keep up with by hand.

• 
  Corentin (@corxntyn) reported 30 minutes ago

  The easiest way I've found to make Hermes much more powerful is Composio. Hermes already has a browser, terminal, files, memory, cron jobs, etc. The problem is that most of my actual work lives inside Gmail, Google Calendar, Granola, Slack, ClickUp, GitHub and a bunch of other apps. Composio gives your Hermes access to more than 1,000 apps through one MCP connection. This opens up requests like: "Find the email about this project, check my calendar and draft a reply with 3 available times" "Read this Slack thread, turn it into a ClickUp task. Assign it to X and set the due date for next week" "Research this lead, find our previous conversations, draft an offer and host it on our server" Hermes searches Composio for the exact action it needs. If an account is not connected yet, it gives you an OAuth link. Simple as that. You don't have to manually build a separate integration for every app or load thousands of tools into every conversation. Just send this to Hermes: "Open composio․dev/hermes and set up Composio for me. Once connected, find the apps and workflows that would be most useful based on how I use Hermes." That's it!

• 
  CATO ✨ (@Cato_0001) reported 31 minutes ago

  @github @Microsoft don't bow down to these clowns. Do better.

• 
  Quantum Rose (@zhang_xiao18313) reported 32 minutes ago

  ChatGPT Work is my execution layer for complex, cross-platform workflows. • Cloud Infrastructure Management: I gave it high-level intent to migrate an AWS service. Codex parsed multi-account IAM dependencies, executed Terraform scripts in terminal, validated state locks, handled remote backend drift, ran dynamic smoke tests against staging endpoints, and sent an auditable diff report to Slack. • E-commerce Operations: I provided a list of product URLs with pricing anomalies. Using desktop control, it logged into the merchant portal, cross-referenced inventory logs via SQL, identified coupon stack glitches, updated real-time pricing rules, and auto-generated an incident summary PDF for the ops team. • Full-Stack Feature Rollout: On a Next.js + Python microservice repo, it picked up a Jira issue, updated backend GraphQL schemas, patched frontend state management, fixed 4 TypeScript type errors, triggered local Docker builds, pushed to GitHub, verified the Vercel preview deployment, and updated the Jira status to QA. • Data Pipeline & BI: It audited an unstable Python ETL pipeline, identified API rate-limit bottlenecks, refactored pandas transformations to polars for speed, added automated retry logic, backfilled 3 months of historical data, and verified dashboard sync on Metabase. • Personal Workflow Automation: On my local environment, it audited outdated Homebrew packages, cleaned up Docker dangling images, removed orphaned *** branches, synced 2FA backup codes to an encrypted vault, and sent a system health digest to my Telegram bot. Even this summary was extracted from my local Codex CLI logs, structured, and published via browser automation. The shift: I manage context and policy; ChatGPT Work handles end-to-end execution across terminal, cloud APIs, IDEs, databases, and local system environments.

• 
  Francis Rafal (@francisrafal) reported 32 minutes ago

  I just ran a real-world test of GPT-Live voice in @ChatGPTapp. I described a bug in ParseHawk out loud. From a voice conversation, it found the GitHub issue, reproduced the bug, fixed it, added regression coverage, validated it, created a PR, opened it in my browser, and I merged it. I am really amazed by this @thsottiaux @OpenAI!