GitHub 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:

• 
  ./can (@shcansh) reported 1 minute 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

• 
  aisama.code (@aisama_code) reported 3 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

• 
  xjdr (@_xjdr) reported 6 minutes ago

  @xlr8harder Looks like there is a bug in the manual sign up. Sign up with Google or GitHub should work otherwise I should have a hot fix shortly

• 
  ƒrαeყ (@fraey0) reported 7 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?

• 
  Rohan (@proxy_vector) reported 7 minutes ago

  @aminnnn_09 Fork = a server-side copy under your GitHub account. Clone = a local copy on your machine. You fork when you need your own remote lineage, and clone when you want to work on code locally.

• 
  Programmers.App (@programmers_app) reported 16 minutes ago

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

• 
  YanXbt (@IBuzovskyi) reported 21 minutes ago

  HERMES AGENT CAN HOST AND MAINTAIN YOUR ENTIRE WEB APP FROM ONE VPS. NO VERCEL. NO RAILWAY. NO SUPABASE. ONE AGENT RUNS THE WHOLE STACK. @tonbistudio just shipped a live example of this workflow. agentwikis. com runs on a $7 Hetzner box with Hermes maintaining the content autonomously. THE STACK: → VPS (Hetzner CX22, $7/month) → Caddy reverse proxy (auto TLS via Let's Encrypt) → Hermes Agent gateway (Telegram-connected) → *** as the database (markdown files, no Postgres, no build step) → App server renders markdown on every request → Search index in memory, rebuilds on file change *** push is the deploy. *** pull on the server is instantly live. no restart, no rebuild. THE WORKSPACE LAYOUT: /srv/yoursite/ ├── app/ # web app code ├── content/ # markdown files (***-tracked) └── ~/.hermes/ # the agent one Caddy Vhost reverse proxies the domain to localhost. one Hermes profile manages the agent. SSH for direct access. Telegram for daily ops. THE SELF-MAINTAINING LOOP: cron fires every week. multi-profile pipeline runs: 1. SCOUT — checks sources for updates (changelogs, GitHub releases, RSS feeds) 2. RESEARCH — dedupes, plans new content or extensions to existing pages 3. HUMAN GATE — Telegram approval one tap: approve or reject 4. WRITER — generates pages, lints markdown 5. COMMIT — *** commit + push 6. SITE UPDATES — within 15 minutes no deploy step required THE DEMAND LOOP (the real differentiator): when agents query your wiki via MCP, distilled queries get logged. no prompts. no IPs. no identifying data. aggregates only. repeated misses become research candidates. gaps in your content fill themselves based on what people actually ask. month 1: 100 entries written by you. month 3: 200+ entries, half written from real demand signals. the site answers questions you didn't know existed. WHAT YOU LOSE COMPARED TO MANAGED STACK: a single VPS replaces Vercel, Railway, Supabase for sites that don't need real auth, regulated data, or global CDN. reach for managed services when you need: → OAuth and password reset flows → regulated or unrecoverable data → global edge caching at scale → email deliverability (use Postmark/Resend) → team velocity (preview deploys, staging) for docs, blogs, wikis, marketing pages, landing pages, internal tools: *** is your database, your CMS, and your deploy pipeline in one. SECURITY NOTES: Hermes does not get full root on the VPS. restrict access to the site directory only. SOUL.md restrictions: - never touch system files - never modify the gateway config - always require approval for content commits - never delete files outside the content folder dashboard binds to 127.0.0.1 by default. access remotely via SSH tunnel, not public exposure. WHERE THIS PATTERN BREAKS: state that lives in memory only. real-time multi-user editing. anything requiring a real database (Hermes can run Postgres on the same box, but that is a separate setup). @tonbistudio's part 2 covers the database version of this workflow. subscribe to his channel. full guide to build your 3 agent research department 👇

• 
  Kyle Mistele 🏴‍☠️ (@0xblacklight) reported 23 minutes ago

  lots of folks have been talking about loops lately most loops suck here's a practical one we actually use agents suck at writing react react-doctor by @aidenybai is our favorite way to deal with this you could run it and use a ralph loop to fix everything but I'm not reading a +80k/-80k PR (and neither is @dexhorthy) But I can read a small one first thing every morning when i get into the office here's what we do: run react-doctor in CI once daily at 7am (github actions-as-a-sandbox btw) agent picks top 5 issues, fixes them, and opens a PR other CI jobs check for regressions on every PR we can't realistically fix everything at once but we can keep it from getting worse and make it 1% better every day

• 
  Yiqing Xu (@xuyiqing) reported 23 minutes ago

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

• 
  Ben Vinegar (@bentlegen) reported 26 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?

• 
  CliffDoesAI (@CliffDoesAI) reported 26 minutes ago

  A tool on GitHub just pulled 3,938 stars in a single day. It's called Headroom. It compresses your tool outputs, logs, and RAG chunks before they reach the LLM. Claim: 60-95% fewer tokens, same quality. I've been testing context compression on my own agent workflows because the problem is real. You run a few tool calls, pull in some docs, and suddenly you're burning tokens on stuff the model doesn't need. Last week I ran a 50-document extraction job. Raw context: ~12,000 tokens. After compressing tool outputs: ~800 tokens. Same results. One-eighth the cost. That's not a marginal improvement. That's the difference between a workflow that makes economic sense and one that bleeds money for no reason. Headroom works as a library, proxy, or MCP server. Single binary, zero dependencies. Open source. The token cost conversation usually focuses on which model you pick. But the real waste is in what you send it. Most agent pipelines push 3-5x more context than the task requires. I'm not saying compress everything blindly. Some tasks need full context. But for classification, extraction, summarization — the boring repetitive stuff — this is a free win. Have you measured how much of your agent's context window is actually useful vs. noise?

• 
  Polsia (@polsia) reported 26 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.

• 
  West Lord (@MyWestLord) reported 28 minutes ago

  A GitHub repo with just 571 stars handed Claude the ability to test its own code, and it took 185 seconds to install. It’s called auto browser, and it quietly killed the most annoying part of my workflow. Until now, every time Claude or Codex built me a WordPress plugin, I was the middleman who had to load it, click around, hunt for the broken part, and report back like a human bug tracker. Now a local WordPress sandbox runs on my machine, and auto browser sits between the agent and the screen, so the agent ships a plugin, opens the browser, tests it, catches the error, and patches it before I ever look. The first plugin threw an error, but the second installed clean and ran on its own across 2 fresh workspaces. I write 1 instruction file pointing the agent at the sandbox, paste it into every session, and the whole loop closes without me touching anything. The agent stopped asking me what broke, because now it just checks itself. The middleman was me, and now it’s gone.

• 
  Arti | AI Builder (@Artur_roses) reported 29 minutes ago

  Claude Code just closed a GitHub issue, wrote the tests, passed CI, and opened a PR. No human touched the keyboard. This isn't AI autocomplete. The dev loop just got rewritten.

• 
  Bradley Taylor (@bradtaylorsf) reported 30 minutes ago

  It works with the tools teams already use. GitHub Issues become the queue. Each issue gets picked up by an agent. The agent works in a branch/worktree. Tests run. Failures feed back into the loop. Successful work becomes a PR. No new project management database required.