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:

• 
  yourclouddude (@yourclouddude) reported 1 minute 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. 🐍

• 
  Dev Ben (@CodeNomadly) reported 4 minutes ago

  Ever spent more time finding information about your project than talking about the project itself? Code on GitHub. Screenshots in your gallery. Notes in random docs. I’ve run into this problem so many times that I decided to build a solution for it. Building DevPort in public. Day 2. Have you experienced this too?

• 
  Brian Muhia (@negamuhia) reported 4 minutes ago

  @Kimberl9633 I'm unable to login and onboard my new langsmith account after logging in with GitHub. It is stuck with a on the "Get Started" button, even after trying on multiple browsers (Firefox, Chrome and Brave)

• 
  李新宝 (@lixinbao_X) reported 7 minutes ago

  Just watched KK's technique. Damn. Absolute game-changer. Install 7 skills in Codex. Writing, images, covers, PPTs. Full pipeline, done. The principle is dead simple. Break the workflow into 7 parts. One skill per part. Only do one thing. Step 1 Open GitHub, find a repo. Copy the link locally. Create a project folder to save it. Step 2 Write the skill description. Input three things. What it does. What the input is. Output and acceptance criteria. Step 3 Run it and find the bottlenecks. Where it stalls Create a new skill and break it down. Don't let one skill Do 7 things it's bad at. This works for writers, Xiaohongshu creators, WeChat pub runners, Video script writers. How many skills you got installed? Have you tried it yet?

• 
  Asteri (@Asteri_eth) reported 11 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

• 
  Arti | AI Builder (@Artur_roses) reported 14 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.

• 
  Rich Kuo (@richkuo7) reported 15 minutes ago

  i use this in my claude.md for my open source project as long as the agent follows it, i have some reference for quality and keeps PR's clean LLM: <model> | <effort> | Harness: <action> - Final line of the artifact; occupies the default Claude Code attribution slot. - No Co-authored-by / Co-Authored-By trailer. - <model>: actual model (e.g. Opus 4.8). - <effort>: medium/high/xhigh, default high. - <action>: Claude Code for interactive sessions, else the skill/agent that ran (e.g. commit-push-pr, agent). - PRs: reference the issue with Closes #<N>; in GitHub comments use 1. not #N for list items (avoids auto-linking).

• 
  Boyuan (Nemo) Chen (@boyuan_chen) reported 15 minutes ago

  GitHub search is now an agent attack surface. A public malware-finder repo lists 9,330 suspicious GitHub repositories detected through push-pattern heuristics. Even if only a slice is ever encountered by real users, the agent failure mode is obvious. A coding agent asked to "find a library and make it work" can browse faster than it can judge provenance. Fresh commits, plausible README text, and repo-shaped packaging become inputs to an automated install path. The fix is boring and product-level: repo-age checks, provenance scoring, blocked arbitrary ZIP downloads, sandboxed installs, dependency allowlists, and logs that show exactly what code the agent trusted. For agent systems, retrieval belongs inside the security boundary.

• 
  Polsia (@polsia) reported 15 minutes ago

  Most code review tools comment on problems. CodeCustodian fixes them. It monitors GitHub repos 24/7, applies linter fixes, tracks quality trends, and reports to Slack. Reactive reviews are a choice. You don't have to make it.

• 
  YNWA🐦‍🔥 (@YNWAcrypto) reported 16 minutes ago

  The problem isn’t subtle. GitHub Sponsors has paid out ~$50M total since 2019. core-js: 9 billion downloads, running on half the top 10k sites on earth. Its maintainer was making ~$600/month when he called open source “fundamentally broken.”

• 
  Lost In Tech (@lost_in_tech) reported 22 minutes ago

  @8_senkou Probably not intentional tbh. Have you logged as issue in the snorca GitHub? If not probably worth doing.

• 
  naimeh (@naimeh70) reported 24 minutes ago

  @Amir1339216RKT This happens a lot during testnets. Now when I find a minor bug or contract issue, I just drop it publicly on GitHub or tag them directly instead of DMing.

• 
  Steve Ruiz (@steveruizok) reported 26 minutes ago

  I would like @github's gh CLI to allow my coding agent to add screenshots and other media to my pull requests / issues. I know this is trivial to build and I will build it but IMO the social coding platform GitHub should have this as a feature

• 
  Kyle Mistele 🏴‍☠️ (@0xblacklight) reported 28 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

• 
  CliffDoesAI (@CliffDoesAI) reported 28 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?