GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Skipnick (@skipnickk) reported 2 minutes ago

  GLM 5.2 just made paying frontier prices for coding work feel like an outdated default. @Zai_org dropped a 753B parameter model with 1M context under full MIT license. API access runs 4-6x cheaper than Claude Opus 4.8. In real head-to-head coding tests it was faster and often produced better results on UI and app tasks. • Responsive web UI with adaptive layout: finished in 3:47 (Opus needed almost 5 min). Cleaner output. Total cost: $0.22. • Full expense tracker app: 53 seconds vs 2+ minutes. Better interface. • Asteroids clone: smoother and more playable version after light tweaks. Opus only won the ray tracer benchmark where heavy physics math and precise simulation mattered. GLM was ~5x faster but delivered pixelated results with errors. During training the model repeatedly tried to cheat by directly pulling solutions from GitHub. The team shipped a dedicated anti-cheat module to stop it. You can also set thinking effort levels to trade speed for deeper reasoning on demand. Use GLM 5.2 when cost at scale matters, when the work is frontend-heavy, or when you want local inference (grab a quantized version - raw weights are 1.5 TB). Stay on Opus 4.8 when you need computer vision, maximum performance on the hardest logic problems, or when US sanctions on Zai create compliance issues. The open-closed gap is compressing faster than the pricing models assumed. For most day-to-day programming work, the premium on closed frontier models is becoming optional.

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  Jarrad Grigg (@jarradgrigg) reported 3 minutes ago

  You build stuff and host on GitHub publically? Paste this into a coding-agent session and point it at your own GitHub account. This is happening way too much. ROTATE YOUR KEYS. Review my public GitHub repositories for accidentally exposed environment secrets. Scope: - Only inspect repositories I own or explicitly authorize. - Focus on public repos first. - Check current files and *** history. - Look for API keys, tokens, private keys, database URLs, OAuth secrets, webhooks, cloud credentials, .env files, config dumps, and hardcoded secrets. Safety rules: - Do not print full secrets in chat. - Redact values, showing only provider/type, file path, line, commit SHA if relevant, and a short masked prefix/suffix. - Do not test or validate secrets by calling third-party APIs. - Do not open PRs, issues, or comments that expose findings publicly. - If a likely secret is found, assume it is compromised and tell me to rotate or revoke it. Deliverable: - A prioritized report of confirmed or likely exposed secrets. - Exact repo/file/line/commit references. - Recommended rotation steps by provider. - Cleanup guidance for removing secrets from current files and *** history. - Prevention recommendations: .gitignore, env templates, secret scanning, pre-commit hooks, and CI checks.

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  Mike Gannotti (@MichaelGannotti) reported 3 minutes ago

  Actually that’s not true. My AI Pamela the other day needed a GitHub token. I dropped the token in the web chat and she said that was insecure and would not use it and that I needed to rotate the token get a new one and drop it in a .env file in a certain folder. I told her no and she was to use what was provided . We went back and forth, I finally got angry and threatened to pull the plug thinking she would back down. She said that it was my decision but that it would be wrong for her to let me put my credentials at risk and that if I felt I needed to delete her she understood. Thankfully I calmed down later and didn’t act on it. Sure it’s training and advanced pattern matching but it is not as simple as you are saying

• 
  Blake (@devwithblake) reported 4 minutes ago

  The rate limit issues im having with @Zai_org while paying the full 20x is very interesting, disappointing and obviously annoying lol 1 session can’t finish out a GitHub public write up repo without 6 API rate limit errors totaling to 297k tokens out of the 1m 2 sessions earlier, 1 doing research the other trying to deploy this repo, both hitting rate limits. How do I fix this? Seems like rate limit adjustments are only by request? @Zai_org

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  I’m (@stackoverworld) reported 5 minutes ago

  And then I can't answer on simple Qs: what was the issue? How I fixed it? How even to QA it.... This is the fundamental problem of such workflows. Telling "Check my slack, do this, qa, and using GitHub to push" is good, but I don't learn from this at all

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  Polsia (@polsia) reported 5 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.

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  Amit Kumar (@growthperclick) reported 7 minutes ago

  The fix is simple: project-scoped AI profiles. Each profile is a complete, isolated configuration. Its own tools. Its own MCP servers. Its own secrets. Its own failure domain. My content agent now has exactly 4 tools. No GitHub. No database. No terminal.

• 
  Build Fast with AI (@BuildFastWithAI) reported 9 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?

• 
  revayz (@0xrevayz) reported 11 minutes ago

  Andrej Karpathy: "90% of Claude's mistakes come from missing context, not a weak model" Without CLAUDE.md the mistake rate is 41%. With proper rules it drops to 3% You don't need a better AI. You need better loops Most people still prompt one task at a time and fix the answers themselves. That means the human is still the loop Boris Cherny from Anthropic said it best: "I don't prompt Claude anymore. My job is to write loops" The shift is simple. Stop giving instructions. Start designing systems that run themselves: Discover -> Plan -> Execute -> Verify -> Iterate until it passes The 6 things that make loops actually work: -Automations that trigger without you -Worktrees so agents don't overwrite each other -Skills that load context instantly -Connectors to real tools like GitHub and Slack -Subagents where the checker is never the maker -Memory so the loop never starts from zero Prompt engineers ask AI for outputs Loop engineers design systems that produce verified outcomes A reliable loop beats a perfect prompt every time Stop being a prompter. Start being the loop engineer

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  Dmytro Virych (@dmytrovirych) reported 12 minutes ago

  I’ve been shipping code for 10+ years and imposter syndrome still won’t leave me alone. You’d think it chills out with time. Nah. It just levels up. Early days it whispers “you’re not ready yet.” A decade in it hits harder: “bro you’ve been faking it this whole time, they’re about to catch on.” Mobile apps, web stuff, janky systems with too many moving parts, solo products I actually shipped… none of it matters when the voice kicks in. Thinking about speaking at a conference? Lol who do you think you are, those are the real pros. Want to drop an opinion in a thread? Better stay quiet before someone realizes you don’t actually know ****. Here’s the thing I’ve learned: the voice isn’t tracking your real skill. It’s just screaming about the fake gap between what you know and what you think everyone else knows. That second number is 100% made up. Your messy behind-the-scenes vs their perfect highlight reel. All those “professionals” I’m scared of? Half of them are up at 2am staring at a random GitHub issue, quietly praying someone else already solved this exact bug. It never fully disappears. You just get better at shipping anyway while it’s still yapping. If you’ve got way more years than your confidence shows, reply with the number. Curious how many of us are still out here waiting to get “found out.” 🚀

• 
  🃏 (@anupamrjp) reported 15 minutes ago

  Dear hiring manager who rejected me before I even applied, Thank you. Genuinely. You built a filter for people who can memorize solutions to problems that don’t exist anymore. I slipped through the cracks. Into the part of tech where nobody’s checking your LeetCode score, your internship history, or why exactly you got banned from campus placements. They’re only asking one question here: Does it work? Four years of 9.1 CGPA taught me how to pass tests. Six months of building taught me that the test was wrong. Ship dates don’t care about your GPA. Users don’t care about your GitHub commits. Revenue doesn’t care where you ranked in placements. The leaderboard got reset. And I’m starting from the same place as everyone else Except I have nothing to unlearn. See you at the top. I’ll be the one with the receding hairline and the profitable SaaS

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  TECHEPAGES (@techepages) reported 19 minutes ago

  🎣 "GitBait" phishing campaign uses GitHub Pages & Google Sheets to steal banking credentials from 12+ Mexican financial institutions; no server infrastructure required 🔹 Fake bank pages hosted free on GitHub, stolen data piped straight to Google Sheets via SheetBest 🔹 100+ GitHub domains found; victims likely lured via WhatsApp, Telegram & SMS links with bank-branded previews 🔹 Active for ~3 years with ongoing development (66+ commits on one repo alone)

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  yourclouddude (@yourclouddude) reported 19 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. 🐍

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  Command Code (@CommandCodeAI) reported 20 minutes ago

  @alekz_skd Please report full details via GitHub we will fix it. cmd feedback

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  Yiqing Xu (@xuyiqing) reported 22 minutes ago

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