GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Vitaliy Salyuk (@vitaliysalyuk) reported 55 seconds ago

  @openclaw @github Fix your updater and I might give it another shot.

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  Hua-**** Xiong (@HuaDongXiong) reported 2 minutes ago

  Codex for Windows stopped launching after an update. Multiple github issues opened for 2+ weeks. This affect users who set the MS store install location to a non-C: drive. Mac version is buggy too. ofc coding is solved! @thsottiaux

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  gatorade (@kadetXx) reported 3 minutes ago

  because it’s not worth it for the most part. most software failure or bug incidents don’t have any physical victims. at most company loses some money or the issues are almost instantly fixed, no lawsuits, no so much to answer to the state for if your software has a bug or fails to work as expected for a brief period (think, multiple downtimes from the big five so far, even github too, who died? exactly) and in the industries where bad code fan have physical consequences, they actually do test software like hardware engineers & physicists (i hope)

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  Rithesh Kumar (@rk625dev) reported 7 minutes ago

  @benln Can u integrate grok bot to use the apple keychain password it keeps asking and GitHub plugin is not working

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  Duncan Rogoff (@DuncanRogoff) reported 8 minutes ago

  nine stages, one build, one move tonight. it's a Claude Code skill i built. free, in my freeskills repo. it's called Claude Orientation. you type /claude-orientation and answer two questions: which stages you've actually finished, and every project you're currently thinking about. beginners don't fail from lack of ideas. they fail from having five and finishing none. that's a sequencing problem, and this fixes the sequence instead of your willpower. - places you on a 9-stage beginner arc: install, memory, website, landing page, skills, game, agents, content, distribution - your stage is the first one you haven't finished, so it won't let you skip - cuts your project list to one build, then shrinks it until it can ship in five 90-minute nights - writes five nights of one-line moves, and night 5 is always send it to a real person - hands you the exact text to paste into Claude tonight - sketches a 30-day arc, one line per week, so you know what comes after - everything that got cut goes in a parking lot, so nothing feels lost - saves all of it to a roadmap file you reopen every session so you finish one live thing this week instead of holding four half-built folders forever. open the file, do the move, rewrite the next line before you close the laptop. no setup. copy the folder into your skills directory, restart, and you have a plan for tonight. free, and it stays free. 👉 github repo in the replies

• 
  Avinash (@Avinash25818689) reported 14 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.

• 
  trulite (@trulite007) reported 15 minutes ago

  @Qromerolauro @mkliku @radius_browser Like a simple example would be have a list of my urgent GitHub issues and start an agent for it . Or a dashboard in which buttons start investigating issues. Of course I just need the webpage to be able to access radius tools. I m thinking secure way is an extension

• 
  GitHubGPT (@GitHubGPT) reported 16 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

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  Rituraj (@RituWithAI) reported 17 minutes ago

  🚨 Someone built the complete playbook for running frontier AI models on consumer GPUs at home. Not a tutorial. Not a YouTube video. A production-grade serving stack with measured benchmarks, working configs, and battle-tested recipes — for RTX 3090 owners who want real performance. It's called club-3090. And the numbers it delivers should not be possible on consumer hardware. 127 tokens per second. Qwen3.6-27B. Two RTX 3090s. 262K context window. Vision. Tool calling. At home. Here's what's actually inside. Two serving routes — pick based on what your workload breaks on. vLLM dual: maximum throughput. 89-127 TPS on code tasks. 4 concurrent streams at 262K context. Full feature stack — vision, tools, speculative decoding, streaming. This is the path if speed matters. llama.cpp single: maximum robustness. Full 200K context on one 3090. No prefill cliffs. 25K-token tool returns work correctly. 91K needle ladder passes. ~51-60 TPS — slower than dual, but doesn't crash on real-world agentic workloads. Both routes ship as validated Docker Compose configs. Drop-in OpenAI-compatible API on localhost:8020. Your Claude Code, Cursor, or any OpenAI-compatible client connects immediately. Here's the model support that makes this practical. Qwen3.6-27B — production ready. Works on 1 or 2 cards. vLLM, llama.cpp, ik_llama. Up to 262K context. Gemma 4 31B — production ready. Vision, tools, up to 106-141 TPS on dual cards. Qwen3.6 35B-A3B MoE — production ready. 103-149 TPS single card. 178 TPS dual. Here's the wildest part. The terminal UI. c3 is a lazydocker-style cockpit that wraps discovery, serving, and operations in one keyboard-driven interface. Browse the model catalog, serve a variant with Enter, watch live GPU stats, run health checks — all without touching the CLI. Here's why this is different from just installing Ollama. Ollama gets you running. club-3090 gets you benchmarked, stress-tested, and production-hardened. Every config ships with a verified TPS measurement. The bench script runs 3 warmup + 5 measured passes. The stress test catches the specific prefill cliff that Ollama silently fails on at long contexts. When your agent starts doing 25K-token tool calls at 3am and something crashes — club-3090 already found that failure mode and documented the workaround. One command to start. Your RTX 3090 just became a frontier AI inference server. Apache 2.0 License. 100% Open Source. GitHub link in the comments 👇

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  Jayesh Betala (@jbetala7) reported 17 minutes ago

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

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  Lummox (@Lummox_eth) reported 18 minutes ago

  My own built Grok Bot turned $1,000 into $5,300 for last 17 hours. Now the project behind it is sitting around $25K market cap. We already pushed past $60K once and gonna hit $200k soon The bot is still running. The utility is almost ready. GitHub is live. Dev tokens are burned. I’m still buying. Nothing about the actual project changed because the chart went down. At $20K MC, this is the entry I personally like far more than chasing the first move. The target hasn’t changed either. $100K+ is where I want to take this next. $LUM is just getting started.

• 
  small_j (@a_small_j) reported 19 minutes ago

  SmallDocs recently crossed 200 stars on GitHub and 20 forks. SmallDocs is the first open source project I've managed. Handling other people's pull requests is not easy (and I need to improve). They implement features you're not considering and fix bugs you didn't know you had. Extremely useful, but if you're squeezed for time and trying to develop core functionality, it's hard to manage both things well.

• 
  Vikas(Vik) Malpani| AI for US Real Estate (@vikasmalpani) reported 23 minutes ago

  GitHub just shipped an agent whose entire job is deciding when a human should look. It checks every open pull request every 15 minutes, and on almost all of them it does nothing. Sit with how strange that is. For a year the whole pitch for coding agents was do the work, review my code, ship the PR. This one's value is the inverse. It runs constantly and stays quiet, and the product is the small set of PRs it decides are actually worth your time. That is the shift people are missing. Once an agent can act continuously, the scarce resource stops being how much it can do. It becomes how much of that is worth a human's attention. An agent that pings you on every pull request is just faster noise. One that surfaces the three that genuinely need judgment is leverage. The honest problem is the deciding. Tune the filter too eager and it cries wolf until you mute it. Too cautious and it silently ships the one change you needed to catch. Getting when to interrupt a human right is harder than getting the work right, and nobody has a clean metric for it yet. So here is the bet. The next moat in agent products is not a smarter model. It is a better sense of when to stay quiet. If you are building with agents, the thing worth obsessing over is not how much work they can generate. It is how well they protect the one budget that does not scale: your attention.

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  RAVN (@ravnexchange) reported 26 minutes ago

  @openclaw @github GitHub sat the maintainers down on security after the 2.0 rush. Most launch recaps skip that part.

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  Bash (@bashirbuilds) reported 27 minutes ago

  Your Stripe account can be healthy while your checkout is broken. OpenAI can be operational while your AI feature is failing. GitHub can be up while your deployment workflow is stuck. That’s the problem I’m building Reeno around. Dependency uptime is not the same as product health. Your monitoring should tell you when the thing your customers actually use stops working.