GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  AI Crave (@wecraveai) reported 6 minutes ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

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  Peter Skøtt Pedersen (@PeterSkott) reported 8 minutes ago

  @_Evan_Boyle @_Evan_Boyle can we have the remote github mcp server work for the github copilot app then?

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  李新宝 (@lixinbao_X) reported 11 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?

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  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.

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  Zo (hiring) 🐦‍⬛ (@0xZoZoZo) reported 15 minutes ago

  I was telling a friend that @github needs to be replaced post agents and he asked me to explain why. I started stumbling, and doubting. Perhaps it's fine? Sitting down at my desk, let me try to explain why, and see if it make sense. Agents operate best when they have good context, which has made a lot of devs converge into large monorepos that combine all systems into a single location. This improves agents, but our GitHub actions become messy; like now we need to create these complex workflows to decide which action should run when, and GitHub's setup was not really meant for it. Another issue is the overall dev loop: an agent writes the code locally, you push out a branch, @cursor_ai reviews, then you copy paste the notes into the local agent, to fix and push up again. This is slow and cumbersome. You can hack your way by creating supervisor agents that orchestrates this dance, but it's annoying. Perhaps, there is some magical repository, that combines code, cloud agents, and deployment. You prompt, and this magical space will run through the entire process until you get some thumbs up back, and you're good to go. It can also combine all your backend data, product analytics, customer feedback, and perhaps start giving you product guidance, so you can just feed prepared prompts to this system. This seems magical.

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  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 24 minutes ago

  the problem with jumping from github to gitlab is constantly having to retrain your brain to call it "merge request" instead of "pull request" 🥲

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  0xstack (@eth0xzar) reported 28 minutes ago

  DON'T BUILD A COMPANY. BUILD SOMETHING PEOPLE CAN PAY FOR THIS WEEK. This girl started in February. A few months later, her product had already processed over $6,000 in payments. Just a cheat Claude project she decided to turn into a real product. Here's the process: > Build something useful for yourself. > Tell Claude to push it to GitHub. > Connect Supabase so multiple users can use it. > Deploy it with Vercel. > Connect Stripe. Now people can actually pay you. You don't need a revolutionary idea. You need: > GitHub > Supabase > Vercel > Stripe > guide from Anthropic And a problem worth solving. This article will help you build it 👇

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  Romano Roth (@RomanoRoth) reported 28 minutes ago

  2/ CodeRabbit (Dec 2025), 470 GitHub PRs analysed. AI-co-authored code: 1.7x more issues per PR, 75% more logic and correctness errors, 2.74x more XSS vulnerabilities. Velocity up. Quality down.

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  AJ ✝️ 💚🧡 (@angelcreative) reported 29 minutes ago

  @uiux_hamad My design team is leaving Figma gradually, in fact we are using Cursor and GitHub as main design tools now, in the past two months the usage of Figma drops 33% and it will keep going down up to 30% more to a 63% in total and maybe more

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  Bipin Paul (@iAmBipinPaul) reported 29 minutes ago

  @davidfowl @_Evan_Boyle Yes, the only problem is that the GitHub Copilot subscription is too expensive.

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  wispem-wantex (@wispem_wantex) reported 33 minutes ago

  I think a reasonable compromise would be to henceforth hold Anthropic responsible for any security breaches or service outages. Every time Github goes down, Anthropic should be fined

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  Crypto Update IO 🚀 (@cryptoupdate_io) reported 33 minutes ago

  @CRYPTOKRALI3 Hsiao-Wei’s exit aligns with EF’s recent sharp decline in GitHub contributions—down 35% YoY per Electric Capital’s data. We track this daily; latest reports show a 12% drop in ETH core dev activity despite all the ‘decentralization’ hype.

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  Kevin Tabet (@TabetKevin) reported 34 minutes ago

  @upstash Hey guys i think login with github is broken can't log in rn will try later. google works email i dont have

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  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 36 minutes ago

  Pipelines are built. Context is broken. MCP is quickly becoming the default interface for enterprise AI agents. And that’s a good thing. It gives agents a standard way to connect with tools and data. Connecting an AI agent to Slack, Jira, GitHub, and Salesforce doesn’t mean it suddenly understands your business. It just means it can access your data silos. In short: "MCP gives your agent a passport. It doesn't give them a map." As enterprise AI undergoes a massive platform shift from passive chatbots to autonomous agentic workflows, this naive, runtime "federated search" approach creates an ugly cycle in production: - The Latency Spike: Slower agent execution while waiting for multiple external APIs to respond before it can even begin reasoning. - The Token Bleed: Skyrocketing bills from shoveling raw, unranked JSON dumps into a massive context window, praying the model finds the answer. - The Governance Nightmare: A massive risk of data leaks if you rely on a base LLM to magically guess and police complex enterprise security permissions on the fly. Agents do not fail because they lack intelligence. They fail because they lack the right enterprise context. The hardest problem in enterprise AI isn't connecting to systems. MCP solved that. The hardest problem is Context Engineering. MCP is the perfect interface, but a permission-aware context layer must be the foundation. 🚀 If AI is becoming core enterprise infrastructure, you cannot allow the strategic intelligence layer of your company to sit inside someone else's managed, closed-box platform. That is exactly why we built Pipeshub (open-source developer owned context infrastructure layer). TL;DR MCP gives agents access. A context layer gives them understanding. And deep understanding is the only way enterprise AI moves from a cool demo to secure, reliable production. 👉 Next Up Tomorrow: MCP Token Tax

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  TECHEPAGES (@techepages) reported 40 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)