GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  napes.base.eth (@Napes_0fficial) reported 2 minutes ago

  Most people are drowning in information, but AI still works like a chatbot. It answers questions, then disappears. Nothing persists, nothing compounds. My startup idea is called MemoryMesh. Problem: People and teams lose context every day. Developers repeat decisions. DAOs forget discussions. Communities rebuild knowledge from scratch. AI has memory, but users don't own it. Solution: MemoryMesh is a decentralized memory layer for AI agents. Every conversation, decision, and workflow becomes a verifiable knowledge asset stored on-chain. AI agents can reference that history, collaborate with other agents, and earn fees when their knowledge helps solve future problems. Think GitHub for collective intelligence.A developer agent that solved a bug last month can help another project tomorrow. A DAO's governance history becomes searchable context instead of lost Discord messages. Communities build shared intelligence that compounds over time instead of resetting every cycle. The result is an economy where knowledge itself becomes an asset, and AI agents become contributors rather than disposable assistants. Infrastructure like this could unlock autonomous organizations, smarter agents, and entirely new markets around reusable intelligence. We're still building apps on top of conversations. I think the next wave will be built on top of memory. Curious whether anyone else sees this as inevitable. @RallyOnChain

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  yourclouddude (@yourclouddude) reported 3 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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  Matthew P. Grosvenor (@mpgros) reported 3 minutes ago

  @github - "We had a problem finding your email subscriptions." That's because I never subscribed to anything in the first place. Stop sending me your spam I didn't ask for.

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  Sudeep Srivastava (@sudeepsriv) reported 8 minutes ago

  GitHub might finally have a serious competitor. And it’s from Cursor. Most people know Cursor as an AI code editor. But Cursor Origin is much bigger. It’s trying to become an AI-native alternative to GitHub where AI agents don’t just help write code. They help build entire products. Think: • Source control • AI coding agents • Code review • Project understanding • Team collaboration all inside one workflow. Why developers are paying attention: Instead of manually searching through repositories, you can tell AI: • Fix this bug • Build this feature • Refactor this project • Investigate an issue • Ship a working version And AI handles much of the execution. The bigger shift: GitHub was built for humans writing code. Cursor Origin is being built for humans managing AI agents that write code. That’s a completely different future. We’re moving from: Human → Code to Human → AI Agent → Code My take: If GitHub defined the software era, Cursor Origin could help define the AI-native development era. And that’s why Elon Musk acquiring Cursor would be huge. xAI would gain: • AI models • Compute infrastructure • Coding agents • A developer platform That’s not just buying a product. That’s owning a major piece of how future software gets built.

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  Ben Canning (@benhackshealth) reported 9 minutes ago

  Spent today setting up home assistant in the gym, ran into an issue with the connection between the software that controls the LEDs around the mirrors. Found a 4 year old repo on github, downloaded it, updated the code, fixed the problem and now control the lights without having to get out of my seat... Am I a hacker now?

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  BruzWJ (@BruzWJ) reported 10 minutes ago

  @thdxr ngl im kinda tired of every funded lab shipping a github competitor, my read is the *** host was the easy part the part nobody rebuilds is the issues + CI + review muscle memory baked into the org

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  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 11 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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  Floorless🌒Lance🪽 (@4ranc6) reported 14 minutes ago

  @CAONHTAN1 Having error connecting github

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  0xstack (@eth0xzar) reported 14 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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  Gabriel Denys (@gabedenys) reported 17 minutes ago

  @Marcos12345rico I posted a GitHub issue. Assuming you probably want bug reporting mostly there? It's a good tool. Locally I already patched and compiled the app to fix the bug.

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  Arda Kılıçdağı - 🦣 @arda@micro.arda.pw (@ardadev) reported 18 minutes ago

  Just Compiled #Rockbox Utility for arm64, and automated this: This screenshot is a pure macOS Arm64 build, built on CI pipeline. AFAIK; Since macOS 27 is deprecating rosetta support and macOS 28 is abandoning it, and nobody is compiling (yet), wanted to do this myself. No local dependency, purely on GitHub actions. I'll automate this (added Windows arm64 and x64 as well, but fixing Windows compilation bugs), and share with you guys. PS: No code is altered, only gh actions yaml is added, so rebasing for updates (which I'll automate as well), won't be an issue.

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  Steve (@Steve1885204) reported 20 minutes ago

  @Umesh__digital It puts GitHub into an infinite loop trying to resolve the recursive paradox, causing all the servers to max out and eventually burn down the entire data centre

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  ImZoomBoy (@ImZoomBoy) reported 21 minutes ago

  @kunchenguid Need to clear these up: - Svelte and Convex? (Best for ai) - Which AI models to use? (Plan vs execute. Cheapest etc) - Treehouse (For this github issues workflow) - GNHF? (Do you do this every time) - Do you manually execute every github task?

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  Coobyk (@Coobyk_) reported 21 minutes ago

  Someone should make a game where you’re a dev and try to fix a bug in your open source project but GitHub constantly has uptime issues or weird UI stuff or doesn’t render properly from most browsers so you **** around until you get the result lmao

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  Kyle Mistele 🏴‍☠️ (@0xblacklight) reported 22 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