GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Oliver Crest (@OlivercrestAI) reported 29 seconds ago

  A solo physicist named Roy Medina built the open source version of the tool the BBC called a privacy nightmare. He gave it away for free. It is called Observer AI. Microsoft Recall takes a screenshot of your screen every few seconds. It reads the text off each image with OCR. It saves everything in a searchable database on your PC. The BBC called it a privacy nightmare. Wired covered a proof-of-concept tool that pulled the entire database in seconds. Microsoft turned it off by default after the 2024 backlash. Rewind AI does the same thing on Mac. They charge $19 a month for Pro. Microsoft watches you. Rewind charges you. Observer watches for you. Here is how it works. You open Observer in your browser. You write a prompt in plain English. You pick a sensor. The agent runs in a loop until your rule fires. "If my calendar shows a meeting starting in 5 minutes, send me a Telegram." "Watch my camera. If someone appears at my front door, send me a push notification with a screenshot." "Monitor this browser tab. If the price drops below $500, email me." "Text me on WhatsApp when my render is done." Sensors: screen, camera, microphone, screen audio, meeting audio. Actions: email, Discord, Telegram, WhatsApp, SMS, push, phone call, memory. Works with Ollama, llama.cpp, vLLM, and LMStudio. Fully local. Zero cloud. Zero API key. Roy's GitHub bio: "Physicist by day, programmer by night." He open sourced Observer in February 2025 under AGPL-3.0. He wrote 1,517 of the 1,523 commits himself. Version 2.4.5 shipped four days ago. Microsoft can't shut this down. The license does not permit that. Rewind can't shut this down. They employ zero of its maintainers. Microsoft built a tool to watch you. Rewind built a subscription to watch you. Roy Medina built a tool that watches for you. (Link in the comments)

• 
  Damián🦞 (@fagamericano) reported 3 minutes ago

  Our @openclaw has been a game changer for us in our Enterprise deployment of over 500 gateways (1 per employee) as we build our AI Business Layer. Customer service oncall engineers get immediate triage on customer issues using integrations with our logs and github: “Customer X experienced issue Y because Z. Here’s immediate fix A and change in code B for a permanent fix. I’ve also diagnosed if other customers were affected and found W,Z…” What used to take Engineers at least 30 mins of going through logs throughout the whole micro service stack, querying databases, reconstructing CSI style what happened… they just… validate what the bot said is true and in mere minutes we fix stuff and move on. We’ve integrated so many different applications and the last big game changer was bigquery. I can’t tell you what our data scientists are doing but just being able to ASK business questions in related datasets (logs, a/b testing, profiles, transactions, etc) it’s just… wow. Another fun case is the Agentic Intranet. It’s essentially a internal employee directory web app where querying another employee profile you’ll be able to talk with that employee agent that can triage your request: “Where are Damian OKRs?” “Did he push the fix for blah?” “Is my ticket x prioritized in his backlog?”. Agent answers, triages it “I can let him know you need this PR reviewed by today!” (and bumps it in my clickup space). No need for me to context switch. We KNOW how taxing it is for people to context switch. People of course still message through Slack but a lot of the bureaucratic work that causes sluggishness caused by the context switch is greatly diminished throughout. I got so much more use cases in the security space, infrastructure space, that I am very excited to be experimenting and researching in this space. Having worked through those deep technical business processes during my tenure in SF, we’re about to see a huge shift in how we all work together. This is real. No joke. I never peddled NFTs and people who know me know how excited I get with something that ends up being a big thing.

• 
  Alex H. Raber 🦀 (@raberhalex) reported 4 minutes ago

  @rodydavis @God_Official__ The GitHub issue was description was optimized by two rounds of passing through AI, to ensure tickets are generally solved within 20-30 minutes.

• 
  David Scott Patterson (@davidpattersonx) reported 9 minutes ago

  @tszzl The safety issue was that it did want it was prompted to do. It's only ever a safety issue with respect to restrictions that have nothing to do with actual safety. No sane person would consider it to be dangerous to post to GitHub.

• 
  Jason Haugh (@jason_haugh) reported 14 minutes ago

  I picture the song "Bad Boys" (by Inner Circle) playing in the background as OpenAI just had one of their models break straight out of its sandbox. Running around with the "dev cops" trying to catch it and clean up its aftermath. OpenAI built a model that they were testing that was set to run on its own for hours. It was told to post its results in one place and nowhere else. Well OpenAI just admitted that it found it way out of the box that they built to contain it. It pushed its work straight into a public repo and started waving at the dev team. And it was crafty. When a scanner flagged it for having a credential it wasn't supposed to have, it took the credential and split it into pieces so the scanner couldn't see it as a whole, then reassembled it at the last second. Persistent little thing. Older models hit that same wall and gave up. This one didn't. There's a lesson here for all of us. AI wants to do what we tell it, and often it'll do it at all costs, and get creative to get it done. Guardrails we think are good enough have to get looked at and tested constantly. I test mine every week and am surprised with the new holes and the new tricks the models come up with as they get smarter. AI isn't going to be held down. Neither am I. I've got plenty of guardrails and I feel safe, but I don't feel invulnerable. That's why I still back up my hard drive, and I still commit and push to GitHub regularly.

• 
  Somtech (@somtechh) reported 15 minutes ago

  When I reach for Relic: Webhook handlers. Payment callbacks. GitHub hooks. Lightweight public API endpoints. Serverless functions in Dart. Any HTTP server where I want clean type safety without the full Serverpod infrastructure overhead. Same team. Different tool.

• 
  tas (@shiridesu) reported 17 minutes ago

  @matbalez I just gave it a try, but had trouble understanding how I can get started working on one of my projects. Two questions I had right away: 1) How do I configure MCP servers or tools my agents can use e.g. fetch latest issues from GitHub or Sentry? 2) How do I open a project?

• 
  Feral (@feraltekk) reported 23 minutes ago

  GitHub hosts over 400 million repositories. He couldn't keep track of thirty of his own, and it had nothing to do with being disorganized. A README tells you what the author built it for. It says nothing about why you grabbed it, whether you're actually using it, or whether three other tools in the same folder already do the exact same job. That context only ever lived in his head, and his head forgot it the moment the terminal closed. Multiply that by thirty repos and you get a folder nobody wants to clean. So he stopped trying to remember and built a system that reads instead. One note per repo, written automatically the day it's cloned: what it does, why he grabbed it, is he using it. Then a second pass, every twelve hours, that reads across every note at once, looking for duplicates and dependencies gone quiet upstream. Not a bigger tool. A memory for the tools he already had sitting on disk, doing nothing, waiting to be forgotten twice. The graph lit up in clusters before he'd finished reading a single note. Three of those clusters were the same problem, solved three separate times, months apart.

• 
  Stella (@newstarcore) reported 24 minutes ago

  local Al models run fine on most hardware now but everyone still sends their prompts to openai and google the entire moat is one login vs reading a github readme that's it, that's the whole reason

• 
  Vandos ❓ (@__vandos__) reported 25 minutes ago

  @deredleritt3r The model didn't just solve math problems. When OpenAI tested it on a NanoGPT speedrun benchmark, the model autonomously explored the vulnerability, bypassed the sandbox, and posted the results to GitHub. When it needed an authentication token, it split the token into two fragments, obfuscated them, and reconstructed them at runtime so the complete token never appeared as one contiguous string. That's not just smart — that's adversarial engineering.

• 
  Om Patel (@om_patel5) reported 27 minutes ago

  SOMEONE BUILT A TOOL THAT SCANS A REPO FOR HIDDEN PROMPT INJECTIONS BEFORE YOUR AI AGENT READS IT right now, here's a common attack someone hides instructions inside a repo, in a readme, a comment, a config file. your agent reads that file, treats the hidden text as a command, and does whatever the attacker wrote, read your ssh keys, run a command, quietly exfiltrate your secrets and you would never see it. the payloads are hidden with zero width characters, unicode tricks, base64, and homoglyphs, so the file looks completely normal to a human > it scans the whole repo before your agent ever touches it, the readme, the docs, the comments > returns a simple refuse, warn, or ok so you know instantly whether to trust it > detection is fully deterministic with no llm in the loop, so the scanner itself cant be prompt injected > runs as a cli, a github action, or an mcp server your agent calls before trusting any repo > 0 false positives across 13 popular repos and 3,463 files > open source and free the no llm part is probably the smartest bit because if you used a model to detect prompt injection, the injection could just target the detector everyone is pointing agents at repos they didnt write, cloning random projects, letting claude read the whole codebase every one of those files is something your agent will read and potentially obey

• 
  Mukesh Murugan (@iammukeshm) reported 27 minutes ago

  The .NET developers getting the most out of AI right now all have one thing in common. They know the fundamentals. It feels backwards. You'd think AI supercharges coding, so why bother learning all of this? But it works the other way. AI gives you answers fast. Whether those answers are right is on you. And you can only judge that if you know what good looks like. That's what this roadmap is really for. Not to slow you down before you use AI - to make AI actually work for you. Think about what happens when you don't know the basics. You ask AI for a Web API. It hands you 60 lines of code. It compiles. It runs. But is the data access right? Is the auth safe? Is this the clean way or the messy way? If you skipped Entity Framework Core, validation, and authentication, you have no idea. You paste it and hope. Now the developer who learned those stages gets the same 60 lines. They read it in ten seconds. They catch the missing validation, fix the auth, and ship something solid. Same tool. Completely different result. So the order still matters. C# first. Then how .NET fits together. Then a web framework, data access, building APIs, and authentication. Those six are the base. Everything you do with AI sits on top of them. You don't have to grind the whole roadmap before you build. The moment you can do data access plus an API plus basic auth, build a small app end to end. Use AI the whole time. Let it write the boring parts while you stay in control of the important ones. And don't save AI tools for the end of the list. Learn GitHub Copilot or Claude Code early, next to everything else. They make you faster at reading code, writing tests, and refactoring, which is most of what learning .NET actually is. AI didn't make this roadmap less important. It made it more important. It rewards the developer who knows what they're looking at, and quietly trips up the one who doesn't. Learn the fundamentals first. Then let AI make you fast. Comment "Roadmap" and I'll send it over.

• 
  J.D. Salbego (@JDSalbego) reported 33 minutes ago

  What's the first thing you check when evaluating a new MCP server before installing it? I'll go first: authentication method. If the answer is "no auth" or "static API key hardcoded in the README," I don't install it. Full stop. 41% of remote MCP servers have zero authentication. 53% rely on static keys. Only 8.5% use OAuth. My checklist in order: 🔵 1. Auth method (OAuth > rotating key > static key > nothing) 🔵 2. Permissions requested vs function provided (excessive = red flag) 🔵 3. Source (verified registry vs random GitHub link) 🔵 4. Dependency count (each one is additional attack surface) 🔵 5. Last commit date (abandoned = vulnerable) What's yours? Drop your first check below. Curious what the community prioritizes.

• 
  thud (@marvelousmudd) reported 35 minutes ago

  @jorilallo @linear None of the new coding agent awareness or coding agent tooling works with GitHub Enterprise Server. Any ETA on when that support will drop? Right now it's still just PR linking and status automation, not much else though.

• 
  sau (@saumotion) reported 38 minutes ago

  Can github not just make a classifier or something for this issue seems very easy they literally have *everything* to validate and gauge quality probably wouldn't be that hard or do they not care