Top creators don’t guess LinkedIn hooks. They reverse-engineer them. Most people open ChatGPT and ask it to “write a hook.” That’s the mistake. Because strong hooks come from patterns. Not inspiration. Here’s the simple system I use: Step 1: Collect viral hooks Start saving strong first lines. Look for: → curiosity gaps → bold claims → numbers → contrarian opinions That’s your hook library. Once you have 20–50 examples, you’ll start seeing patterns immediately. Step 2: Let AI analyse the patterns Upload your hook collection. Then ask: “Analyse these LinkedIn hooks and identify their patterns.” Now AI becomes your strategist (not just your writer). It shows you: → what repeats → what structures work → why they work That’s your framework. Step 3: Generate hooks on demand Once patterns are clear, you stop guessing and start generating. Now your prompt becomes: “Use these hook patterns to generate 3 distinct hooks for this post.” This turns AI into your personal hook engine. Not a content slot machine. I’ve collected 100+ high-performing hooks from top creators and added them into my Hook GPT. If you want access, like this post + comment HOOKS (Save this post for later so u don't lose it - top right 3 buttons)

🚨 BREAKING: Yann LeCun just raised $1.03B, one of the craziests rounds in AI history 🔥 $1.03B seed round. ~$3.5B valuation. For a company that basically just started. The startup is called AMI (Advanced Machine Intelligence), and it’s backed by top investors: Bezos Expeditions, Temasek, Nvidia, Cathay Innovation, Greycroft… But the most interesting part isn’t the money. It’s the thesis. LeCun believes the current AI paradigm is flawed. Most frontier labs today are scaling LLMs that predict the next token. But according to him, that won’t lead to true machine intelligence. So AMI is betting on something different - World Models These are AI systems that learn how the physical world actually works by training on: •⁠ ⁠video •⁠ ⁠spatial data •⁠ ⁠sensor inputs •⁠ ⁠real-world interactions Instead of just predicting text. The goal is AI that can: •⁠ ⁠reason about environments •⁠ ⁠plan actions •⁠ ⁠understand causality •⁠ ⁠operate in the real world Robots. Autonomous systems. Industrial AI. Investors are funding it like a frontier research lab, not a normal startup. Which tells you something important: The industry is starting to hedge that LLMs alone might not be the endgame. A second paradigm may be coming. And now there’s $1B betting on it. If you want a full breakdown of the deck behind Yann LeCun and the AMI thesis, comment “DECK” and I’ll send you the link 👇