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)

I spent 3 months evaluating conversational video AI for Synnc. The decision came down to one question: is perception a feature, or a foundation? 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Every creator joining Synnc needs a profile before our agentic GTM layer matches them with AI brands. → Personality, content style, brand voice, audience signal. Manual Zoom calls don't scale. Forms miss nuance. We needed AI interviews that read the room, adapt in real time, and run 24/7. 𝗪𝗵𝘆 𝗡𝗼𝘁 𝗛𝗲𝘆𝗚𝗲𝗻 HeyGen is excellent at what it was built for. $100M+ ARR, 100K+ businesses, SOC 2 Type II, and LiveAvatar now ships real-time. But HeyGen was architected rendering-first. Real-time conversation was layered on. 𝗪𝗵𝘆 𝗧𝗮𝘃𝘂𝘀 Tavus was built perception-first. Three foundational models, one pipeline: ↳ Raven-1 reads facial cues and environmental context ↳ Sparrow-1 handles conversational timing and turn-taking ↳ Phoenix-4 renders at sub-600ms end-to-end latency SOC 2 + HIPAA. $40M Series B led by CRV last month, $64M total raised. 𝗧𝗵𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗥𝘂𝗹𝗲 → Automating content generation? HeyGen. → Automating two-way conversation? Tavus. The pattern repeating at the agentic layer: ↳ Chatbots → AI agents ↳ Video generation → AI humans ↳ Static onboarding → Adaptive interviews 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗡𝗼𝘁𝗲 Conversational AI touching creator data needs SOC 2, data residency, and audit trails per session. Non-negotiable. Ours runs behind isolation, consent capture, and full transcripts. 𝗧𝗵𝗲 𝗦𝘆𝗻𝗻𝗰 𝗦𝘁𝗮𝗰𝗸 Daily → Deepgram → Pipecat → LangGraph → Mem0 → Tavus → ElevenLabs Built to profile creators at scale without a scheduling link in sight. PS → What's the one onboarding step that still needs a calendar invite? Drop "manual" below.