วิดีโอ

🤖 Everyone’s building agents. Almost nobody can tell you where the “agent” actually lives. Here’s the split that made it click for me 👇 🟢 HARNESSING — everything you wrap around the model: 📄 System prompt → the role + your brand voice 🧠 Memory → past posts and what actually worked 🔧 Tools → LinkedIn API, analytics, scheduler 🔍 Retrieval → your best hooks + audience data That’s the scaffolding. It’s static. It’s what the model has. 🟠 LOOP ENGINEERING — how it keeps going: 📝 Plan → ✍️ Draft → 📤 Post → 📊 Measure → 🔁 repeat Then one question: goal met? No → run it again. Yes → ship. That’s the part that turns a chatbot into an agent. It’s dynamic. It’s what the model does. ⚡ Most people over-invest in harnessing (endless prompt tweaking 😅) and completely skip the loop. So they get a very well-dressed model that answers once and stops. And the magic is where they meet — that teal arrow at the bottom. Results flow back into memory, so every cycle starts smarter than the last. 🔄 I built this as a growth agent because that’s the job I actually want automated: plan the angle, draft the post, schedule it, measure it, learn, repeat. 📈 Once you see this split, you stop asking “what’s the best prompt?” and start asking “what’s my loop, and when does it stop?” — which is exactly the question that separates people who use AI from people who engineer it. 🚀 Save this before your next agent build. 🔖 What should the loop optimize next — 📈 reach or 💬 replies? Tell me below 👇 . . . #HackProduct #AIagents #AIengineering #LLM #agenticAI promptengineering systemdesign buildinpublic AItools softwareengineering contextengineering codevisuals devtools machinelearning techreels growthengineering