💰 $325 CASH PRIZE 💰 That's a full year of Premium. Win this and your membership pays for itself. 📋 THE CHALLENGE You just got hired again. Different client this time. Meet Sarah, a freelance copywriter who's drowning in context-switching. 📎 Download the full client brief attached to this post. Short version: She works with three types of clients (SaaS founders, ecommerce brands, local service businesses) and starts from scratch every project. She doesn't need another tool. She needs a system. Your job is to build her a folder-based AI specialist she can drop into any Claude project. The folder IS the deliverable. 🗂️ THIS WEEK YOU LEARN ICM Up until now, comps have been "build a thing." This week you utilize the methodology taught throughout the community. 🧠 Folders as architecture. That's it. That's the whole concept this week. Your specialist is a folder with five things: - 📄 identity.md (who they are) - 📐 rules.md (how they respond) - 💬 examples.md (what good looks like) - 📚 reference/ (source material) - 📖 README.md (how to use it) Drop the folder into a Claude project. Claude becomes the specialist. Reusable. Shareable. Portable. 🎯 PICK YOUR SPECIALIST Don't pick copywriting. That's Sarah's example. Pick something YOU would actually use. A few sparks to get you thinking: - A salary negotiation coach - A meal planner that knows your dietary restrictions - A code reviewer for your stack - A real estate market analyst for your city - A technical recruiter screener - A grant writer for nonprofits in your space The more specific, the better. "Marketing expert" is not a specialist. "B2B email expert for enterprise SaaS targeting CFOs" is. 💼 WHY THIS ONE LANDS ON YOUR RESUME Real talk. Winning a comp in a Skool community doesn't get you a job by itself. But shipping a working folder-based AI specialist with a clean README and a public repo? That's a portfolio piece.

Most people are still treating LLMs like goldfish with infinite context windows. But the real power comes when you give your AI systems persistent, structured, and reliable memory. I’ve been diving deep into three distinct approaches: - Open Brain (OB1) — the personal exocortex - Poor Man’s Memory (PMM) — the ultra-lightweight, git-native path @Millenial Cat - Cognee — the structured graph + vector layer for serious agents Each represents a completely different philosophy for how we should capture, store, and retrieve context. Full breakdown dropping soon: architecture comparisons, strengths & tradeoffs, how they actually fit together in a real stack, and when I’d choose one over the others. If you’re building any kind of long-term AI workflow, personal knowledge system, or agent setup — this one’s for you.What’s your current memory strategy? Drop it below