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7d •
Welcome to Clief Notes. Here's where to start.
1. Go check out 📚Navigating The Course to see how to get around and what's here. 2. Start with The Foundation. Concepts, folder architecture, prompting framework. Everything else builds on this. 3. Check in at the bottom of each lesson. Polls, discussion posts, other members working through the same stuff. Use them. 4. When you're ready to build real things join in on our Biweekly competitions and win some real cash. ⭐ Competitions Mega Thread 5. If you are wanting to dive into the masterminds, grab all the past templates, artifacts and resources. Upgrade and head into the The Vault for Premium and The Drawing Room (VIP) for VIP 6. Post your work. Ask questions. Help others when you can. What are you here to build?
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21h •
📢 Recordings of Tea Masterminds are live: The Second Brain
🧠 This round was about what a second brain actually is: a context layer you and your AI both read, not a notes app. The Afternoon Tea is the teaching. The High Tea is the room putting it to work on scale, memory, trust, and security. Here is what I want you to understand about these drops, because it is the whole point of being in here. While the videos are valuable and being able to sit and answer your questions is a big reason for them that's not the only value they hold. 📄 Every drop is a set of working files. Markdown built to be used and reused. Each one ends with the exact data to give your AI for your own situation. This round also ships a starter folder you can open, run the self-audit on, and walk away with the skeleton of your own second brain in a sitting. 🤖 I build them expecting you to feed them to your AI. That is the design. Hand a whole round to Claude in a few minutes, whether or not you made it live. The room's thinking is in the files, so you lose almost nothing by catching it later. 🔄 They adapt. A prompt pack is frozen. These are meant to be reshaped: update the context, swap in your own work, bend the templates to your process. And they grow on my side too, as we learn together in these calls. The call is dialogue. The package is that dialogue, crystallized into something you can run. Next round builds on this one. ☕ Afternoon Tea 6 →Afternoon Tea 6 (Second Brain Chat) 🫖 High Tea 10 → High Tea 10 (Second Brain Deep Dive) 🧭 How you should use these: 🔹 Show up live when you can. Your questions shape the next drop. 🔹 When you can't, rewatch, or drop the files into your AI and run the prompt at the bottom. 🔹 Open the starter folder and build your own version. Rename it to your work. It is yours to keep. 📚 A mastermind ends when the call ends. What you get here keeps working after: a structured version of your own thinking (and some of my own thinking!) that improves every round. In my opinion that is worth more than the hour in the room. (or three as some of you stick around in these calls to chat)
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2d •
Ai before ChatGPT: The Interview.
In this interview I sit down with Matt from NLP Logix. He's been working in the AI space longer than most people have been working in general. We dive into what changed and what is it going to be important about the future. This is a three part series, I will be posting another two videos from another two experts in mathematics and Engineering! Please like and comment on YouTube if you have time as well!
8d •
Stretching Claude Further: ICM to Orchestrate 2,350 Local Workers
We've been experimenting with treating ICM not as the whole system, but as one layer inside a larger orchestration architecture. For us, ICM solved something much bigger than prompting. It solved context. How do you keep models focused? How do you stop them from reading entire repositories? How do you bound work? How do you reduce drift? How do you move toward convergence? ICM gives us work packets, context contracts, routing, validation, and controlled handoffs. Once we started implementing it, we found ourselves asking: What happens if we build around that? Internally we've been experimenting with a governance layer we call AQ-CMF (just our internal name for it), but I think the more interesting thing to share is the orchestration itself. Right now it's basically a small "Swarm Orchestration Starter Pack." The idea is simple: Use the smallest model capable of doing the work. Reserve larger models for judgment and reasoning. Current setup: RTX 3060 12GB • 2,200 binary filtering workers • Qwen 0.6B • yes/no decisions • triage • filtering • classification RTX 5060 Ti 16GB • 150 structured extraction workers • Qwen 4B • schema completion • information extraction • template generation Cloud reasoning layer (introduced to me by @Ari Evergreen 's post https://www.skool.com/cliefnotes/i-run-100-agent-workflows-on-a-budget-model-heres-the-catch) • up to 200 Kimi 70B workers • interpretation • reasoning • code generation • higher-complexity analysis Claude Code • orchestration • synthesis • validation • architecture decisions • final judgment The smaller models don't really "think." They observe. They classify. They extract. They filter. Claude assembles. Claude validates. Claude decides. One thing we've noticed is that this also changes the economics considerably. Instead of paying frontier-model prices for every operation, we let local models perform the cheap labor.
2d •
Your AI Content Might Be Fine. Maybe That's The Problem.
If you use AI to help write anything, whatever your niche is, you've probably had this moment: the draft looks fine, technically correct, nice sentences, but something about it feels a little off. Maybe a little too polished, a little too generic, but you can tell - or maybe just sense - that it's written by an AI bot. I ran into this when setting up my voice.md file, so I built something to fix it. The guide I created doesn't just describe the tone I want, it defines it with real examples. Here's the structure that actually worked: 1. Gold standard examples: 2 to 3 pieces of writing that are exactly the tone I'm going for, used as a reference before writing anything new. Each example is followed by a "Why it's good" explanation. 2. Bad examples with annotations: writing that looks fine on the surface but fails in a specific, named way. 3. A drift patterns table: short phrases that sound right but aren't, next to the actual reason they don't work. 4. Mechanical rules: specific, almost boring rules that are easy to forget but change everything once you write them down The biggest shift for me was realizing tone can't just be described, it has to be demonstrated. Telling an AI to "sound warm" or "sound authentic" doesn't work nearly as well (or at all) as showing one good example and one bad example side by side, then naming exactly what's different between them. More examples = better output. Explaining the failures is huge. If you're using AI for anything where tone actually matters, I'd genuinely recommend creating something like this before you let it write your first real piece. It saved me a ton of revision time and kept my content sounding like an actual person instead of a generic AI draft. Happy to answer questions if anyone wants to build their own version of this.
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