It’s live. Starting today, everyone in Clief Notes has access to the new Clief Notes AI. The easiest way to use it? Don’t overthink it. Ask it the question you would normally ask me. “Where should I start?” “What should I focus on next?” “Where did you talk about [topic]?” It’ll use what’s already inside Clief Notes to help answer you and point you toward the right lesson or resource when there’s something worth going deeper on. The goal isn’t to give you another AI tool to play with. It’s to make everything already inside this community easier to actually use. If you want Access to it Comment "READY" and we'll send you access to it!

📋 The brief I wanted to run a basic data exercise: take one dataset, organize it three different ways, and measure what each structure actually costs an AI agent to search. A controlled comparison I could point to instead of arguing about it in the abstract. Three versions of the same data: 1️⃣ Raw — the dataset exactly as downloaded. One file, no structure, no metadata. 2️⃣ ICM — files and folders. A mechanical breakdown into a directory hierarchy, the kind of structure I've been building into ICM workspaces for a while now. 3️⃣ "Second Brain" — an Obsidian-style vault. Same content, but with per-item notes, cross-linked [[wikilinks]], character/theme pages, the whole living-notes treatment. Then I built a small tool that fires the same question at all three, using a real agent for each run (not a canned lookup), and logs tokens, time, and cost per stage. Point the same question at raw, ICM, and Obsidian, and see what each structure actually buys you. 📚 Why I chose Shakespeare I needed something big enough to be a real test, public domain, and — critically — already broken down at a fine grain (act, scene, sonnet) so I wasn't inventing structure that wouldn't exist in a messier real dataset. I looked at the U.S. Code first. It's the right shape (title → chapter → section mirrors book → chapter → verse almost exactly), but it's enormous. Shakespeare's complete works are a fixed, known-size corpus (5.4MB, Project Gutenberg, public domain) that will never change, never need re-downloading, and never go stale. That "always useful" property means this test is reusable as a reference point for other structure comparisons later, not a one-off. 💰 A bit on the cost of setting up The ICM layer cost almost nothing to build. It's a mechanical script — split on ACT/SCENE headers, extract speaker names by regex, write files. No model calls. 1,579 files, and the token footprint came out at 1.01x the raw file's size. Structure that's this close to free is easy to underrate.

Been heads down on something the past month plus. Not a lesson, nor a Tea round but more like the layer under all of it. Kept seeing the same thing in comments and onboarding calls: People not knowing where to even start or if something’s already been covered somewhere in here. We're working on fixing that. Not ready to put it in your hands yet.

Honestly super happy about how little our token cost is despite having almost 100 people in! Plenty of things to fix and solve but thats what alpha is about, onboarding people to their own orginzations slowly as well but check out the update!

Most AI workflows fail for the same reason: they treat context as a blob instead of a structure. ICM solves part of that problem by staging context through folders, markdown files, and step-by-step workflow design. But ICM becomes much more powerful when you add ontology, because ontology gives the workflow a shared semantic model: what the entities are, how they relate, and which concepts belong in each stage. That is the difference between “a well-organized prompt system” and “a structured reasoning system.” Why ontology matters Ontology is not just vocabulary. It is the relationship layer that tells an AI system how to interpret a domain. For example, in a hospital policy workflow, ontology can distinguish: - Policy - Goal - Constraint - Regulation - Requirement - Evidence - Workflow - Approval - Audit Without ontology, the model may understand these as related words. With ontology, it understands them as connected concepts with specific roles. That makes reasoning more reliable, especially when the task requires multiple hops across a domain. Why this matters in ICM ICM already helps by loading only the context needed for the current stage. Ontology makes that staging meaningful. Instead of loading everything at once, the workflow can load a scoped ontology for each step: - Intent stage: Policy, Goal, Stakeholder - Compliance stage: Regulation, Requirement, Evidence, Violation - Operations stage: Workflow, Role, Approval, Audit Now the model is not just reading context. It is traversing a domain structure in small, controlled steps. That reduces noise, improves traceability, and makes each intermediate reasoning step easier to inspect. Concrete example Imagine a demo project that analyzes a hospital AI policy. Stage 1 extracts the policy intent. Stage 2 checks compliance against regulations. Stage 3 translates the findings into operational guidance. Each stage uses a different ontology slice, but all three stages share the same semantic backbone. That means the model can move from “what is this policy trying to do?” to “does it meet the requirement?” to “how should the team implement it?” without losing meaning between steps.