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.

A priming bundle grows without anyone ever deciding to grow it. Mine reached a 106 KB markdown file going into every fresh chat before I noticed I was spending most of an opening context window on orientation the job did not need. - The files grow, the bundle never gets re-scoped - each markdown earns a few lines a week and nobody re-reads the total. - Orientation is paid per spawn - every new chat pays the full size again, whether that job touches any of it or not. - Size is checkable, relevance is not - a byte count is trivial to gate on, whether a file earns its place in this specific job is not. What I run now is a size gate rather than a curation step. The priming script assembles the bundle, checks the total, and if it is under threshold it goes. If it is over, the loop running in the background trims on its next pass rather than blocking the spawn. Crude, but it fails in the right direction, because I never get stopped from starting work and the trim happens on the loop's clock instead of mine. It is still a blunt instrument. Size is a proxy for the thing I actually care about, which is whether a given file is load-bearing for this particular job, and a byte count cannot tell me that. So this one is a question rather than a share. How are you handling it - retrieval instead of pasting, per-job bundles assembled by task type, a summarisation pass, or third-party tooling I have not looked at properly? I am more interested in what has actually held up in practice than in what should work on paper.

Hi all, Before I get to my questions, I want to thank Jake for such a valuable resource in his ICM / F&F method. I am an AI and coding newbie, and this has definitely put me on the fast track. I knew I needed something like this, I just didn't know how to even start looking for it, and then the YouTube algorithm served it up for me. The moment I saw what Jake was teaching, I knew it was my next step in the journey. Thank you! On to my questions. I have gone through the Foundation Course, and I have the full Workspace-Blueprint template. The three-layer method (Map, rooms, tools) has become how I actually work, and I have been running it for a little while on smaller projects. Here is my situation. I am building the operating system for a multi-industry business group, and it has grown into several distinct domains: - an identity and philosophy layer: governing principles, values, and leadership and management philosophy, synthesized from a body of books and courses, - a knowledge layer: a large reference library plus custom tools that extract and organize knowledge from books and transcripts (several thousand files), - a research layer, - an operations layer: SOPs and playbooks for running the businesses, - a learning layer that captures lessons and feeds improvements back in, - a governance layer that holds the enterprise map. Right now this lives across roughly ten separate Claude projects that grew organically. Some began as tools, some as libraries, and each became its own project. Many are stages in a workflow. So, work completed in one "stage" needs to move into another. Most of this was built before I discovered Jake. I am fairly certain I need to reorganize what I have already built, and I am also considering turning some, if not all, of the skills I have created into file-based workflows. The method is taught at the scale of one folder with a few rooms, and mine is well past that, so I want to be sure I am applying it correctly rather than drifting into the "too many workspaces" problem at the project level.

Stop README-shopping. Start scoring the repos you actually trust. Most of us still pick open-source like restaurants. Stars. Nice README. Maybe a glance at the last commit. That friction is real, and it's how vibe-coded slop ends up in the stack. New classroom piece is live: Git Repo Security: Why It Matters, and How We Actually Do It https://www.skool.com/cliefnotes/classroom/c7f102c7?md=43a45577d5cb4096b040770491d87b50 Why this hits us: we pull public git constantly. Agents clone third-party skills. Volunteers don't have an AppSec team. Stars are a weak signal. The article folds OpenSSF / SLSA / CISA-style checks into two portable skills so the discipline gets cheap and repeatable. How you run it (newbie path) 1. Open the article and download the zip. 2. Drop the zip into your harness (Claude Code, Cursor, OpenCode, Grok, etc.). 3. Prompt the agent like a colleague: run repo-scorecard Egonex-AI/Understand-Anything (github username/repo) or run repo-eval Egonex-AI/Understand-Anything ============================================================= Here's what you get from repo-scorecard: Scorecard: Egonex-AI/Understand-Anything Score: 9.50 / 10.00 — healthy, well-run project What's good: • Actively worked on — last update was yesterday. • Very busy recently: 355 commits in the last 90 days. • Many people involved — about 70 different contributors this year (though one person still does most of the commits). • Has a clear license, a README, and a security contact policy. • Has automatic quality checks and tests set up. • Ships tagged releases (8 tags) and keeps a fixed parts-list for its building blocks. Things to know: • No automatic updates for the building blocks it depends on. Score based on publicly visible project activity and housekeeping. It does not include a security scan of the code itself. ==============================================================

Come chat about your goals for the week! If you are new here, this is a chat room for general talk about the past week and the week ahead. Share what you’re working on and network with others! If you‘ve been here before, you know the drill. Leave a comment below. Some ideas to get the juices flowing: - What you’re working on - Something you hope to ship - Blockers you’re running into at the moment - Where you’re looking for help currently - Anything else you’d like to share Have fun and enjoy your week everyone! This week’s poll: Do you feel like you have the fundamentals down at this point or are you still learning any basics? If you feel you’re missing something, comment it below 👇