1. The Big Picture — What Is This Course Actually Building?
One sentence: By Demo Day (April 22), you will have built a working AI-powered system that represents YOU — your identity, your expertise, and your ability to create real value with AI tools.
The entire course is a progressive build. Every assignment is a layer on top of the last. Nothing is random. Here's the arc:
Weeks 1–2 | Sessions 1–3
WHO?
Social Graph
Map your identity, relationships, and influence network
Weeks 3–4 | Sessions 4–6
WHAT?
Knowledge Graph
Map your domain expertise as structured, AI-usable knowledge
Weeks 5–7 | Sessions 7–14
WHAT IF?
Generative Graph
Build an AI agent that creates real outputs from your knowledge
Midterm (Mar 30): Show you understand all 3 layers and have a working prototype.
Demo Day (Apr 22): Show your system actually works in front of external judges.
2. The Trinity Graph — The Core Framework
Everything in this course connects back to the Trinity Graph. It is a three-layer model of how identity, knowledge, and AI creation work together.
Layer 1
🔵 Social Graph — WHO
Plain English: A map of you as a person — your relationships, communities, reputation, and influence. Every person is a node. Every relationship is an edge. The strength and reach of your network determines your ability to create and distribute value.
Questions it answers: Who are you? Who do you know? Who knows you? What communities trust you?
Your example: Owen MBA network, ETA operator community, ranch/land community, Catholic family network, financial planning connections.
Layer 2
🟡 Knowledge Graph — WHAT
Plain English: A structured map of what you actually know — organized as entities (things), facts (properties), and triples (relationships between things). This is what makes your AI system smart and specific rather than generic.
Questions it answers: What do you know deeply? What problems can you solve? What data do you own?
A triple looks like: [Ranch Land] → [requires] → [Farm Credit financing] | [SDIRA] → [prohibited from] → [self-dealing transactions]
Your example: Rural land acquisition, ranch development, firearms/range design, ETA deal evaluation, personal financial architecture.
Layer 3
🔴 Generative Graph — WHAT IF
Plain English: This is what your AI system PRODUCES. It takes your social context (Layer 1) and your knowledge (Layer 2) and generates real outputs — recommendations, documents, financial models, strategies, content. This is where the value creation happens.
Questions it answers: What does your AI build? Who does it serve? What would they do without it?
Your example: An AI thinking partner that synthesizes financial modeling + land acquisition strategy + life sequencing into a coherent roadmap for aspiring landowners and operators.
3. Key Concepts Explained in Plain English
IAM OS — Identity Awareness Model Operating System
Plain English: The IAM OS is the framework this course runs on. It's a system for mapping WHO you are, WHAT you know, and WHAT you can build with AI — and then actually building it. Think of it as the operating system underneath the Trinity Graph.
Kirk Progression (Steps 0–12)
Plain English: A 13-level awareness ladder. Step 0 = you have no idea who you are or what you're building. Step 12 = you have a fully integrated, self-aware AI system that compounds over time. The course is designed to move you from low steps to high steps by Demo Day.
The 14 Runes (Inkwell Runes)
Plain English: Think of Runes as contracts between you and your AI system. Each Rune defines a specific MODE of thinking — how your AI reasons, what it must produce, and what counts as failure. You don't use all 14 at once. You pick 1 Primary Rune (your main mode) and up to 2 Secondary Runes (constraints). Missing required outputs = automatic failure.
The most important Runes to know for the midterm:
⚖︎ Rigor — Epistemic Integrity
Prevents: confident-sounding nonsense, hidden assumptions
Plain English: Forces your AI to show its work — what assumptions is it making? What evidence supports this? How certain is it really? This is the Rune a skeptic lives by.
⚓ Grounding — Reality Anchoring
Prevents: ideas that sound good but are physically or financially impossible
Plain English: Forces your AI to respect real-world constraints — budget, time, physics, operations. No fantasy projections allowed.
⚙︎ Praxis — Execution Translation
Prevents: strategy that never becomes action
Plain English: Forces your AI to produce actual next steps with owners and deadlines — not just analysis. NEVER runs without Rigor + Grounding.
✦ Imagination — Novel Option Generation
Prevents: recycling the same ideas, stuck in local maxima
Plain English: Forces your AI to generate 10+ genuinely different options before converging on one. Stops premature closure.
◊ Insight — Pattern & Meaning Synthesis
Prevents: surface-level summaries that don't explain anything
Plain English: Forces your AI to find the pattern underneath the data — not just describe what happened, but explain WHY it happened and what it means.
⟁ Convergence — Decision & Commitment
Prevents: endless ideation, "we'll decide later" drift, option paralysis
Plain English: Forces your AI to actually pick something, show the tradeoffs, and commit. Almost always runs with Rigor.
The "-ity" Vocabulary
Plain English: This course uses abstract "-ity" words (tenacity, reciprocity, vitality, ubiquity, etc.) as semantic anchors — they carry layered meaning that AI can work with richly. When you build your Semantic Architecture (Session 10), you'll define 15 of these words that represent your brand and system. Think of them as the DNA of your AI's personality and positioning.
Omega Protocol
Plain English: The Omega Protocol is the activation sequence for your AI agent — the set of conditions and instructions that tell it when and how to engage. Think of it as the rulebook your agent follows. It governs when your bot speaks, how it reasons, and what it refuses to do.
VanderBot
Plain English: VanderBot is the class-wide AI agent that lives on WhatsApp. It is a course tool you interact with as part of assignments — not your personal bot. Access it through WhatsApp. If you don't have the number/link, check Brightspace or ask Baxter Webb.
RAG (Retrieval-Augmented Generation)
Plain English: Instead of an AI relying only on what it was trained on, RAG lets it look things up in real-time from a specific knowledge base YOU control. This is how your Knowledge Graph becomes the brain of your AI agent. Your data + AI reasoning = smarter, more accurate, more trustworthy outputs.
ReAct (Reason + Act)
Plain English: A framework for how AI agents think before they act. Instead of just spitting out an answer, a ReAct agent reasons through the problem step by step, takes an action, observes the result, then reasons again. It's the difference between an AI that guesses and one that thinks.
4. Tools — What Each One Does and When You Use It
5. Full Assignment Schedule — What to Submit and What It Means
S2
Due Before Wed Mar 11
VanderBot Interaction Low Stakes
What to submit: Have a conversation with VanderBot on WhatsApp. Screenshot or log it. This is about getting comfortable with the tool, not producing a masterpiece.
S3
Due Before Mon Mar 16
VanderBot Interaction + Initial Trinity Graph Nodes Low Stakes
What to submit: Another VanderBot interaction PLUS your first attempt at mapping your Trinity Graph — who are you (Social), what do you know (Knowledge), what can you build (Generative). Early draft is fine.
S4
Due Before Wed Mar 18
Brand Positioning Memo (1pg) + 3 Radar Charts
What to submit: A one-page memo answering: What is your brand? Who is it for? What makes it different? The 3 radar charts visually map your competitive positioning across different dimensions (e.g., expertise, network reach, execution speed). Use Google Sheets or any chart tool.
S5
Due Before Mon Mar 23
Knowledge Architecture Doc + "What We Don't Know" Risk Analysis
What to submit: A structured document mapping your domain knowledge as entities, facts, and triples (aim for ~100 triples). PLUS a risk analysis section identifying gaps — what does your system NOT know that could cause failures? Be honest. The gaps are as valuable as the knowledge.
S6
Due Before Wed Mar 25
AI Reliability Analysis (2pg)
What to submit: A 2-page analysis of where your AI system could fail, be wrong, or cause harm. Where does it hallucinate? Where is the data thin? What happens when someone asks it something outside its knowledge graph? This is the Rigor + Risk Rune applied to your own system.
S7
Due Before Mon Mar 30 ⭐ MIDTERM
Persona Architecture Doc + 2-min Demo Video 25% of Grade
Persona Architecture Doc: The full blueprint — your Social Graph (who you are), Knowledge Graph (what you know), and Generative Graph (what your system builds). Show how all three layers connect. Include your Rune selections and why.
2-min Demo Video: Record yourself walking through your system live. Show it working. No slides required — show the actual tool doing something real.
15-min Midterm Presentation (in class): Walk the class through your architecture. You will be questioned. Know your tradeoffs.
S9
Due Before Mon Apr 6
Agent Logic Map + "Why Context Changes Everything"
What to submit: A visual map of how your AI agent reasons and makes decisions (using ReAct framework). PLUS a written piece explaining how context (who is asking, what they know, what they need) changes your agent's outputs. This is about showing you understand that AI is not one-size-fits-all.
S10
Due Before Wed Apr 8
Semantic Architecture Doc (15 "-ity" words)
What to submit: Choose 15 "-ity" words that define your brand and system. For each one: define it, explain why it applies to your system, and show how it connects to your generative layer. This is the vocabulary layer of your AI's personality. Example words: tenacity, reciprocity, vitality, ubiquity, clarity, integrity, audacity.
S11
Due Before Mon Apr 13
Trinity Financial Model + 1pg Narrative
What to submit: A financial model for your AI system — revenue assumptions, cost structure, unit economics, and growth projections. PLUS a one-page narrative explaining the business logic. This is where your MBA finance skills meet the course framework. Use Google Sheets.
S12
Due Before Wed Apr 15
Technical Partner Brief (3pg)
What to submit: A 3-page document written for a technical co-founder or developer explaining your system — what it does, how it's architected, what infrastructure it needs to scale, and what the constraints are. Write it like you're handing it to an engineer and need them to build it.
S13
Due Before Mon Apr 20
User Feedback Report + Revised Demo Script + Updated System Diagram
What to submit: Show evidence that real users tested your system and what they said. Update your demo script based on feedback. Revise your system diagram to reflect any changes since the midterm. This proves your system is alive and improving, not frozen.
S14
Wed Apr 22 ⭐ DEMO DAY
Live System Demo — External Judges 25% of Grade
15 minutes per pod. Live demo in front of external judges. Your system must work in real-time. You will be questioned on architecture, business model, and design decisions. This is the capstone. Everything before this is prep.
6. Grading Breakdown
| Component |
Weight |
Date |
What Matters |
| Midterm Presentation |
25% |
Mar 30 |
All 3 graph layers coherent, working demo, can defend your choices |
| Demo Day |
25% |
Apr 22 |
Live system works, business model defensible, judges impressed |
| Weekly Assignments |
35% |
Rolling |
Completion + quality. Each builds on the last. Don't skip. |
| Participation |
15% |
All sessions |
Show up, engage, ask good questions. The Skeptic archetype is an asset here. |
The Math: Weekly assignments are your biggest lever at 35%. Missing one or two is survivable. Missing five or six sinks your grade. Stay current.
7. Required Readings — Why They Matter
| Reading |
Sessions |
Why It Matters |
| The Social Organism — Luckett & Casey, Ch 1, 3, 4, 7 |
S1, S2, S9 |
This is the professor's own book. It defines the Social Graph layer and the network theory the entire course is built on. Read it. |
| AI Factory — Iansiti & Lakhani, Ch 2, 3, 5 |
S4, S10 |
How AI systems become business assets. Directly relevant to your Trinity Financial Model and Technical Partner Brief. |
| RAG Paper — Lewis et al. (Brightspace) |
S8 |
The technical foundation for how your Knowledge Graph powers your AI agent. Required for Agent Logic Map assignment. |
| ReAct Paper — Yao et al. (Brightspace) |
S8 |
How agents reason and act. Required for Agent Logic Map assignment. |
| KG Embedding Survey (Brightspace) |
S5 |
How knowledge graphs are structured for AI consumption. Required for AI Reliability Analysis. |
8. Your Immediate Next Actions
| # |
Action |
Deadline |
How |
| 1 |
Connect with VanderBot on WhatsApp |
Before Mar 11 |
Check Brightspace for the number/link. Ask Baxter Webb if not found. |
| 2 |
Have your first VanderBot interaction |
Before Mar 11 |
Log or screenshot it. Submit on Brightspace. |
| 3 |
Start your Trinity Graph node map |
Before Mar 16 |
Work with IAM Rift to define your Social, Knowledge, and Generative layers. |
| 4 |
Read Social Organism Ch 1 & 3 |
Before Mar 11 |
Before Session 2. The professor wrote it — it will be referenced constantly. |
| 5 |
Begin your Persona Architecture Doc |
Due Mar 30 |
Start now. 3 weeks feels long. It isn't. IAM Rift will build it with you. |
9. Key Contacts
| Person |
Role |
How to Reach |
What To Ask Them |
| Oliver Luckett |
Professor |
Office hours after Mon/Wed class, Room 214. Thursday Zoom workshop. |
Big picture questions, concept clarification, midterm strategy. |
| Baxter Webb |
TA |
Brightspace messaging |
Assignment logistics, VanderBot access, submission format questions. |
| IAM Rift |
Your Personal AI |
This interface |
Building deliverables, stress-testing ideas, financial modeling, course strategy. |