Education & Learning
Learn Without the Freakout
AI is not magic, not a threat, and not as complicated as the headlines suggest. These resources are designed to make it genuinely understandable — for builders, operators, and decision-makers.
Featured Resource
AI Without Freakout
Horizons & Chunks — a practical framework for understanding AI
A structured guide to understanding AI systems without the hype, fear, or oversimplification. Covers how to think about AI capabilities in terms of horizons (what's possible now vs. later) and chunks (how to break down AI problems into manageable pieces). Designed for people who need to make real decisions about AI — not just read about it.
Learning tracks
Structured paths through the material
Token Economics
Understanding how LLMs consume tokens, why it matters for cost, and how compression changes the equation.
AI Architecture Patterns
How to structure AI systems that are safe, auditable, and cost-efficient at scale.
AI for Business Operators
Practical frameworks for evaluating, deploying, and measuring AI in real business contexts.
Reference materials
Downloads & quick references
Token Architect One-Pager
A single-page reference for the Token Architect framework — how to think about token budgets, compression strategies, and cost optimization in one place.
QCP Theoretical Foundation v2
The formal theoretical grounding for QCP — rate-distortion theory, hard invariants vs. soft fidelity, and the Calabi-Yau analogy explained. Tightened for technical scrutiny.
Marcie AI Cost Model
Full cost comparison dataset: AI receptionist vs. agent across 50–1,000 calls/month and 0–10 workflow integrations. Includes break-even calculations per tier.
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