Music Theory

A working understanding of calculus — the mathematics of change and accumulation. The trunk gives you the throughline from limits and derivatives through the Fundamental Theorem to the borderlands where calculus hands off to analysis. Clusters dive into rigorous limits, integration techniques, multivariable and vector calculus, differential equations, series, and complex analysis.

How markets, brokers, and order books actually work — and what the evidence says about trying to beat them. The trunk covers market mechanics, asset classes, leverage, and the risk math every participant needs; clusters dive into microstructure, execution, the retail arenas, sizing math, psychology, manias, and strategy evidence.

A beginner-friendly curriculum that builds deep intuition for how AI actually works — not through math or code, but through mental models that make effective use a natural consequence of understanding. Covers how language models think, why they hallucinate, the art of prompting, practical AI workflows for writing, images, and code, and the societal shifts AI is driving. Designed for anyone curious enough to want more than tips and tricks.

Legal literacy — how law actually works. The trunk gives you the operating system: where law comes from, how courts and precedent work, contracts, torts, property, criminal vs civil, and constitutional structure. Clusters dive into reading cases, contract mechanics, IP, your rights with police, and the startup legal toolkit.

A coherent, mechanistic picture of how the Earth actually works as a coupled system — its biogeochemical cycles, climate physics, ecosystems, pollution pathways, and the leverage points where solutions live. The trunk gives you the throughline a working professional needs to reason about environmental claims; clusters dive into circulation, ecology, paleoclimate, climate modeling, pollution, energy, and mitigation.

A comprehensive, 3-pillar curriculum that builds durable understanding of artificial intelligence and machine learning — from the mathematical foundations and classical algorithms, through modern deep learning architectures, to the societal implications of deploying AI in the real world. Emphasizes timeless principles over transient tools so knowledge remains valuable as the field evolves.