Reference Track

The Expert Library

Go under the hood. How models actually work — parameters, weights, types, and quantization — explained in plain language.

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What is this?

The main Academy teaches you to work with AI. The Expert Library teaches you how the AI itself works — the knowledge that lets you pick the right model, read a model card without blinking, and understand exactly what you're running (and where). Read the six pages in order, or jump to whatever you need. No account, no quiz — this is a reference shelf.

Parameters & Model Sizes

What a parameter actually is, what the "B" in 8B means, and which sizes run on which hardware.

Start Here

Open Weights & Model Families

What "open weights" really means (and doesn't), the major model families, and the license fine print.

Explore Open Weights

Model Types

Chat models, reasoning models, embedding models, vision models — what each one is for, and when to use which.

Learn the Types

Quantization

How a 70-billion-parameter model shrinks to fit modest hardware — and what the compression costs you.

Understand Quants

Choosing a Model

Tokens and context windows, how to read a model card, why benchmarks lie a little, and a decision table that matches tasks to model sizes. The capstone — everything else builds to this.

Read the Capstone

Long-Horizon Work

How agents carry real projects across hours and days: why long tasks fail (drift, context exhaustion, credit assignment), the five-pattern scaffold that fixes it, and how "time horizon" gets measured. The bridge from model mechanics to finished work.

Learn Long-Horizon Patterns

Reading Order

1. Parameters 2. Open Weights 3. Model Types 4. Quantization 5. Choosing a Model 6. Long-Horizon Work
Previous: The Glossary