AI · Knowledge track

Understand
AI models.

Model names change quickly. The concepts underneath them change more slowly. This track explains what an AI model is, what parameter counts do and do not tell you, how context windows shape a run, and what open-weight or closed access means in practice.

Start here

What is an AI model?

Separate the trained model from the application, runtime, API and surrounding tools.

Build the mental model →
Scale

Model parameters

Understand what billions of parameters represent — and why parameter count is not a quality score.

Understand parameters →
Runtime

Context window

Learn what occupies context, why usable context differs from a headline number and how long inputs affect a run.

Explore context →
Access

Open-weight vs closed

Compare weight access, local deployment, API dependence and the separate question of licensing.

Compare access models →
MODEL ≠ PRODUCT
A model is one component of an AI system. Interfaces, retrieval, tools, safety layers, prompts and inference settings can substantially change the behavior users observe.