# Reference

Background material to dip into when your project pulls you toward something you don't yet know. None of it is required reading. The class is driven by your project; this folder exists so that when a project hits a wall, there's something concrete to point at.

There are two flavors of reference here, and they're meant to be used differently.

## Tools and tech (hands-on)

Short, self-paced primers on the things you'll most often end up touching. Read them when you have a reason to, not before.

| Folder | What it covers |
|--------|----------------|
| [`python/`](python/) | Python basics — enough to read and tweak the code AI writes for you |
| [`git/`](git/) | Tracking changes, undoing mistakes, working on more than one thing at once |
| [`github/`](github/) | Putting code somewhere others (or future-you) can find it |
| [`huggingface/`](huggingface/) | Where most open-weight models live; how to grab one and use it |
| [`pytorch/`](pytorch/) | The framework most modern models are written in |
| [`docker/`](docker/) | Running other people's software without polluting your own machine |

More will appear here as projects surface the need for them.

## Papers (reading)

A small, opinionated set of papers that, taken together, give you a feel for *how we got here*. Skim them, read the abstracts, or just look at the dates and the names — the trajectory matters more than any individual result.

See [`papers/`](papers/).

## How to use this folder

- **Don't binge it.** Reading reference material in the abstract is the slowest way to learn it. Wait until your project gives you a reason.
- **Skim, then dive.** Read the README of a topic before reading any of the lessons. You may find you only need one section.
- **Ask AI as you go.** These primers are starting points, not textbooks. If something doesn't click, paste the snippet at an AI and ask.