veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

eyeseast

I have an updated version of this on my blog here: https://chrisamico.com/blog/2023-01-14/python-setup/.

Python

This is my recommended Python setup, as of Fall 2022. The Python landscape can be a confusing mess of overlapping tools that sometimes don't work well together. This is an effort to standardize our approach and environments.

Tools and helpful links:

• Python docs: https://docs.python.org/3/
• Python Standard Library:  - Start here when you're trying to solve a specific problem

patriciogonzalezvivo

Please consider using http://lygia.xyz instead of copy/pasting this functions. It expand suport for voronoi, voronoise, fbm, noise, worley, noise, derivatives and much more, through simple file dependencies. Take a look to https://github.com/patriciogonzalezvivo/lygia/tree/main/generative

Generic 1,2,3 Noise

float rand(float n){return fract(sin(n) * 43758.5453123);}

float noise(float p){
	float fl = floor(p);
  float fc = fract(p);