Papers-in-100-Lines-of-Code: minimal implementations of over 60 influential AI research papers each under 100 lines of code

What it solves

This project provides concise, minimal implementations of a wide variety of influential AI and machine learning research papers, reducing complex theoretical concepts to manageable codebases of 100 lines or fewer.

How it works

The repository serves as a collection of standalone implementations of algorithms and architectures described in academic papers. It covers a broad spectrum of AI research, including generative models (GANs, Diffusion), reinforcement learning (DQN, PPO), neural radiance fields (NeRF), and optimization methods (Adam).

Who it’s for

It is designed for developers and researchers who want to understand the core mechanics of AI papers by reading a simplified version of the code rather than wading through dense academic text or massive production libraries.

Highlights

• Over 60 implemented papers.
• Focuses on extreme brevity (100 lines of code per implementation).
• Covers diverse domains including 3D reconstruction, image synthesis, and deep reinforcement learning.
• Includes implementations of landmark papers like Stable Diffusion v1-5 and 3D Gaussian Splatting.