One Small Step is an educational resource that explains core AI and large language model concepts through short, accessible articles designed to be read in under five minutes. It covers the structure and function of key LLM components like attention mechanisms and tokenization, as well as foundational machine learning mathematics such as matrix rank and overfitting.
The project also serves as a guide to the GGUF file format, which packages all model parameters and metadata into a single compact binary file for cross-platform deployment without external dependencies. It explains how this format enables efficient model storage, fast loading through memory-mapped file access, and local inference on consumer-grade hardware including CPUs and GPUs.
Beyond AI education, One Small Step functions as a static site generator that builds a complete website from Markdown files at build time. It uses file-based routing to map each Markdown file directly to a URL path, applies reusable HTML templates with content injection, and bundles CSS and JavaScript assets during the build process to reduce client-side load times. The documentation covers both the AI concept explainer series and the static site generation tooling.
