A newly released guide provides a comprehensive introduction to diffusion language models and the research advances driving them. Adapted from major machine learning conferences, the resource details the core building blocks of modern open-source implementations. It covers essential techniques including masking diffusion, iterative refinement, post-training, and variable-length generation.