1.4 About the Authors
The principal author, Dr. S. Salewski, studied Physics, Mathematics, and Computer Science at the University of Hamburg (Germany), receiving his Ph.D. in experimental laser physics in 2005. His professional experience includes research on fiber lasers, electronics design, and software development using various languages, including Pascal, Modula-2, Oberon, C, Ruby, Nim, and Rust. Some of his open-source projects—such as GTK GUI bindings for Nim, Nim implementations of an N-dimensional R-Tree index, and a fully dynamic constrained Delaunay triangulation algorithm—are available on GitHub at https://github.com/StefanSalewski. This repository also hosts a Rust port of his simple chess engine (with GTK, EGUI, and Bevy frontends), selected chapters of this book in Markdown format, and materials for another online book by the author about the Nim programming language, published in 2020.
Naturally, much of the factual content and conceptual explanations in this book draw upon the wealth of resources created by the Rust community. This includes numerous existing books, the official online Rust Book, Rust’s language reference and standard library documentation, Rust-by-Example, the Cargo Book, the Rust Performance Book, blog posts, forum discussions, and many other sources.
As mentioned previously, this book was written with significant assistance from Artificial Intelligence (AI) tools. In the current era of technical publishing, deliberately avoiding AI would be highly inefficient and likely counterproductive, potentially even resulting in a lower-quality final product compared to what can be achieved with AI augmentation. Virtually all high-quality manufactured goods we use daily are produced with the aid of sophisticated tools and automation; applying similar principles to the creation of a programming book seems logical.
Initially, we considered listing every AI tool used, but such a list quickly became impractical. Today’s large language models (LLMs) possess substantial knowledge about Rust and can generate useful draft text, perform sophisticated grammar and style refinements, and answer specific technical questions. For the final editing phases of this book, we primarily utilized models such as OpenAI’s ChatGPT o1 and Google’s Gemini 2.5 Pro. These models proved particularly adept at creating concise paraphrases and improving clarity, sometimes suggesting removal of the author’s original text if it was deemed too verbose or tangential. Through interactive prompting via paid subscriptions to these services, we guided the AI towards maintaining a concise, neutral, and professional technical style throughout the final iterations, ensuring a coherent and consistent presentation across the entire book.