Brevity is underrated in technical writing. Most books on this subject are eight hundred pages, and most readers stop at page ninety. Burkov's format is a deliberate response to that, and the reason it works is that the difficulty is not in explaining things, it is in deciding what not to explain. He is good at that decision, and it showed in the hundred page machine learning book which many people, including me, still consider the clearest short introduction to the field.
This one applies the same discipline to language models. The structure is historical and I think that is the right call. You start with machine learning foundations, then count based language models, then recurrent networks, then transformers, then the modern practices of fine tuning and prompting. Learning it in that order means that when the attention mechanism arrives you already understand the problem it solves, because you have watched recurrent models struggle with exactly that problem.
A book that opens with transformers gives you the answer without the question, and readers of those books tend to end up with a description of an architecture rather than an understanding of why it exists. The implementations matter too. There is PyTorch code and there are notebooks, so the ideas are runnable rather than decorative, and modifying a small working implementation is one of the fastest ways to find out whether you actually understood the explanation. The obvious cost of a hundred pages is omission.
You will not come out of this an expert in anything specific. Coverage of the frontier is thin, so agents, evaluation methodology, serving, and the operational realities of running these systems get very little space, and that is where a lot of the current professional work sits. The correct way to read this is as a foundation you build on, not a destination. It also asks something of the reader.
The explanations are intuitive where they can be, and there is still mathematics, and you need Python and reasonable comfort with linear algebra and probability to get full value. Someone entirely new to programming should start elsewhere. The commercial model is worth praising. All chapters stay available on the site under an explicitly stated read first buy later principle, so you can read the whole thing, decide whether it earned your money, and then pay.
That is a rare degree of confidence in one's own work and it should be more common. The e-book is pay what you want through Leanpub, and physical editions are priced at a premium that is high per page and typical for self published technical print runs. Hardcover buyers can request a PDF, which is the right way round. My 4.4 reflects an excellent, honest, well judged short book with real limits that it is upfront about.
If you have been using language models for a year without understanding them, this is the most efficient way I know to fix that, and you should plan for what you read next before you finish it.