Supposedly this is a roman a clef about Jesse Livermore's career. There's a lot of stuff in this book that makes sense of markets in ways that pretty much no other investing book I've ever read does. Some what I remember are bucket shops, tape sense, marketing campaigns for new stocks, risk of ruin (Livermore went bust over and over), and what amounts to compulsive gambling.
None of this is about an end user in the sense of the user of an LLM. This is aimed at the prospective user of a training framework which implements backpropagation at a high level of abstraction. As such it draws attention to training problems which arise inside the black box in order to motivate learning what is inside that box. There aren't any ML engineers who shouldn't know all about single layer perceptrons I think, and that makes for a nice analogy to real life issues in using SGD and backpropagation for ML training.