Taste-wise, I'd prefer these sorts of books to just briefly state facts and spend most of the time on building intuition, but just putting it all together in one place is nice in and of itself. I've had to re-derive all this shit on my own the last six months, and it would have been helpful to have a moderately complete compendium of the average practitioner's working knowledge available as a starting reference (admittedly, the first half is a hodge-podge that sort of backgrounds the second). It serves the same purpose as a good "survey" paper.

Taking ordinary calculus knowledge and expanding it to differentiable programming is hard. Even if-else branches have nuances which, at first glance, it looks like the authors didn't handle. They confidently state, for example, that the derivatives of predicate variables are uninformative, but the veracity of that statement depends on the local properties of the branches in question. It's often true for "mathy" conditional statements and often not true for more computer-programming-esque if-statements. Separating a particular representation of a computation from what it actually computes and obtaining accurate partial derivatives is _hard_. Jax and Julia both have bugs on that front, and it's good that people are trying to make this knowledge more accessible (admittedly, not very well on that front IMO, but the other sections I spot-checked all look to be higher quality).