About a 4.4/5 for me
Really valuable information. He's right when he says you shouldn't read it cover to cover but just use for reference for what you're currently working on. (This was for a UX book club so I didn't have much of a choice, and as a warning it's a bit of a slog to get through in one go.)
A number of the suggestions I was able to put into practice, as well as a few reminders of best practice to emphasize with my team (such as the the repeated reminders to run a pilot test, please and thank you), and I'm particularly interested in particular in using the lostness metric in the future.
I disagreed on a couple of the minor takes. For example, that you shouldn't use quantitative questions when identifying needs. Quant questions can be hugely helpful for this, not just in contextualizing information, but also giving a sense of how severe an issue is. For example, asking how many times a week someone has to call in for specific information from shelters. If it's once a month, yeah there might be something there, but any new solution would likely more of a hassle than a help. If it's 18 times a day, that's more likely a need that needs to be addressed.
Also minor issue but an MVP is. Literally a "minimal version of a product with the smallest possible feature set." Like it's even in the name. Minimum Viable Product. Though this could also be lack of consistency within the field (or even within a single organization in one past case).
Other takes I heavily endorse. Such as personas are not a research methodology, and their primary use (alongside user stories) is as a communication tool to gain empathy from the product team for the actual users.
I also appreciated the very specific examples and advice, especially around things like expected time for particular analyses when split different ways, artifact templates, or social etiquette during in person interviews that isn't always explicitly taught. Also convenient to have the recommended sample sizes for particular techniques as well (such as the 500 users for the lostness metric or other findability-based quant studies.)
I would be interested to read more about the tie-in with product metrics (KPIs/OKRs), especially on the quantitative side and benchmarking over time.