I have preordered, and am looking forward to reading my copy when it arrives. Seems like a way to buy lightcone-control-in-expectation-points very cheaply.

I admit I'm worried about the cover design. It looks a bit... slap-dash; at first I thought the book was self-published. I'm not sure how much control you and Eliezer have over this, but I think improving it would go a long way toward convincing people to spread it & its ideas as mainstream, reasonable, inside-the-overton-window.

I'm not sure about that; it seems like there's lots of instances where just a few bits of knowledge gets you lots of optimization power. Knowing Maxwell's equations lets you do electronics, and knowing which catalyst to use for the Haber process lets you make lots of food and bombs. If I encoded the instructions for making a nanofactory, that would probably be few bits compared to the amount of optimization you could do with that knowledge.

Registering that I will attempt this. Not sure if I will be able to produce something publishable in a reasonable amount of time, but I expect to learn from the attempt.

Also, I think some probabilities were left out of some sums there? Was that intentional, or a typo?

You can also use "focus mode" in the digital wellbeing section to disable apps during a certain window. I'm guessing it's intended for use during work, which is why the name doesn't really reflect its broader uses.

I just finished Andrew Ng's course as well, and had a similar experience to you. I do have a math background, so in retrospect it was probably a mistake to take it, but I saw it recommended so highly by people. I think the main value I got from it was the heuristics for debugging models and such, but I'm left wondering how many of those are even still relevant.

I'm still trying to learn ML though, so I'll take a look at your CS+ML guide. I remember trying fastai a few months ago and I felt there like I wasn't learning much there either, again other than debugging heuristics. I also don't like their special library, because I can't remember which things are part of the library and which are just pytorch (they're essentially teaching you two libraries at once, plus all the ML concepts--it's kind of a lot to keep in your head). Maybe I'll take another crack at it.

If you want another guide to pull from, I was following this one a few months ago. It stood out to me from the millions of other "86 bajillion books to learn computer science NOW" lists online because they intentionally limited it to a few subjects, and give their reasoning for each choice (and the reason some other popular books may be bad choices). It's much more CS focused, rather than programming focused, which is why I'm not following it now, but I plan to return to it when I actually have a job :)

These look amazing! I'm really blown away by the designs. I know it's early days, but do you think there will likely be stock left after preorders are filled? I generally don't like preordering things, but I will likely buy a copy once it's on sale unless there's an unforeseen quality issue, so if there won't be stock left then I'd like to just preorder.

Also, I took a look at the sample chapter and noticed a possible typographical error: on the 8th line of text on the back cover, it looks like there's two spaces between "blog" and "devoted". Same on line 12 between "essays" and "of". I might just be seeing things though.