Left out of that history is the branch that almost happened: for quite a while the smart money was that Apple would buy Be, Inc. and use BeOS as the basis for their future OSes. More than a few developers (myself included) based their business models on this happening.

Grover's search algorithm gives only a quadratic speedup.

Exactly. That was the big problem I had with the book: it's written for Java programmers. I am intrigued by the language, but I would much prefer a book that treats the language on its own terms.

It's on my list...

I'll mention it to my publisher, but honestly it would lose a lot without all the color figures.

The book is based on my Ph.D. thesis, which you can download for free:

http://www.swiss.ai.mit.edu/~bob/hearn-thesis-final.pdf

The reason that fun games tend to be NP-hard (or harder) is that if a game's "physics" supports interesting constructions requiring complex reasoning to solve, then probably that same physics can be used to build computational gadgets, which is how you show hardness of the generalized version. This quality expresses itself even on small, fixed-size board.

Ah, it doesn't mean that either. :-)

If a problem is NP-hard, it means it is at least as hard as any other problem that can be solved in polynomial time on a nondeterministic computer.

It is an open question (P=NP) whether this is equivalent to saying that there is no deterministic polynomial-time algorithm.