I actually got the link to his code yesterday and it's eerily close to what I wrote a few months ago, but I rejected that approach because I didn't like that any of the numbers in [0.5,1.0) were so much more likely to be returned than 0.0. This gives the highest possible entropy and all numbers representable by floating points in that range can be returned, but the trade-off is that certain values will be returned vastly much more often than others. It's this discussion of trade-offs that I'm actually missing.

I don't feel competent enough to write that paper/article/rant about the trade-offs myself, I barely understand math and randomness, but I'm getting tired of all the libraries out there that say "uniform[1] random[2] floating points[3] [1] - not actually uniform [2] - not actually random [3] - terms and conditions apply, YMMV, not valid where invalid".

What do you mean when you say you don't like that some numbers are returned more often than others? This seems completely inevitable for a uniform distribution because floating point numbers don't have a uniform density across the real line.

Only if you choose a range like [1,2] with a uniform density of floating point numbers you will see all floating point numbers in the interval - except both bounds of course - with equal probability.

You would have to not return some floating point numbers ever to see all returned floating point numbers in the interval with equal probability but that of course hardly qualifies as a uniform distribution if some perfectly valid floating point numbers will never occur.

It totally qualifies as a uniform distribution. Is it the case that generating [1, 2) generates a uniform distribution, but subtracting 1 from that does not produce a uniform distribution?