As a side note, the program is amazingly performant. For small numbers the results are instantaneous and for the large number close to the 2^32 limit the result is still returned in around 10 seconds.

[–] caellian 14 points 11 months ago (1 children)

Really makes you question your sanity when optimizing jumps in code without benchmarks.

[–] [email protected] 15 points 11 months ago (2 children)

For a long time I've been of the opinion that you should only ever optimize for the next ~~sucker~~ colleague who might need to read and edit your code. If you ever optimize for speed, it needs to be done with massive benchmarking / profiling support to ensure that the changes you make are worth it. This is especially true with modern compilers / interpreters that try to use clever techniques to optimize your code either on the fly, or before making the executable.

[–] [email protected] 7 points 11 months ago* (last edited 11 months ago)

The first rule of optimization: Don’t do it
The second rule of optimization: Don’t do it yet (experts only)

[–] [email protected] 1 points 11 months ago (1 children)

I'm absolutely on-board ...in application code.

I do feel like it's good, though, when libraries optimize. Ideally, they don't have much else to do than one thing really well anyways.

And with how many libraries modern applications pull in, you do eventually notice whether you're in the Python ecosystem, where most libraries don't care, or in the Rust ecosystem, where many libraries definitely overdo it. Because well, they also kind of don't overdo it, since as a user of the library, you don't see any of it, except the culmulative performance benefits.

[–] [email protected] 1 points 11 months ago

Libraries are also written and maintained by humans.

It's fine to optimize if you can truly justify it, but that's going to be even harder in libraries that are going to be used on multiple different architectures, etc.

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