Measuremancer

tailed and oddly shaped distributions that occur in real world data { IF the expense doesn't get in your way some other way, that is, as per Senior Engineer in the article }.

ct uncertain numbers and then say y/x to get a ratio with uncertainty propagated. Function call syntax is more flexible (and arises more often than uncertain value algebras), e.g. "f(x)", "x.f", "f x", and even "f: x" all work for any kind of routine call. The need to be specific about let/var constancy & types is slightly more verbose but adds semantic/correctness value (e.g. first use). Just generally Nim feels like "much more choice" than Python to me. In fact, the leading whitespace itself can usually be side-stepped with extra ()s which is not true in Python. I am speculating, but I think what might make you feel Python is more expressive may come from ecosystem size/support effects such as "3 external libraries exist that do what I want and I only need to learn the names of how to invoke whichever". So, you get to "express" what you "want" with less effort (logic/coding), but that's not really PLang expressivity. Python is more dynamic, but dynamism is a different aspect and often leads to performance woes.

1.550 -0.7495 0.2023 L2:bat/examples$ ./closes|awk '{print $3,$2,$1*$2}'|fitl -s0 -c,=,n,b -b100 1 2 3 $1= 787.77 *$2 + 48.745 *$3 bootstrap-stderr-corr matrix 1.350 -0.6207 0.1635 L2:bat/examples$ a (48.861 +- 0.2023)-(48.745 +- 0.1635) 0.12 +- 0.26 # i.e. run-to-run consistent L2:bat/examples$ a (695.3265 +- 0.05302)/(48.745 +- 0.1635) 14.265 +- 0.048 # i.e. batching makes it 14X faster! One can go further to get a mean +- std.err of ~695.52 +- ~0.04 nanosec hot-cache time per syscall overhead or even do point-weighting there, but it's probably more important to check fit residuals for serial autocorrelation (very do-able with https://github.com/c-blake/fitl, EDIT: oh, and if you care `a` is basically shown in https://github.com/SciNim/Measuremancer/pull/12 which uses standard error propagation). The bigger point would not be ever more careful measurement of the cost if you do not, but rather the ease of "batchifying" if you do of any highly regular call interface similar to syscalls with high latency and also the "follow on" API design, granularity-wise. The inner code of a mini-assembly language letting you implement, say, open/fstat/mmap/close all in one syscall crossing is only about 30 lines of C. { Yes, yes..It could use multi-CPU-arch and syscall auditing integration.. and sure, ebpf & io_uring alter the Linux landscape these days }. Also, @exDM69 is very correct that pure hot cache-hot loop numbers are only one part of the cost story (https://news.ycombinator.com/item?id=39188551).

will either break or you'll need to work around it anyway (e.g. calling into some C library for part of the code). [0]: https://github.com/SciNim/astGrad#extra-fun [1]: https://github.com/SciNim/Measuremancer/

pirations. EDIT: https://nim-lang.org/ has much more detail and there are over 50 small CLI programs at https://github.com/c-blake/bu as rather extended examples. Using that musl approach I get tiny little 100 kB ELF executables that would work on practically any Linux you can `scp` them to with none of the boilerplate/language limits/hassles or speed-limits of, say, Go and run-time start-up times on the order of 100 microseconds. There are all kinds of great libs in Nim, too like @V1ndaar's https://github.com/SciNim/Measuremancer or all sorts of goodies over at https://nimble.directory/