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> after some consideration of OCaml, but unfortunately the multi-core story still isn't there yet
It is supposed to land in the release after 4.13, which is the next one.
Regarding the scientific computations library there is Owl[1][2] which now has an almost finished book[3].
I'm just curious. Do you do symbolic math in Rust? I found a crate [1] that is a wrapper for SymEngine. However, it is no longer maintained.
> Us CLI folks are not even asking for much. Just a way to call a program with arguments and so that you don't have to do compile identical code every time you do something. That's it. Is that unreasonable?
Look, everyone wants static compilation, and it's almost certainly going to happen eventually. It's not unreasonable at all to want that. The only thing that would be unreasonable is to (apparently repeatedly!) imply or assert that this lack of static compilation is due to some bizarre perversion of the community.
If you want it to happen faster, then stop complaining about it on HN, get familiar with the current attempts at true static compilation in e.g. https://github.com/tshort/StaticCompiler.jl/pull/46 and start contributing yourself.
If you want performance benchmarks vs Fortran, https://benchmarks.sciml.ai/html/MultiLanguage/wrapper_packa... has benchmarks with Julia out-performing highly optimized Fortran DiffEq solvers, and https://github.com/JuliaLinearAlgebra/Octavian.jl shows that pure Julia BLAS implementations can compete with MKL and openBLAS, which are among the most heavily optimized pieces of code ever written. Furthermore, Julia has been used on some of the world's fastest super-computers (in the performance critical bits), which as far as I know isn't true of Swift/Kotlin/C#.
Expressiveness is hard to judge objectively, but in my opinion at least, Multiple Dispatch is a massive win for writing composable, re-usable code, and there really isn't anything that compares on that front to Julia.
For a non-numerical benchmark where Julia does really well, you should also check out https://h2oai.github.io/db-benchmark/. There's a bunch of work to be done to improve DataFrames.jl more, but it's already one of the fastest tools for what it does (despite having only reached version 1.0 a few weeks ago).