End result: code that is uglier and still much slower than C++. Kind of a shame.

My work is more combinatorial. Julia does excel at numerical computation. There's a tribal divide in math between people who can't go 30 seconds away from the real or complex numbers, and those whose tolerance is about that long. I try to keep an open mind, but I'm closer to the second camp. Julia is good enough to consider either way.

A development in recent months, AI can assist in general purpose Lean 4 programming, no longer getting confused by the dominant proof-oriented training corpus. If one is a functional programmer who believes that Haskell was on the right track, then Lean is the most interesting language choice for shaping one's thoughts. Benchmarks are inherently misleading if a better language makes it possible to express algorithms out of reach of more primitive languages.

https://github.com/Syzygies/Compare

            C++  100    13.08s  ±0.08s
           Rust   99    13.16s  ±0.02s
          Julia   90    14.54s  ±0.01s
             F#   90    14.54s  ±0.04s
  Kotlin-native   88    14.79s  ±0.01s
         Kotlin   86    15.18s  ±0.01s
          Scala   79    16.50s  ±0.08s
   Scala-native   76    17.14s  ±0.02s
            Nim   65    20.17s  ±0.01s
          Swift   64    20.54s  ±0.04s
          Ocaml   52    25.38s  ±0.04s
           Chez   49    26.64s  ±0.02s
        Haskell   37    34.96s  ±0.06s
           Lean   29    45.39s  ±0.15s

To those who regularly write Julia code, what is your workflow? The whole thing with Revise.jl did not suit me honestly. I have enjoyed programming in Rust orders of magnitude more because there's no run time and you can do AOT. My intention is not write scripts, but high performance numerical/scientific code, and with Julia's JIT-based design, rapid iteration (to me at least) feels slower than Rust (!).

And that's a good thing, because Python+NumPy syntax is far more cumbersome than either Julia or MATLAB's.

You can see this at a glance from this nice trilingual cheat sheet:

https://cheatsheets.quantecon.org/

One could say that we can almost replicate the semantic of a C++ program, but writing in Julia. For example we can remove bounds checks in arrays or remove hidden memory allocations.

But the goal of a language for numerical computing is capturing the mathematical formulas using high level constructs closer to the original representation while compiling to efficient code.

Domain scientists want to play with the math and the formulas, not doing common subexpression elimination in their programs. Just curious to see how it evolves

- not a single post has anything inside here https://flow.byu.edu/posts/

Prelude of what's to come in the self-reinforcing cycle of machines talking to machines and drowning everything else.