[rotorblade]

I agree. I meta-programmed (enormous) expressions from analytical expressions exported from Mathematica to Julia, because I found Julia to be ~3000 times faster than Mathematica when it comes to calculating eigenvalues. Using BigFloat for higher precision, my matrix function in Julia took ~20 minutes to compile on the first run and ~20 GB of RAM. Smooth once compiled, but I was the only one of my collaborators that had the capacity to run it.

[rotorblade]

Note, that I still do like using Julia. I'm a physicist and need to do a lot of computations in a hassle-free way, then jupyter+Julia (+ SymPy) is(/are) the best available tool(s).

The above may only be an issue of BigFloat, to be fair, since Float64 compiled in an instant (never measured, and the time never bothered me).

So Julia has solved a lot of problems for me, and I see great potential for it in the future.