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I don't agree at all with your last paragraph. MATLAB and Julia are lovely for numerics, maths is terse and looks like maths! Mojo is hobbled by choosing Python style syntax, which does not lend itself well to maths.

WRT the type system, I agree MATLAB is a mess but Julia's multiple dispatch feels more like maths than any other language I've tried.

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I coded Matlab in uni, never got it. And had to look Julia code. I have to learn it to read. I am more SEng rather then Sci or Quant. And on compile time types. So for me Julia is foreign and forces me to look both Python and Julia.

What problems do you run into for maths with Python? For linear algebra and ML with Jax/Numpy I find it quite readable.

I think problems is too strong a word, it's just how the syntax looks. And of course it's far better than say C++. But specifically, the lack of native vectors/matrices and very few infix operators (e.g dot product, and matrix multiplication with @ is clunky). Explicit but generic broadcasting using `.` also helps.

The way that types interact with methods _feels_ like maths. I.e. you have a method made up of mathematical operators, if you swap out an input, it typically does the natural and correct thing.

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