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A subset of Python for one, and debugging stays awkward with gdb.

Makes me think of this little pun: https://github.com/FluxML/Flux.jl/blob/master/src/utils.jl#L...

The subset of Python compatible with Julia's whole performance story (mostly, structs are the missing piece), yes sure.

You mean the type system and multiple dispatch? I guess those are fairly minor points, yes.

One performance feature I find really nice in Julia, is that you can write your own function (cost function, function to integrate, whatever) that you can pass into a solver/integrator/etc. Then it will be inlined and compiled into that external library.

Does this work with numba? Are your jit-compiled functions compiled into external modules?

Even if structs worked, they wouldn't be part of a good type system, don't work with abstract interfaces/inheritance,traits, multiple dispatch, fast union types, zero allocation immutables or any myriad of other things

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