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Comment by Fomite | Hacker News Reader

For non-CS scientific computing users, "a high performance language with easy syntax" is a big feature.

Exactly, that's why I think Julia is a good idea.

For scientific users used to R or Python, the performance should knock their socks off. (But users doing their work in C, C++ or Fortran will probably see little or no improvement).

Do we really need Julia given that Python has Numpy which does a lot of the heavy lifting in numerical computation and is pretty fast?

Numpy is great as long as you're using Numpy. There's lots of aspects of scientific computing that may not be using Numpy.

Numpy is good for vectorized calculations on dense multidimensional arrays of homogenous 64 or 32 bit floating point numbers, or integers. If that describes your problem and you like Python, by all means use numpy. If that doesn't describe your entire problem, you'll need more tools.

It should be understood that numpy is written in C, and is an example of the "2 language problem" that Julia solves rather well.

The question really is the converse ... given how deft Julia is with general programming as well as numerically intensive programming, do you really need Python + numpy(C).

On top of what others have mentioned, there is something to be said about getting numerical linear algebra tools as native citizens of a language.

There are many things I hate about MATLAB, having matrices built into the syntax is nice.

It's why the horror that is MATLAB exists. I'm looking forward to replacing MATLAB with the much more ergonomic Julia soon though.

Yes, do it! It was an easy transition years ago, now Julia seems light-years ahead.

Would that not be true for all technical / scientific use cases if not most use cases in general?

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