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Like I said in a another comment. To me Julia has the potential to be the modern fortran. Python versatility is miles ahead. You can build Web APIs, Web apps,

Like I said in a another comment. To me Julia has the potential to be the modern fortran. Python versatility is miles ahead. You can build Web APIs, Web apps, automation scrips, data scrapping and so on. Python is not the de facto language of ML nowadays because its performance. All the underlying performance sensitive code is written is C/C++/Fortran. Also, cython, which has great integration with python, is growing a lot and has been used in some cool projects such as aiohttp, fastapi, and spacy. So one more alternative to write fast code without giving up on python entirely.

pjmlp 6y ago
Modern Fortran and C++ can be just as productive, and C++ based repls are starting to appear as well, which removes the dual language headache. Python community better embrace JIT tooling, now that projects like TensorFlow and Torch are going polyglot.
- zo1 6y ago
  What do you mean by JIT tooling?
  - pjmlp 6y ago
    Stuff like PyPy being used alongside CPython instead of a very interesting project that very few bother to use.
dunefox 6y ago
> Python versatility is miles ahead. You can build Web APIs, Web apps, automation scrips, data scrapping and so on. What? And you somehow cannot do that in Julia? > So one more alternative to write fast code without giving up on python entirely. I don't have to give up on Python, I can just use all libraries from Julia with PyCall. I'm just glad I don't have to use Python the language anymore than absolutely necessary.
- miguendes 6y ago
  > What? And you somehow cannot do that in Julia? You definitely can. My point is that python libs are more mature and there are more options to choose from than Julia.
  - dklend122 6y ago
    Julia libraries are plenty mature enough to be useful and Julia's 10x advantage in numerical computing far outweighs the difference in many cases.
kwertzzz 6y ago
I would argue that Julia can be similarly flexible than Python, but you have to be more explicit that you need this flexibility. For instance, in python you happily add a string to the list [1,2,3], but not in Julia because the list has been created as a vector of integers. However, if you create the list as Any[1,2,3], then you can append a variable of any type.

pjmlp 6y ago

Modern Fortran and C++ can be just as productive, and C++ based repls are starting to appear as well, which removes the dual language headache. Python community better embrace JIT tooling, now that projects like TensorFlow and Torch are going polyglot.

zo1 6y ago
What do you mean by JIT tooling?
- pjmlp 6y ago
  Stuff like PyPy being used alongside CPython instead of a very interesting project that very few bother to use.

dunefox 6y ago

> Python versatility is miles ahead. You can build Web APIs, Web apps, automation scrips, data scrapping and so on. What? And you somehow cannot do that in Julia? > So one more alternative to write fast code without giving up on python entirely. I don't have to give up on Python, I can just use all libraries from Julia with PyCall. I'm just glad I don't have to use Python the language anymore than absolutely necessary.

miguendes 6y ago
> What? And you somehow cannot do that in Julia? You definitely can. My point is that python libs are more mature and there are more options to choose from than Julia.
- dklend122 6y ago
  Julia libraries are plenty mature enough to be useful and Julia's 10x advantage in numerical computing far outweighs the difference in many cases.

kwertzzz 6y ago

I would argue that Julia can be similarly flexible than Python, but you have to be more explicit that you need this flexibility. For instance, in python you happily add a string to the list [1,2,3], but not in Julia because the list has been created as a vector of integers. However, if you create the list as Any[1,2,3], then you can append a variable of any type.