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The article describes Julia as "a general-purpose programming language aimed at science, engineering, and related arenas of technical computing". I think this

The article describes Julia as "a general-purpose programming language aimed at science, engineering, and related arenas of technical computing". I think this is about right. If you are going to solve differential equations or run some numerical weather codes or solve an integer program or simulate an electrical circuit, it would be a good idea to consider Julia rather than reaching for Fortran, C++, or MATLAB. It is a much nicer language and still very fast. My experience is that people who want to use Julia for data science and especially for deep learning, usually find that you can get the job done, but often it's easier to use PyTorch or Jax and just accept the limitations of Python.

TheRealKing 2y ago
The comments further above on the inherent limitations of the Julia language as a replacement for Fortran or C++ seem to contradict your opinion.
- currymj 2y ago
  those limitations are sometimes real. However, I have read enough Fortran and C++ written by scientists, to fervently wish that their code had been written in Julia instead. In Julia, I don't think they could manage to write code so debauched. If you just do things the naive way, usually life is good and code is both readable and fast. The pitfall is type instability and memory allocation, but I think with a few basic tricks, people can learn to avoid this, especially for simpler tasks. (edit: also, if people do mess this up, they notice because their code is slow. whereas people don't notice if their code is fast but incomprehensible and impossible to maintain or extend.)