>Why use it, when there are so many other good languages out there with more community/support? Honest question.
Such a question seems sort of in bad faith (or loaded), since the selling points of Julia have been hammered time and again on HN and elsewhere, and are prominent on its website. It's a 1 minute search to find them, and if someone is already aware that there's this thing called Julia to the point that they think it's made to be "a big deal", they surely have seen them.
So, what could the answer to the question above be? Some objective number that shows Julia is 25.6% better than Java or Rust or R or whatever?
But first, who said it's a "big deal"? It's just a language that has some development action, seems some adoption, and secured a modest fundng for its company. That's not some earth shattering hype (if you want to see that, try to read about when Java was introduced. Or, to a much lesser degree, Ada, for that matter).
You use a language because you've evaluated it for your needs and agree with the benefits and tradeoffs.
Julia is high level and at the same time very fast for numerical computing allowing you to keep a clean codebase that's not a mix of C, C++, Fortran and your "real" language, while still getting most of the speed and easy parallelization. It also has special focus on support for that, for data science, statistics, and science in general. It's also well designed.
On the other hand, it has slow startup/load times, incomplete documentation, smaller ecosystem, and several smaller usability issues.
Such a question seems sort of in bad faith (or loaded), since the selling points of Julia have been hammered time and again on HN and elsewhere, and are prominent on its website. It's a 1 minute search to find them, and if someone is already aware that there's this thing called Julia to the point that they think it's made to be "a big deal", they surely have seen them.
So, what could the answer to the question above be? Some objective number that shows Julia is 25.6% better than Java or Rust or R or whatever?
But first, who said it's a "big deal"? It's just a language that has some development action, seems some adoption, and secured a modest fundng for its company. That's not some earth shattering hype (if you want to see that, try to read about when Java was introduced. Or, to a much lesser degree, Ada, for that matter).
You use a language because you've evaluated it for your needs and agree with the benefits and tradeoffs.
Julia is high level and at the same time very fast for numerical computing allowing you to keep a clean codebase that's not a mix of C, C++, Fortran and your "real" language, while still getting most of the speed and easy parallelization. It also has special focus on support for that, for data science, statistics, and science in general. It's also well designed.
On the other hand, it has slow startup/load times, incomplete documentation, smaller ecosystem, and several smaller usability issues.