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As an enthusiastic Julia user, I can definitely attest to this. It's frustrating to just want to share with people what you find cool about a language, only to

As an enthusiastic Julia user, I can definitely attest to this. It's frustrating to just want to share with people what you find cool about a language, only to be accused of being part of some astroturfing campaign, or for any discussion of the language being immediately derailed by people complaining about 1-based indexing. Granted, there are definitely cases out there where Julia users have been overzealous and undersensitive when trying to convince others to come try out the language, but my experience with these people is that they're almost always coming at it from a place of just being excited about finding a language that works well for them and solved so many of their issues, and so they want to share that with others. ____ D is a cool language and it's a shame it seems to get ignored so much. I hope D people continue to work at it.

xyproto 3y ago
Julia is the only programming language that makes me annoyed when I think of it, because it was so troublesome to package for Arch Linux and because of how little they care about increasing the safety in the language (like Rust or Ada).
- majoe 3y ago
  What made Julia so difficult to package? I'm genuinely curious. > ... because of how little they care about increasing the safety in the language (like Rust or Ada). I think it's a bit harsh to compare Julia to Rust on that front. Julia has completely different design goals. Compared to Rust's borrow checker and C++'s move semantics, Julia's immutability-by-default (+ clever compiler optimizations) gives a good trade off between performance, safety and ease of use. While Rust goes for safety + performance, Julia goes for performance + ease of use (respectively dynamicity). Nevertheless, I miss some features related to type driven development in Julia, which would improve safety: - A newtype idiom - Sum types - Explicit, type-checked interfaces : I actually would prefer something like C++20 concepts (a blacklist approach) to Rust's trait (or typeclasses, a whitelist approach), because it would be a better fit for Julia's design goals.
  - adgjlsfhk1 3y ago
    The main problem with packaging julia is that it requires a custom version of LLVM (since LLVM ships breaking versions every 2 years and doesn't do downstream testing for projects other than Clang). Lots of linux distros have repeatedly tried shipping Julia with random LLVM versions and as a result, shipped broken Julia versions to people.
bachmeier 3y ago
I think Julia has a very bright future. There's no fundamental reason for Python to be so popular in the number crunching sector. I expect a lot of that to migrate to Julia over the next decade. Unlike D, I believe it has significant funding.
- pjmlp 3y ago
  I used to think likewise, until NVidia and Microsoft decided it was about time to make Python implementations actually embrace JITs.
  - eigenspace 3y ago
    If all Python needed was a JIT, this would have been addressed over a decade ago. The problem isn't a lack of a compiler, it's language semantics that make the job of a JIT nearly impossible.
    - pjmlp 3y ago
      The usual urban myth, ignoring JIT work in language semantics like Smalltak, Common Lisp, SELF, where anything goes and the whole process space can change in a method call. JIT doesn't happen in Python because the community rather rewrites code in C, than support projects like PyPy. NVidia and Microsoft had to come into play to change this.
      - eigenspace 3y ago
        None of the languages you mention there get even close to C levels of performance when you hit "anything goes" dynamic language features. Those JITs are only competitive (i.e. within an order of magnitude perf) when you are accelerating very barebones code on native types and non-generic functions, or you strip out dynamism. In julia, there is no separation between generic functions and fast functions, and no separation between user defined types and inline allocated structs. This is not the case with Python, Smalltalk, Common Lisp, etc. To actually take hold and stop people from relying on C libraries for everything in Python, the Python JIT is going to have to be as fast as C, and it's also going to need to have a CPython compatible ABI because people are not going to abandon all these pre-existing libraries overnight. Python's semantics make both of those prospects incredibly dubious.
machinekob 3y ago
I think biggest Julia problem is deployment and focus on Academia/HPC, its nice language and pretty fun to write, but it seems to be not good enough to replace Python and lost tons of momentu from hype train in 2017-2018?. Julia still have a sucess story and is a lot bigger compared to D.

xyproto 3y ago

Julia is the only programming language that makes me annoyed when I think of it, because it was so troublesome to package for Arch Linux and because of how little they care about increasing the safety in the language (like Rust or Ada).

majoe 3y ago
What made Julia so difficult to package? I'm genuinely curious. > ... because of how little they care about increasing the safety in the language (like Rust or Ada). I think it's a bit harsh to compare Julia to Rust on that front. Julia has completely different design goals. Compared to Rust's borrow checker and C++'s move semantics, Julia's immutability-by-default (+ clever compiler optimizations) gives a good trade off between performance, safety and ease of use. While Rust goes for safety + performance, Julia goes for performance + ease of use (respectively dynamicity). Nevertheless, I miss some features related to type driven development in Julia, which would improve safety: - A newtype idiom - Sum types - Explicit, type-checked interfaces : I actually would prefer something like C++20 concepts (a blacklist approach) to Rust's trait (or typeclasses, a whitelist approach), because it would be a better fit for Julia's design goals.
- adgjlsfhk1 3y ago
  The main problem with packaging julia is that it requires a custom version of LLVM (since LLVM ships breaking versions every 2 years and doesn't do downstream testing for projects other than Clang). Lots of linux distros have repeatedly tried shipping Julia with random LLVM versions and as a result, shipped broken Julia versions to people.

bachmeier 3y ago

I think Julia has a very bright future. There's no fundamental reason for Python to be so popular in the number crunching sector. I expect a lot of that to migrate to Julia over the next decade. Unlike D, I believe it has significant funding.

pjmlp 3y ago
I used to think likewise, until NVidia and Microsoft decided it was about time to make Python implementations actually embrace JITs.
- eigenspace 3y ago
  If all Python needed was a JIT, this would have been addressed over a decade ago. The problem isn't a lack of a compiler, it's language semantics that make the job of a JIT nearly impossible.
  - pjmlp 3y ago
    The usual urban myth, ignoring JIT work in language semantics like Smalltak, Common Lisp, SELF, where anything goes and the whole process space can change in a method call. JIT doesn't happen in Python because the community rather rewrites code in C, than support projects like PyPy. NVidia and Microsoft had to come into play to change this.
    - eigenspace 3y ago
      None of the languages you mention there get even close to C levels of performance when you hit "anything goes" dynamic language features. Those JITs are only competitive (i.e. within an order of magnitude perf) when you are accelerating very barebones code on native types and non-generic functions, or you strip out dynamism. In julia, there is no separation between generic functions and fast functions, and no separation between user defined types and inline allocated structs. This is not the case with Python, Smalltalk, Common Lisp, etc. To actually take hold and stop people from relying on C libraries for everything in Python, the Python JIT is going to have to be as fast as C, and it's also going to need to have a CPython compatible ABI because people are not going to abandon all these pre-existing libraries overnight. Python's semantics make both of those prospects incredibly dubious.

machinekob 3y ago

I think biggest Julia problem is deployment and focus on Academia/HPC, its nice language and pretty fun to write, but it seems to be not good enough to replace Python and lost tons of momentu from hype train in 2017-2018?. Julia still have a sucess story and is a lot bigger compared to D.