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What you've pointed out might all be valid. But they seem to be issues with maturity. When Python was at a similar age, did it have all these issues worked out?

What you've pointed out might all be valid. But they seem to be issues with maturity. When Python was at a similar age, did it have all these issues worked out? Not sure if you can generalize it to "Julia community isn't interested". If we have more ppl liking Julia and using it for general purpose computing (which it is capable of), then more of these tools will become mature overtime. Also, Julia is not in the same niche as Rust though, it's more like a Python, so not sure if the comparison is apt.

pythrowaway 5y ago
Problem for Julia is, the Python ecosystem gets better every year. Python might be getting better faster than Julia can catch up.
- m_mueller 5y ago
  I highly doubt it for one core feature: performance. For a little while it looked like Python 4 might fix this (by using type hints to JIT) , but it was quickly given up. IMO a big shame as it is the one thing holding python back.
  - oscardssmith 5y ago
    I think Julia actually does a pretty good job of showing that the problem isn't types. The problem is semantics. Julia's restriction is eval to the global scope is a perfect example of this. It has a pretty minor on code, but a massive effect on performance. Python has a ton of things like this where a slightly different (and totally breaking) semantic change prevents optimization.
    - m_mueller 5y ago
      My poinr wasn’t about type system per se but the ability to compile code down to natively execute without going through an interpreter. Whether you solve that through type inference or type hints I honestly don’t care, but afaik Julia has an easier job by allowing you to make types explicit on the data structure side while being flexible for the algorithms. Semantics are certainly also a big part, but it would be nice to have first party support for what Numba does, JITing a reduced set of semantics but keep everything else compatible.
- pjmlp 5y ago
  Julia runs fast enough to catch Python, because Python will never improve the main reason why Julia was created in first place, the community embracing a JIT compiler instead of re-writing working code into C.

pythrowaway 5y ago

Problem for Julia is, the Python ecosystem gets better every year. Python might be getting better faster than Julia can catch up.

m_mueller 5y ago
I highly doubt it for one core feature: performance. For a little while it looked like Python 4 might fix this (by using type hints to JIT) , but it was quickly given up. IMO a big shame as it is the one thing holding python back.
- oscardssmith 5y ago
  I think Julia actually does a pretty good job of showing that the problem isn't types. The problem is semantics. Julia's restriction is eval to the global scope is a perfect example of this. It has a pretty minor on code, but a massive effect on performance. Python has a ton of things like this where a slightly different (and totally breaking) semantic change prevents optimization.
  - m_mueller 5y ago
    My poinr wasn’t about type system per se but the ability to compile code down to natively execute without going through an interpreter. Whether you solve that through type inference or type hints I honestly don’t care, but afaik Julia has an easier job by allowing you to make types explicit on the data structure side while being flexible for the algorithms. Semantics are certainly also a big part, but it would be nice to have first party support for what Numba does, JITing a reduced set of semantics but keep everything else compatible.
pjmlp 5y ago
Julia runs fast enough to catch Python, because Python will never improve the main reason why Julia was created in first place, the community embracing a JIT compiler instead of re-writing working code into C.