[throwaway894345]

Yes, JIT would work well also, and I would strictly prefer a JIT, but I don’t think we’re likely to see a JIT Python with good ecosystem compatibility in the next decade. Good luck to the people who are using Python these days, but I’m tired of fighting the same major problems we had 15 years ago. Other ecosystems solved those problems and they actually improve materially.

[pjmlp]

That is why I am so much into Julia, even with its adoption bumps.

The problem is not that Python lacks JITs, rather the community culture of rewriting code in C instead of contributing to JIT efforts.

Personally I just use a JVM/.NET based language, and if I need I can use the same C, C++ and Fortran libraries that Python uses anyway.

[johnnycerberus]

Julia was created to tackle problems in applied disciplines (physics, neuroscience, genetics, material engineering, etc.). I was expecting it not to be picked up by your everyday app developer or by the overly abstract functional programmer. As an afterthought, personally I think Julia can do much more than that, I would say it can do at least as much as Python is capable today, but better.

[pjmlp]

The ecosystem is slowly expanding beyond applied disciplines, because when those people need to code something else, e.g. a Web site for their research data, then as usual they try to use the hammer they already know.

[throwaway894345]

I'm really interested in Julia's performance for general purpose application development. It's great that it can work with large numerical arrays very efficiently, but what about large, diverse graphs of small objects like you commonly find in general purpose application development? I think I want a hybrid between Julia and JVM or something.