[jakobnissen]

There are even more issues with Julia than you mention there. But the point is there are _many issues_ with _every_ programming language: Python is slow, its package management story is a dumpster fire, its object model is bizarre and confusing, its typechecking mechanism sucks. Also, its documentation is terrible (docstrings and documentation of functions) and the built-in REPL is basically useless. Rust has slow compile times, too much macro magic leading to unreadable code, the 90% of what the borrow checker rejects is perfectly fine code, its trait coherence rules are too restrictive, async sucks, and it's totally unsuitable for quick experimentation.

These are all real limitations, but users of these languages learn to live with them. Rustacenans learn to start a build, then so something else while it builds. That is, on its face, totally unacceptable to a Pythonista. Pythonistas learn to always ship performance sensitive applications with _another language_ doing all the hard work: Totally unacceptable for a Rustacean.

If someone tries out Python, spends five minutes getting package management to work and fails, they have not seriously thought about Python as a language. I feel people do just that with Julia: Try it out, then reject it on the first rough edge.

Fair enough: There are many people for whom lack of static type checking or Julia's latency is a showstopper, making the language unsuitable. But I'm still firmly convinced that for scientists/engineers at least, Julia, on balance, offers a better language than Python. I hope you're wrong that it won't gain much popularity.

[xiaodai]

i work in a bank leading some numerical work and I've been training my staff on Julia so we can write monte-simulation code that is fast. heck, the team builds their model in C++ and we soundly beat them with Julia on the code that generates the same results.

when we looked at it, it was because c++ is hard af to learn and the developer dont use it to its full potential. they just use some quantlib which where you look under the hood has many unoptimized parts.

with julia, the code is so simple and clean we even put in some GPU code in one place using CUDA and complete blows the C++ out of the water.

I did achieve some good performace with numba once thought with the avx so pythoni snot all bad but numba is only a small subset of python but with julia i can do crazily fast stuff that looks like python and is readable.

[acmj]

It seems that Julia has been used for numerical stuffs often. Its redmonk ranking is above some great languages like nim and erlang. This is already an achievement for a community-backed open-source language. By "much popularity", I meant "as popular as Java or Python". I had a high hope for Julia.