[new4thaccount]

Julia JITs to native LLVM code and is really fast for a lot of use cases. For things where the JIT warming up takes less time than the full job, it can be better than Python. It was also developed with numerical computation in mind, so it was designed for that performance wise and has so many brilliant people working on the language and library. You can use macros for awesome DSLs and run on the GPU and in parallel a lot easier than Python in some cases. It has a first class package manager, great REPL and doesn't need an installer.

[patrick5415]

I didn’t find the REPL that great, because it takes forever to jit anything. My laptop is not that old and creating an array with 5 elements takes over a second. If I type a syntax error, it takes tens of seconds to produce an error. This yields a very frustrating experience and doesn’t lend itself to an effective prototyping environment. For now at least I’ll be sticking with matlab.

[improbable22]

There are indeed frustrating lags due to JIT, but they have got better lately & are being worked on -- if I understand right this is now one of the priorities, after focusing on getting the breaking changes done before 1.0.

Here's how long a vector of 5 random numbers takes, after a cold start, on 1.1:

    $ julia -e '@time rand(5)'
    0.054343 seconds (121.03 k allocations: 6.219 MiB)

[laughfactory]

Yeah, this has been my experience as well. I really _want_ to like Julia. But so far the JIT experience has been exceptionally miserable--and unfortunately for me, my typical approach to development is quite interactive. I'm on 1.1.1.

[eigenspace]

That sounds really bizarre. What version of which OS are you using? I've heard that older versions of Windows (I think 7) have problems with the REPL that cause extreme latency.

I don't think the latency you're experiencing is normal.