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Comment by kwk236 | Hacker News Reader

Why can't people just settle on Python? Julia is going to die anyways in the near future.

The same reasons people migrated to Python in the late 90s from matlab, Fortran and C++: Python addressed pain points in those languages and ecosystems. Julia addresses deficiencies in Python the language. Unfortunately, it lacks the ecosystem: until it gets the rich library set that Python has, we're stuck with Python since ecosystem is arguably more important for practical traction than the base language.

I don't know how well it works, but Julia does let you use python libraries.

    @pyimport matplotlib.pyplot as plt
    x = linspace(0,2*pi,1000); y = sin(3*x + 4*cos(2*x));
    plt.plot(x, y, color="red", linewidth=2.0, linestyle="--")
    plt.show()

https://github.com/JuliaPy/PyCall.jl

Works really well and a lot of python scientific software is part of a typical julia install.

Glad to hear that. Will it be possible to overload . in the future?

There's been extensive discussion of that and yes, it may well happen [1]. The biggest concern at this point is how to avoid having people abuse such a feature by trying to fake out class-based o.o. code in Julia in a way that will look right but work poorly.

[1] https://github.com/JuliaLang/julia/issues/1974

On an infinite time scale, you are certainly right. We're all going to die pretty soon, and so will our programming languages.

In the meantime, though, Julia seems rather healthy, and this particular Python fanboy is nevertheless interested where Julia will take us!

Julia is not an aggressor. It's not even a competitor. Rather, it is another stab at improving scientific computation. Let's focus on trying to learn from Julia instead of spreading FUD.

I fully agree with this statement. We hope to move forward and help create new things rather than replace something today. And of course, Julia is not the last word on this innovation either!

Julia is better than Python for a lot of things - basically when you can't vectorise your algorithm and don't feel like writing C++.

Python has numba, which is equally backed by LLVM. What makes Julia interesting compared to Python IMO is its macro system much more than its performance benefits.

Although unequally inconvenient when working with user-defined types. Possible now (wasn't always) but not seamless. Are there examples of libraries that depend on Numba?

What makes you think that about Julia?

If Julia's death was a foregone conclusion then you wouldn't have to ask this question!

I see Python fan following everywhere when there is a discussion on languages!! Someone is always there to defend :P

Some time back, I saw an Ask HN post by someone who wanted to know how to become an evangelist for a language or a framework. I wonder if that's something that motivates comments in forums such as this.

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