Comment by amkkma | Hacker News Reader

Comment by amkkma | Hacker News Reader

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As a Julia evangelist, your point is well received.

I've definitely been making an effort to temper any discussion of benefits with associated drawbacks.

Though, I just want to express WHY I'm so enthusiastic. I've used both R and Python for DS. I'm familiar a bit with matlab.

I've played with Keras and Pytorch.

Aside from all the technical and productivity benefits Julia just feels GOOD to write in, compared to all of the above. It's a real quality of life improvement + a feeling of freedom in that I can express my ideas, compose them with others all without worrying about a different compiler or framework or array type system or having to code units in c++.

This is freeing and honestly, it would be sad if it's relegated to niche status forever. There's definitely a selfish component here, but I'd like everyone to feel the same benefits.

It's hard to go back to pandas or tf or pytorch after using chain.jl, dataframes.jl and flux.

Yes, there are drawbacks with compile times and things like that, which can be annoying. But I keep working in julia because the benefits are worth it, those aren't intrinsic to the language, and most importantly, there's a roadmap to fixing them: https://discourse.julialang.org/t/precompile-why/78770/8 with progress that is tantalizingly close to the end goal of easy dev and easy deployment.

> It's a real quality of life improvement + a feeling of freedom in that I can express my ideas, compose them with others all without worrying about a different compiler or framework or array type system or having to code units in c++.

As someone who spends 98% of his time in Python and the remaining 2% in R I'd love to switch to a more nicely designed, ergonomic, high-performance language. Adopting Julia at work is a nonstarter for now, but what really holds me back from learning it is how thoroughly academic all the evangelism seems to be. Most working data scientists aren't doing things like physics simulations and won't be terribly interested in solving differential equations; even in grad school the topic rarely came up for my econometrics research.

To put it bluntly: reading about Julia kind of makes me feel a bit dumb, not excited.

When I do work with neural networks, the networks just aren't going to be small because the value in them -- for the kind of work I do -- is in their capacity to use large amounts of data to find useful representations. So the benefit of Julia over PyTorch described in the link is only interesting to me in a "huh, I guess that's a bit cool" kind of way.

I guess it's mostly academics who have unusual problems that demand custom solutions, but which are small enough so people can switch to a language their lab group is not using and still be successful with their projects. I also wish for more common adoption but it is hard to make headway against python and R that have years of development for "basic packages" for typical users in them, which is just not the kind of stuff most academics can spend their time on.

I appreciate your honestly and clarity about your intentions.

Maybe this my biggest gripe when someone claims Julia is objectively better than Python or R. The answer to this question is highly subjective, heavily depends on whether you use VSCode or not, and people that usually answer this question are heavily invested in getting other people to use Julia.

But the answer is usually presented as an objective answer, and I guess that just kind of rubs me the wrong way.

It's definitely not objectively better. For normal workflows, the startup latency is a real hindrance, even if the community has collectively built norms around that which help a bit (keeping a long running repl session open, etc.). But in terms of expressiveness plus attainable performance it really is hard to beat. I think it's nice that the path from quick draft to really performant code is continuous, and not a big gap like switching languages.

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