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Comment by mjn | Hacker News Reader

I read that as being about what language industry uses to write ML applications, not about technical feasibility of integrating machine learning methods into a codebase. Put differently: industry most often uses Python (especially in ML), therefore the author wants to target Python in order to maximize uptake outside of academia. They even admit that doing it in Python is technically harder than doing it in Julia ("Unfortunately, this type [Type III] is also the hardest to get right"), but consider it worth the trouble for the broader accessibility.

(That's more or less the direction I've been going with research code lately too, so I can sympathize, although I'm not entirely happy with the situation and definitely also sympathize with the Julia folks being unhappy about it.)

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Somehow I don't think an ocean simulation needs to be in Python so some startup can use it to... what, sell ads or something?

Anyone interesting enough to be looking at your ocean simulation code can probably handle it being in Julia, and may even prefer it, since the language is so much better designed for this kind of thing than Python.

hopefully if enough people are unhappy about it && sees future in alternative (i.e. critical mass), we can collectively have a "phase transition".

It may take a while, however. 15-20 years ago, you kind of had to use Python on the sly in the scientific setting vs the incumbents (MATLAB, C++, Fortran). Julia seems to be in a similar phase.

That being said, Python does have some structural advantages since it positions itself as a universal glue. It's much easier to gain a critical mass in that regard vs a niche area like scientific or numerical computing. That being said, Julia is probably underrated in general purpose usage.

I think Julia has a much better path to wide adoption for numerical computing/HPC. It is a much better language for package developers (you pretty much never have to go to a lower level language and everything can compose together with much less work). If you look at Julia and Python packages with similar functionality, the Julia one will typically be much more general and 1/10th the lines of code. This is a pretty powerful incentive for on-boarding package devs.

I'm very excited about this, but my guess is it will take years for the packages to be so broadly and clearly superior that there's a mass migration to Julia. And even then people may prefer to just call Julia from a language they're more comfortable with. Still, it would be amazing for Julia to become the single go-to high-performance language of ML/DL/AI, advanced statistical modeling, HPC, etc.

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