4 ms·

I cannot resonate more with this argument. Everytime we had some issue with performance with Python, instead of rewriting from scratch, I wrote it in C and crea

• bonoboTP 6y ago
  Yes, there is a very long tail of libraries scattered across GitHub and the web in general. Snippets of obscure algorithms, APIs for various things, like ML datasets from 5 years ago etc. I heard there's some interop between Python and Julia, but I guess it comes with plenty of caveats, like it works for most cases, but will give you enough headache like "oh, of course this use case is not supported like that" etc. Once you're in Python you can do anything. Spin up a web server, render something in 3D, read obscure data formats... When I see academic code from 10 years ago written in Matlab I cringe because I know it will be a pain to work with that code. There was a big reason people moved away from Matlab as it cannot support large applications. In Python you can build huge code bases with modular structure, sane classes etc. as opposed to Matlab. And it's free and open source unlike Matlab. So the switch there was worth it. But I don't want all of today's great Python long tail ecosystem to be lost to obsolescence in 5 or 10 years. It's just sad to see so much effort go down the drain.