FluxML is amazing, so much nicer than using TensorFlow.

I will have to disagree. FluxML is indeed great, but it changes often and it does not support many of the advanced features of TensorFlow (neither is there a package that seamlessly works with Flux in order to support these features). It is getting there, but Tensorflow v2 is pretty great itself, and frequently faster. But FluxML might soon be as good or better.

Also, to be fair, FluxML is backed by a couple of people while Tensorflow is backed by megacorps, so it is already impressive how much they have done.

I mean to be fair I think the person you are responding too just finds flux more pleasant to use, not more feature complete.

I use pytorch mostly and always find tensorflow very cumbersome, but I assume it’s because I haven’t spent enough time with it yet.

How much of the BioJulia stuff would you say currently works? It looks like a lot of repos have been created, and the scope is pretty impressive (looks like there are repos for everything from structural bioinformatics to population genetics), but a lot of them look to be basically empty(https://github.com/BioJulia/PopGen.jl), or have really scary looking issues:(e.g https://github.com/BioJulia/GeneticVariation.jl/issues/25).

% of the repos in the org on github? That number is lower than I'd like. % of the repos that are actively maintained? Much higher.

One of the great things about julia is that it's really easy to throw together a package and register it. One of the bad things about julia is how easy it is for those one-off projects or idea dumps to pollute the space. We could definitely do a better job labeling the repos that are no longer being maintained or that aren't actually ready for prime time. There's a tradition in julia of a lot of really functional libraries to stay < v1.0, because we all take semver seriously, and if the interface is still in a bit of flux, making the switch to 1.0 is a big deal (DataFrames.jl, looking at you). But it does make it hard for new users to distinguish between a super robust package and someone's weekend hobby.

Not my field, but at least some of it appears to be worked on seriously. This was an interesting recent blog post about making DNA-sequence processing go fast:

https://biojulia.net/post/seq-lang/

I've always found Julia a little lacking for bioinformatics, but I'm not doing ML. I have very high hopes for Nim, which I think has better performance potential than Julia across domains and can produce binaries.

Nim made me really sad when they gave up on multiple dispatch. I has such potential as a language but I just can't imagine using one without multiple dispatch anymore