Bdamkin54

What are the plans/thinking about traits, interfaces and static typing type things?

That's great for custom algorithms, but there is a still a significant place for framework facilitated canned non diffable algorithms, model selection/eval and other aspects of more mundane data science. For Julia to gain mainstream data science appeal, it will have to approach and exceed scikit learn for these sorts of tasks. (and I think it will soon, given the way things are moving.)

I have two suggestions that would trigger a positive phase change in Julia's growth:

1. Enable useful web assembly support for front and back end Julia programs.

2. Convince a large company to use and work on Julia for their machine learning stack.

Any plans to pursue these?

Really glad to hear all that.

And obviously #1 (along with multithreaded web server capability) will help with #2 .

Regarding the compiler passes, which aspects do you think will have to wait for 2.0? It seems like a lot of it could be non breaking- Do you think a better cassette could appear before then?