Built-in autodifferentiation and amazing libraries built around it, plus tons of cutting edge applied math libraries that interoperate automatically, thanks to

Built-in autodifferentiation and amazing libraries built around it, plus tons of cutting edge applied math libraries that interoperate automatically, thanks to Julia's well conceived approach to the expression problem (multiple dispatch). Aside from that, the language itself is like a refined python so it should be pretty friendly off the bat to ML devs. What Julia needs though: wayyyy more thorough tooling to support auto generated docs, well integrated with package management tooling and into the web package management ecosystem. Julia attracts really cutting edge research and researchers writing code. They often don't have time to write docs and that shouldn't really matter. Julia could definitely use some work in the areas discussed in this podcast, not so much the high level interfaces but the low level ones. That's really hard though!