Exactly. To me they have a rather big blind spot when it comes to reaching out to users with docs, and understanding their user needs, exactly because they focu

Exactly. To me they have a rather big blind spot when it comes to reaching out to users with docs, and understanding their user needs, exactly because they focus so much on these hard challenges. You can see it in pt 2: "where is the Julia ML ecosystem currently inferior?". They identify some gap with using cuda optimized kernels, where Julia doesn't make use of built in cuda magic; a solution is proposed where users might manually override compiler behavior (if I understand correctly). However, no mention at all about the gap in dev experience. Some examples: - TERRIFIC efforts of various groups in porting ml models from research to easy-to-use torch code - the super ergonomic data wrangling tools in torch. Practically speaking, ml ecosystems need quality interop: do i get my data in and out of the ml framework in a sensible way - the ease of getting started building your first toy ML model in torch because the docs and the API are so great Getting the ball rolling, adoption-wise, means overcoming the cost of switching to something new. And I don't see that happening soon because python+torch is just so comfy.