>This sounds like magic bullet / handwaving. It's not handwaving when it already exists. CUDAnative.jl gives native compilation of Julia code to GPUs, which al

>This sounds like magic bullet / handwaving. It's not handwaving when it already exists. CUDAnative.jl gives native compilation of Julia code to GPUs, which allows CuArrays.jl to do efficient loop fusion among other tricks. This is then enough for DifferentialEquations.jl, Flux.jl (the big neural net library, think PyTorch), CLIMA (a MIT-CalTech next-gen climate model), etc. (too many packages to list) to all utilize the same underlying GPU framework. It's as if someone got rid of the GPU functionality of Numba, PyTorch, TensorFlow, etc. and just made one good enough Python library for everyone to share. As you can imagine, that reduces the amount of work to do package development but gets the same functionality in the end with a lot less maintenance issues. You can also say the same for how many JITs and transpilers to C there are in Python libraries vs the single Julia JIT. The real danger of the Python monoliths is that when something as simple as a neural network package starts to include a JIT, a GPU compiler, and automatic differentiation library, and it's all written in C++, it gets really hard to attract developers because the code base is so hard to understand. This means that instead of getting new developers and more maintenance, people just spawn new libraries all of the time in Python, starting from scratch to write a new JIT, new GPU kernels, etc. I just cannot see how that's a good thing.