I think you are roughly right that more expertise in this area would be beneficial, but you also don't seem to understand what the core strengths and goals/shor

I think you are roughly right that more expertise in this area would be beneficial, but you also don't seem to understand what the core strengths and goals/short comings of Julia are. I would love to see more engagement, but if it comes in the style of "you don't know what you're doing" then it wont work, especially if it's in contrast to practitioners experience in using the language. For example base functions being untyped is exactly how good Julia library code must look. Julia wants your code to be generic. It will get specialized (and compiled) when called with concrete types. The type system is not designed to encode invariants about the library, but to pass through information from the call site to the compiler when encountering inner functions. From a design perspective it is duck typed. A fundamental difference to compiled languages is that the compiler doesn't need to reason about the types ahead of time because it only gets triggered when the function is called, at which time the concrete type information is available. From my perspective this interaction of parametric type system, JAOT compilation, and multiple dispatch/ubiquitous generic code looks like they are interacting in a way that is genuinely new and exciting. At least I don't know any language that does something similar. One way to think of it is that Julia only has template functions, with many the draw backs and problems that entails. C++ 20 introduced Concepts to improve this aspects of the language, and I really believe Julia is in need of something along those lines, but this is more for humans than for the compiler. And obviously Julia is doing very well when it comes to enabling reuse and composability for example. I mean, I can throw a Neural Network into a Differential Equation, solve it using a state of the art solver, differentiate through the whole thing to do a gradient descent, and run all that on the GPU or CPU with the same code. So it's pretty absurd to claim that the duck typing in the base library is a problem for reuse or parallelism. BTW we also prototyped our problem space with Fortran and Julia, and Julia actually ended up faster than the Fortran implementation for the same algorithms. So compiler optimization also is not constrained in this way. Finally, Rust is a language built on the principle to not look at the cutting edge of PL research but instead to look at established things and implement them in a sound and relatively conservative way.