Fellow Julia user. Quite excited about Mojo. Been following Chris from his MLIR days at Swift 4 TF and his course with Jeremy on fastai. I hope we can also derive some exciting learning from Mojo :)
As package developers we need to optimize our packages for 1.9. It's quite a task but I am excited what's ahead in Julia. Matlab(is not open-source, R is slow and not really a general purpose language, Python is great but same issue 2-lang problem...why should I implement CUDA in C++ or Numpy in C. I want to be able to modify lower back-end code but with Python it's not possible. Julia fixes all of these problems and I am quite happy I invested my time in Julia. Present/Future is bright :)
what are some of these libraries that are written in C but don't have python bindings if I may ask. Genuine question the once I have seen have python binding and that's why they are super popular and widely used.
sorry for confusion what just pointing to one of recommendations from the blog post that I thought you mind wanna re-consider to overcome "multiple dispatch correctness bugs" i.e. "I'd say that there is a huge combination of packages that can be used together providing the language with an enormous amount of possibilities. It is up to the programmer to verify the interaction before use and, preferably, add tests to one of the packages." Yuri's criticism of composability came from packages as Viral already mentioned
but the whole point of using Julia is that you don't need C(relatively same speed). Why would you interface it a Scientific Computing library written in C. Also C doesn't really sound like a good language for Scientific Computing. People do SC in Python and which is why Julia i.e. to solve two language problems and mind boggling speeds like C with syntax like Python