I didn’t express that correctly. What I meant to say was that I investigated Julia as a possible “use it for everything language.”
For me, general purpose has to include deep learning tools.
I really prefer Lisp and Haskell, but although Haskell’s TensorFlow support is pretty good, I found it easier to just use Python. I have had problems with the SBCL Common Lisp TensorFlow C bindings, but perhaps that was my fault.
AFAIK Clojurians doing some interesting stuff in that space. They've figured out Python Interop, writing books on Deep learning, Linear Algebra, etc. all that in Clojure.
A little of both, I guess. The biggest issue is that I, personally, get uncomfortable with dynamic typing when the project starts becoming over 1-5 Kloc. So I tend to stick with stuff like Scala, Rust, or OCaml for non data science tasks.
You mean Julia as language is not enough good for general purpose or it lacks ecosystem (frameworks, libraries) for general purpose use?