Neither is python - it just relies on universal availability. Over time…
> Multiple dispatch, it's system of abstract inheritance and lack of static analysis and tooling makes it very difficult to write correct code. It's even hard to read Julia code due to dispatch labyrinths, macros that obfuscate IDEs, and lack of good IDE tooling.
There is support for solving some of this but it’s obscure and needs more attention.
Overall I think the growth will continue, specific fields having best of any language software packages (DifferentialEquations.jl,…) written in Julia starts to create somewhat of a network effect.
And imo the packages being written in Julia are quite specialized, unlike much of the famed “Python ecosystem”, half of which is wrapper to C++ functions and another big chunk being HTTP calls to external APIs..
https://github.com/dmolina/DaemonMode.jl
> portable
Neither is python - it just relies on universal availability. Over time…
> Multiple dispatch, it's system of abstract inheritance and lack of static analysis and tooling makes it very difficult to write correct code. It's even hard to read Julia code due to dispatch labyrinths, macros that obfuscate IDEs, and lack of good IDE tooling.
There is support for solving some of this but it’s obscure and needs more attention.
Overall I think the growth will continue, specific fields having best of any language software packages (DifferentialEquations.jl,…) written in Julia starts to create somewhat of a network effect.
And imo the packages being written in Julia are quite specialized, unlike much of the famed “Python ecosystem”, half of which is wrapper to C++ functions and another big chunk being HTTP calls to external APIs..