Earlier quoted context omitted.
You have to use it inside a REPL, like R or Matlab. Julia using LLVM and compiling on-the-fly is not sloppy design, it's a design tradeoff (and a great one, if you really need speed). Julia is not a scripting language, it's a language for mathematical analysis. Also going back to your example the solution would be to use ImageMagick.jl from Julia, and extend the library (4 extra lines with ccall) if it's not availabl…
> Julia is not a scripting language, it's a language for mathematical analysis. Ok, this clarifies the matter a lot. So julia is not intended to be a general-purpose programming language. Are you involved in julia development? (Besides, I strongly dislike the design tradeoff of not being a good unix citizen.)
https://docs.julialang.org/en/latest/manual/running-external...
The only trade-off here is the restricted resources the Julia devs can afford. Focusing on the scientific computation niche, the Julia compiler was built to compile code just ahead of time, since this way it can create code as fast as compiled languages while still being dynamic like any interpreted language (such as Matlab and Python). But there is nothing in the language that prevents it from being fully interpreted (and therefore having minimal compile time overhead, but generating poorly optimized code for long running processes), or alternatively pre-compiling and caching to have both (which is being worked on: https://github.com/JuliaLang/PackageCompiler.jl )