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Comment by mark_l_watson | Hacker News Reader

Julia does support REPL style development, and the little I have used it, it was effective for deep learning, text processing, and SPARQL/linked data clients.

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I would say that Julia not only supports a REPL, but is heavily biased towards its usage - enough to be detrimental IMO.

To clarify (Correct me if this isn't what you mean), Julia compiles not only program code, but dependency code each time you run a program. This can make simple programs and scripts take a long time to start. A common response is "Use the REPL", where it compiles the dependencies once at load.

Julia has a JIT compiler but it doesn't store the compiled code between sessions. This can add a delay on the first time you call a function. By keeping the REPL open you can avoid having these delays multiple times.

My biggest complaint is static compilation. There are things you can do with PackageCompiler, but they don't work for some of the code I write. This makes deployment of artefacts harder than it needs to be.

Most of the infrastructure is there in the code, it just needs to be tied together, in such a way that juliac file.jl -o file.x results in a deployable (preferably static) binary.

but its REPL is slow and not as interactive.

I've been writing "scripts" with it that extract multi GB data sets using various APIs, perform relevant processing, and spit out actionable visualizations and data.

The APIs are (these days) mostly web/REST-ish, though I'm starting to work on writing some wire-protocol stuff as well.

It (Julia) is an amazingly powerful language today. Excellent for data scientists and engineers, for deep and complex analytics. I am using it in a professional (e.g. paid) setting. Along side python, C++, and other things.

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