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Comment by npr11 | Hacker News Reader

I work at company using Julia in production. What sort of recommendations are you looking for? :)

Mainly, am I going to spend hours fussing with the basics of the language. For example in R, it’s too slow, so I had to write Rcpp, only to discover there’s no real way to debug it from RStudio, down into the rabbit hole, lldb, Rinside, everything that can crash does, terse errors messages with out line numbers, opaque SEXP types XCode can’t decode etc. So basically are the normal everyday use cases fraught with quirks you need to know more about the internals of Julia or can you just be a user? If that makes sense.

“Writes like Python runs like C” is one tagline I’ve seen them use. Julia gives you a lot of control over how fast you want your code to be. You can write some code without really thinking too much and it’ll be fast and work just fine. If you get to a point where it’s not fast enough then there’s a whole host of tools to help you speed it up.

At any stage you can call @code_{warntype, lowered,...} and inspect exactly what code has been produced by the compiler to find bottlenecks.

I’ve not written much R before but in my experience I’ve never had to go to those lengths to debug Julia code! (Although the error messages can sometimes be a bit of a mouthful, I think because it uses LLVM)

Not the parent, but I am interested in a good list of libraries that might be non obvious to find.

You can use the search tools to find libraries for the categories you're interested at:

https://juliaobserver.com/packages

https://pkg.julialang.org/docs/

As a personal answer, I really like unitful for keeping track of physical units and libraries like MLStyle.jl or Match.jl which add stuff like pattern matching to the language (though they are more general helper libraries compared to stuff like Turing.jl and DifferentialEquations.jl):

https://painterqubits.github.io/Unitful.jl/stable/highlights...

https://github.com/thautwarm/MLStyle.jl

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