julia

We use Julia (running in IJulia) with D3 using either iframe communication, window.postMessage or hooking directly onto the websocket to communicate between the D3 viz and the Julia backend.

For visualizations that need to be shared IPython notebooks are great and work with Julia & R as well.

All the statistical languages like R and Julia should be able to get something out of that.

A big thing Julia has going for it is that it plays nicely with Fortran.

So unless D or Rust have macros, you end up with Julia and Clojure.

Then I found this image mapping Julia sets on the complex plane by their seed values.

I guess it's the convenience of having a single language that speaks for a pure Julia solution, more than anything else.

Why would you use Julia over something like Erlang for parallel?

For a purely parallel task Julia should be much faster.

I'm also particularly impressed to see Julia Serrano being quoted here.

> So unless D or Rust have macros, you end up with Julia and Clojure.

What students cannot understand is that after passing the classic CS61A in Scheme, languages such as Python or Julia could be picked up in a few hours with occasional consulting of reference manual for particular details.

I just finished re-watching "Julie and Julia" before I sat here and saw this.

> “The potential for weirdos is too great, and $20 or $30 simply isn’t worth it,” says Julia Schweitzer, a Brooklyn-based startup employee who works remotely.

That not many languages do that (one notable exception is Julia) is a shame!

Julia's startup time has been fast for over a year – accomplished by pre-compiling the base language as a shared library (.so,.dll, or.dylib).

Julia is an example of an imperative one that tries to do that.