Julia looks to me like a very promising programming language. Its main appeal is bringing C-like speed to a high-level, well-designed language that is tailored to scientific computation. From the article:
I hesitate to make the comparison, but it’s poised to do for technical computing what Node.js is doing for web development — getting disparate groups of programmers to code in the same language. […]
If you work in a technical group that’s in charge of a dizzying mix of Python, C, C++, Fortran, and R code — or if you’re just a performance-obsessed gunslinging cowboy shoot-from-the-hip Lone Ranger like me — I encourage you to download Julia and take it for a spin.
The “dizzying mix” is very much how I would describe a lot of the code I use and write on a daily basis. R is too slow for intensive computations, so I drop down to C to write most of my algorithms and then wrap the C code in a higher-level R interface (via FFI); a similar state of affairs exists for Python. Getting communication right between the two worlds (the low-level C and the high-level language) can be daunting, and frankly, the mental context switch is exhausting. (Let’s not even bring Fortran into the discussion.)
I really hope Julia will succeed. What it needs right now is a first-class plotting facility and a bit more widespread adoption, but once that is achieved, I think that Julia will be a serious contender for mindshare.