This is very good news for people like me who would like to play with GPU kernels and arrays but usually have no access to NVIDIA/AMD hardware. (Although specific Intel hardware and Linux is being required for now.)
I'm waiting for KernelAbstractions.jl integration.
Or maybe it should be "When on an online discussion someone mentions Julia, the probability of that discussion turning into a 0-based versus 1-based indexing discussion is 1"
There seems to be a kind of Godwin's law for Julia that states that "When on an online discussion someone mentions Julia, the probability of a complaint about 1-based indexing is 1".
Ha, ah! After a more careful read, I noticed this: "with the speed of C or Ruby"... I'm pretty sure Ruby performance was never a positive characteristic anywhere (right?). I wonder if this is a writing error or the journalist ignorance. Anyway, the article uncovers very nice facts about the history of Julia.
“Those people were our early converts—people who came for performance".
That was entirely my case. After being using Python for a while and dropping to Cython in the performance bottlenecks (and evaluating other options), I was really longing for a language where I could have it all.
When I first heard of Julia, in a highlighting package for Latex in the summer of 2013, my first thought was "oh no!, yet another high-level language, we already have Python people!".
However, as soon as I read the "Why we created Julia" I just knew it was it, I finnaly could have my cake and eat it too. And in a matter of months I substituted almost all codes used in my half-the-way PhD to Julia.