I don't find it strange: I wrote this to say why I like using Julia and point out what the community is missing, not as a comparison to every other JIT in existence. But if you want to know why I gave up on Cython, I'll lay it out for you. I tried it almost 2 years ago because some documents in a course had IPython notebooks which used it. So I did some standard scientific computing stuff like write some Runge-Kutta methods and yes its speed was fine (that was a pretty big part of the blog post: if you try hard and use the right tools you'll get pretty much the same performance anywhere). That's not the problem at all.

The problem was extending it to be more widely useful in my own research. I wanted to make those same compiled functions also work with complex numbers to integrate spectral discretizations of a stochastic PDE (instead of the finite difference one from before). I found some SO posts like:

https://stackoverflow.com/questions/30054019/complex-numbers...
https://stackoverflow.com/questions/27906862/complex-valued-...

At that point it stopped looking like Python at all. I always found the SciPy syntax a little verbose since I had used a lot of MATLAB before (but I wanted to make this project not require a license to run) (this QuantEcon cheatsheet is a good demonstration of what syntax is like in my domain: https://cheatsheets.quantecon.org/). But to make this kind of "complex or not" logic work in compiled Cython, I resorted to conditional compilation (http://cython.readthedocs.io/en/latest/src/userguide/languag...). These days I understand that what I created was essentially a multiple dispatch mechanism.

Anyways, at around that time I started experimenting with other tools, especially Julia, because I really was getting frustrated whenever I had to "go beyond doubles" and write something that was extendable instead of a one-use script. Maybe there's some tricks I was missing, but I found it really hard in Python and MATLAB. Soon after, my PhD adviser and I started arguing about whether one of the properties in the simulation's solution was due to floating point errors. I couldn't convince him, so I wanted to write this integrator so it was fast and compiled, but allowed arbitrary precision so that way I could prove that it still existed even with very high precision. I couldn't find a page which explained how to do high precision arithmetic in Cython or Numba, so I completely gave up. Needless to say, I decided to re-write a small portion of this in Julia and it worked really well, pretty much instantly. Then I was digging around the Julia package listing and Viral pointed me to a big opportunity (https://github.com/JuliaDiffEq/ODE.jl/issues/64) and I have been developing a lot of Julia differential equation solvers ever since.

Obviously YMMV, but after computing a lot without a "first-choice language" (between R, Python, C, MATLAB, Mathematica) for quite awhile, I kept with Julia because I didn't have issues when I hit less standard tasks, and I found it very easy to contribute fixes to other people's projects because it was just Julia code. This stochastic PDE integrator story is just one (significant) project that led me in this direction.