I quite liked this article, but both the article and the comments here mostly skip over my absolute favorite Julia feature: because Julia is fast end-to-end, you can code in the style that naturally matches your use case and mental models without sacrificing performance. Functional patterns and imperative patterns, vector-based or element-by-element, your types or built-in: they all work well and run quickly!

I do most of my programming in Julia and Python. Python libraries like numpy and pandas are fast and efficient—if you're staying "within the lines" of how the library is designed to work. And most of the time this is okay! But not irregularly I want to do array operations on arrays of user-defined types, or I want to walk through a dataframe row by row without paying a huge performance penalty, etc. And all the sudden the well-tuned Python ecosystem feels very restrictive.

In Julia, my workflow is roughly: 1) think about the problem. 2) Code an intuitive solution. 3) If necessary, tweak a little bit of code to improve performance by reducing allocations or type instability.

That's a lot less mental work than my Python/Matlab/Java workflow of 1) think about the problem. 2) Think about how the solution can be expressed in the paradigm the language supports performance with. 3) Write a solution in this particular paradigm. 4) Tune for performance, which may be awkward if the initial solution was not intuitive.