Yeah the poor performance in this blog post is a total misunderstanding of why and how julia is performant. Is printing "hello world" fast really that important? OK. Then don't use Julia.
You pay for it by having the compiler JIT the code in a highly optimized fashion. If you have actual numerical calculations that are compile-once, run-many-many-many-times, then you will see a huge performance benefit, amortizing the cost of expensive compilation and optimization that happens once at the beginning of the program cycle.
The very title of what he links to "How To Make Python run as fast as Julia" betrays the problem. The goal of Julia is to not have to do that sort of boilerplate/arcane tweaking to get really good performance - the system will do it out of the box.
I'll have to disagree with the notion that Julia is hard to read. I'm currently deploying Julia to run automated hardware verification on a computer chip. Effectively, I've written a DSL using Julia macros that generates assembly code files, compiles, and executes it, and my coworkers (who do not use julia) have found it easy to read my code and understand what's going on. Far easier, in any case, than the equivalent C code using asm blocks.
I do agree about the one-based indexing. I get it, it's what matlab does. But it would be nice to say, be able to throw an option at the top of a program that forces the appropriate indexing.