[jakobnissen]

I don't think that's right. Well, you're right that some enthusiasts of Julia are too quick to say "fast as C, easy as Python" without appending an asterisk to that statement. You can't get really fast performance without paying any attention to the hardware.

Performance is on a spectrum, and usually a tradeoff against readability and conciseness. I think it IS true that Julia excels in that it gives, by far, the best expressibility/performance tradeoff.

Also, often, you really can get "free lunch" - there are many times where if you just do the obvious thing, Julia and its backend LLVM can optimise it to extremely efficient code. For a simple example, just summing an array with a for loop, for example.

[th0ma5]

To me, that's the big hand wavy thing is that that thing where if you just do the straightforward obvious thing it's fast, most real world problems aren't straightforward and obvious and therefore turn into something more complex. And now you've got a big can of worms because it was quick in the unspecial case. But now you have no real easy way to get to your special case working correctly without learning more and spending more time just like any other language or switching to a lower level.

[sundarurfriend]

> But now you have no real easy way to get to your special case working correctly without learning more and spending more time just like any other language

That's a big hand wavy thing right there: "spending more time" is not a binary, and "just like any other language" ignores the massive differences there are in how much time you have to spend, what resources are available to you from the language, and how easy the ecosystem makes it.

[th0ma5]

At every level of abstraction there is a lot of learning in that context, but now you demand it across all levels simultaneously.