> The truth is, Big-O is just the name of the notation used to describe how efficient an algorithm is.
The truth is that this is absolutely unequivocally unabashedly WRONG. Big O (there's no dash, first of all), describes the behavior of some function f as f tends to infinity (but we can actually look at Big O when f approaches arbitrary values, as well). The "O" in Big O references the function order of the limiting function --, e.g. logarithmic, quadratic, cubic, and so on. If you're going to make a hubbub about truly understanding Big O, at least get the math bits right.
> Learning about how they work without understanding the associated implications of time and space complexity misses the point.
Some fresh grad probably wouldn't realize how laughable this claim really is, but seasoned developers do. 99.9% of times you don't care (or even understand; some are quite hairy) about the (micro-)optimizations found inside standard library functions or data structures.
> It's like memorizing dates in roman history without having heard of Julius Caesar...
Yeesh, capitalize "Roman" please. What a low-quality trash-heap of an article.
> And on top of making learning easier, understanding the point of Big-O (and algorithms in general) will seriously help you avoid mistakes in interviews.
Ah yeah, here we go. Finally, we get to the only practical use case of Big O unless you're a CS PhD: getting through interviews.
Overall, this is a zero effort pseudo-marketing blog post that hasn't been researched, edited, or proof-read. It's all so Triplebyte can make the top of HN, get a bit of traffic, and keep reinforcing horrible interviewing practices that don't work. I swear, one of these days I'm going to write a book about how to actually do a good job of interviewing software engineers.