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Big-O notation has been drilled into us since our first data structures class. O(1) good.O(n) acceptable.O(n log n) fine.O(n²) bad. We memorize it for interviews, use it to compare algorithms, and treat it as the final word on performance. But once y...

Introduction When you first start programming, you're mostly focused on getting your code to work. Does it produce the right output? Does it handle the basic test cases? That's great, but there's another question you should be asking: how fast does i...

If you’re anything like me, the first time someone explained Big-O notation, it felt like they were trying to measure my code using math from another galaxy. “This algorithm is O(n log n).”“Bro… I just want my code to work.” Same energy. But the deep...

Entender la base de Logaritmo Antes de explicar el Big O notation es necesario explicar qué es un logaritmo. Un logaritmo es preguntar cuántas multiplicaciones del mismo número X son necesarias para llegar a un número Y. EJ: \(2^x= 8\)¿Cuántos 2 ten...

Understanding the Base of a Logarithm Before explaining Big O notation, it's necessary to explain what a logarithm is. A logarithm asks how many multiplications of the same number X are needed to reach a number Y. Ex: \(2^x = 8\) How many 2s do I ne...
