How a Sorting Project Revealed Why Algorithmic Thinking Beats Writing More Code

A developer working on a Push-Swap project — which requires sorting numbers using two stacks and a restricted set of operations — found that choosing the right algorithm matters far more than simply writing functional code. The exercise highlighted how different sorting approaches, such as Bubble Sort, Selection Sort, and Merge Sort, all produce correct results but differ significantly in performance as input size grows. Time complexity notation like O(n²) versus O(n log n) does not measure exact runtime in seconds, but rather describes how the workload scales relative to input size. At a thousand elements, an O(n²) algorithm may perform a million operations while an O(n log n) one performs around ten thousand. The key takeaway is that correct code can still be a poor solution if it fails to scale efficiently with larger datasets.