Space complexity

Big-O measures memory as well as time. Space complexity counts the extra memory your code needs as the input grows, not counting the input itself. A few variables are O(1).

A new list, a set of seen items or a copy of the input is O(n). An n by n grid is O(n²). Recursion counts too, because each unfinished call keeps a frame on the stack.

You often trade one cost for the other. A set makes duplicate checking O(n) time, but it costs O(n) space. Sorting in place and then comparing neighbours takes O(n log n) time with little extra memory.

Stating both costs for every solution, as the milestone asks, is the habit this stage builds.