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Big-O notation (time and space)
Big-O describes how work grows as your data grows. O(1): same speed no matter the size (looking up a key in a map). O(n): work grows in step with the data (one pass through a list). O(n²): a loop inside a loop. 1,000 items means 1,000,000 steps. O(log n): halve the problem each step (binary search).
Why it matters when you build with AI: Ask the AI 'what's the Big-O of this?' for any loop over user data. Nested loops over two lists are the number-one cause of 'it was fast in dev, slow in prod'.
Quick check
A loop inside a loop over the same list of n items is…
n × n steps. 1,000 items → 1,000,000 steps.