Big-O

Read growth curves at a glance instead of memorising a table.

1. 1What Is Big-O?Stop timing code. Start predicting how it scales.4m
2. 2O(1) and O(n)One step, or every step? That's the whole difference.4m
3. 3O(n²) and Nested LoopsEvery pair costs you. Nested loops explode fast.5m
4. 4O(log n) and HalvingThrow away half the problem, every single step.4m
5. 5Comparing ComplexitiesO(n log n) beats O(n²) long before you notice.5m