Use Big-O thinking to compare how code grows as input size increases.
If input grows, the cost shape matters more than the happy-path sample. Big-O is a vocabulary for growth. It strips away machine speed and constant overhead so you can compare structures: one pass, repeated search, nested comparison, indexed lookup. That abstraction is powerful because production data rarely matches local samples. Look for Repeated Search find inside map is a common signal. Each item in one list scans another list. That may be fine for 20 rows and painful for 20,000. Spend Work Once A lookup table, set, or map front-loads organization. You pay one pass to make future reads cheap.…
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