The original title is about Big O notation and slow loops. Let me craft a concise headline that preserves key facts.

Short summary

A practical guide to Big O notation focusing on three key complexity classes: O(1), O(n), and O(n²). The author shows how a nested loop for duplicate detection ran in 9 seconds on 14K rows but was instant on 60 test rows, then fixed it to 40ms using a Set. The article explains how hash tables achieve O(1) lookups versus arrays and linked lists.

• •O(n²) loops are invisible at small scale but explode at production data volumes
• •Swapping array.includes() for Set.has() collapses O(n²) to O(n) — 9s to 40ms in real example
• •Hash tables (Set, Map, dict) give O(1) read/write/membership by computing memory positions directly

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