The Sliding Window Technique: Explained with 5 Interview Problems
A complete guide to the sliding window technique for coding interviews. Explains why windows beat recomputing every subarray from scratch, the crucial difference between fixed-size and variable-size windows, and the grow/shrink template that solves nearly every variable-window problem. Works through five interview problems with full Python solutions: Maximum Sum Subarray of Size K, Longest Substring Without Repeating Characters, Minimum Size Subarray Sum, Longest Repeating Character Replacement, and Minimum Window Substring. Covers the amortized O(n) argument, window-state bookkeeping with hash maps, and the signals that tell you a problem wants a sliding window.