Search Insert Position Coding Challenge in JS, Prepare for Coding Interview

Crack the Search Insert Position challenge in JavaScript. We restate the task in plain English, run a quick O(n) sanity check, then build the optimal O(log n) binary search with the low–mid–high template. Includes real examples on [1,3,5,6] with targets 5, 2, 7, plus off-by-one fixes and interview narration.

What you’ll learn:
• Binary search pattern and why we return low
• Edge cases: before first, after last, exact hit
• Time O(log n), space O(1)
• How to explain it clearly in an interview (QA/SDET friendly)

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Timestamps:
00:00 What is “Search Insert Position” and why O(log n)?
00:22 How do we restate the task and set examples (1,3,5,6 with 5, 2, 7)?
00:47 First solution start: can a linear scan return index or insert spot?
01:35 What outputs do we get for targets 5, 2, and 7 with the linear scan?
02:25 Second solution start: how does binary search find index or insertion point?
03:28 Why return low (edge cases, off-by-one fixes) and what’s the complexity?
04:18 How should you narrate this in interviews (quick checklist) and wrap?

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