Big O notation describes the upper bound on how an algorithm time or memory grows with input size, ignoring constants. It lets you compare algorithms independent of hardware.
Big O notation - Interview Concepts - ExtraBrain is part of ExtraBrain's local-first Mac workflow for live interviews, meetings, transcription, provider control, and responsible AI use.
Big O notation describes the upper bound on how an algorithm time or memory grows with input size, ignoring constants. It lets you compare algorithms independent of hardware.
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From fastest to slowest: O(1), O(log n), O(n), O(n log n), O(n squared), and O(2 to the n).
Analyze both runtime and extra memory; a faster algorithm may use more space.
Count how work grows with n, keep the dominant term, and drop constants and lower-order terms.
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Short answers for people and crawlers comparing ExtraBrain with other live AI assistants.
Interviewers expect you to state and improve the time and space complexity of your solution, not just make it work.
It depends on the problem, but reducing an O(n squared) brute force to O(n log n) or O(n) is a common goal.
