Find the Big O of ANY code in 10 seconds

4 rules. That's all.

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Loops multiply

A loop inside a loop: multiply their counts. n × n = O(n²).

Steps in a row add

One loop, then another loop: n + n = 2n, which is still O(n).

Doubling or halving = log n

If i doubles or n halves each step, that loop is O(log n).

Recursion: count the calls

Calls × work per call. Fibonacci with two calls each time grows like 2ⁿ.

The mistake

Calling a loop O(n) when its counter doubles. That's O(log n).

Can you get it?

Tap an answer. The full video explains why.

Cheat sheet

• Nested loops: multiply
• Loops in a row: add
• Doubling/halving: log n
• Drop constants
• Recursion: calls × work

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