A beginner-friendly introduction to algorithm complexity analysis. Covers why correctness alone isn't enough when evaluating code, and introduces Big O notation as a language-agnostic way to measure how solutions scale. Explains the most common complexity classes — O(1), O(n), O(n²), O(log n), and O(n log n) — with clear code examples in JavaScript. Also covers space complexity and the practical rule of dropping constants and lower-order terms when simplifying Big O expressions.
Table of contents
Episode 1:What Are We Actually Measuring?Big O NotationThe Most Common Complexities You’ll SeeO(1): Constant TimeO(n): Linear TimeO(n²): Quadratic TimeO(log n): Logarithmic TimeGet Chaitanya.V’s stories in your inboxO(n log n)Visualizing the DifferenceWhat About Space Complexity?One Practical Rule to RememberThe Habit That Will Change How You Read Code156 Impressions