Using Big O notation to improve app performance

In modern software development, user experience is heavily influenced by performance, which can significantly impact user engagement and retention. The guide emphasizes the importance of considering the performance implications of code and introduces Big O notation as a tool to measure algorithm complexity, which is crucial for writing efficient code. Big O notation helps determine how the complexity of an algorithm scales with the size of its input, addressing space and time complexity through various scenarios like best, average, and worst cases. Examples include O(1) for constant time complexity, O(n) for linear time complexity, O(log n) for logarithmic time complexity, O(n^2) for quadratic time complexity, and O(n!) for factorial time complexity, highlighting the importance of avoiding unnecessarily complex code. The document encourages developers to balance readability with performance and utilize Big O notation to optimize code, ultimately fostering the creation of engaging and high-performing software.