Blog

Mert Özen's blog posts on software development, backend, frontend, and DevOps.

Series

Deriving Time Complexity from Code: Loops, log n and Recursion

To read complexity from code, add sequential blocks, multiply nested loops, call a loop that halves every step log n, and write a recurrence for recursion. With real counts: triangular loops, naive Fibonacci, merge sort, the master theorem, LINQ's hidden loops and an order-matching benchmark going from O(n·m) to O(n+m).

8 Nov 2026 13 min 5

Computer Science Fundamentals PART 01

What Is Big-O Notation: Growth Rate, O, Ω, Θ and Worst Case

Big-O describes an algorithm's cost not in seconds but by how its step count grows as the input size n grows. The difference between O, Ω and Θ, choosing n correctly, best/average/worst case, and the real growth of List, HashSet and binary search from 10 to 1,000,000 elements measured with BenchmarkDotNet.

8 Nov 2026 11 min 4