Understanding time & space, visually

Source: Dev.to

Overview

Ever looked at code and wondered why some runs instantly while others slow down as input grows?

A new section has been added to make time and space complexity easier to understand visually.

Decoded App – Complexity Visualizer

What’s included

Instead of treating Big‑O as pure theory, this section focuses on how algorithms actually behave.

Visual comparison of complexities

You can see how different complexities grow as input increases:

• O(1)
• O(log n)
• O(n)
• O(n log n)
• O(n²)

The idea is simple — understand how the number of steps increases with input size.

Code → complexity mapping

Common patterns are broken down so it’s easier to identify complexity directly from code:

• Constant operations
• Single loops
• Nested loops
• Input‑shrinking patterns

Space complexity

A separate section covers memory usage:

• Constant space
• Linear space
• Recursive space

Focused on how and when memory grows.