6 min read
Hash Tables Explained Visually — Hashing, Collisions, Chaining
How do hash tables work? A visual guide to hash functions, collisions, separate chaining, linear probing, load factor, resizing — and when a BST is the better choice.
Algorithm Tutorials & Visual Guides
Step-by-step explanations of the algorithms and data structures that matter — each one paired with an interactive visualization you can run, pause, and rewind.
6 min read
Binary Search Trees Explained — Insert, Search, Delete Visually
Learn how binary search trees work, visually: the left-right invariant, insert and search traced step by step, all three delete cases, and why AVL trees keep it fast.
6 min read
BFS vs DFS — A Visual Guide to Graph Traversal
BFS vs DFS explained visually: trace both on the same graph, see why BFS finds shortest paths, compare memory trade-offs, and learn exactly when to use each one.
6 min read
Dijkstra's Algorithm Explained — Shortest Paths, Step by Step
Learn how Dijkstra's algorithm finds shortest paths, step by step: the greedy insight, relaxation, priority queues, O((V+E) log V), and why negative edges break it.
7 min read
Merge Sort Step by Step — Visual Guide with Animation
Learn merge sort step by step with a visual walkthrough: the divide phase, the merge traced pointer by pointer, why it is O(n log n) in every case, and stability.
7 min read
Quicksort Explained Visually — Step-by-Step with Code
See how Quicksort works step by step: a full partitioning walkthrough, pivot choice, why the worst case is O(n²), and why it still beats Merge Sort in practice.
5 min read
How Bubble Sort Works — A Visual Step-by-Step Guide
Learn how Bubble Sort works with a step-by-step visual walkthrough: comparisons, swaps, passes, why it is O(n²), and when it is actually worth using.
6 min read
Quicksort vs Merge Sort — Which Is Faster? (Visual Comparison)
Quicksort vs merge sort compared visually: worst-case behaviour, memory use, stability, cache locality, and what real standard libraries actually ship.
6 min read
Big O Notation Explained Visually — A Practical Guide
Big O notation and time complexity explained visually: O(1) to O(n²) grounded in real algorithms you can watch run, a growth table, and the classic beginner mistakes.