This is not a collection of textbook answers. It is a translation layer. I am taking the core concepts from Grokking Algorithms (2nd Ed) and reimplementing them as Production Utilities using Python.
Goal: To move beyond "LeetCode" style puzzles and understand how algorithms power real backend systems (Log analysis, Caching, Routing, etc.).
| Day | Topic | Real-World Application | Status |
|---|---|---|---|
| 01 | Binary Search | Log Hunter: Finding 1 transaction in 10M logs | Completed |
| 02 | Selection Sort | Music Sort: Why O(n²) kills page load speed | Completed |
| 03 | Recursion | The Crawler: Searching deep nested directories | Completed |
| 04 | Quicksort | The Divide: Optimizing large data processing | Completed |
| 05 | Hash Maps | API Caching: Building a Redis-lite layer | Completed |
| 06 | Breadth-First Search | Social Graph: Finding shortest connections | Completed |
| Refit | Trees (Ch. 7) | Visualizer: CLI tool to print directory trees | Completed |
| 07 | Dijkstra’s Algorithm | GPS Navigation: Finding the fastest route | Completed |
| 08 | Greedy Algorithms | Schedule Optimizer: Managing limited resources | Completed |
| 09 | Dynamic Programming | The Knapsack: Maximizing inventory value | Pending |
| 10 | K-Nearest Neighbors | Recommendation Engine: "You might also like..." | Locked |