Algoritma Practice in the Age of AI

Description and use

The author uses AI to generate LeetCode-style problems but solves them manually, focusing on time‑complexity optimizations and clean code.

Problem

Balancing AI assistance with manual algorithmic practice to deepen understanding of algorithmic logic and performance.

Outcome

The author produced a collection of manually solved algorithmic problems that emphasize efficient coding practices, demonstrating improved understanding of algorithmic complexity and performance.

Workflow

• 1. Prompt AI to generate a LeetCode‑style problem focused on a specific concept.
• 2. Review the AI‑generated problem statement and constraints.
• 3. Manually design an algorithmic solution, aiming for optimal time complexity.
• 4. Implement the solution in Python, applying hash maps, two‑pointer techniques, or in‑place array manipulation as needed.
• 5. Test the solution against provided test cases and edge cases.
• 6. Refactor code for clarity and performance.
• 7. Commit the solution to a GitHub repository for review and feedback.

Evidence

I asked AI to generate LeetCode-style algorithm problems focused on specific concepts to keep my problem-solving skills sharp, and I solved each of them manually, step by step.

Source

My goal wasn’t simply to find a working solution, but to write clean and performant code while reducing Time Complexity wherever possible.

Source