Beyond Implementation: I Built a Tool to Master Algorithm Selection for Coding Interviews
Every developer who has prepared for a technical interview knows the routine: grind through problems, practice implementing sorting algorithms from scratch, and try to wrap your head around dynamic programming. But there’s a crucial step that often gets less attention than it deserves: the moment you read a problem and have to decide which algorithm to use in the first place.
This is where the real challenge often lies. It’s not just about knowing how to write a BFS traversal; it’s about recognizing that a problem asking for the "shortest path in an unweighted graph" is a signal to use BFS. This pattern-matching skill is what separates a good candidate from a great one.
To address this specific gap, I built Think2Algo, a web platform designed to train that exact "algorithmic intuition."
The Problem: From "How" to "Which"
While practicing, I found myself getting stuck not on the implementation details but on the initial decision-making. I’d see a problem and think, "Should this be solved with a sliding window, or is it a two-pointers problem? Is this a classic case for backtracking?" The cost of choosing the wrong path is significant, both in an interview and in real-world development.
I realized what I needed was a way to practice this diagnostic step in isolation. I didn't want to just solve another problem; I wanted to build a mental map that connected problem types to algorithmic solutions.
The Solution: Think2Algo
Think2Algo is an interactive learning tool that flips the script. Instead of asking you to code a solution immediately, it presents you with a problem and forces you to first select the most appropriate algorithm. It’s a "flashcard" system for high-level problem-solving.
The core workflow is simple:
Read a typical interview-style problem.
Analyze its constraints and requirements.
Choose the most suitable algorithm from a list.
Get immediate feedback on your choice, along with a detailed
explanation of why a particular algorithm is the best fit.
This process helps reinforce the patterns, keywords, and signals associated with each algorithm, making the selection process faster and more accurate over time.
Key Features
To support this learning model, I built out a few core features:
Interactive Challenges: A collection of problems where the primary goal is to match the problem to the right algorithm, complete
with difficulty levels.
Detailed Algorithm Guides: For each algorithm, there’s a dedicated page explaining what it is, its Big O complexity, and—most
importantly—a "When to Use It" section with common keywords and
problem types.
Code Templates: Once you've made the connection, you can view clean, commented code templates in both Python and JavaScript to see
the implementation.
Bilingual Support: The entire platform is available in both English and Portuguese to reach a wider audience.
For those interested in the technical side, the project was a great opportunity to work with a modern web stack:
- Framework: Next.js (using the App Router)
- Language: TypeScript
- UI: React
- Styling: Tailwind CSS with ShadCN for UI components
- Internationalization (i18n): React Context API to manage language state.
The focus was on creating a clean, fast, and fully responsive user interface that works seamlessly on both desktop and mobile devices.
Conclusion
Building Think2Algo was a rewarding experience. It helped me solidify my own understanding of algorithms and create a tool that I hope can help other developers bridge the gap between theory and application.
I would appreciate any feedback from the CoderLegion community.
You can try out the live version here: https://think2algo.vercel.app/
