I built a free tool to practice the hardest part of coding interviews: choosing the right algorithm

Question

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:

1. Read a typical interview-style problem.

2. Analyze its constraints and requirements.

3. Choose the most suitable algorithm from a list.

4. 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/

Comments

I'll check and give you feedback

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Thanks, i preciate

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I could not DM you since we don't follow each other yet. I like what you have built, nice tool to become good in DSA, especially choosing best approach in solving DSA problem. However it can better if there's better/deep explanation on why choosing this approach is better than others.

In addition, giving code with multiple approach can be another add on. For example, I wrote an article on array rotation problem, you can see how I've shared code in different space time complexity from brute force to the most efficient one here.

Overall it's good and if you are planning to extend the project, there are many ideas I can share and perhaps other users will too.

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Hi! Thanks so much for the feedback and the kind words. I'm glad you're finding the tool useful!

Those are excellent suggestions. You're right, deeper explanations on the trade-offs and showing multiple coding approaches would be fantastic additions. I'll definitely keep that in mind for future updates.

Thanks again for reaching out!