Why Real-World Coding Beats LeetCode: The Case for Practical Technical Interviews

Introduction

Let’s cut to the chase: nobody’s job as a software engineer is to reverse a binary tree on a whiteboard. Yet, the tech industry is obsessed with algorithmic brainteasers and data structure trivia. The result? A hiring process that rewards rote memorization and test-taking skills, not actual engineering ability. If you want to know if I can do the job, show me the job. Give me a bug, a messy codebase, a half-baked feature spec. That’s where the real skills show up.

The Problem with LeetCode Interviews

Algorithmic interviews are everywhere, and they’re fundamentally flawed:

• Artificial problems: Most LeetCode-style questions are academic exercises. When was the last time you had to balance a red-black tree at work?
• Test prep over talent: Success is about grinding hundreds of problems, not about being a good engineer.
• Unrealistic environment: Whiteboards, time pressure, and zero context—nothing like real development.
• Excludes real talent: Great engineers who build, debug, and ship code get filtered out because they didn’t memorize the right trick.

The bottom line: these interviews select for the best test-takers, not the best problem solvers.

Real-World Coding: What Actually Matters

Here’s what you actually do as a software engineer:

• Read and understand existing code—often messy, undocumented, and full of surprises.
• Fix bugs—identify, reproduce, and patch issues in complex systems.
• Implement features—translate vague specs into working software, with all the ambiguity and trade-offs that entails.
• Collaborate—work with other developers, review code, and communicate clearly.
• Deal with legacy—wrestle with technical debt, outdated libraries, and weird edge cases.

None of this is tested by “find the shortest path in a graph” under a stopwatch.

Practical Interviews: Why They Win

A real-world technical interview looks like the job:

• Bug fixing: Give the candidate a real bug from your codebase. See how they approach, investigate, and solve it.
• Feature implementation: Ask them to build or extend a feature. Are they thoughtful? Do they ask clarifying questions? Can they write maintainable code?
• Code review: Present a pull request with issues. Can they spot bugs, suggest improvements, and explain their reasoning?
• System debugging: Hand over a broken app and ask them to diagnose the problem.

Why is this better?

• Relevance: You see how candidates handle the exact challenges they’ll face on the job.
• Depth: You assess real skills—reading code, debugging, problem solving, communication.
• Fairness: No advantage for those who spent months grinding LeetCode. It’s about engineering, not exam prep.
• Signal, not noise: You filter for people who can actually do the work—not just recite trivia.

Counterarguments and Myths

Let’s address the usual objections:

• “But algorithms matter!”
  Sure, but most jobs don’t need advanced algorithmic knowledge. If you’re building a search engine or working at a hedge fund, fine—test for it. For everyone else, it’s overkill.

• “LeetCode is objective.”
  Only if you think test-taking is the job. Real engineering is subjective, messy, and collaborative.

• “We need a filter.”
  You already have one: resumes, portfolios, GitHub, and practical interviews. If you want to filter for real talent, test real skills.

• “What about junior candidates?”
  Even juniors can show how they approach a bug or a simple feature. It’s not about perfection—it’s about process and potential.

Conclusion

It’s time to stop pretending that algorithm puzzles are a proxy for engineering. If you want to hire people who can build, fix, and ship real software, give them real problems. Drop the LeetCode theater. Start interviewing like you actually care about what happens after the hire. The best engineers will thank you—and so will your codebase.