The Feynman Technique - Explaining Code Quality to My 10-Year-Old Self

Recently, I’ve been learning to program in Python to improve my AI skills, as most AI libraries are Python-friendly. Deep down, I’ve always been curious about Python because of its flexibility and history. As part of this journey, I’ve been researching scientifically backed learning techniques, including the Feynman Technique.

This technique, developed by Nobel Prize-winning physicist Richard Feynman, focuses on breaking down complex ideas by explaining them in plain language. It has four core steps:

1. Identify the concept.
2. Teach it to a child (or pretend to).
3. Identify gaps in your understanding.
4. Review & simplify.

I decided to put this technique to the test by explaining code quality to my 10-year-old, always-dreaming self. Code quality can be subjective, but three main pillars are universally important: complexity, errors, and readability.

---

Complexity: How Complicated Is Your Code?

Complexity measures how intricate the code logic is—things like decision paths and dependencies. Some key metrics include:

• Cyclomatic Complexity: Counts the number of independent paths in the code (loops, conditionals).
• Cognitive Complexity: Measures how hard it is for a human to understand the logic.
• Nested Layers: Tracks how deep loops and conditionals go.

---

Errors: What Can Go Wrong?

Errors represent different types of issues that break your code. Here are the most common ones:

• Syntax Errors: When code breaks the language's rules (e.g., missing semicolons, brackets, or incorrect indentation).
• Logical Errors: When the logic is incorrect, leading to unexpected outputs despite the code running.
• Runtime Errors: Issues encountered during execution, like division by zero or null pointer references.
• Time Limit Exceeded: When code takes too long to execute, often due to inefficient algorithms.

---

Readability: How Easy Is It to Understand?

Readability determines how simple it is for others (or future you) to understand the intent of your code. Some key factors:

• Naming: calculate_order_total() vs. calc()—descriptive names matter!
• Formatting: Consistent indentation and whitespace improve clarity.
• Structure: Modular functions are better than one giant block of code.
• Comments: Explain the why, not just the what.

---

Complexity vs. Readability: What’s the Difference?

At first, I struggled to distinguish between complexity and readability—they seemed like the same thing. But this table helped me clarify:

In short, readability is for humans, while complexity is about code structure.

---

Explaining Code Quality Using My Childhood Bike

Now, how do I explain all this to a hyperactive 10-year-old version of myself? If I start talking about "code complexity," he’ll probably throw something at me. So, let’s use something I loved as a kid—my bike.

Errors: The Flat Tire Problem

One Sunday, I woke up excited for a bike ride, only to find a flat tire. Bummer! I had to fix it (with my dad’s help) before I could ride again.

A flat tire is like a code error—when something is broken, the bike (or program) won’t work properly.

Complexity: The Rusty Chain Struggle

I loved my bike, but I didn’t always take care of it. I left it outside in the rain, dropped it on the ground instead of using the kickstand, and only washed it when my dad was washing his car. Over time, the chain rusted, making pedaling much harder.

This is just like code complexity—if a program has too many nested loops or convoluted logic, it makes the computer work harder.

Readability: Sticky Handlebars & Messy Code

After playing in the dirt or eating candy, my bike’s handlebars would get sticky, making it hard to steer. If I had cleaned them regularly, I wouldn’t have struggled!

The same applies to code readability. If your code is a mess, with unclear variable names (num instead of clientPhoneNumber), future programmers—including future you—will struggle to understand it.

---

Final Thoughts: Teaching to Learn

I enjoyed this exercise and plan to try it with my kids. Teaching—whether to children or adults—helps cement concepts in our own brains. Associating ideas with real-world objects makes learning easier and more fun.

If you want to learn more about Feynman’s technique, check out this article:
🔗 The Feynman Technique and How to Use It for Learning

P.S. Take care of your code and your bikes! ❤️