Breakfast and Code: How Intentional Practice Transforms Your Skills

Ever feel like your coding has hit a plateau, no matter how many projects you churn out? Like your breakfast, it's consistent but uninspired. What if you could make it exceptional—one small tweak at a time?

Imagine this: every morning, you wake up and make breakfast. After a while, the process becomes second nature. You crack the eggs the same way, toast the bread to the same shade of golden brown, and pour the coffee just like yesterday. It's reliable, sure—but is it truly better? Probably not. Unless you experiment—try a new spice, switch up your technique—your breakfast stays, well, fine.

Programming is no different.

You might write code day after day, pushing out projects or fixing bugs. But unless you deliberately focus on improving, you risk stagnating. It's easy to mistake repetition for growth, but they're not the same. This is where deliberate practice steps in—it's the intentional effort to sharpen your skills, one focused improvement at a time.

Deliberate practice isn't about logging endless hours. It's about identifying your weak spots, breaking them down into manageable goals, and refining your approach. Think of it like crafting the perfect breakfast: instead of going through the motions, you experiment, learn, and iterate.

In this post, we'll explore how you can transform your programming skills through deliberate practice. Whether you're a beginner or building advanced projects, this approach can help you grow in ways you didn't think possible.

1. Analyze Your Work

The first step to improvement is understanding where you are now. Think of it as taking inventory before a big recipe experiment: What do you have? What's missing? Look at your recent projects and reflect on these questions:

• What went well? What are you proud of?
• Where did you struggle? What could be better?
• If you started again today, what would you change?

Let's ground this in a practical Python example.

Example: A Simple Calculator Script

Suppose you wrote this calculator to perform basic arithmetic operations:

1def calculator(a, b, operation):
2    if operation == "add":
3        return a + b
4    elif operation == "subtract":
5        return a - b
6    elif operation == "multiply":
7        return a * b
8    elif operation == "divide":
9        if b != 0:
10            return a / b
11        else:
12            raise ZeroDivisionError("Cannot divide by zero")
13    else:
14        raise ValueError("Invalid operation")
15
16result = calculator(10, 5, "add")
17print(f"The result is {result}")  # The result is 15

What went well?

Great! You've got a functional calculator. Celebrate the wins:

• It works! The calculator can handle basic operations like addition, subtraction, multiplication, and division.
• Some validation is present. You've added a check for division by zero, which prevents unexpected crashes.

What didn't go well?

Now for some constructive reflection. This script, while functional, has room for improvement:

• Hard to extend. Adding new operations, like exponentiation, requires editing the function.
• Unfriendly errors. The script raises exceptions, but they'll crash the program without guiding the user.
• Testing is tricky. There's no structure for testing individual operations, which complicates debugging.

What would you do differently?

This is where the magic of deliberate practice begins. Based on your observations, here are some ideas:

• Refactor the code into smaller, reusable functions for modularity.
• Add error handling to provide user-friendly feedback.
• Write unit tests to catch issues early and ensure reliability.
• Add docstrings to explain the purpose of each function.

2. Set Improvement Goals

I remember refactoring my first project—a messy, overcomplicated API. Breaking it into modular components felt tedious at first, but the satisfaction of seeing clear, reusable code was worth every minute.

Improvement doesn't happen by accident—it requires focus and direction. After reflecting on your work, the next step is to break down your observations into specific, actionable goals. Think about making your breakfast better: instead of vaguely deciding to “make it tastier,” you might aim for fluffier eggs or perfectly golden toast. It's the same with code: the more targeted your goals, the easier it becomes to improve.

Today, perfect the eggs; tomorrow, experiment with a new coffee blend.

How to Define Goals

Start by picking one or two areas that will make the most impact. Let's revisit our calculator script to see this in action.

From This:

• "I want to write better code."

To This:

• "I will refactor the script into modular functions."
• "I will add error messages for invalid inputs."

Clear goals give you a direction and make progress measurable. For example:

1. Error Handling: Provide feedback instead of abrupt crashes.
2. Modularity: Refactor the calculator into reusable functions.
3. Testing: Ensure each operation handles edge cases correctly.
4. Documentation: Use docstrings to explain function purposes.

Example: Error Handling Goal

If we focus on error handling, a rewritten calculator might look like this:

1def calculator(a, b, operation):
2    try:
3        operations = {
4            "add": lambda x, y: x + y,
5            "subtract": lambda x, y: x - y,
6            "multiply": lambda x, y: x * y,
7            "divide": lambda x, y: x / y if y != 0 else float('inf'),
8        }
9        return operations[operation](a, b)
10    except KeyError:
11        return "Invalid operation! Try add, subtract, multiply, or divide."
12    except Exception as e:
13        return f"An error occurred: {e}"

Here, error handling becomes more user-friendly. Instead of crashing, the script provides helpful guidance.

3. Learn Intentionally

Goals alone won't take you far if you don't have the tools or knowledge to act on them. Imagine wanting to perfect your toast but not knowing the difference between toasting on high versus medium heat. Intentional learning means identifying and purposefully closing your knowledge gaps.

Identify What You Need to Learn

Each goal should guide your learning focus. For instance:

• Error Handling: Research Python's exception handling (try, except, finally).
• Testing: Learn how to write unit tests using unittest or pytest.
• Refactoring: Explore how modular design improves maintainability.

Find Quality Resources

The internet is overflowing with information, but not all resources are created equal. Here's how to curate your learning:

• Documentation: Python's official docs are a goldmine (e.g., exception handling).
• Books: Effective Python by Brett Slatkin offers actionable advice.
• Videos: Platforms like Real Python break down complex topics visually.
• Open Source Projects: Studying popular repositories helps you see best practices in action.

Balance Theory and Practice

For example, if you're learning about pytest, don't stop at reading—apply it:

1import pytest
2
3def test_add():
4    assert add(3, 4) == 7
5
6def test_invalid_operation():
7    with pytest.raises(KeyError):
8        calculator(3, 4, "power")

Combining theory with hands-on practice solidifies new skills.

4. Practice Systematically

Think of systematic practice like preparing breakfast every day with a plan to improve one small aspect at a time. You might aim for a perfectly scrambled egg on Monday, then master the art of crispy bacon on Tuesday. It's the same for coding: consistency and focus turn good intentions into measurable growth.

If you wouldn't skip breakfast while trying to perfect it, why skip a coding session?

Build a Routine

Consistency is your secret weapon. Dedicate specific time slots to practice. For example:

• Daily: Dedicate 20–30 minutes to refining a single skill (e.g., error handling).
• Weekly: Work on a mini-project to apply multiple skills.

Even a short, focused session can produce exponential results over time.

Focus on Specific Exercises

Coding Katas: Websites like Codewars or Exercism offer small challenges that target specific skills.
Example Kata: Implement a Fibonacci sequence calculator with edge case handling.

Mini-Projects: Build small tools, like:

• A weather app that fetches data from an API.
• A CLI-based task tracker with persistent storage.

These exercises are more engaging than abstract examples and simulate real-world problems.

Iterate on Real Projects

Take an old project and apply deliberate improvements:

• Refactor messy sections for clarity.
• Add meaningful error messages.
• Write tests for critical functionality.

For instance, revisit our calculator example:

Day 1: Split operations into individual functions.
Day 2: Add a feature for exponentiation.
Day 3: Write tests for all operations.

Seek Feedback

Imagine someone tasting your breakfast and saying, "It's good, but a dash of salt would elevate it." That's what feedback is—small tweaks that make a big difference.

• Ask a mentor to critique your refactoring.
• Submit your work to an online community like Reddit or GitHub.
• Pair-program with a peer to tackle challenges collaboratively.

The Meta-Skill: Deliberate Practice

Deliberate practice isn't just a strategy—it's a meta-skill. Whether you're debugging, writing tests, or collaborating on code reviews, this approach transforms how you learn and grow.

Next time you code, treat it like making the perfect breakfast—ask yourself, "What's the one ingredient I can refine today?"

Final Thoughts

Becoming a better programmer isn't about writing more—it's about writing better. By analyzing your work, setting goals, learning intentionally, and practicing systematically, you can elevate your skills faster than you thought possible.

Your move: Pick a project, find one thing to improve, and dive in. Repeat tomorrow.

You can find me as J1Loop on GitHub or connect with me on LinkedIn.