The Evolution of My Coding Muscle

I've spent the last 20 years coding.

My brain thrived on programming languages: C, Java, Python, Dart/Flutter, JS/TS.

I learned tricks, optimized algorithms, and debated design patterns.

Crafting solutions was a meticulous skill—like building precise machinery.

Then AI-powered coding tools arrived. GitHub Copilot. Cursor.

Suddenly, my routine transformed.

Today, my primary skill isn't recalling syntax or hand-optimizing code.

It's communicating effectively with AI tools.

It's guiding them clearly toward my goals.

A New Coding Muscle Emerges

The coding muscle itself has changed.

In the past, mastering loops, memory management, or complex data structures and frameworks was essential.

Now, AI handles those technicalities.

Our role has evolved: articulate goals, specify constraints, steer and critique the AI.

For instance, when quickly prototyping, I ask the AI to prioritize readability and flexibility.

In mature production scenarios, I request optimized solutions that consider performance and resources.

A real world Example

Take sorting in Python.

The built-in sort() uses Timsort. It's great for most situations: complexity O(n log n).

But not always optimal.

Imagine a real-time leaderboard tracking millions of players.

Scores range narrowly from 0 to 10,000.

Sorting these repeatedly every second can create latency.

Enter Counting Sort

Counting Sort excels precisely here.

Here's how it looks in Python:

def counting_sort(scores, max_score=10000):
    counts = [0] * (max_score + 1)
    for score in scores:
        counts[score] += 1

    sorted_scores = []
    for score, count in enumerate(counts):
        sorted_scores.extend([score] * count)

    return sorted_scores

# Example usage:
player_scores = [5000, 4500, 9999, 4500, 7500, 5000, 4500, 9999, 0, 5000]
sorted_player_scores = counting_sort(player_scores)
print(sorted_player_scores)
# Output: [0, 4500, 4500, 4500, 5000, 5000, 5000, 7500, 9999, 9999]

Counting Sort reduces complexity dramatically with the cost of additional memory.

Sorting 1,000,000 scores with Timsort means around 20,000,000 operations.

Counting Sort requires roughly 1,010,000 operations—a nearly 20-fold efficiency boost.

This means lower latency and happier users.

Did I write this code?

No, the AI did.

The trend is clear: AI is getting better at writing code.

And new benchmarks like SWE-Lancer are being set to write always better code.

Verify for yourself

Yet, a vital question arises: do we trust AI completely?

That's our new challenge.

We must review the logic, revisit mathematics, and validate AI suggestions.

Sometimes, the solution exceeds our current understanding—and that's okay.

When that happens, humility is key.

We either learn the necessary skills or seek expertise.

Experts remain crucial. Lifelong learning is indispensable.

A Moment of Reflection

Recently, I faced a coding challenge without AI.

Honestly? I felt lost.

It reminded me of a student who relied too heavily on a calculator.

I realized how deep this transformation was.

But it doesn't worry me.

Humans always adapted.

We no longer obsess over transistors or manual memory management.

Soon, manual code optimization will feel equally distant.

Embracing the New Paradigm

Effectively managing AI-driven assistants will soon be crucial.

The future doesn't belong to those recalling syntax perfectly.

It belongs to those thinking strategically.

It belongs to clear communicators.

It belongs to those who skillfully integrate AI into their workflows.

The true evolution isn't losing coding muscle.

It's about building new ones.

Sharpening judgment.

Mastering collaboration between human insight and artificial intelligence.

That's the journey we're on—and it's just beginning.