Vibe Coding Is a Great Start—But Discipline Is How You Level Up

I’m going to say this clearly: I approve of vibe coding.

If AI-assisted coding is what gets new developers in the door, that’s a win. I’m self-taught, and I care more about people building real things than passing some purity test about how they got started.

But after moving from self-taught developer, to engineer, and now into architecture-level thinking, I can tell you this:

vibe coding is a great beginning, not a complete method.

If you want to level up—and keep control as your projects grow—you need structure.

Why This Matters Now

A lot of new developers are entering software through prompts, copilots, and LLMs. That’s not a problem. In many ways, it’s progress.

The risk is not the tool. The risk is staying in a workflow where you can ship code but can’t reliably explain, verify, or evolve it.

You can absolutely keep using AI (you should), while still operating like an engineer.

The Same Engineering Problem, New Interface

This is not a new problem. It’s the same one engineers at every level have faced forever:

How do we get results faster?
How do we get results more accurately?
How do we keep improving both without losing control?

Whether it was manufacturing systems, civil infrastructure, embedded software, or modern AI-assisted coding, the tension is familiar: speed vs. accuracy vs. reliability.

The interface changed. The fundamentals did not.

That’s why older engineering tools still matter so much. Version control, testing discipline, and iterative planning are not legacy rituals—they are proven control systems that help us move fast without flying blind.

Rule 1: Always Use Version Control

No exceptions. Every project. Every time.

Use Git from day one, even for toy projects. If you can’t answer “what changed, when, and why?” then you don’t control your system.

At minimum:

Create a repo immediately.
Commit in small chunks with meaningful messages.
Use branches for features or experiments.
Open your own pull requests (yes, even solo) for review context.

Why this helps with AI:

You can roll back bad generations instantly.
You can compare AI-generated alternatives safely.
You can give better context to the model by sharing diffs and commit history.

Version control turns random coding sessions into traceable engineering.

Rule 2: Always Use TDD

If you only adopt one engineering habit beyond Git, make it TDD.

Write the test first. Watch it fail. Implement just enough to pass. Refactor. Repeat.

That cycle gives you three superpowers:

Clarity — you define behavior before implementation details.
Safety — you can change code (or regenerate it with AI) without fear.
Focus — tests keep prompts and generated code aligned to outcomes.

Why this helps with AI:

AI is much better when you specify behavior precisely.
Tests prevent “looks right” from replacing “is correct.”
You can ask AI to satisfy failing tests instead of rewriting whole modules.

If you don’t test, you’re not steering. You’re hoping.

Rule 3: Use Agile Practices, Even Solo

People hear “agile” and think team ceremonies. That’s optional.

The core agile mindset is still powerful when you work alone:

Keep work in small, prioritized stories.
Plan in short cycles (daily or weekly).
Review outcomes, not intentions.
Adapt quickly based on feedback.

A solo agile loop can be simple:

Define the smallest valuable outcome.
Write acceptance criteria.
TDD your way through implementation.
Demo it (to yourself, users, or stakeholders).
Retrospect and improve your process.

Why this helps with AI:

Smaller stories produce better prompts.
Clear acceptance criteria reduce hallucinated scope.
Iteration keeps AI output aligned with real user value.

“But What If I Never Dive Deep Into the Code?”

That’s exactly why these rules matter.

Even if you don’t inspect every line, Git + tests + agile structure give you operational control:

You know what changed.
You know what still works.
You know what to do next.

Without that, you’re not building software—you’re accumulating uncertainty.

The Upgrade Path

Vibe coding can get you from zero to momentum.

Engineering discipline gets you from momentum to mastery.

If you’re new, keep your excitement. Keep using AI. Keep shipping. Just add rules that protect your future self.

Start here:

Use Git on everything.
Practice TDD on every feature.
Run agile loops even when you’re a team of one.

If you want help implementing this workflow, check out NebulaKit—it’s built to make this process easier.

Do that consistently, and you won’t just generate code—you’ll architect systems.

These “old” tools are exactly how engineers have always handled the speed and accuracy challenge. They still work now, and they work even better with AI in the loop.