Vibe Coding Bible: A Programming Paradigm for AI-Written Software

Evgeniy Kormin — Mon, 15 Jun 2026 18:49:49 +0000

Vibe Coding Bible: Rethinking Software Architecture for AI-Generated Code

For the last year I noticed something consistent while working with AI-generated code.

The more I used AI to write code, the more my systems started to break in a very specific way:

small changes caused unexpected side effects
refactoring became increasingly risky
context grew until it became difficult to manage
AI sometimes “helped” by breaking nearby modules
fixing one thing often broke something else

At some point I stopped thinking of this as a prompt engineering issue.

It felt more like a fundamental mismatch in the programming model itself.

The core mismatch

Most software architecture today is built around one assumption:

humans are the primary authors of code

We expect that:

humans can understand the system over time
humans can safely refactor large dependency graphs
humans can maintain mental models of complexity
humans can coordinate changes across modules

This works reasonably well when humans are the main drivers of development.

But with LLMs involved, a different behavior emerges:

every interaction effectively starts from a partial or reconstructed context

Even if the full codebase exists, the model operates with limited visibility at any given moment.

As systems grow, this leads to predictable patterns:

increasing coupling
fragile refactoring
hidden dependencies
loss of global consistency

The idea behind Vibe Coding Bible

This led me to a different question:

What if we design software assuming AI is the primary code author, and humans are system designers?

That shift changes many assumptions.

Some familiar practices start to behave differently:

flexible interfaces become harder to reason about
deep abstraction layers introduce fragility
refactoring increases cognitive cost
shared mutable structures amplify unexpected behavior

Core principle: nailed interfaces

The central idea is simple:

interfaces should not evolve

Once defined:

they are never modified
they are never extended
they are never refactored

If requirements change, we do not modify the interface.

We create a new block with a new interface.

Even if it feels redundant.

Because AI makes duplication cheap — but coupling remains expensive.

Block structure

Every unit of code is a block:

interface.py → immutable contract
implementation.py → AI-generated logic
tests.py → optional generated tests

Key rule:

the interface is frozen forever

No exceptions.

Tree instead of graph

Most real-world systems naturally evolve into dependency graphs:

modules depend on each other
dependencies spread in multiple directions
small changes produce system-wide effects

In AI-heavy development, this becomes especially unstable.

So instead we enforce a simpler structure:

strict tree hierarchy
level-based imports only
no cross-branch dependencies
no lateral coupling

This is not elegant.

But it is predictable.

And predictability matters more than flexibility when AI writes most of the code.

No refactoring rule

In this model:

If something is wrong:

we do not refactor it
we replace it

If behavior changes:

we do not extend existing modules
we create new ones

If an interface no longer fits:

we do not modify it
we define a new block

At first this feels inefficient.

But the cost model shifts:

writing new code is cheap
managing coupling is expensive

So we optimize for regeneration instead of evolution.

What changes for developers

In this model, developers stop being:

refactorers
dependency graph managers
large-codebase navigators

And become:

system decomposers
interface designers
constraint engineers
AI operators

The job shifts from writing code to shaping boundaries.

Migration strategy

Existing systems are not migrated directly.

Instead:

freeze problematic modules
stop modifying legacy code
build new AI-native blocks alongside it
gradually shift functionality over time

Legacy code becomes background context, not something to constantly fix.

This is not a universal rule

This is an experiment.

It may not apply to all systems.

Some domains may still require:

complex shared state
deep optimization
tightly coupled systems

But in many AI-heavy workflows, traditional assumptions start to break down.

Why I’m writing this

I call this experiment:

Vibe Coding Bible

It is not a finished framework.

It is a set of assumptions I am testing in practice.

Related projects

Programming paradigm for AI-written software:

https://github.com/evgeniykormin86-stack/Programming-Paradigm-for-AI-Written-Software
Incident-driven AI system (Golden Armada):

https://github.com/evgeniykormin86-stack/golden_armada

Final thought

The question I’m exploring is simple:

What does software architecture look like when AI becomes the primary programmer?

This is one possible answer.

Not the final one.

But a starting point.

DEV Community: Evgeniy Kormin