๐๐ผ๐ผ๐ฝ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐๐ป๐ฎ๐บ๐ถ๐ฐ ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐๐
I realized I was doing Loop Engineering manually.
Most people use AI to write code. I use AI to build architectures.
I do not ask an AI to build a system. I ask it to build parsers, converters, and compilers. This creates deterministic results instead of random code.
My process follows a strict loop:
- Define an architectural intent.
- Debate the solution with AI.
- Test the solution against new concepts.
- Turn the solution into a reusable pattern in my DSL.
I use a concept I call VibeCoding Engineering. It is not about letting an agent program everything. It is about a continuous loop between intention, research, and verification.
I am moving from manual debate to Adversarial Verification.
Instead of me trying to break a solution, I use specialized agents to attack it: โข Type Adversary Agent: Tries to break logic with nulls or wrong structures. โข Security Adversary Agent: Looks for leaks or policy violations. โข Semantic Equivalence Agent: Ensures the logic is identical across different languages. โข Linear Usage Agent: Checks if memory is handled correctly.
A solution only succeeds if it survives this war.
I also use Intent-based Healing. In most systems, an error is the end. In my system, an error is just another event. When a validation fails, the system triggers a self-healing pipeline. It tries to extract primitives, convert types, or normalize data. It only returns a final error when no safe path remains.
The goal is to transform opinion into measurement. I use a Tournament pattern where multiple agents generate competing strategies. They compete on speed, security, and memory usage. The winner is chosen by data, not by feeling.
AI is great at generating possibilities. But the filter must be the final authority.