Most AI coding examples stop after generating code.

The model receives a prompt, returns a proposed implementation, and the application prints the result. That is useful for experimentation, but it is not a complete engineering workflow. Code is only useful after it has been written to the repository, compiled, tested, and reviewed.

I built Loop Engine to explore a more practical approach:

plan → edit → verify → review → reflect

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