Show HN: A SQLite graph that captures why AI-generated code exists (github.com)

A new repository has been launched, introducing a SQLite graph designed to capture the rationale behind AI-generated code. While AI coding agents like Codex and GitHub Copilot excel at quickly generating code, the underlying engineering decisions often become obscured during the process. This repository aims to address that issue by providing a structured framework that logs requirements, decisions, and traces, allowing for better retention of contextual information that can inform future engineering work. Significantly, this initiative offers a pre-ALM (Application Lifecycle Management) review mechanism that complements existing AI coding flows. By maintaining a local graph of decisions and policy triggers, it allows engineers to enhance their interaction with AI tools through improved contextual awareness, ensuring that outcomes are based on sound engineering principles rather than just acceleration in code generation. The technical implementation includes functionalities for reverse engineering imports, impact analysis, and LLM context reviews, all without altering the AI model weights. Overall, this repository represents a strategic step towards a more sustainable approach to AI-assisted coding, emphasizing the importance of understanding "why" generated code exists, not just "what" it produces.