AI coding tools are proficient at understanding individual files but often fail to grasp the overall architecture of a project. This limitation can lead to inefficiencies and errors in coding assistance, as the tools do not have a comprehensive understanding of the project's structure.
This problem affects developers who rely on AI tools for coding assistance, leading to potential misunderstandings and errors in the codebase.
Pain Points
- AI tools do not understand project architecture.
- Inefficiencies in coding assistance due to lack of architectural context.
- Potential errors in code due to incomplete understanding by AI tools.
I’ve been working on a side project called LogicStamp. The idea came from a frustration with AI coding tools - they understand files pretty well, but they don’t really understand the architecture of a project. LogicStamp analyzes a TypeScript codebase using the TypeScript AST and compiles structured architectural context into deterministic, diffable JSON bundles describing components, props, hooks, dependencies, and contracts. This makes it possible to run architectural drift checks, compare against git baselines, or provide structured context to AI tools. Curious what other builders think about this approach. Website: https://logicstamp.dev Repo: https://github.com/LogicStamp/logicstamp-context
LogicStamp is a tool that analyzes TypeScript codebases and provides structured architectural context to AI tools, enhancing their understanding and capabilities.