Turning a React/TypeScript Codebase Into Deterministic AI Context
LLMs are surprisingly bad at understanding real codebases.
Not because they’re “dumb”, but because the way we provide context today is fragmented, lossy, and hard to reason about - pasted files, partial trees, snapshots, or vague summaries.
After running into this repeatedly while working with React/TypeScript projects, I ended up building a CLI to make context explicit and deterministic instead.
The problem
Most AI-assisted coding workflows rely on some variation of:
- pasted files
- large snapshots
- “just include the folder”
- implicit assumptions about project structure
This breaks down quickly:
- context gets truncated
- important relationships are lost
- results vary between runs
- CI or automation becomes impossible
You can often feel that the model is missing something - but you can’t point to a stable, inspectable source of truth for what it actually knows about your codebase.
The idea
Instead of snapshots, I wanted structured context.
So I built LogicStamp, a small CLI that analyzes a React/TypeScript project and produces deterministic, machine-readable context bundles.
Rather than shipping raw files, it extracts structure:
- components
- hooks
- props
- dependencies
- relationships
The output is JSON, designed to be stable across runs and usable by both humans and machines.
What LogicStamp does
- Zero-config CLI
- Static analysis via the TypeScript AST
- Deterministic output (no “best effort” guessing)
- Produces AI-ready context bundles instead of raw files
No magic, no agents, no memory tricks - just structure.
The goal isn’t to replace models or tools, but to give them better input: explicit, inspectable, and reproducible context.
Why deterministic context matters
Once context is deterministic:
- results become reproducible
- automation becomes possible
- CI pipelines can reason about code structure
- tools stop relying on implicit assumptions
Even outside of AI, having a stable representation of a codebase’s structure turns out to be useful in surprising places.
Open source
The project is open source:
https://github.com/LogicStamp/logicstamp-context
I’d love feedback - especially from people building devtools or working with AI-assisted workflows.
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