Show HN: Deterministic, machine-readable context for TypeScript codebases (github.com)
Hi HN,
I built a CLI that extracts a deterministic, structured representation of a TypeScript codebase (components, hooks, APIs, routes) directly from the AST.
The goal is to produce stable, diffable “codebase context” that can be used in CI, tooling, or reasoning workflows, without relying on raw source text or heuristic inference.
It supports incremental watch mode, backend route extraction (Express/Nest), and outputs machine-readable data designed for automation.
Repo + docs: https://github.com/LogicStamp/logicstamp-context
Happy to answer questions or hear where this would (or wouldn’t) be useful.
2 comments
[ 4.5 ms ] story [ 13.4 ms ] threadI put this information in my AGENTS.md, for similar goals. Why might I prefer this option you are presenting instead? It seems like it ensures all code parts are referenced in a JSON object, but I heavily filter those down because most are unimportant. It does not seem like I can do that here, which makes me thing this would be less token efficient than the AGENTS.md files I already have. Also, JSON syntax eats up tokens with the quotes, commas, and curlies
Another alternative to this, give your agents access to LSP servers so they can decide what to query. You should address this in the readme as well
How is it deterministic? I searched the term in the readme and only found claims, no explanation
> Pre-processed relationships - Dependency graphs are explicit (graph.edges) rather than requiring inference
I suspect this actually is the opposite. Injecting some extra, non-standard format or syntax for expressing something requires more cycles for the LLM to understand. They have seen a lot of Typescript, so the inference overhead is minimal. This is similar to the difference between a Chess Grandmaster and a new player. The master or llm has specialized pathways dedicated to their domain (chess / typescript). A Grandmaster does not think about how pieces move (what does "graph.edges" mean?), they see the board in terms of space control. Operational and minor details have been conditioned into the low level pathways leaving more neurons free to work on higher level tasks and reasoning.
I don't have evals to prove one way or the other, but the research generally seems to suggest this pattern holds up, and it makes sense with how they are trained and the mathematics of it all.
Thoughts?