A context optimization layer for LLMs that enables full-codebase reasoning without losing logic or requiring indexing. FullScope compresses code structurally so LLMs can process significantly more context β without losing executable logic.
Hey everyone π
I built FullScope after running into the same issue over and over: LLMs are great at coding⦠until they need to understand a real codebase.
You usually have to choose:
* send full files β too expensive
* use search/RAG β lose the reasoning thread
* use summaries β lose accuracy
FullScope is my attempt to remove that tradeoff.
It lets models see more code without losing logic or modifying files.
The part Iβm most curious about:
π does preserving the reasoning thread across files actually improve how you use AI in your workflow?
Would love to hear:
* how you're currently handling large codebases with AI
* where things break down
* what you wish worked better
Thanks for checking it out π
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