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 — without losing executable logic.

getviktor.com — An AI coworker that actually does the work

An AI coworker that actually does the work

Hey everyone 👋 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 → too expensive * use search/RAG → lose the reasoning thread * use summaries → lose accuracy FullScope is my attempt to remove that tradeoff. It lets models see more code without losing logic or modifying files. The part I’m most curious about: 👉 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 🙏

getviktor.com — An AI coworker that actually does the work

An AI coworker that actually does the work

FullScope

See more code. Get better answers.

See more code. Get better answers.