Wanderland: An Executable Knowledge Graph for AI-Native Documentation

We present Wanderland, an executable knowledge graph that treats structured markdown as a programming language. Unlike systems that extract knowledge graphs from documents or generate code from documentation, Wanderland makes the document itself the computational substrate. Fenced code blocks execute as functions, navigation constitutes query execution, and a multi-level cache implements compilation semantics. The architecture exhibits strong structural parallels to fifty years of research in compilers (streams with relocations), databases (query plans with operators), and distributed systems (event sourcing with projections). We argue that these parallels are not merely analogical but reflect shared computational invariants: the same design patterns that optimize binary linking can be adapted to optimize document rendering. A novel three-state provenance system (unverified → reviewed → verified) with hash-based drift detection provides continuous human verification without external tooling