Looskis

Probelm Large codebases are hard for both developers and AI tools to understand. Keyword search misses meaning, relationships between files are scattered, and navigating imports, calls, and dependencies is slow and error-prone. LLMs lack the structural and semantic context needed to give accurate suggestions. Full rescans don’t scale as repositories grow. Probabilistic heuristics rather than deterministic parsing can lead to subtle bugs and missed references Solution Hugging Tree builds a semantic knowledge graph of your codebase by combining graph traversal like follow imports, calls, and dependencies with vector search. It scans incrementally using git, and provides rich “context trees” that give LLMs deep awareness of structure, semantics, and relationships—resulting in precise code understanding, navigation, analysis, and planning.