Making the Context Across 46 Repositories Semantically Searchable for AI (Part 2)
7.9 relevance
Score Breakdown
technical depth 9
novelty 8
actionability 8
community 5
strategic 6
personal 9
Scored daily by a customisable AI persona to surface the most relevant engineering leadership news.
Deep technical solution for making multi-repo codebases AI-searchable, directly actionable for platform engineers.
Summary
Ryan Tsuji, CTO at airCloset, solved the entry-point problem for a knowledge graph spanning 46 repositories by joining it with an existing db-graph that already had AI-generated semantic descriptions for 1,133 tables. Rather than annotating all functions, he focused annotations only on boundary nodes (APIs, events, pages), enabling natural-language semantic search without overwhelming teams. The approach treats multiple graphs as peers joined by SAME_ENTITY edges, reusing existing context rather than building from scratch.