posts
filtered by #knowledgegraph clear
AI-Native Redesign: The Principles Don't Change — Only the Machinery Does
Every organization eventually faces the same design problem: keeping important information stored, current, and reachable at scale. This post frames that as a three-node loop (creation / maintenance / consumption) and works through what redesigning it with AI as a given looks like, as opposed to bolting AI onto a human-first setup. Along the way it recasts earlier posts on cortex — code-graph, product-graph, AI-Observability, auto-review harness — as different applications of the same underlying principle.
Making the Context Across 46 Repositories Semantically Searchable for AI
The biggest issue Part 1 left open was that AI couldn't reach the 46-repo codebase by natural-language query (the entry-point problem). This post is how I solved it — by reusing the pattern proven in db-graph (1,133-table semantic search), then layering minimal annotations only around boundary nodes. Covers the separate-branch operation that keeps engineers' daily workflow untouched, the SLO that protects the joins between three graphs, the SAME_ENTITY normalization, and the April–May trial-and-error timeline traced through real commits.
Building One Knowledge Graph Across 46 Repositories With Static Analysis
A static-analysis approach to unifying 46 repositories (37 air-closet-side + 9 mall-side) of legacy production code into one knowledge graph. Why simply 'letting AI read the code' isn't enough, why I had to chase down boundary nodes (API endpoints, DB tables, Event topics), how I dealt with framework and library diversity, and what 3 months of trial and error solved or didn't solve — looking back through actual git history.