Dead exports are unreferenced exports in JavaScript—clutter that slows AI-assisted development and increases risk. Detect them with project-wide static analysis, using Knip for monorepos and ts-prune for TypeScript, while watching for false positives from barrels and dynamic imports. Tree-shaking wo
Dead code in fast-moving TypeScript projects slows builds and confuses AI-driven development. It piles up from rapid changes, fear of breaking unseen consumers, AI duplicates, and hidden abstractions. Automated tools like Knip (and ts-prune, unimported) plus graph maps (ENRE-ts, Pharaoh) reveal
Before refactoring with AI agents, you must check blast radius to avoid silent breakages. The article promotes deterministic knowledge-graph blast-radius analysis (Pharaoh) that parses code with Tree-sitter and stores relationships in Neo4j, enabling multi-hop reachability for changes. Integrating t
AI coding accelerates regressions due to limited context, causing silent breakages and duplication in small teams. The guide promotes an architecture-first approach: model the codebase as a living knowledge graph that agents can query to guide changes. It adds guardrails—blast radius, reachability,
AI agents work best with true codebase context—a structured, graph-backed map of modules, dependencies, and endpoints—not file dumps. Flat context misses links and drifts while inflating token costs; deterministic parsing (Tree-sitter) keeps facts reliable. Pharaoh converts TS/Python repos into a Ne
Dead code detection MCP gives real-time, deterministic identification of unused or unreachable code, making its truth accessible to AI agents and tools like Claude Code and Cursor. It exceeds traditional tools by cross-repo reachability with Tree-sitter and graph databases and eliminates runtime LLM
The piece outlines eight strategies to search, validate, and reuse functions at scale in AI-augmented codebases, emphasizing speed, accuracy, and deterministic results. It centers on Pharaoh search_functions, a Neo4j knowledge graph with MCP that delivers import-path aware results and zero per-query
Code Dependency Graph MCP servers turn a codebase into a deterministic, queryable knowledge graph of modules, functions, and endpoints, giving AI tools a reliable architectural map instead of scattered snippets. They reduce blind spots, enable blast-radius analysis, and improve security by exposing
The article promotes a codebase intelligence platform that gives AI agents architecture-aware, live context via a knowledge graph. This context prevents hidden dependency gaps, reduces duplication, and enables deterministic, safer refactors. Pharaoh offers automatic TS/Python parsing, 13+ graph tool
An MCP server for code understanding turns your codebase into a structured Neo4j knowledge graph that AI agents query for ground-truth architecture (call graphs, ASTs, dependencies). It delivers deterministic answers with zero per-query token cost after indexing and strict tool boundaries via a mani
New posts on codebase intelligence, AI coding, and building Pharaoh.