NODES 2025 - Beyond Vibe Coding: AI Agents for Technical Debt Detection with Neo4j

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9 learners

What you'll learn

This course includes

51.3 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

neo4j graph databases graphs LLM retrieval augmented generation RAG vector database vector search GenAI AI Artificial intelligence nodes25

Full Transcript

In the fast-paced world of software development, ""vibe coding""—where intuition and momentum drive decisions—can lead to rapid progress but often accrues unseen technical debt. This session unveils a suite of AI agents I've developed to detect and analyze the subtle signs of such debt using Neo4j's graph capabilities. I've built an extensible framework using Dagger and Neo4j that: - Parses diverse codebases (Python, JavaScript, TypeScript, etc.) using specialized parsers - Constructs a comprehensive code graph in Neo4j including: - File nodes with language and path metadata - Symbol nodes (Functions, Classes, Interfaces) with scope, signature, and line positioning - Relationship edges: DEFINED_IN, IMPORTS, CALLS, CONTAINS, and re-export patterns - Executes Cypher queries through a dedicated cypher-shell client container - Live Demonstration Speaker: Kambui Nurse Resources: Get Started with Aura - https://bit.ly/3LOLrjh Deployment Center - https://bit.ly/4jOelM3 Ground AI Systems and Agents with Neo4j - https://bit.ly/4oVsnyb #nodes2025 #neo4j #graphdatabase #graphrag #knowledgegraph