Lenny RAG / EigenPM Explorer

Knowledge-graph RAG over 297 Lenny's Podcast transcripts, plus a 3D PCA map of those guests you can place yourself inside.

Problem

• How do you turn 297 hour-long conversations into coordinates where "close" means "thinks alike"?
• Entities cluster by topic, an AI cluster here, a B2B cluster there; I wanted a map of how people think.

Approach

• The retrieval half is built on top of traversaal-ai/lennyhub-rag, an open-source LightRAG + Qdrant pipeline.
• I built the guest space two ways from the extracted knowledge graph: from what people talk about (roughly 1,700 entities) and from how they connect ideas (the keywords on relationship edges).
• TF-IDF over 604 relationship keywords, each used at least 15 times across the corpus, reduced to 20 principal components with a Varimax rotation so the axes stay interpretable.
• A short quiz maps your answers into the same 20 dimensions and drops you on the map beside the guests you cluster with.

Outcome

• The relationship space explained meaningfully more variance, 25.9% vs. 14.8% in the top 20 components, and it separated guests by philosophy rather than subject.
• A 3D map of those 297 guests projected into a psychographic space, live as Latent Lenny.
• The retrieval half shipped without a formal eval suite.