Vector Databases - GoKawiil

Startup positioning advice: speak to what customers already want, veteran advisor says

A go-to-market consultant who has worked with over 100 startups—including Temporal, Netlify, Pinecone, and Pydantic—argues that founders routinely fail to communicate in terms customers actually care about. The core prescription is deceptively simple: instead of pitching your product's features or vision, describe it using the language and priorities of the people you're trying to reach.

gkogan.co · 2026-08-26

RAG systems: engineers urged to skip vector databases and embeddings by default

A technical guide argues that most retrieval-augmented generation (RAG) implementations are over-engineered, with teams reaching for embeddings, vector databases and reranking before checking if simpler tools suffice. It lays out decision factors—data freshness, corpus size and churn, query patterns, scale and team ML skill—to determine which retrieval approach fits, starting with plain full-text search like BM25, Elasticsearch or Postgres search before adding complexity.

lighthousenewsletter.com · 2026-08-26