Building Production Full-Stack AI Apps with Gemini API & Supabase
A practical guide to building production-grade full-stack AI apps with Gemini API and Supabase—covering auth, pgvector, Edge Functions, RLS, and cost control, plus the tuning lessons (IVFFlat to HNSW recall recovery, the service_role RLS bypass) you only learn in production.
When Your pgvector Search Quietly Gets Worse — Field Notes on Protecting Recall with Gemini Embeddings
A semantic search built on Gemini Embeddings and PostgreSQL pgvector tends to lose precision over months without throwing a single error. These are field notes on the real causes — model pinning, operator/index mismatch, HNSW reindexing, and recall collapse under filters — with working code.
Building a Production RAG System with Gemma 4: Local LLM + Vector Search Architecture
A complete guide to building production RAG systems with Gemma 4, ChromaDB, and pgvector. Covers architecture design, chunking strategies, Long-Context RAG using the 256K window, hybrid search, and performance optimization.
Gemini API Embeddings vs Vector Databases: Pinecone, Qdrant, pgvector, and Cloud Spanner Compared for Production
Benchmark Pinecone, Qdrant, pgvector, and Cloud Spanner Vector using Gemini text-embedding-004 with real latency, cost, and code. The definitive production selection guide.