pgvector
PostgreSQL extension for vector similarity search
Episodes
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#4340: Building an AI Tutor That Teaches You to Code Without Writing It For YouHow to build an AI tutor that assigns projects, reviews code, and remembers what you've learned — without writing your code for you. -
#2755: How to Build AI Memory That Actually WorksStop jumping to conflict resolution. The real challenge is getting data in and out cleanly. -
#2677: Memory Layers for AI Agents: SaaS vs Self-HostedZep, mem0, Letta, Graphiti, Cognee — which memory layer should you commit to for your AI agent? -
#2676: Vector Database Schema Design for AI Memory LayersStop dumping vectors blindly. Design metadata schemas and namespaces for retrieval that actually works at scale. -
#2674: Why Your Agent's Context Window Is Getting Eaten Before You StartStop shipping the whole toolbox to every session. A bridge plugin pattern that fetches skills on demand instead. -
#2634: The Two-Stage Pipeline for Persistent User MemoryHow to extract durable personal context from raw prompts and build a self-healing memory layer for AI systems. -
#2466: The Hidden Trap of Embedding Model Lock-InWhat happens when your vector database works great — until your embedding model gets deprecated and your vectors become useless. -
#2271: Vector Search in a Single FileWhat if you could do vector search with just SQLite? We explore sqlite-vec, the extension that adds embeddings to the world's simplest database, an... -
#1944: PostgreSQL: The Thirty-Year MiracleHow does a volunteer-run database power the New York Stock Exchange and survive every tech trend without burning out? -
#1592: The Vector Debt Trap: Choosing Embeddings That LastStop treating embedding models like plumbing. Learn how to navigate vector debt, multimodal retrieval, and database configuration for RAG. -
#1483: The Recall-Per-Dollar Era: Mastering Vector Database TuningStop burning money on unoptimized vector searches. We dive into HNSW tuning, distance metrics, and the vital "recall-per-dollar" metric. -
#1215: The Vector DB Hangover: Scaling Without Going BrokeStop overpaying for your AI's memory. We break down the math of self-hosting vectors and the rise of serverless search. -
#1213: Is Your JSON Store Just a Postgres Feature Now?Explore why the NoSQL revolution didn't kill SQL and how document databases are finding a new second life in the era of AI and RAG pipelines. -
#1212: The Postgres Vector Revolution: Killing the SprawlIs your tech stack a sprawling suburb of microservices? Discover why a 40-year-old database is winning the AI infrastructure war. -
#1211: Escaping JOIN Hell: The SQL Developer’s Guide to Neo4jStop struggling with 15-deep JOINs. Learn how Neo4j turns relationships into first-class citizens for faster, more intuitive data modeling. -
#1124: The Database Explosion: Why One Size No Longer Fits AllFrom vector stores to edge computing, discover why the world now has over 1,000 databases and why Postgres isn't always the answer. -
#1123: When One Database Isn't EnoughCan Postgres 18 finally replace the data warehouse? We dive into data gravity, columnar storage, and the physics of scaling in the AI age. -
#695: Behind the Curtain: How My Weird Prompts Gets MadeCorn and Herman explain exactly how each episode of My Weird Prompts is produced, from voice recording to published podcast. -
#263: Why AI Needs a New Kind of MemoryHerman and Corn explore why SQL is losing ground to graph and vector databases. Discover why the "edge" is the new gold standard for AI accuracy. -
#200: Why Graph RAG Beats Flat Vectors for AI MemoryExplore how the AI stack is evolving from simple vector search to complex Graph RAG and why the "boring" Postgres database is winning the race.