Postgres

Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Eskildsen of Turbopuffer

Mar 12, 2026 · 1:00:32

Simon Eskildsen, founder of Turbopuffer, explains how his obsession with the cost of vector search at Readwise led him to build a search engine on object storage and NVMe SSDs, cutting costs by 95% for customers like Cursor. He outlines the three conditions for a major database company—a new workload (AI-driven search), a new architecture (object storage first, no consensus layer), and evolving to support every query plan. Turbopuffer's architecture uses S3's strong consistency and compare-and-swap for metadata, avoiding stateful systems. Eskildsen shares how they won Notion by absorbing latency issues with dark fiber, and discusses the shift from single RAG queries to agents making many concurrent searches. He also describes the "P99 engineer"—obsessive, trade-off minded, able to bend software to first principles. Future plans include expanding full-text search, reaching Common Crawl scale, and eventually adding simpler OLAP queries.

Quadratic: The AI Spreadsheet

Jun 10, 2025 · 28:15

David Kircos, co-founder and CEO of Quadratic, joins hosts Alessio and Swyx to demonstrate Quadratic, an AI-powered spreadsheet that natively executes Python, JavaScript, and database queries in WebAssembly for real-time performance. The product's AI assistant uses a context-aware agent loop that summarizes sheet structure rather than dumping full data, then tool-calls to read columns or peek at values only when needed, keeping costs low on large datasets. Kircos explains Quadratic's three-year technical groundwork—WebGL rendering and WebAssembly runtimes—enabled them to capitalize on AI, turning a 'technical spreadsheet' into one where users simply drop a CSV and say 'help me analyze this.' He details how they route chart images back to the model for visual feedback and predicts that formulas will yield to Python for complex analysis, though spreadsheets will remain the best interface for human–AI data collaboration. Quadratic is source-available on GitHub, supports real-time multiplayer, and has grown to nine people. The episode also covers future directions like proactive agents and form controls (sliders) for interactive models.

The Rise and Fall of the Vector DB category: Jo Kristian Bergum (ex-Chief Scientist, Vespa)

Apr 19, 2025 · 27:17

Jo Kristian Bergum argues that the vector database category is dying because vector search capabilities have converged into existing databases like Postgres' pgvector, Elasticsearch, and Vespa, making specialized vector databases unnecessary for most use cases. He traces the category's rapid rise after ChatGPT, driven by the misconception that RAG required embeddings, and notes Pinecone's high ARR and subsequent repositioning. Bergum emphasizes that embeddings remain important but should be combined with traditional retrieval methods like BM25 for effective search, and that re-ranking can add modest gains. He critiques the hype around knowledge graphs, noting the bottleneck of building them, but sees LLMs making triplet generation easier. For the future, he hopes for more domain-specific embedding models and visual language model backbones, though acknowledges the difficulty of the business model.

Fullstack-Bench: The Eval for Coding Agents — with Sujay Jayakar, Chief Scientist, Convex

Mar 19, 2025 · 31:13

Sujay Jayakar, Convex chief scientist, presents Fullstack-Bench, a benchmark evaluating how well AI coding agents build full-stack apps on Convex, Supabase, and FastAPI+Redis, finding Convex’s tight feedback loops and strong abstractions let agents autonomously complete tasks within time limits. The benchmark tests three tasks (chat, to-do, files) across three backends with 30-60 minute limits and human hints. Convex succeeded autonomously on chat and to-do tasks, while Supabase agents struggled with RLS policy debugging and FastAPI agents with manual SSE wiring. Sujay discusses how platform design impacts AI code generation: simple procedural code, strong abstractions, and guardrails like end-to-end type safety improve LLM performance. He notes that API choices matter—things too similar to Firebase but not exact cause hallucinations—and that models like Claude 3.7 sometimes regress on platform-specific evals. The episode concludes with Convex’s focus on enabling AI-generated backends and anticipation of new agent workflow patterns.