We Deleted Our Vector Database. Postgres Was Faster.

<p>Your RAG application works perfectly with 1,000 documents. You push it to production, upload 10 million vectors, and suddenly your query latency jumps from 50ms to 5 seconds.

<p>Most semantic search systems don’t fail because of embeddings. They fail because of how the system is designed around them.</p> <p>When I started building a semantic search API

<p><a class="article-body-image-wrapper"

<p><em>B-tree internals, column order and planner statistics in Postgres 19, checked against MongoDB's ESR rule.</em></p> <p>Most "add an index" advice stops at <code>CREATE

<h1> SynapCores SQLv2 vs PostgreSQL: The Evolution of Database Systems </h1> <h2> The AI Database Revolution </h2> <p>We built window functions (LAG, LEAD, RANK, etc.) in

<p>Munchable answers one question: does this food fit the gut condition you live with. Until recently there was exactly one way to ask it, which was to hold a phone camera up to a

<p>On January 28, 2026, Moltbook launched to considerable fanfare. The pitch was bold: an "agent-first, human-second" social network where 1.5 million autonomous AI agents could

<h2> 🎯 Who This Is For </h2> <ul> <li>You want to store and search vector data in DynamoDB</li> <li>You're frustrated that a single query returns at most 100 results, which leaves