I couldn't touch the database, so I built search next to it

<p>Adding full-text search to Postgres is a two-line migration. Running it in production without surprises is not. The GIN index that makes search fast also adds write

<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

<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

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<p>We paid for a vector database for a year. It was faster than Postgres at the one thing it does. We deleted it anyway.<br /> </p> <div class="highlight js-code-highlight"> <pre

<h2> Search Is Harder Than It Looks </h2> <p><code>LIKE '%query%'</code> works until:</p> <ul> <li>Your table has 100k rows (Seq Scan, slow)</li> <li>Users misspell words (no

<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>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