Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise.

Time codes:

00:00 Intro

00:15 Napkin Problem 4: Throughput of Redis

01:35 Episode intro

02:45 Simon's background, including implementation of Turbopuffer

09:23 How Cursor became an early client

11:25 How to test pre-launch

14:38 Why a new vector DB deserves to exist?

20:39 Latency aspect

26:27 Implementation language for Turbopuffer

28:11 Impact of LLM coding tools on programmer craft

30:02 Engineer 2 CEO transition

35:10 Architecture of Turbopuffer

43:25 Disk vs S3 latency, NVMe disks, DRAM

48:27 Multitenancy

50:29 Recall@N benchmarking

59:38 filtered ANN and Big-ANN Benchmarks

1:00:54 What users care about more (than Recall@N benchmarking)

1:01:28 Spicy question about benchmarking in competition

1:06:01 Interesting challenges ahead to tackle

1:10:13 Simon's announcement

Show notes:

Economical way of serving vector search workloads with Simon Eskildsen, CEO Turbopuffer

Vector Podcast