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

Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise.Craft decaf & half caf coffee, 25% discount: https://savorista.com/discount/VECTORThis episode on YouTube: https://youtu.be/I8ZtqajighgMedium: https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3Dev: https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfaIf you are on Lucene / OpenSearch stack, you can go managed by signing up here: https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcastTime codes:00:00 Intro00:15 Napkin Problem 4: Throughput of Redis01:35 Episode intro02:45 Simon's background, including implementation of Turbopuffer09:23 How Cursor became an early client11:25 How to test pre-launch14:38 Why a new vector DB deserves to exist?20:39 Latency aspect26:27 Implementation language for Turbopuffer28:11 Impact of LLM coding tools on programmer craft30:02 Engineer 2 CEO transition35:10 Architecture of Turbopuffer43:25 Disk vs S3 latency, NVMe disks, DRAM48:27 Multitenancy50:29 Recall@N benchmarking59:38 filtered ANN and Big-ANN Benchmarks1:00:54 What users care about more (than Recall@N benchmarking)1:01:28 Spicy question about benchmarking in competition1:06:01 Interesting challenges ahead to tackle1:10:13 Simon's announcementShow notes:- Turbopuffer in Cursor: https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223stranscript: https://lexfridman.com/cursor-team-transcript- https://turbopuffer.com/- Napkin Math: https://sirupsen.com/napkin- Follow Simon on X: https://x.com/Sirupsen- Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696/