Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise.This episode on YouTube: <a target="_blank" rel="noopener noreferrer nofollow" href="https://youtu.be/I8Ztqajighg">https://youtu.be/I8Ztqajighg</a>Medium: <a target="_blank" rel="noopener noreferrer nofollow" href="https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3">https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3</a>Dev: <a target="_blank" rel="noopener noreferrer nofollow" href="https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfa">https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfa</a>If you are on Lucene / OpenSearch stack, you can go managed by signing up here: <a target="_blank" rel="noopener noreferrer nofollow" href="https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcast">https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcast</a>Time 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: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223s">https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223s</a>transcript: <a target="_blank" rel="noopener noreferrer nofollow" href="https://lexfridman.com/cursor-team-transcript">https://lexfridman.com/cursor-team-transcript</a>- <a target="_blank" rel="noopener noreferrer nofollow" href="https://turbopuffer.com/">https://turbopuffer.com/</a>- Napkin Math: <a target="_blank" rel="noopener noreferrer nofollow" href="https://sirupsen.com/napkin">https://sirupsen.com/napkin</a>- Follow Simon on X: <a target="_blank" rel="noopener noreferrer nofollow" href="https://x.com/Sirupsen">https://x.com/Sirupsen</a>- Not All Vector Databases Are Made Equal: <a target="_blank" rel="noopener noreferrer nofollow" href="https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696/">https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696/</a>

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

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