Vector Podcast
Claim This Podcastby Dmitry Kan
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Podcast Overview
<p>Vector Podcast is here to bring you the depth and breadth of Search Engine Technology, Product, Marketing, Business. In the podcast we talk with engineers, entrepreneurs, thinkers and tinkerers, who put their soul into search. Depending on your interest, you should find a matching topic for you -- whether it is deep algorithmic aspect of search engines and information retrieval field, or examples of products offering deep tech to its users. "Vector" -- because it aims to cover an emerging field of vector similarity search, giving you the ability to search content beyond text: audio, video, images and more. "Vector" also because it is all about vector in your profession, product, marketing and business.</p><p>Podcast website: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.vectorpodcast.com/">https://www.vectorpodcast.com/</a></p><p>Dmitry is blogging on <a target="_blank" rel="noopener noreferrer nofollow" href="https://dmitry-kan.medium.com/">https://dmitry-kan.medium.com/</a></p>
Language
🇺🇲
Publishing Since
12/6/2021
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Recent Episodes
June 15, 2026
Beyond Hyperspace - Ohad Levi on Hardware Accelerated Search and Agentic Memory
<p>In this episode we sat down with Ohad Levi, co-founder and CEO of Hyperspace, to discuss the harware-accelerated search product he has built to address the search latency problem.</p><p></p><p>Ohad also shares his thoughts on Agentic memory and what keeps him at night these days.</p><p></p><p>Podcast design by <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/srbhr/">https://www.linkedin.com/in/srbhr/</a></p><p></p><p>Timecodes:</p><p>00:00 Intro</p><p>01:35 Ohad's background</p><p>03:30 How idea was born: what was missing in the search landscape</p><p>06:52 Top 3 issues with existing search solutions</p><p>10:52 The importance of search latency</p><p>13:41 Ohad's solution for latency</p><p>19:22 Was Hyperspace up for the challenge?</p><p>22:12 New approaches to handling massive scale</p><p>26:12 Does latency matter for new agentic AI?</p><p>32:12 Agentic AI vs SaaS</p><p>35:03 Ohad's learnings from Hyperspace</p><p>38:37 Friction points for the hardware-accelerated search </p><p>42:40 Product-led growth way</p><p>47:43 What keeps Ohad excited about the AI / search field</p><p>51:43 Ohad's message to the Search community</p><p></p><p>Shownotes:</p><p>Ohad Levi on LinkedIn: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/ohad-levi/">https://www.linkedin.com/in/ohad-levi/</a></p><p>Hyperspace: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.hyper-space.io/">https://www.hyper-space.io/</a></p><p>Dmitry's blog on Medium: <a target="_blank" rel="noopener noreferrer nofollow" href="https://dmitry-kan.medium.com/">https://dmitry-kan.medium.com/</a></p><p>Dmitry on LinkedIn: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/dmitrykan/">https://www.linkedin.com/in/dmitrykan/</a></p>
April 10, 2026
AI Webinar - Building an AI-Ready Data Backbone
<p>Webinar I gave with AI Camp and Aiven on AI-ready data backbone, and specifically how OpenSearch unlocks AI-powered search and log analytics: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.aicamp.ai/event/eventdetails/W2026032610">https://www.aicamp.ai/event/eventdetails/W2026032610</a></p><p></p><p>Blog post: <a target="_blank" rel="noopener noreferrer nofollow" href="https://dmitry-kan.medium.com/webinar-building-an-ai-ready-data-backbone-with-aiven-google-cloud-4629f97f69bd">https://dmitry-kan.medium.com/webinar-building-an-ai-ready-data-backbone-with-aiven-google-cloud-4629f97f69bd</a></p><p></p><p>LLM/RAG/AI Agents course: <a target="_blank" rel="noopener noreferrer nofollow" href="https://dmitry-kan.medium.com/course-large-language-models-and-generative-ai-for-nlp-2025-98e31780de30">https://dmitry-kan.medium.com/course-large-language-models-and-generative-ai-for-nlp-2025-98e31780de30</a></p><p></p><p>Free tier OpenSearch: <a target="_blank" rel="noopener noreferrer nofollow" href="https://aiven.io/free-opensearch">https://aiven.io/free-opensearch</a></p><p></p><p>Time codes:</p><p>1:01 Dima's intro + Vector Podcast</p><p>4:56 About Aiven</p><p>7:06 Why best? - Question from the audience</p><p>10:22 Free Tier OpenSearch!</p><p>11:57 Aiven's unifed platform</p><p>12:58 OpenSearch: What and Why</p><p>17:00 Why OpenSearch is AI-Ready?</p><p>18:26 What Aiven's OpenSearch gives you</p><p>20:44 Lexical vs semantic search</p><p>22:51 Technical use cases of OpenSearch </p><p>24:17 Reference Architecture with Kafka as event processor, and OpenSearch as storage and search layer</p><p>25:37 Aiven's case studies for OpenSearch</p><p>26:27 When to choose OpenSearch?</p><p>28:21 Demo of OpenSearch query UI</p><p>32:12 Is there any advantage in using Qdrant over OpenSearch? - Question from the audience</p><p>34:30 What is the vector lenght (in this demo)? - Question from the audience</p><p>36:27 What are the main advantages of Aiven's OpenSearch compared to Elasticsearch? - Question from the audience</p><p>32:11 Demo of Search Relevancy Workbench: visual way of searching</p><p></p><p>Show notes:</p><p>- User Behaviour Insights: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.ubisearch.dev/">https://www.ubisearch.dev/</a></p><p>- Webinar's demo code part 1: Episode download / transcribe / index: <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/dimakan-dev/conduit-transcripts/blob/main/DATA_PROCESSING_GUIDE.md">https://github.com/dimakan-dev/conduit-transcripts/blob/main/DATA_PROCESSING_GUIDE.md</a></p><p>- Webinar's demo code part 2: Main UI and quality dashboards: <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/dimakan-dev/preparing-data-for-opensearch-and-rag/blob/main/workshop/STREAMLIT_README.md">https://github.com/dimakan-dev/preparing-data-for-opensearch-and-rag/blob/main/workshop/STREAMLIT_README.md</a></p>
November 7, 2025
Trey Grainger - Wormhole Vectors
<p>This lightning session introduces a new idea in vector search - Wormhole vectors!</p><p>It has deep roots in physics and allows for transcending spaces of any nature: sparse, vector and behaviour (but could theoretically be any N-dimensional space).</p><p></p><p>Blog post on Medium: <a target="_blank" rel="noopener noreferrer nofollow" href="https://dmitry-kan.medium.com/novel-idea-in-vector-search-wormhole-vectors-6093910593b8">https://dmitry-kan.medium.com/novel-idea-in-vector-search-wormhole-vectors-6093910593b8</a></p><p></p><p>Session page on maven: <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructor">https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructor</a></p><p></p><p>To try the managed OpenSearch (multi-cloud, automatic backups, disaster recovery, vector search and more), go 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></p><p></p><p>Get credits to use Aiven's products (PG, Kafka, Valkey, OpenSearch, ClickHouse): <a target="_blank" rel="noopener noreferrer nofollow" href="https://aiven.io/startups">https://aiven.io/startups</a></p><p></p><p>Timecodes:</p><p>00:00 Intro by Dmitry</p><p>01:48 Trey's presentation</p><p>03:05 Walk to the AI-Powered Search course by Trey and Doug</p><p>07:07 Intro to vector spaces and embeddings</p><p>19:03 Disjoint vector spaces and the need of hybrid search</p><p>23:11 Different modes of search</p><p>24:49 Wormhole vectors</p><p>47:49 Q&A</p><p></p><p>What you'll learn:</p><p></p><p>- What are "Wormhole Vectors"?</p><p>Learn how wormhole vectors work & how to use them to traverse between disparate vector spaces for better hybrid search.</p><p>- Building a behavioral vector space from click stream data</p><p>Learn to generate behavioral embeddings to be integrated with dense/semantic and sparse/lexical vector queries.</p><p>- Traverse lexical, semantic, & behavioral vectors spaces</p><p>Jump back and forth between multiple dense and sparse vector spaces in the same query</p><p>- Advanced hybrid search techniques (beyond fusion algorithms)</p><p>Hybrid search is more than mixing lexical + semantic search. See advanced techniques and where wormhole vectors fit in.</p>
35 total episodes available
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Frequently asked questions
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- What is Vector Podcast?
<p>Vector Podcast is here to bring you the depth and breadth of Search Engine Technology, Product, Marketing, Business. In the podcast we talk with engineers, entrepreneurs, thinkers and tinkerers, who put their soul into search. Depending on your interest, you should find a matching topic for you -- whether it is deep algorithmic aspect of search engines and information retrieval field, or examples of products offering deep tech to its users. "Vector" -- because it aims to cover an emerging field of vector similarity search, giving you the ability to search content beyond text: audio, video, images and more. "Vector" also because it is all about vector in your profession, product, marketing and business.</p><p>Podcast website: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.vectorpodcast.com/">https://www.vectorpodcast.com/</a></p><p>Dmitry is blogging on <a target="_blank" rel="noopener noreferrer nofollow" href="https://dmitry-kan.medium.com/">https://dmitry-kan.medium.com/</a></p> - How often does this podcast release new episodes?
This podcast updates bi-weekly.
- Where can I listen to this podcast?
This podcast is available on 8 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Information about guest appearances is not available.
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