Vector Podcast

This episode was recorded LIVE during Berlin Buzzwords 2026

Charlie's site and blog: https://thesearchjuggler.com/

LinkedIn: https://www.linkedin.com/in/charliehullsearch/

Search Relevance Slack (7000 search professionals): https://join.slack.com/t/relevancy/shared_invite/zt-42nhd2z7n-c8QAsDCYU8hFi8c2pRrXKg

Charlie's panel at bbuzz, featuring yours truly among a group of distinguished search and ML people: https://www.youtube.com/watch?v=StaPk0k-52Y

Time codes

00:00 Intro

00:42 Charlie's take on this year's Berlin Buzzwords

01:55 Based on the panel: Charlie's first encounter of AI

04:55 Is preserving search still a question?

5:48 Charlie's "old" search technique to improve quality of search

7:58 Explainability gap

9:03 Did LLMs change Charlie's life

12:07 Query Understanding is still a complex topic

13:14 Vector search or not

17:28 Meetups and places to learn with Charlie

19:25 Charlie's message to the search makers

23:00 What is fundamentally missing in our space

26:03 Where to follow Charlie's work

Show notes:

- Upcoming Vespa conference: http://www.vespaai.live/

- Where find Charlie? On meetups: http://www.searchmeetups.com/

- http://www.thesearchjuggler.com/

- https://searchmeetups.com/

- A neat trick for protecting numbers when using fuzzy search: https://www.youtube.com/watch?v=s34afYwYqyM

This is a LIVE discussion with Julien Nioche, the creator of SPRUCE, open source greenops framework. We recorded LIVE at Berlin Buzzwords 2026.

Cameraman is Zoom's AI and it goes a little crazy at times zooming on Julien and zooming back out. As usual, audio version will be available. Go to https://www.vectorpodcast.com/

He gave a talk about SPRUCE at the conference: https://2026.berlinbuzzwords.de/session/spruce-it-up-open-source-greenops-at-scale/

Timcodes:

00:00 Intro

00:16 Julien's background

01:36 What is SPRUCE

2:51 Greenops vs FinOps

6:09 Reasons cloud providers won't go deeper on greenops

7:09 Ways to deploy SPRUCE

15:06 Cultural shift needed for Greenops

17:39 How to measure impact of AI agents and LLMs

Shownotes:

- SPRUCE and green ops: https://opensourcegreenops.cloud/latest/

- SPRUCE on GitHub: https://github.com/digitalpebble/spruce

- Julien's company: https://digitalpebble.com/

- Apache StormCrawler: https://stormcrawler.apache.org/ and GitHub: https://github.com/apache/stormcrawler

- SPRUCE is based on Apache Spark: https://spark.apache.org/

- "AI is here - time to throw away our search engines?" panel hosted by Charlie Hull at Berlin Buzzwords 2026: https://www.youtube.com/watch?v=StaPk0k-52Y

- Estimate and track the environmental footprint of GenAI models at inference. Ecologits: https://ecologits.ai/latest/

- Evaluate the environmental impact of digital technologies across organizations. Boavizta: https://boavizta.org/en

This episode was recorded LIVE at the Berlin Buzzwords 2026

YouTube version: https://youtu.be/acOGVynTVpM

The cameraman is pure Zoom's AI ;)

The Course: "AI-Powered Search: Modern Retrieval for Humans & Agents"

aipoweredsearch.com/live-course?promoCode=vector-podcast

Discount Code for course (20% off): "vector-podcast"

AI-Powered Search (Book, Content, Community): https://aipoweredsearch.com/

Timecodes

00:00 Intro

00:30 Doug's and Trey's impression of the conference

01:16 How modern AI changed search (if any)

05:48 How to bring AI techniques into existing search engines on a budget

10:44 What the AI-Powered Search course includes

18:02 Staying hands-on

18:47 Guest's favourite topic that keeps them up at night

27:40 Message to the builders of search tech

32:55 Search continues to be challenging and exciting

Shownotes:

- Upcoming course in detail (grab promo code above to save 20%): https://aipoweredsearch.com/articles/the-frontier-of-ai-search-ai-powered-search-modern-retrieval-for-humans-agents/

- Doug's blog on search, agents, RAG, LLM as a judge and more: https://softwaredoug.com/

- AI-Powered Search: https://aipoweredsearch.com/

- Berlin Buzzwords:

- MICES: https://mices.co/

- Future of Search conference: https://berlinsearchweek.com/future-of-search/

- Women of Search: https://www.women-of-search.org/

- "AI is here - time to throw away our search engines?" panel hosted by Charlie Hull at Berlin Buzzwords 2026: https://www.youtube.com/watch?v=StaPk0k-52Y

- Dmitry's prototype of Wormhole vectors idea with OpenSearch: https://aiven.io/blog/beyond-hybrid-search-traversing-vector-spaces-with-wormhole-vectors

- Dmitry's blog on Medium: https://dmitry-kan.medium.com/

- Dmitry's Tech Stories on Substack: https://substack.com/@dmitrykan

- Follow me on LinkedIn for Search updates: https://www.linkedin.com/in/dmitrykan/

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.

Ohad also shares his thoughts on Agentic memory and what keeps him at night these days.

Podcast design by https://www.linkedin.com/in/srbhr/

Timecodes:

00:00 Intro

01:35 Ohad's background

03:30 How idea was born: what was missing in the search landscape

06:52 Top 3 issues with existing search solutions

10:52 The importance of search latency

13:41 Ohad's solution for latency

19:22 Was Hyperspace up for the challenge?

22:12 New approaches to handling massive scale

26:12 Does latency matter for new agentic AI?

32:12 Agentic AI vs SaaS

35:03 Ohad's learnings from Hyperspace

38:37 Friction points for the hardware-accelerated search

42:40 Product-led growth way

47:43 What keeps Ohad excited about the AI / search field

51:43 Ohad's message to the Search community

Shownotes:

Ohad Levi on LinkedIn: https://www.linkedin.com/in/ohad-levi/

Hyperspace: https://www.hyper-space.io/

Dmitry's blog on Medium: https://dmitry-kan.medium.com/

Dmitry on LinkedIn: https://www.linkedin.com/in/dmitrykan/

Webinar I gave with AI Camp and Aiven on AI-ready data backbone, and specifically how OpenSearch unlocks AI-powered search and log analytics: https://www.aicamp.ai/event/eventdetails/W2026032610

Blog post: https://dmitry-kan.medium.com/webinar-building-an-ai-ready-data-backbone-with-aiven-google-cloud-4629f97f69bd

LLM/RAG/AI Agents course: https://dmitry-kan.medium.com/course-large-language-models-and-generative-ai-for-nlp-2025-98e31780de30

Free tier OpenSearch: https://aiven.io/free-opensearch

Time codes:

1:01 Dima's intro + Vector Podcast

4:56 About Aiven

7:06 Why best? - Question from the audience

10:22 Free Tier OpenSearch!

11:57 Aiven's unifed platform

12:58 OpenSearch: What and Why

17:00 Why OpenSearch is AI-Ready?

18:26 What Aiven's OpenSearch gives you

20:44 Lexical vs semantic search

22:51 Technical use cases of OpenSearch

24:17 Reference Architecture with Kafka as event processor, and OpenSearch as storage and search layer

25:37 Aiven's case studies for OpenSearch

26:27 When to choose OpenSearch?

28:21 Demo of OpenSearch query UI

32:12 Is there any advantage in using Qdrant over OpenSearch? - Question from the audience

34:30 What is the vector lenght (in this demo)? - Question from the audience

36:27 What are the main advantages of Aiven's OpenSearch compared to Elasticsearch? - Question from the audience

32:11 Demo of Search Relevancy Workbench: visual way of searching

Show notes:

- User Behaviour Insights: https://www.ubisearch.dev/

- Webinar's demo code part 1: Episode download / transcribe / index: https://github.com/dimakan-dev/conduit-transcripts/blob/main/DATA_PROCESSING_GUIDE.md

- Webinar's demo code part 2: Main UI and quality dashboards: https://github.com/dimakan-dev/preparing-data-for-opensearch-and-rag/blob/main/workshop/STREAMLIT_README.md

This lightning session introduces a new idea in vector search - Wormhole vectors!

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).

Craft decaf & half caf coffee, 25% discount: https://savorista.com/discount/VECTOR

Blog post on Medium: https://dmitry-kan.medium.com/novel-idea-in-vector-search-wormhole-vectors-6093910593b8

Session page on maven: https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructor

To try the managed OpenSearch (multi-cloud, automatic backups, disaster recovery, vector search and more), go here: https://console.aiven.io/signup?utm_source=youtube&utm_medium&&utm_content=vectorpodcast

Get credits to use Aiven's products (PG, Kafka, Valkey, OpenSearch, ClickHouse): https://aiven.io/startups

Timecodes:

00:00 Intro by Dmitry

01:48 Trey's presentation

03:05 Walk to the AI-Powered Search course by Trey and Doug

07:07 Intro to vector spaces and embeddings

19:03 Disjoint vector spaces and the need of hybrid search

23:11 Different modes of search

24:49 Wormhole vectors

47:49 Q&A

What you'll learn:

- What are "Wormhole Vectors"?

Learn how wormhole vectors work & how to use them to traverse between disparate vector spaces for better hybrid search.

- Building a behavioral vector space from click stream data

Learn to generate behavioral embeddings to be integrated with dense/semantic and sparse/lexical vector queries.

- Traverse lexical, semantic, & behavioral vectors spaces

Jump back and forth between multiple dense and sparse vector spaces in the same query

- Advanced hybrid search techniques (beyond fusion algorithms)

Hybrid search is more than mixing lexical + semantic search. See advanced techniques and where wormhole vectors fit in.

YouTube: https://www.youtube.com/watch?v=fvDC7nK-_C0

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

Craft decaf & half caf coffee, 25% discount: https://savorista.com/discount/VECTOR

This episode on YouTube: https://youtu.be/I8Ztqajighg

Medium: https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3

Dev: https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfa

If 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=vectorpodcast

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:

- Turbopuffer in Cursor: https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223s

transcript: 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/

Vector Podcast website: https://vectorpodcast.com

Haystack US 2025: https://haystackconf.com/2025/

Federated search, Keyword & Neural Search, ML Optimisation, Pros and Cons of Hybrid search

It is fascinating and funny how things develop, but also turn around. In 2022-23 everyone was buzzing about hybrid search. In 2024 the conversation shifted to RAG, RAG, RAG. And now we are in 2025 and back to hybrid search - on a different level: finally there are strides and contributions towards making hybrid search parameters learnt with ML. How cool is that?

Design: Saurabh Rai, https://www.linkedin.com/in/srbhr/

The design of this episode is inspired by a scene in Blade Runner 2049. There's a clear path leading towards where people want to go to, yet they're searching for something.

00:00 Intro

00:54 Eric's intro and Daniel's background

02:50 Importance of Hybrid search: Daniel's take

07:26 Eric's take

10:57 Dmitry's take

11:41 Eric's predictions

13:47 Doug's blog on RRF is not enough

16:18 How to not fall short of the blind picking in RRF: score normalization, combinations and weights

25:03 The role of query understanding: feature groups

35:11 Lesson 1 from Daniel: Simple models might be all you need

36:30 Lesson 2: query features might be all you need

38:30 Reasoning capabilities in search

40:02 Question from Eric: how is this different from Learning To Rank?

42:46 Carrying the past in Learning To Rank / any rank

44:21 Demo!

51:52 How to consume this in OpenSearch

55:15 What's next

58:44 Haystack US 2025

YouTube: https://www.youtube.com/watch?v=quY769om1EY

https://www.vectorpodcast.com/

I had fun interacting with NotebookLM - mostly for self-educational purposes. I think this tool can help by bringing an additional perspective over a textual content. It ties to what RAG (Retrieval Augmented Generation) can do to content generation in another modality. In this case, text is used to augment the generation of a podcast episode.

This episode is based on my blog post: https://dmitry-kan.medium.com/the-rise-fall-and-future-of-vector-databases-how-to-pick-the-one-that-lasts-6b9fbb43bbbe

Time codes:

00:00 Intro to the topic

1:11 Dmitry's knowledge in the space

1:54 Unpacking the Rise & Fall idea

3:14 How attention got back to Vector DBs for a bit

4:18 Getting practical: Dmitry's guide for choosing the right Vector Database

4:39 FAISS

5:34 What if you need fine-grained keyword search? Look at Apache Lucene-based engines

6:41 Exception to the rule: Late-interaction models

8:30 Latency and QPS: GSI APU, Vespa, Hyperspace

9:28 Strategic approach

9:55 Cloud solutions: CosmosDB, Vertex AI, Pinecone, Weaviate Cloud

10:14 Community voice: pgvector

10:48 Picture of the fascinating future of the field

12:23 Question to the audience

12:44 Taking a step back: key points

13:45 Don't get caught up in trendy shiny new tech

YouTube: https://www.youtube.com/watch?v=403rxbWZK9Y

Vector Podcast website: https://vectorpodcast.com

Get your copy of John's new book "Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications": https://amzn.to/4fMj2Ef

John Berryman is the founder and principal consultant of Arcturus Labs, where he specializes in AI application development (Agency and RAG). As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools. John is coauthor of "Prompt Engineering for LLMs" (O'Reilly).Before his work on Copilot, John's focus was search technology. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of "Relevant Search" (Manning), a book that distills his expertise in the field.John's unique background, spanning both cutting-edge AI applications and foundational search technologies, positions him at the forefront of innovation in LLM applications and information retrieval.

00:00 Intro

02:19 John's background and story in search and ML

06:03 Is RAG just a prompt engineering technique?

10:15 John's progression from a search engineer to ML researcher

13:40 LLM predictability vs more traditional programming

22:31 Code assist with GitHub Copilot

29:44 Role of keyword search for code at GitHub

35:01 GenAI: existential risk or pure magic? AI Natives

39:40 What are Artifacts

46:59 Demo!

55:13 Typed artifacts, tools, accordion artifacts

56:21 From Web 2.0 to Idea exchange

57:51 Spam will transform into Slop

58:56 John's new book and Acturus Labs intro

Show notes:

- John Berryman on X: https://x.com/JnBrymn

- Acturus Labs: https://arcturus-labs.com/

- John's blog on Artifacts (see demo in the episode): https://arcturus-labs.com/blog/2024/11/11/cut-the-chit-chat-with-artifacts/

YouTube: https://youtu.be/60HAtHVBYj8