Weaviate Podcast

00:51:36

SkyDiscover with Alexander Krentsel, Shu Liu, Shubham...

Weaviate Podcast ·

2026/10/05

Shu Liu, Alexander Krentsel, Mert Cemri, and Shubham Agarwal, PhD students at U.C. Berkeley's Sky Computing Lab, join the Weaviate Podcast to discuss SkyDiscover and what it means for AI to build entire systems, not just code! AI coding went from Copilot-style line completion to function- and file-level generation with Claude Code. The next step is to optimize or synthesize systems that used to take PhD-level expertise, such as databases, inference engines, and key-value stores. With coding

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00:55:07

In-Context Retrieval with Siddharth Gollapudi -...

Weaviate Podcast ·

2026/10/01

Siddharth Gollapudi, a researcher at UC Berkeley, joins the Weaviate Podcast to discuss in-context retrieval and his paper "Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale." The idea is simple and radical: instead of encoding documents into embeddings and building a nearest neighbor index, put the entire corpus into an LLM's context window and use attention itself as the retriever. The conversation opens by separating this from generative

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00:51:42

humans& with Alexis Ross, Manya Bansal, and Niloofar...

Weaviate Podcast ·

2026/09/21

Alexis, Manya, and Niloofar from humans& join the Weaviate Podcast to introduce Persimmon, a user model built to simulate how humans actually behave in multi-turn, multi-party conversations. Persimmon is explicitly not an assistant, a companion, or a Character AI-style stand-in, it is a research preview aimed at faithfully capturing the distribution of human behavior. This includes the natural friction of frustration, excitement, and group dynamics that assistant chatbots trained to be helpful

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01:00:15

Delegance Brokerage with Alex Ledbetter - Weaviate...

Weaviate Podcast ·

2026/09/17

Alex Ledbetter is the founder of Delegance Brokerage, an AI-native commercial insurance brokerage he built entirely on his own. The conversation opens with his origin story: an internship on a political risk, credit, and bond underwriting team in New York, where he watched a three-trillion-dollar portfolio run off the back of Excel and realized commercial insurance brokers earning 15–35% annual commissions could be disintermediated the way Robinhood disintermediated retail stock brokers. After

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00:57:45

AutoIndex with Sam O'Nuallain - Weaviate Podcast #143!

Weaviate Podcast ·

2026/09/07

Sam O'Nuallain joins the Weaviate Podcast to discuss AutoIndex, research from UMass Amherst and Databricks on learning representation programs for retrieval. Instead of tuning the retriever or re-ranker, AutoIndex asks how the data itself should be represented: a two-agent system, an analysis agent and a code agent, writes and refines Python programs that chunk, enrich, and reorganize a corpus to optimize downstream metrics like Recall and nDCG.The conversation opens with why indexing is such a

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01:04:55

Recursive Language Models with Alex Zhang - Weaviate...

Weaviate Podcast ·

2026/08/26

Alex Zhang, a PhD student at MIT, joins the Weaviate Podcast to discuss Recursive Language Models (RLMs), a new abstraction for designing agent harnesses. Instead of the standard ReAct-style loop that stuffs every tool observation into an ever-growing prompt, an RLM treats the prompt as a variable in a program. The model writes code that manipulates its own context and spawns recursive LLM calls over pieces of it. The published headline was long-context performance, but the deeper intention,

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00:56:40

Drowning in Documents with Mathew Jacob - Weaviate...

Weaviate Podcast ·

2026/08/17

Mathew Jacob, lead author of "Drowning in Documents: Consequences of Scaling Reranker Inference" and now a PhD student in ML systems at the University of Washington, joins the Weaviate Podcast to unpack one of the most surprising results in modern search: cross-encoder rerankers get worse as you give them more documents. The paper began during his Databricks internship, where scaling reranking past roughly 100 documents sent recall@10 plummeting, a result so counterintuitive he assumed it

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00:55:08

Founding Weaviate with Bob van Luijt and Etienne...

Weaviate Podcast ·

2026/07/27

Weaviate co-founders Bob van Luijt and Etienne Dilocker return to the Weaviate Podcast to celebrate seven years of building the company, answering questions submitted by the community. The conversation opens with what excites them most in AI right now: Etienne on agentic coding and the "Moore's law" of how long models can sustain autonomous loops, and Bob on world models, new architectures that could slash training energy costs, open source frontier models, and inference on new

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01:00:39

Knowledge Engineering with Bradley Allen - Weaviate...

Weaviate Podcast ·

2026/06/01

Dr. Bradley Allen brings five decades of AI history into a deep conversation on knowledge engineering, neurosymbolic AI, and the future of enterprise intelligence. The discussion begins with the boom-and-bust cycle of rule-based expert systems, AI winters, and why today’s large language model wave may be different. The conversation then turns to how knowledge is organized in practice, from personal piles of papers searched on demand to formal knowledge graphs built with classes, relations,

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00:41:40

Booking.com and Weaviate with Başak Eskili - Weaviate...

Weaviate Podcast ·

2026/05/18

Başak Eskili joins the Weaviate Podcast to explore how one of the world’s largest travel platforms adopted vector search, retrieval-augmented generation, and agentic AI at production scale. The conversation begins with Booking.com’s shift from keyword matching to semantic retrieval as internal teams needed embeddings, similarity search, and eventually GenAI RAG workflows. Başak explains why OpenSearch was a practical first step on AWS, how adoption grew across teams, and why hundreds of millions of

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01:01:16

Search Agents with Nandan Thakur - Weaviate Podcast...

Weaviate Podcast ·

2026/05/05

Dr. Nandan Thakur returns to the Weaviate Podcast fresh off defending his dissertation to discuss the evolution from neural retrieval to agentic search and his new work on Orbit, a synthetic training data pipeline for search agents. The conversation opens with reflections on his PhD journey, tracing the field's shift from ColBERT-style models and sparse retrievers through RAG and into today's agentic search paradigm where LLMs iteratively search, reason, and refine.The discussion dives deep

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01:03:16

AgentIR with Zijian Chen and Xueguang Ma - Weaviate...

Weaviate Podcast ·

2026/04/27

Zijian Chen and Xueguang Ma from the University of Waterloo join the Weaviate Podcast to discuss AgentIR and why retrieval systems need to be redesigned from the ground up for AI agents. The conversation opens with a striking reframe: agents have become the primary consumers of search, inserting themselves as middleware between humans and information. Humans used to query search engines directly, now they delegate to ChatGPT, which searches on their behalf. This means retrieval algorithms are no

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00:57:23

Data Agents with Shreya Shankar - Weaviate Podcast #135!

Weaviate Podcast ·

2026/04/06

Shreya Shankar from UC Berkeley joins the Weaviate Podcast to discuss data agents, the Data Agent Benchmark, and DocETL. The conversation opens with defining what a data agent actually is, not just text-to-SQL over a single table, but an AI system that can reason across dozens of heterogeneous databases, flat files, and knowledge repositories to answer complex organizational questions. Shreya explains why this multi-database reality makes existing benchmarks insufficient, motivating the Data Agent

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01:21:13

Multi-Vector Search with Amélie Chatelain and Antoine...

Weaviate Podcast ·

2026/03/23

Amélie Chatelain and Antoine Chaffin from LightOn are leading the way in the next generation of search powered by Multi-Vector representations and Late Interaction. The podcast begins with what motivates them to work on Multi-Vector Search, continuing to discuss particular details such as the combination between lexical and semantic search, as well as bi-encoder speed with cross encoder accuracy. This discussion continues to present insights about training multi-vector models and how they differ

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00:53:20

AI-Powered Search with Doug Turnbull and Trey...

Weaviate Podcast ·

2026/03/01

Doug Turnbull and Trey Grainger join the Weaviate Podcast to discuss all things AI-Powered Search! The conversation kicks off with designing search experiences, not all search queries are the same! Sometimes the user knows exactly what they want (a product ID, a specific file), other times they're exploring a broad category, and other times they need to compare and contrast options. AI is now making it possible to dynamically construct UIs around search results, moving toward what Trey describes

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01:00:31

Pyversity with Thomas van Dongen - Weaviate Podcast...

Weaviate Podcast ·

2025/12/08

Thomas van Dongen is the head of AI engineering at Springer Nature and the creator of Pyversity! Pyversity is a fast, lightweight open-source Python library for diversifying retrieval results. Retrieval systems often return highly similar items. Pyversity efficiently re-ranks these results to encourage diversity, surfacing items that remain relevant but less redundant. It implements several popular diversification strategies such as MMR, MSD, DPP, and Cover with a clear, unified API.

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01:02:25

Semantic Query Engines with Matthew Russo - Weaviate...

Weaviate Podcast ·

2025/11/18

Matthew Russo is a Ph.D. student at MIT where he is researching the intersection of AI and Database Systems. AI is transforming Database Systems. Perhaps the biggest impact so far has been natural language to query language translations, or Text-to-SQL. However, another massive innovation is brewing. AI presents new Semantic Operators for our query languages. For example, we are all familiar with the WHERE filter. Now we have AI_WHERE, in which an LLM or another AI model computes the filter value

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01:00:00

REFRAG with Xiaoqiang Lin - Weaviate Podcast #130!

Weaviate Podcast ·

2025/11/03

Xiaoqiang Lin is a Ph.D. student at the National University of Singapore. During his time at Meta, Xiaoqiang lead the research behind REFRAG: Rethinking RAG-based Decoding. Traditional RAG systems use vectors to retrieve relevant context with semantic search, but then throw away the vectors when passing the context to the LLM. REFRAG instead feeds the LLM these pre-compute vectors, achieving massive gains in long context processing and LLM inference speed! REFRAG makes Time-To-First-Token (TTFT) 31x

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00:43:55

Weaviate and SAS with Saurabh Mishra and Bob van...

Weaviate Podcast ·

2025/10/13

This episode dives into Weaviate's partnership with SAS! We are super excited about our recent collaboration on the SAS Retrieval Agent Manager (RAM), featuring a first party integration with Weaviate! The podcast dives into all sorts of aspects of Enterprise AI adoption from what has changed, to what has NOT changed with recent breakthroughs in AI systems!

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01:01:32

Weaviate's Query Agent with Charles Pierse - Weaviate...

Weaviate Podcast ·

2025/09/22

Charles Pierse is the Director of the Weaviate Labs team, where he has recently lead the GA release of the Weaviate Query Agent. The podcast begins with the journey from alpha to GA release, discussing unexpected lessons and the collaborations between teams at Weaviate. Continuing on the product design, we cover the design of the Python and TypeScript clients and how to think about response models with Agent products. Then diving into the tech, we cover several different aspects of the Query Agent

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