HeyJaredWeaviate Podcast
Join Connor Shorten as he interviews machine learning experts and explores Weaviate use cases from users and customers.
- Indexed episodes, last 90 days
- 4
- Latest publication
- Sep 7, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 27, 2026
Latest episodes
AutoIndex with Sam O'Nuallain - Weaviate Podcast #143! (opens the original)
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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
Recursive Language Models with Alex Zhang - Weaviate Podcast #142! (opens the original)
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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, ins
Drowning in Documents with Mathew Jacob - Weaviate Podcast #141! (opens the original)
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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 was a
Founding Weaviate with Bob van Luijt and Etienne Dilocker - Weaviate Podcast #140! (opens the original)
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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 chips.From there,
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