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High scale agentic memory with Turbopuffer

Hosted by Doug Turnbull (Maven)

Thu, Oct 1, 2026

5:00 PM UTC (1 hour)

Virtual (Zoom)

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Doug Turnbull
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What you'll learn

How to iterate on agentic memory problems

How to debug agentic memory problems with a real dataset. Labeling evidence. Debugging why its not retrieved.

Take advanatge of scale to just "index everything"

How far can we get indexing everything into Turbopuffer, instead of modeling the perfect knowledge base?

Think in terms of layers of knowledge caches

Remembering evolving knowledge as a disposable cache, not a pristine beautiful organized platonic set of fats

Why this topic matters

Agents don't remember what they learn Jargon. Personal preferences. What works / doesn't work. In this talk, we'll explore one approach: just index all agentic traces into a high-scale vector database like Turbopuffer. Then reconstruct only the knowledge that we need, treating these neat and tidy bits of knowledge like a cache that expires, not facts that live forever. We'll discuss pros / cons of such an approach, and play with a fun solution.

You'll learn from

Doug Turnbull (Maven)

Led teams at Shopify, Reddit, Wikipedia

In 2012, Doug got bit by the search bug and he's still trying to keep up. From full-text search, to Learning to Rank models, to search agents that generate their own code, he knows the endless landscape first hand. Yet Doug wants to deeply understand the what / how / why, and help teams use these technologies practically, distinguishing hype from reality.

He’s led search at Reddit, Shopify, and Wikipedia, authored Relevant Search and AI Powered Search, and advised 100+ organizations over the years - all in pursuit of the same question: how does search actually work?

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