Jul 23, 2026

Building an Autonomous Delivery Experience with DoorDash Co-Founders Andy Fang and Stanley Tang

DoorDash co-founders Andy Fang and Stanley Tang discuss how AI and autonomous technology are transforming consumer behavior and delivery logistics. They detail the impact of "Ask DoorDash" on restaurant discovery and grocery orders, and the operational challenges and strategic advantages behind their autonomous delivery robot, Dot. The conversation highlights DoorDash's 'use case first' approach to autonomy, the critical role of data in scaling physical AI, and their surprising prediction that more Dashers, not fewer, will be part of DoorDash's future multimodal delivery strategy.

Jul 23, 2026

AI on Your Lakehouse: Context Comes in Shapes, Not Queries — Zach Blumenfeld, Neo4j

AI agents often struggle with context in lakehouses, leading to confident but incorrect answers. This workshop by Zach Blumenfeld introduces three Neo4j graph shapes built on lakehouse data—Connections (semantic layer), Trees (document outlines), and Communities (themes)—to provide essential context, enabling agents to accurately navigate structured and unstructured data, answer complex estate-level questions, and overcome limitations of traditional Text2SQL and vector search.

Jul 23, 2026

How Supabase Became One Of The Fastest Growing DevTool Companies In The World

Supabase CEO Paul Copplestone details how a frustrating Firebase migration sparked an open-source side project that grew into a decacorn. He explains the strategic choice of PostgreSQL and open source, the evolution of developer experience to achieve 5-second time-to-value, and the transformative impact of AI agents, which now launch millions of Supabase databases monthly. The conversation also covers the challenges and advantages of a fully distributed workforce and the future bet on 'self-driving databases' to address the complex 'operate' stage of AI-driven development.

Jul 23, 2026

Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AI

Daniel Chalef discusses how LLM synthesis destroys data provenance, making it difficult to trace information back to its sources. He introduces Graphiti, an open-source temporal graph framework, and Zep, an agent memory infrastructure built upon it. This system models provenance as a graph, treating sources as 'episodes' and derived facts as interconnected nodes and edges. This approach enables robust lineage tracing, handles entity merges and fact invalidation through temporal mechanisms, facilitates metadata-driven trust evaluation, and allows for compliant data deletion (e.g., GDPR) based on source support. The benefits include enhanced compliance, improved data veracity, and debuggable AI agents, addressing a critical engineering challenge in LLM applications.

Jul 23, 2026

Local Agentic Theory For Mobile Games — Shafik Quoraishee & Joanne Song, The New York Times

This presentation explores the innovative concept of running agentic AI entirely on mobile devices to enhance game accessibility and personalization. It delves into the technical challenges of local AI (space, time, energy budgets), contrasts agentic systems with traditional reinforcement learning, and demonstrates practical applications with a Space Invaders agent and a crossword solver. A key takeaway is the transformation of accessibility from fixed toggles to dynamic, real-time adjustments based on player needs, ultimately envisioning a future of billions of small, personalized local AI brains.

Jul 23, 2026

Why Agentic Systems Need Ontologies — Frank Coyle, UC Berkeley

Frank Coyle discusses the inherent flaws of purely probabilistic LLMs in agentic systems and proposes a neuro-symbolic solution. He advocates for wrapping LLM tool-use loops with external ontologies and validation mechanisms (like RDFS, OWL, and Pydantic) to provide logical guardrails, preventing common, critical errors that probabilistic reasoning alone cannot reliably avoid.