Jul 22, 2026

Your Moat Is Your Data Model — Mike Phipps, Gates Foundation

Mike Phipps from the Gates Foundation details how they built a Strategic Intelligence Platform (SIP) using a Neo4j knowledge graph to serve AI agents. He argues that the true "moat" in an AI-commoditized world lies in an organization's unique data model and tacit knowledge, not in generic AI tools. The platform unifies 25 years of siloed grantmaking data, integrating structured and unstructured information through a rigorous curation pipeline, and is refined via continuous retrieval evaluations to ensure alignment with organizational reporting standards.

Jul 22, 2026

Active Graph Agent Runtime (BabyAGI 4) — Yohei Nakajima, Untapped Capital

Yohei Nakajima, creator of BabyAGI, introduces ActiveGraph, a novel agent architecture that builds around an immutable event log rather than the LLM. This log-centric approach enables auditable agents with native replay, rollback, and self-improvement capabilities, drawing inspiration from blackboard systems and Kafka.

Jul 22, 2026

CrabRAG: Why Automated Assistants Need Graph Memory, Not More Tokens — Stephen Chin, Neo4j

Stephen Chin demonstrates how current LLM agent memory systems, relying on markdown files or vector databases, lead to token inefficiency, hallucinations, and a lack of multi-hop reasoning. He showcases graph databases as a superior alternative, providing precise, explainable, and auditable answers for complex, large-scale problems through a live demo of a home lab digital twin.

Jul 22, 2026

Claude for Long-Horizon Tasks — Lance Martin, Anthropic

Lance Martin from Anthropic shares insights into building reliable and secure long-horizon agents with Claude. He details architectural principles like decoupling the 'brain' from the 'hands' for reliability and security, implementing independent verifiers for self-correction, and developing advanced self-learning memory systems akin to human memory's in-band writing and offline 'dreaming' consolidation. The talk concludes with a vision for evolving agent harnesses towards organizational-level, proactive, and multiplayer capabilities.

Jul 22, 2026

Expanding Flows for Fast and Flexible Generation Beyond the Fixed Canvas

This talk introduces Expanding Generative Flows (EFlows) and Expanding Flow Maps (EFMs), a novel framework for flow-based generative models that overcome the limitation of fixed-canvas generation. It proposes decomposing the generative process into an 'expand' operation (to increase dimensionality with new coordinates/tokens) and a 'transport' map, allowing for fast and flexible generation of variable-sized outputs across both continuous and discrete state spaces. The framework leverages local time clocks, piece-wise deterministic Markov processes, and specific training objectives for consistency, demonstrated through applications in conformer generation, molecular graph generation, and language modeling.

Jul 22, 2026

Open Models: Kimi K3, Qwen 3.8, Xi's WAIC Speech, Distillation, The Open-Closed Gap, and What's Next

Nathan Lambert and Florian Brand discuss the accelerating open model landscape, focusing on the surge in Chinese models like Kimi K3 and GLM 5.2. They explore the reasons behind China's progress, the evolving US ecosystem, the cybersecurity implications of open-source bans, and debunk common misconceptions about distillation, particularly challenging Ben Thompson's recent claims. The episode concludes with predictions and a frontier model tier list.