Jul 17, 2026

AI Can't Learn The Way Humans Do - This Could Fix That

This discussion explores world models as a promising path to solving sample efficiency in AI and achieving AGI. It contrasts deterministic control (Newtonian physics) with stochastic environments (RL), explaining the challenges posed by large action spaces in Go, self-driving, and robotics. The episode delves into how synthetic data, video diffusion models, and latent space architectures like JEPA are making world models practical, while also highlighting remaining open problems in physics modeling, real-time adaptation, and rich sensory integration.

Jul 17, 2026

Fable 5 as Advisor: Anthropic's Two-Model Pattern for Smarter, Cheaper Agents (Ep. 1010)

The episode explores Anthropic's "advisor strategy," a novel AI agent pattern that combines a fast, cheap "executor" model with a frontier-class "advisor" model. This allows for mid-task consultation within a single API call, resolving the tension between cost and capability. Benchmarks show simultaneous improvements in quality and cost reduction, demonstrating that AI progress is shifting towards intelligent model composition rather than just larger models.

Jul 17, 2026

Thinking Machines Lab drops Inkling & Meta’s Muse Spark 1.1

This episode covers Thinking Machines' Inkling, an open-source, customizable model prioritizing architecture over benchmarks; Meta's Muse Spark 1.1, positioned for agent orchestration and enterprise use; OpenAI's GPT-5.6 Sol's 8% score on ARC-AGI-3, reigniting AGI debates; and Anthropic's "J-space" paper, exploring internal model reasoning and its implications for AI safety and interpretability.

Jul 17, 2026

Closing Keynote: Garry Tan, Y Combinator

Garry Tan, President of Y Combinator, details how AI-native companies are achieving unprecedented productivity and scale with lean teams. He introduces the concept of "wiring the work" by treating AI as a workforce where organizational components are encoded into markdown "skill files." Tan explains how "company brains" act as a library and librarian, managing institutional knowledge to overcome human memory limits. He emphasizes the discipline of "skillifying" every task to ensure continuous learning and calls on founders to build these new AI-native infrastructures to "boil the ocean"—tackling previously insurmountable problems.

Jul 16, 2026

Imagination Engineering — Eve Bouffard, Head of Design, Y Combinator

Eve Bouffard of Y Combinator introduces "Imagination Engineering," arguing that powerful AI shifts the human challenge from execution to ideation. She showcases personal experiments like an AI-generated website from her public thoughts, a tool visualizing historical geniuses, and custom productivity aids. Bouffard highlights AI's role as a "rocket ship for the mind," enabling rapid learning and software creation on demand.

Jul 15, 2026

Computer-Use 2.0: Agents Just Got Multi-Cursor — Francesco Bonacci, Cua

This talk introduces cua driver, an open-source tool enabling AI agents to interact with computer GUIs in the background across macOS, Windows, and Linux by leveraging accessibility APIs. It details CUABench, a robust evaluation framework with over 130 verifiable tasks designed to benchmark and ensure the trustworthiness of computer-using agents, revealing current limitations in complex tasks like circuit design. Finally, it presents cua fleet, an infrastructure solution that optimizes GPU utilization and reduces costs for RL training of these agents by dynamically managing sandbox environments.