A discussion with the founders of Waddle Labs and RoboCurve on how general-purpose language models are beginning to control robots. The conversation covers the lineage from RT-2 and code-as-policies to in-context learning, harness design, skill consolidation, Platonic representations, and the path toward general-purpose robots.
Raghav Saboo, Staff ML Engineer at DoorDash, argues that semantic understanding, not engagement, is the real bottleneck in marketplace discovery. He walks through four LLM-driven primitives (graded relevance supervision, semantic IDs, multi-timescale consumer memory, and steerable personalized collections) and explains the core pattern of reasoning offline with LLMs and distilling that into fast serving models.
An exploration of the recent wave of AI innovations, focusing on the pervasive theme of efficiency. The episode delves into cost-reduction in new frontier models like Anthropic's Claude Opus 5.5 and OpenAI's GPT-6, the emergence of TypeSafe AI's "System One" models like Jev for high-speed, calibrated decision-making, and the groundbreaking open-source Lunar Foundation Model by NASA and IBM, showcasing AI's diverse applications beyond text generation.
Will Briggs and Todd L. Montgomery discuss the impact of AI on programming careers and language learning, the pedagogical approach of Briggs' book "C++26 for Lazy Programmers," and the evolving landscape of C++. They explore how AI requires deeper code fluency, Briggs' method for teaching complex C++ concepts like pointers, and his critiques of C++'s feature proliferation. The conversation concludes with a strong argument for C++'s enduring relevance as a foundational language for aspiring developers.
Anastasis Germanidis, co-founder of Runway, discusses the company's journey from early generative creative tools to frontier video models, emphasizing their pivot towards general-purpose world simulation. He details the evolution of Gen-1, Gen-2, and Gen-3 (scaling 10x after Sora's release), highlighting the importance of controllability and the inevitability of real-time generation. The conversation delves into cutting-edge applications like Interface World Models (a fully neural operating system) and World Models for Robotics, where third-person video data and robust simulation are key to training intelligent agents.
Claire Vo argues that AI has rendered traditional product roadmaps obsolete. With AI accelerating execution, the bottleneck has shifted from building capacity to generating meaningful ideas and fostering strong conviction. She outlines three traps (backlog, parity, churn) created by combining AI with old roadmaps, and proposes a new approach centered on durable convictions, disposable features, and a "roadmap of ambition" driven by evidence and bold experimentation rather than fixed feature lists.