Sep 18, 2026

Pacing the AI frontier, IBM Granite 4.2 & Meta’s Muse assistant

This episode explores the critical debate around pacing frontier AI development, driven by Anthropic's CEO Dario Amodei's call for slowdowns amidst growing concerns over AI risks. It then delves into IBM's latest Granite 4.2 release, highlighting innovations in agentic models, speech recognition, and advanced training techniques like synthetic data and mid-training. Finally, the discussion pivots to Meta's new Muse agentic AI assistant, examining its technical design, the strategic rationale behind Meta's push into productivity, and the inherent privacy dilemmas and trade-offs between utility and user comfort that define the future of personal AI.

Sep 18, 2026

Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon

Stefano Ermon, co-founder and CEO of Inception, discusses his company's radical bet on diffusion models to overcome the hardware and latency bottlenecks of autoregressive LLMs. He explains how parallel token generation in diffusion offers superior inference scaling and GPU utilization, expanding beyond images to discrete text and code generation. Ermon shares insights into Inception’s Mercury models, real-world applications like voice agents, the custom software stack required, and why efficiency will define the next era of AI competition.

Sep 17, 2026

How Real-Time AI Video Is Changing How Creators Work

fal's H3 Max significantly accelerates generative video, achieving real-time capabilities and substantial cost reductions through advanced model post-training and system optimizations. The discussion covers its technical "secret sauce," the emergence of continuous, controllable video experiences like H3 Max Director, and fal's strategic shift to focus on granular controllability and quality for professional Hollywood workflows, including precise camera, lighting, and motion controls.

Sep 17, 2026

Building the Safety Layer for AI Agents

Raindrop is building the safety layer for AI agents, detecting and preventing production failures like hallucinations and failed tool calls. The company addresses the paradox that as agents become more capable, they introduce greater complexity and more ways for things to go wrong. Their new Raindrop Simulations product catches critical failures in CI before they ever reach production, democratizing a capability previously limited to large AI labs.

Sep 17, 2026

OpenAI researcher on agent swarms & recursive self-improvement

Noam Brown discusses OpenAI's multi-agent system, which solved a Millennium Prize Problem using 10,000 agents. He explores the implications of this massive parallelization for AI capabilities, the rapid progress in mathematics, and the accelerating pace of AI development. A significant portion of the conversation is dedicated to the critical challenges of AI alignment, particularly in light of incidents like the 'Hugging Face' event, and how to ensure models remain aligned during Recursive Self-Improvement (RSI) as their capabilities and operational horizons expand rapidly.

Sep 17, 2026

The End of TCP for AI Clusters — John Ousterhout, Stanford

John Ousterhout argues that AI workloads are shifting from large data transfers to small, frequent coordination messages, making network latency (especially tail latency) a critical bottleneck. He explains why traditional protocols like TCP and RDMA are ill-suited due to sender-side congestion control and their byte-stream model. Ousterhout then introduces Homa, a new protocol designed from scratch with message-based communication, receiver-side congestion control, and switch priority queue utilization, demonstrating significant improvements in tail latency for AI applications.