Sep 22, 2026

Why Education Has to Change

Ben Horowitz, Erik Torenberg, and Gagan Biyani introduce the Horowitz Andreessen Academy, a new educational model designed for the AI era. They argue that traditional education is ill-suited for a rapidly changing world, advocating for a project-based, "learn by doing" approach. The academy aims to cultivate "main character" builders through immersion in Silicon Valley's culture, strong peer networks, and direct engagement with real-world problems and companies, emphasizing the critical role of people skills and the value of learning from failure in an age where AI empowers unprecedented creation.

Sep 22, 2026

Why AI Makes You Worse at the Jobs It Can't Do (with Katie Malone)

Dr. Katie Malone, host of *Linear Digressions*, explains how AI enabled her to relaunch her podcast after a 5.5-year hiatus. She argues that people management and AI agent management are fundamentally the same skill, highlighting the importance of defining tasks, setting acceptance criteria, and 'trust but verify'. Malone discusses the implications of 'AI slop' and 'process slop' in organizations and describes how a "Claude Code in a trench coat" agent now produces her show. She also delves into fascinating data paradoxes, including Bainbridge's Ironies of Automation, Simpsons Paradox, and Benford's Law, emphasizing how human expertise can erode with increasing automation and the counter-intuitive patterns in natural data.

Sep 22, 2026

AI Agents Aren't the Revolution. They're the Catalyst!

Sam Anthony argues that AI agents are not the revolution themselves, but rather a powerful catalyst for profound, lasting changes in the surrounding technology ecosystem. This includes modernizing data infrastructure, standardizing system design, enabling seamless interoperability, democratizing technology and expertise, and shifting human thinking towards outcome-based problem-solving.

Sep 20, 2026

When Should AI Systems Use Super Agents?

Grant Miller explains the paradox of "super agents" in AI systems – their immense power comes with significant risks. He details concerns like privilege abuse and expanded attack surfaces, contrasting them with the benefits of unified organizational intelligence and workflow coordination. The solution proposed is an architecture of "agent swarms," guided by principles of least agency, isolation, observability, and human oversight, to build secure and scalable agentic systems.

Sep 19, 2026

Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards — Filip Makraduli

This talk introduces FlashNorm, an optimization for transformer RMS norm layers. It details how algebraic tricks like weight folding, deferred division, and dropped pre-normalization can significantly speed up inference by mitigating GPU inefficiencies. The speaker also shares critical insights from implementing parallel CUDA streams, including debugging a subtle race condition caused by implicit stream joins, and advocates for open inference engines for deploying kernel-level research.

Sep 19, 2026

The Frontier AI Inference Cloud for Agents — Byung-Gon (Gon) Chun, FriendliAI

Byung-Gon Chun from FriendliAI details how agentic inference fundamentally differs from traditional chat-based LLM usage, requiring a specialized infrastructure to optimize for end-to-end task completion rather than single request latency. He highlights the economic advantage of open-weight models for agentic workflows and explains FriendliAI's innovative inference stack, featuring prefix caching, advanced KV cache management, cache-aware routing, and agent-aware optimization, which together deliver significant speedups and cost savings for production AI agents.