Aug 11, 2026

How Companies Are Building Their Own Intelligence | Sonya Huang, Sequoia Capital

Sonya Huang of Sequoia Capital discusses the strategic imperative for companies to embrace "sovereign AI" by owning their AI models and weights. She identifies cost, speed, performance, and controlling destiny as the four driving forces behind this shift. Huang argues that the competitive landscape is moving towards owning the intelligence layer, positioning application companies as the new innovation labs. She provides a practical, opinionated framework covering strategy (what to own vs. rent), team building, ensuring external legibility of research, and a technical roadmap for implementation, emphasizing how open-weight models now enable frontier-level performance through ownership and customization.

Aug 10, 2026

Why Deep Networks Don’t Need to Memorize Everything — Matthieu Wyart

Matthieu Wyart, a statistical physicist, argues that deep networks discover abstractions by recovering hidden data hierarchies, allowing them to escape the curse of dimensionality. He explains how this mechanism, combined with predicting latent representations instead of raw tokens, can significantly improve sample efficiency. The discussion also covers the physics of rough loss landscapes, machine creativity, diffusion models, and a theoretical framework for neural scaling laws.

Aug 10, 2026

Taking Reinforcement Learning Cross Datacenter — Nan Jiang, Modal

Nan Jiang introduces "Adam absorption" to revolutionize RL model synchronization. By exploiting finite precision serving and small Adam steps, less than 1% of served model weights actually change, allowing for 500MB patches instead of 500GB checkpoints. This enables a distributed "bulletin board" architecture, decoupling trainers from global rollout fleets and unlocking elastic, cross-region GPU capacity for RL.

Aug 10, 2026

Codex, Behind the Harness — Dominik Kundel, OpenAI

Once GPT 5.3 Codex Spark achieved 1000 tokens/sec on Cerebras, network latency superseded inference as the bottleneck for agents. This talk details how the Codex harness addresses this and other agentic challenges through innovations like WebSocket mode for stateful context, deferred tools for efficient context construction, robust sandboxing (Seatbelt, Bubblewrap, custom Windows solution), and an auto-review subagent to mitigate approval fatigue while ensuring security. It also covers structured actions via 'apply patch' for file edits, shell tools for system interaction, and sophisticated long-horizon goal management, with most distinct features exposed through the open Responses API.

Aug 10, 2026

5 Best Practices for Building AI Agent Skills

This video outlines five essential best practices for developing reliable, secure, and effective AI agent skills. It covers optimizing skill triggering through descriptive metadata, leveraging real-world domain expertise over generic LLM output, managing context windows efficiently by writing lean skills and using progressive disclosure, implementing deterministic logic with scripts for fragile operations, and critically vetting all skills for security vulnerabilities before deployment. These practices are crucial for professionals building robust agentic systems.

Aug 09, 2026

Velocity Sickness: What Happens When Your Whole Team Gets 10x Faster — Matt Dailey, Ref.

Matt Dailey introduces "velocity sickness" – the stress of increased AI output without impact. He proposes shifting from ephemeral chat-based agent interactions to durable, shared documents as the "decision layer" to separate planning from implementation, enabling teams to own their code and prioritize ideas effectively.