Sep 19, 2026

Routing LLM Inference in Production: From Engine Signals to Policy — Qianru Lao & Lu Zhang, OpenAI

This talk outlines OpenAI's journey in building a robust inference load balancer. It details the transition from an initial feedback-loop-driven system, which suffered from explainability issues and disruptive oscillations, to a sophisticated control plane/data plane architecture. The new system leverages a global view to optimize end-to-end latency across all traffic, balancing network distance with engine-side queuing, and includes critical protection mechanisms like outlier penalties, dynamic retry budgets, and load shedding to ensure stability in production.

Jul 29, 2026

Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil

Jerry Tworek and Rohan Anil, founders of Core Automation, argue that the transformer architecture has reached its limits and the primary bottleneck to smarter AI systems is now architectural. They contend that current models lack continual learning and test-time adaptation capabilities, critical for real-world deployment. They advocate for new architectures that learn from experience more efficiently than current reinforcement learning, optimize pre-training and RL end-to-end, and build a highly automated lab focused on accelerating innovation through kernel generation, aiming for models that can improve themselves without human intervention.

Apr 20, 2026

Can we AI our way to a more sustainable world?

Microsoft experts Doug Burger, Amy Luers, and Ishai Menache discuss the dual role of AI in sustainability. They analyze the environmental footprint of datacenters and explore how AI-driven optimization and materials discovery can be pivotal in decarbonizing global systems like energy, industry, and food production.

Dec 09, 2025

Efficient Reinforcement Learning – Rhythm Garg & Linden Li, Applied Compute

A deep dive into the challenges and solutions for efficient Reinforcement Learning (RL) in enterprise settings. The talk contrasts synchronous and asynchronous RL, explains the critical trade-off of "staleness" versus stability, and details a first-principles system model used to optimize GPU allocation for maximum throughput.

Nov 18, 2025

I’m Teaching AI Self-Improvement Techniques

Aman Khan from Arize discusses the challenges of building reliable AI agents and introduces a novel technique called "metaprompting". This method uses continuous, natural language feedback to optimize an agent's system prompt, effectively training its "memory" or context, leading to significant performance gains even for smaller models.

Aug 01, 2025

Solving AI Video: How Fal.ai is making AI Video Generation Fatser & Easier

Fal co-founder Burkay Gur and head of engineering Batuhan Taskaya discuss their journey building a high-performance generative media cloud. They cover their strategic pivot to media models, core optimization principles born from early GPU scarcity, and the development of a customer-obsessed culture to navigate the fast-paced AI model landscape.