Sep 23, 2026

Skill issue: stop deploying vision language models, use them with Skills — Merve Noyan, Hugging Face

Merve Noyan discusses the pitfalls of directly using large Vision Language Models (VLMs) at runtime due to performance limitations and restrictive licensing. She introduces "Web Vision," a toolkit that enables efficient training of smaller, task-specific computer vision models using a novel "vibe training" methodology. This approach leverages VLMs for initial labeling and judgment, followed by training efficient models like RF-DETR, all while adhering to Apache 2.0 licenses and costing only a few dollars.

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 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.

Sep 18, 2026

The Next Breakthroughs in AI with Yossi Matias | The Google Research Podcast

Yossi Matias, VP and GM of Google Research, discusses how Google prototypes the future by inventing it, emphasizing the current "Golden Age of Research." Key themes include the seamless integration of AI through ambient intelligence and adaptive Generative UI, architectural advancements that push beyond scaling laws with techniques like Speculative Decoding, and multi-agent AI systems such as AI Co-Scientist and Empirical Research Assistance for accelerating scientific discovery. He also details Google Earth AI for crisis resilience and planetary intelligence, breakthroughs in healthcare diagnostics with MedGemma and the Google Health app, and milestones in quantum computing with the Willow chip, ultimately envisioning a future where AI amplifies human ingenuity and solves humanity's biggest challenges.

Sep 17, 2026

Databricks’ Ali Ghodsi Never Wanted to Be CEO. Now He’s Among the Best

Ali Ghodsi, CEO of Databricks, shares his unconventional rise to leadership, his unique CEO playbook centered on extreme focus on bottlenecks, and how he built Databricks' commercial engine. He details the strategy against Snowflake, the controversial creation of the Lakehouse category, and his views on conflict-averse leadership. Ghodsi also discusses AI's impact on enterprise and organizational design, offering insights from his personal coding experience and operating system.

Sep 16, 2026

If we want them to do Knowledge Work, design them as Knowledge Agents — Benjamin Clavié, Mixedbread

Benjamin Clavié argues that the industry has mistakenly generalized from the "easy case" of coding agents to the much harder domain of general knowledge work. He highlights that code offers durable, explicit cues and narrow tasks, while knowledge work involves implicit meaning, contextual understanding, and agents needing to reconstruct intent. Clavié traces human solutions through "tool loops" and "organization loops," demonstrating how optimized tools and hierarchical agentic structures (like searcher agents generating memos) are essential to overcome performance ceilings and significantly bridge the "oracle gap" between AI and human knowledge workers.