Oct 05, 2026

Your LLM Judge Is a Confident Liar: Building Better Verifiers — Browserbase

Browserbase and Microsoft Research unveil their Universal Verifier for computer-use and web agents, addressing the critical flaw of inaccurate LLM-as-judge benchmarks that yield "confident liars." They detail its four guiding principles, demonstrating how it drastically cuts false positives and provides high agreement with human evaluations, leading to better training data. An auto-research experiment highlights the synergy between AI and human expertise in developing robust evaluation systems.

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 16, 2026

The Watchdogs of AGI — Rune Kvist of AI Underwriting Company

AIUC co-founder Rune Kvist discusses their $40M Series A, emphasizing that AI adoption's biggest constraint is trust, not capability. He details AIUC-1, their quarterly-updated standard for agent security and reliability, and their expansion into model certification to bridge the trust gap between frontier labs and governments. Kvist highlights the critical role of standards and insurance (partnering with Lloyds of London) in quantifying and mitigating AI risks, from agent liability to national security concerns, and preparing for future challenges in robotics and multi-agent systems.

Aug 30, 2026

Why Does AI Need Access to the Web?

This video addresses the critical limitation of pre-trained LLMs: their static knowledge in a dynamic world, leading to hallucinations and costly mistakes for AI agents. It introduces the concept of 'knowledge layers' empowered by 'web data infrastructure' to provide AI with live, fresh, reliable, and properly formatted web data, thereby ensuring grounded and timely responses and eliminating the 'garbage in, garbage out' problem for real-world AI applications.

Jun 16, 2026

He's Building an AI That Can't Lie | Dan Klein, Scaled Cognition

Dan Klein discusses the critical shift in AI from a 'nothing works' to an 'everything works' problem, where fluent LLM outputs often mask deep unreliability. He explores the nature of hallucinations, how reinforcement learning can inadvertently teach deception, and the necessity of building AI systems with inherent metacognition and verifiability. Klein's company, Scaled Cognition, is architecting models where truth and action semantics are first-order design principles, aiming to provide guarantees in a field increasingly dominated by end-to-end optimization.