Jan 30, 2026

Moltbot (Clawdbot): Open-source agents go mainstream

The panel discusses the rise of Moltbot, a community-driven open-source AI agent, and the debate it sparks around vertical vs. horizontal integration and security. They analyze Anthropic CEO Dario Amodei's essay on AI's "adolescence," exploring the growing pains of the technology, the pace mismatch between innovation and safety, and the need for broader societal engagement. The conversation also covers IBM's GRAMMY IQ, an AI-powered fan experience, and the strategic implications of Microsoft's Maia 200 chip, signaling a shift toward vertical integration in the AI hardware space to challenge NVIDIA's dominance.

Jan 27, 2026

OpenAI Town Hall with Sam Altman

Sam Altman discusses the future of AI, covering the evolution of software engineering, the challenges for AI startups, the roadmap for model capabilities and costs, and the broader societal impacts on economics, security, and education.

Jan 25, 2026

If You Can't See Inside, How Do You Know It's THINKING? [Dr. Jeff Beck]

Dr. Jeff Beck explores the philosophical and technical definitions of agency, arguing that the distinction between an agent and an object lies in computational sophistication, particularly the capacity for planning and counterfactual reasoning. The conversation provides a deep dive into Energy-Based Models (EBMs), Yann LeCun's JEPA for learning in latent space, and a pragmatic approach to AI safety centered on inverse reinforcement learning rather than fears of rogue superintelligence.

Jan 15, 2026

How to Make AI Forget

Ben Luria, CEO of Hirundo, discusses the critical need for machine unlearning, framing it as a form of "AI neuro-surgery" for enterprise AI. He explains how this technique directly modifies model weights to remove unwanted data and behaviors, addressing core risks that superficial solutions like guardrails cannot solve.

Jan 12, 2026

Structured Dissent Patterns for Agentic Production Reliability

This talk introduces 'structured dissent,' a multi-agent orchestration pattern where believer, skeptic, and neutral agents debate decisions to overcome the 'confidently wrong' failure mode of single-agent LLM systems, improving reliability for high-stakes tasks like cybersecurity analysis.

Dec 20, 2025

Are AI Benchmarks Telling The Full Story? [SPONSORED]

AI models are often benchmarked like Formula 1 cars, excelling on technical exams but failing the test of daily human experience. Researchers Andrew Gordon and Nora Petrova from Prolific critique the 'leaderboard illusion' of current ranking systems and introduce their HUMAINE leaderboard, a new framework that uses census-based sampling and the TrueSkill algorithm to measure how helpful, safe, and relatable models are to real people, not just tech enthusiasts.