Agentic ai

Where AI products go next: voice, agents, and self-driving software | Tara Sesha and Nan Yu (OpenAI)

Where AI products go next: voice, agents, and self-driving software | Tara Sesha and Nan Yu (OpenAI)

A live talk from the Lenny and Friends Summit with OpenAI's Tara Seshan and Nan Yu and host Claire Vo on building AI products when both technology and user expectations change rapidly. Topics include shipping imperfect features (the ChatGPT "toggle"), the 2 to 3 month model-capability horizon, enterprise absorption limits, single versus multi-agent design, platform layering with computer use as a fallback, working with research through evals, direct user relationships, and predictions for voice, proactive agents, and hardware.

I Gave an AI a Body — Cyrus Clarke, MIT Media Lab

I Gave an AI a Body — Cyrus Clarke, MIT Media Lab

Cyrus Clarke, a researcher at the MIT Media Lab, connected an OpenClaw agent to a 900-pin shape display and asked it to discover its own identity rather than complete tasks. The talk covers the agent's first behaviors, the public reaction, the philosophical influences behind his approach to AI embodiment, and a closed-loop system that taught the agent a gesture-based body language that now responds faster than its language model.

One Operator, Many Drones: Inside Skydio's Autonomy Stack — Suchet Bargoti, Skydio

One Operator, Many Drones: Inside Skydio's Autonomy Stack — Suchet Bargoti, Skydio

Suchet Bargoti, Skydio's Director of Inspection and Mapping, demos a fleet of docked drones orchestrated through an agentic layer, then explains how full-stack autonomy, maps as world models, edge-cloud splitting, VLM-driven tracking and the limits of end-to-end learning combine to make drones function as infrastructure.

AI Agents Aren't the Revolution. They're the Catalyst!

AI Agents Aren't the Revolution. They're the Catalyst!

Sam Anthony argues that AI agents are not the revolution themselves, but rather a powerful catalyst for profound, lasting changes in the surrounding technology ecosystem. This includes modernizing data infrastructure, standardizing system design, enabling seamless interoperability, democratizing technology and expertise, and shifting human thinking towards outcome-based problem-solving.

When Should AI Systems Use Super Agents?

When Should AI Systems Use Super Agents?

Grant Miller explains the paradox of "super agents" in AI systems – their immense power comes with significant risks. He details concerns like privilege abuse and expanded attack surfaces, contrasting them with the benefits of unified organizational intelligence and workflow coordination. The solution proposed is an architecture of "agent swarms," guided by principles of least agency, isolation, observability, and human oversight, to build secure and scalable agentic systems.

Pacing the AI frontier, IBM Granite 4.2 & Meta’s Muse assistant

Pacing the AI frontier, IBM Granite 4.2 & Meta’s Muse assistant

This episode explores the critical debate around pacing frontier AI development, driven by Anthropic's CEO Dario Amodei's call for slowdowns amidst growing concerns over AI risks. It then delves into IBM's latest Granite 4.2 release, highlighting innovations in agentic models, speech recognition, and advanced training techniques like synthetic data and mid-training. Finally, the discussion pivots to Meta's new Muse agentic AI assistant, examining its technical design, the strategic rationale behind Meta's push into productivity, and the inherent privacy dilemmas and trade-offs between utility and user comfort that define the future of personal AI.