Sep 07, 2026

Self-Improving Harnesses, Local Personal AI And YC's Agent For Work | YC Paper Club

A deep dive into the evolving world of LLM harnesses, demonstrating how they've transformed from simple prompt engineering into sophisticated self-improving systems. The discussion covers architectural advancements (e.g., Prime Agent's context as cache, Turing vs. Von Neumann), practical applications (auto-researchers, on-device personal AI like OpenJarvis, YC's QM for work), and the challenges and future directions of agentic AI. It emphasizes that harness innovation is key to unlocking significant performance gains and enabling LLMs to operate autonomously and adaptively.

Sep 04, 2026

Faster Chips That Don't Melt — Anima Anandkumar & Benedikt Jenik, Accelerated Understanding

Accelerated Understanding is building a foundation model for the physical world, leveraging neural operators, resolution invariance, and physics-informed self-improvement to model diverse physical systems, aiming for universal application across domains like semiconductors and energy.

Aug 27, 2026

Invited Research Talk: Measuring Generalization in EEG Foundation Models

This talk presents a multi-dimensional evaluation and interpretability framework for EEG foundation models. It reveals that current models often fail to outperform supervised baselines for BCI tasks, lack robustness to sparse channels, and exhibit an aperiodic low-frequency bias, making them better at capturing subject-specific rather than task-specific information. The analysis highlights critical deficiencies and suggests future directions for pre-training objectives and data collection to improve generalization.

Aug 12, 2026

Chelsea Finn: This is the State of the Art in Robotics

Chelsea Finn, co-founder of Physical Intelligence, discusses the path to building general-purpose robots that operate reliably in the real world. She details how advanced reinforcement learning with human intervention, coupled with multi-scale memory systems, significantly boosts robot autonomy and throughput. Finn argues that robotics is entering its "GPT era," moving from specialized models to powerful, out-of-the-box foundation models like their PIO7, which demonstrates strong compositional generalization across tasks, objects, and robot platforms, matching or exceeding specialist performance without fine-tuning. The talk also covers the unique challenges and opportunities in robotics data, model deployment, and career paths.

Aug 03, 2026

Waymo Co-CEO Dmitri Dolgov: The Demo Is Only 1% Of The Work

Waymo co-CEO Dmitri Dolgov outlines seven crucial lessons from fifteen years of developing and scaling the Waymo Driver, the world's most advanced physical AI. He details the unique challenges of physical AI compared to digital, emphasizing the critical role of reliability, strategic technology choices, continuous innovation through foundation models, structure-augmented learning, high-fidelity simulation, AI flywheels, and robust evaluation frameworks to achieve superhuman safety and build trust in real-world autonomous systems.

Jul 28, 2026

The Next Frontier of AI Is Spatial Intelligence | Fei-Fei Li on a16z

Fei-Fei Li and Yunzhu Li discuss World Labs' acquisition of SceniX, focusing on building "spatial intelligence" and "large world models" to enable robots to understand and interact with the physical world. They elaborate on SceniX's "real-to-sim-to-real" pipeline, emphasizing how simulation, coupled with generative models like Marble, addresses the data bottleneck in robotics by providing consistent, scalable, and efficient training and evaluation environments. The conversation covers the role of counterfactual reasoning, the development of robotics foundation models, and the strategic focus on semi-structured environments for pragmatic, reliable robot deployment.