Posts

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

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.

The State of Frontier Post-Training Recipes | Conversation with Finbarr Timbers

The State of Frontier Post-Training Recipes | Conversation with Finbarr Timbers

This discussion with Finbarr Timbers reviews the evolution of frontier post-training recipes, highlighting the shift from simpler SFT-DPO-RL to complex multi-teacher on-policy distillation (MOPD). It covers the organizational challenges of building models like Olmo, the rise of synthetic data and reasoning-focused RL in DeepSeek, and the complexities of integrating expert teachers, while also exploring open questions on environments, specialized APIs, and career strategies in the rapidly changing AI landscape.

You Might Not Need 50 Diffusion Steps — Ziv Ilan, Nvidia

You Might Not Need 50 Diffusion Steps — Ziv Ilan, Nvidia

Ziv Ilan from NVIDIA details how latency in video diffusion models can be drastically reduced to achieve real-time generation. He presents a layered approach combining dynamic quantization for memory and speed, chunk-based caching to skip redundant denoising computations, and, most critically, step distillation—training models to achieve high-quality output in significantly fewer steps. These techniques, packaged in the open-source FastGen repository, offer additive performance gains, enabling real-time video on a single Blackwell B200 GPU.

Simulating Humans at Scale: Simile's Joon Sung Park

Simulating Humans at Scale: Simile's Joon Sung Park

Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, explains how Simile is building the "GPU of intelligence" to simulate human society, diverging from frontier models that act as the "CPU of intelligence." He details Simile's approach of grounding simulations with real human behavioral data, its diverse corporate applications, and its long-term vision to create a "CERN of human society" to solve fundamental societal challenges.

Context Engineering for Coding Agents

Context Engineering for Coding Agents

A deep dive into advanced engineering techniques for coding agents, focusing on effective context management in LLMs like Claude. The talk introduces a practical framework using a brain-inspired analogy, proposing a Markdown-based 'wiki' as a long-term memory system to augment the agent's limited context window. This approach is demonstrated through a real-world challenge of extracting structured data from technical drawings.

Cloud, Containers & Security • Adrian Mouat, Kief Morris & Sam Newman • GOTO 2025

Cloud, Containers & Security • Adrian Mouat, Kief Morris & Sam Newman • GOTO 2025

In this panel discussion, experts Adrian Mouat, Kief Morris, and Sam Newman delve into the current landscape of cloud technology, container security, and infrastructure automation. They cover key topics such as supply chain security with Sigstore and SBOMs, the practical impact of AI on deterministic systems, the ongoing debate about cloud repatriation, and advanced Infrastructure as Code practices like TDD and managing configuration drift.