Jul 14, 2026

Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden

Katelyn Lesse and Angela Jiang, leaders of Anthropic's developer platform, outline their strategy built on a "three-layer cake": knowledge, execution, and coordination. They emphasize moving towards advanced "strategies" or meta-harnesses that assign distinct jobs to tokens, fostering a robust and open AI ecosystem. The discussion covers empowering builders, setting industry standards, and Anthropic's nuanced approach to an open platform versus a walled garden, focusing on architectural soundness over infrastructure ownership.

Jul 13, 2026

Why a Nation Can't Outsource Its Frontier AI - Alistair Pullen (Cosine AI)

Alistair Pullen, CEO of Cosine, discusses the UK's sovereign AI initiative, born from US export controls. He outlines Cosine's unique economic model, competing with "millions" against "billions" by licensing models instead of hosting inference. Pullen delves into why open models lag frontier systems, emphasizing active parameters and post-training data. He explains Cosine's innovative approach to "slop" through process-based RL and credit attribution, advocating for runtime proof in code review. The conversation covers their hierarchical "Swarm" sub-agent system, the challenges of memory, and advanced synthetic data generation, concluding on the geopolitical impact of export controls as an unexpected catalyst for UK AI.

Jul 13, 2026

The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO

Engram co-founder Dan Biderman discusses building AI that learns from users, critiquing long context, RAG, and compaction's limitations. He introduces Engram's approach of compressing knowledge into "cartridges" and model weights via continual learning and gradient-based updates, aiming for "intuition" over retrieval. The vision extends to personal, "Tamagotchi" AI models and addresses the critical need for token efficiency and "doing more with less" in both enterprise and personal AI.

Jul 13, 2026

Stop Evaluating Models Like It's the 50s - Alejandro Vidal, Mindmakers

This talk introduces the transformative potential of Item Response Theory (IRT), a psychometric model, for evaluating AI models. It addresses the limitations of current evaluation methods by demonstrating how IRT can precisely measure model intelligence, audit and optimize benchmarks, detect data leakage, implement adaptive testing, identify item bias, and even fingerprint models based on their error patterns, opening new avenues for understanding and improving LLM capabilities.

Jul 13, 2026

From fork() to Fleet: Designing an Agent Sandbox Cloud — Abhishek Bhardwaj, OpenAI

This talk by Abhishek Bhardwaj from OpenAI details the architectural considerations for building secure and scalable AI agent sandboxes in the cloud. It explores runtime isolation technologies (from basic process execution to containers, GVisor, and microVMs), emphasizing the superior security of hardware-virtualized microVMs. The speaker then highlights the critical need for persistent storage, outlining explicit (copy-on-write snapshots) and always-on (tiered block storage) solutions as the next major unlock for agent capabilities. Finally, it touches on orchestration challenges for fleet-level management, including low-latency sandbox creation and snapshot-driven scheduling.

Jul 13, 2026

The Prime Intellect Stack — Will Brown, Prime Intellect

Deep dive into Prime Intellect's open-source ecosystem for post-training LLMs, covering the modular Verifiers V1 environment design, the asynchronous and scalable Primer RL training framework, and the Lab platform for hosted training and fine-tuning. Learn about advanced reward systems, the interception server pattern, and tokenization control with the Renderers library, all designed to enable frontier agentic model development.