Oct 22, 2026

Token Maxxing Is Over and Here Is What Comes Next

Pepijn Senders, a principal engineer and former AI enablement lead at HelloFresh, explains how his team rebuilt a banking platform in six months with about 800,000 lines of code, mostly written by Sonnet, and why choosing a model that keeps developers involved matters more than raw autonomy. He covers measuring AI ROI through token spend versus output, keeping humans in control of code, the risks of agent recommendations, team refinement sessions, prompt word effects, and a meta harness built on Claude Code.

Oct 01, 2026

The Caveman Prompting Challenge

James Barney, Head of Forward Labs at MetLife, discusses the operational challenges and strategic considerations for AI enablement in a large enterprise. Key topics include FinOps approaches to model selection based on cost, speed, and accuracy, optimizing LLM token usage through 'caveman prompting' and the proposed 'CaveBench' benchmark, and navigating volatile model release pricing. The conversation delves into measuring AI's value beyond cost, differentiating R&D from production token spend, and establishing robust AI governance using policy as code. A significant shift from traditional UIs to chat-based interfaces is explored, alongside the critical 'latent shopper problem' – the challenge of serendipitous data discovery in an AI-driven, query-focused world.

Jun 28, 2026

Building an Autonomous Engineering Org - Angie Jones, Agentic AI Foundation

Angie Jones details Block's journey in transforming its engineering organization into an autonomous one using AI agents. She outlines a five-stage AI maturity model, the strategic implementation of an 'AI Champions' program, and the technical steps taken to make repositories AI-ready and enable seamless agent delegation directly within developer workflows. The talk covers challenges in scaling multi-agent parallelism, including code review bottlenecks and resource management, and the development of an orchestrator ('Builder Bot') and 'Company World Model' to achieve full autonomy. It concludes with a critical reflection on the ethical implications of achieving such high levels of AI-driven automation.