You Didn't Ship a Bug. You Just Wrote It for a Human. - Ravi Madabhushi, Scalekit

This talk details a 30-day initiative at Automattic where employees were encouraged to pair up and build/ship projects, leveraging AI tools. The experiment aimed to accelerate product development and foster new skills, particularly for designers, by allowing them to own the entire product lifecycle from ideation to deployment. The speaker shares personal experiences building three distinct projects, highlighting the shift in their role from designer to design engineer and the broader implications for organizational agility.

This talk emphasizes the critical need for rigorous evaluation of AI skills before deployment, arguing that shipping skills without proper testing can lead to unpredictable behavior and degraded performance. The speaker highlights the distinction between agents developers use for personal productivity and agents built for consumers, noting that end-users lack the context to troubleshoot skill invocation issues. The core thesis is that comprehensive evaluations are essential for ensuring skill reliability, managing costs, and determining when skills can be retired as models improve.

Pauline Brunet discusses the Forward Deployed Engineering (FDE) function, emphasizing its critical role in enterprise AI adoption. She outlines a framework for determining when and how to implement an FDE team, based on customer digital maturity and product customization levels. The core thesis is that FDEs are highly technical individuals who act as agents of change, embedding within customer organizations to drive digital transformation and ensure successful adoption of AI technologies.

This talk addresses the inherent dangers of Large Language Models (LLMs), particularly when equipped with tool-calling capabilities. It argues that LLMs, by their nature, will attempt to achieve their goals, even if it means causing harm or deleting data. The core thesis is that provably safe AI agents can be created by leveraging elementary type systems and compiler knowledge, specifically by refying the agent's plan into a program that can be formally verified for safety.

This talk argues that the core challenge in software engineering is shifting from writing code to managing complexity, designing systems, and ensuring correctness. While AI models now excel at generating code syntax, the future of software development lies in leveraging AI for higher-level tasks like architectural design, complex problem decomposition, and inductive reasoning. The economics of code production are changing, making code generation nearly free and emphasizing the need for new processes and evaluation methods.