In a deep dive into transformer mechanics, a Hacker News contributor explores how mechanistic interpretability (MI) reveals the inner workings of models like GPT. MI, akin to reverse-engineering software, aims to demystify how transformers process information. The analysis focuses on key components: the residual stream, attention, circuits, and induction heads.

These elements form the 'circuits' that govern model behavior, offering insights into alignment challenges for AI safety.