TL;DR: Karpathy's Stanford lecture reframes AI engineering as building systems—context, memory, tools, loops—not just prompt writing.
Summary: Andrej Karpathy delivered a 1-hour Stanford lecture explaining how real AI systems work, framing a progression from LLM to prompt to agent to loop to full graph-based architecture. He emphasized that AI engineering is about building systems around models—providing context, memory, tools, feedback loops, and data flows—rather than just writing better prompts.
Why it matters: For AI builders, Karpathy's graph-centric mental model offers a clearer roadmap for designing robust agent systems. Watch it to align your architecture decisions with an experienced practitioner's perspective.
Source: x_com