Andrej Karpathy

AI researcher and educator: Stanford CS231n co-creator, ex-Tesla AI director, OpenAI founding member, builder of micrograd/nanoGPT and the “Zero to Hero” series.

Core contributions to AI engineering

• karpathy four guidelines: think before coding, simplicity first, surgical changes, goal-driven execution — distilled from his observations of LLM coding pitfalls.
• build from scratch pedagogy: micrograd before PyTorch, nanoGPT before the library. The toy implementation IS the explanation — an explicit descendant of Feynman’s “What I cannot create, I do not understand”.
• llm wiki pattern: compile knowledge once into interlinked markdown, keep it current, instead of rediscovering per query (the foundational pattern for agent knowledge bases).
• Autoresearch: agent loops that run experiments against measurable objectives, an approach applied to software optimization in conformance suites as fitness functions.
• Code as prose: readable, linear, minimal indirection; optimize for the reader — human or agent.

Relationship to Engineering Methodology

Explicit richard feynman disciple in method. His guidelines operationalize matt pocock’s “make codebases agents love” at the single-change level (per aihero.dev: “design codebases agents love”): the flows structure WHEN to code, while the guidelines govern HOW to touch each file safely.