andrej-karpathy

The build-from-scratch mindset for neural networks involves manually implementing core algorithms to deepen understanding and expose micro-details. This approach helps engineers construct neural networks from first principles rather than relying on abstracted frameworks.

Designing LLM-enabled workflows with Vibe Coding involves applying strategies for AI-assisted coding using partial autonomy and human oversight. This framework uses the autonomy slider to balance AI assistance with human control in real-world projects.

Debugging deep learning pipelines using first principles requires applying Karpathy-inspired mental models to isolate architecture choices and training issues. This framework enforces a structured approach to identifying micro-details in your LLM training pipelines.

Jagged Intelligence in AI agent workflows describes the uneven performance capabilities of LLMs across different tasks. Understanding this mental model helps engineers design better evaluation strategies and anticipate failures in Software 3.0 adoption.

Yes, you can use these mental models for pedagogy and teaching AI concepts. The frameworks provide clear explanations of architecture choices and concepts like Anterograde Amnesia and The March of Nines to effectively communicate deep learning principles.

You should not use fully autonomous AI coding when tasks require high precision and human oversight. The autonomy slider framework helps balance partial autonomy, ensuring Vibe Coding remains effective without introducing unhandled Jagged Intelligence errors.