Karpathy Wiki
Andrej Karpathy is a Slovak-Canadian AI researcher whose work spans deep learning, computer vision, and AI education.[^c1] He was a founding member of OpenAI, served as Director of AI at Tesla leading the Autopilot Vision team, and in 2024 founded Eureka Labs, an AI education platform. His open-source projects — including nanoGPT, llm.c, and the LLM101n course — have become standard educational resources for understanding and building large language models.
Karpathy has been an influential voice in shaping how the AI community thinks about knowledge management and software development. His proposed LLM Wiki pattern reimagines personal knowledge management by having an LLM maintain a persistent, compounding wiki of Markdown files — a "compiler" approach that contrasts with traditional RAG's "interpreter" model of re-discovering knowledge from scratch on every query.[^c2] The concept sparked an ecosystem of community tools and implementations, and has since received academic formalization through a governance framework proposed at CODE University Berlin.[^c5]
He also coined the term "vibe coding" in February 2025, describing a programming paradigm where users build software through natural language prompts, accepting AI suggestions without deep scrutiny.[^c3] This idea is part of a broader framework he developed: beginning with Software 2.0 (neural networks as a new programming paradigm, 2017) and extending to Software 3.0, in which programming large language models using natural language prompts becomes a distinct new form of software development.[^c4] In April 2026, at the Sequoia AI Ascent conference, he introduced the concepts of jagged intelligence — the observation that AI capabilities are uneven, with models capable of refactoring 100,000 lines of code yet failing at basic common-sense tasks[^c7] — and agentic engineering, the professional discipline of coordinating AI agents while preserving human judgment.[^c6] He also introduced [[AI Claws]], persistent autonomous agents that operate as a new architectural layer in the AI stack,[^c10] and proposed that AI models should output HTML as a standard format for human-AI collaboration.[^c11]
In 2026, Karpathy also demonstrated the practical implications of these concepts through an experimental system called "autoresearch," in which an AI agent ran 700 autonomous experiments over two days, discovering optimizations that produced an 11% training speedup.[^c8] A panel at Sequoia AI Ascent 2026, featuring Karpathy alongside Anthropic's Boris Cherny and OpenAI's Greg Brockman, reached a consensus that traditional manual coding is functionally solved as a bottleneck — Cherny stating that Anthropic had "no more manually written code anywhere at the company"[^c9] — while disagreeing on whether organizational structure, human attention, or human understanding constitutes the new limiting factor.