Andrej Karpathy

Andrej Karpathy is a prominent AI researcher, educator, and entrepreneur. He is the author of the llm-knowledge-base pattern described in the “LLM Wiki” gist, which is the founding source of this knowledge base.

Background

Karpathy is known for his work in deep learning and computer vision. He was previously the Senior Director of AI at Tesla, where he led the Autopilot computer vision team. Before Tesla, he was a research scientist at OpenAI. He completed his PhD at Stanford University under Fei-Fei Li, focusing on convolutional neural networks for image and video understanding.

He is widely known for his educational contributions, including popular YouTube lectures on neural networks, his Stanford CS231n course, and pedagogical projects like nanoGPT and micrograd that make deep learning concepts accessible.

The LLM Wiki Pattern

In his “LLM Wiki” gist (April 2026), Karpathy proposed a pattern for using LLMs to build and maintain personal knowledge bases. The key insight is that instead of using rag to retrieve from raw documents at query time, the LLM should incrementally compile a persistent wiki — a structured, interlinked collection of markdown files where knowledge compounds over time (see compiled-wiki-vs-rag).

Karpathy describes his personal workflow as having the LLM agent open on one side and obsidian on the other — the LLM makes edits based on conversation, and he browses the results in real time using Obsidian’s graph view and linked pages. He likens this to programming: “Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase.”

He connects the pattern to vannevar-bush’s memex concept (1945), noting that Bush’s vision of a personal knowledge store with associative trails is closer to the LLM Wiki than to what the web became. The missing piece — who does the maintenance — is now handled by the LLM.

Key Contributions Referenced

• LLM Wiki gist — the founding pattern document for this knowledge base
• Practical workflow insights — ingest one source at a time, stay involved, review summaries
• Division of labor — human curates and thinks; LLM does all the bookkeeping
• Three-layer architecture — raw sources, wiki, schema (see wiki-schema)

Related Pages

• llm-knowledge-base
• compiled-wiki-vs-rag
• wiki-operations
• wiki-schema
• obsidian
• vannevar-bush
• memex
• karpathy-llm-wiki
• notebooklm
• notebooklm-vs-wiki
• rag
• knowledge-compounding
• notebooklm-vs-llm-wiki
• marp
• qmd