Farzapedia Core Philosophy: Build a personal Wikipedia that AI can understand—Karpathy calls out the most practical personalized solution

Amid the rapid development of AI agents, “personalized AI” is moving from slogans to real-world practice. Renowned AI scientist Andrej Karpathy has recently discussed Farzapedia again, and bluntly said that this “personal Wiki” architecture is one of the few solutions that is genuinely feasible in practice.

Unlike mainstream approaches that emphasize “the longer you use it, the more it understands you” with black-box memory, Farzapedia tries to convert personal data into structured knowledge—so the AI can directly read, understand, and operate it—building a more transparent and controllable foundation for personal AI.

Farzapedia’s core idea: create a personal Wikipedia that AI can understand

The key to Farzapedia lies in turning personal data into a set of clearly structured knowledge systems. Through Markdown documents, an index (index.md), and cross-links, users can build their own “Wikipedia.”

In this kind of architecture, AI agents no longer rely on vague contextual memory; instead, they can directly access specific documents, understand the relationships between different topics, and even perform cross-page citations and updates.

Karpathy especially emphasized that this approach allows users to clearly see what information the AI has, and also lets them review and correct it at any time—solving the problem that AI memory was not visible in the past.

Why is this approach more practical? Four key differences

Compared with existing AI personalization solutions, Farzapedia’s advantage does not come from a stronger model, but from a change in data structure.

First is “making it explicit.” All knowledge exists in Wiki form, so users can directly view and edit it, rather than relying on hidden internal memory.

Second is “data sovereignty.” All files are stored locally and are not limited to a single AI platform, avoiding the risk of data being locked in or unable to be migrated.

Third is the “File over App” design. Data exists in universal formats such as Markdown and images, so it can be used directly by various tools—from Obsidian to command-line tools that can be integrated.

Finally is “BYOAI.” Users can freely choose different AI models to connect to the same knowledge base, and even further fine-tune open-source models, enabling the AI to understand personal knowledge at the level of weights.

From RAG to Wiki: a shift in personal AI architecture

Karpathy pointed out that over the past year, the popular RAG (Retrieval-Augmented Generation) has improved information retrieval capabilities, but most still remain at the level of “extending chat logs.”

In essence, this is still fuzzy search: it lacks a clear structure, and it is difficult to maintain and expand.

By contrast, Farzapedia adopts a clear file structure and internal linking, allowing the AI to understand content the way it reads documents, and to establish logical relationships between different topics—significantly improving usefulness and accuracy.

A new skill in the agent era: managing knowledge, not just asking questions

Karpathy also admitted that the barrier to this approach is that it requires a certain level of file management and structural design ability.

But as AI agents advance, these tasks are being automated. Agents can help organize data, generate articles, and maintain links—so users can focus on the content itself.

He believes that “using agents well” will become a key capability. These tools can not only understand language, but also operate computer systems, and they are changing the way people interact with software.

This article Farzapedia core idea: create a personal Wikipedia that AI can understand, Karpathy highlights the most practical personalization solution First appeared in Chain News ABMedia.