Written by: Biteye core contributor Shouyi

*The full text is about 2300 words, expected reading time is 6 minutes

Feeding AI materials every day, but it forgets instantly; Token consumption is high, and the knowledge base ultimately becomes a "failed project"?

Andrej Karpathy @karpathy, former co-founder of OpenAI/Tesla AI director, has just provided the ultimate solution. On April 3rd, he posted a tweet that garnered over 17 million views and open-sourced the hardcore guide llm-wiki.

This guide, which has garnered over 5000 Stars, proposes: use large models to build personal knowledge bases, saying goodbye to "blindly burning Tokens" and allowing knowledge to act like a digital asset that "automatically accrues interest."

Today, I will break down this practical tutorial that even the big shots are using for you!

01 Why have you always failed in creating knowledge bases before?

Before starting to build, understand the two most common failure modes to avoid repeating past mistakes.

1. Traditional RAG (Retrieval-Augmented Generation)

The biggest pain point of this model is that it burns Tokens but is still "forgetful." When you throw tens of thousands of words from the cryptocurrency whitepaper or the latest AI papers at it, it struggles to read through, summarizing a compact version for you. As a result, the next week when you ask, "What's the difference between last week's project and today's competing product?", it only remembers that dry summary from before. Each invocation relies on fragmented retrieval, and knowledge does not form structured sedimentation, leading to excessive Token consumption.

2. Traditional Wiki (Manual Notes)

This model is characterized by pure manual creation: tagging, building links, organizing directories… Karpathy articulated, "The most tedious part of organizing knowledge is not reading and thinking, but 'accounting' (classification, formatting)." Humans get fatigued, while AI is always online. Previously, all this tedious work was handled by humans, resulting naturally in abandonment.

02 Logical Breakdown: The "Fully Automatic Pipeline" of LLM Wiki

The core of Karpathy's plan lies in identity replacement: you only need to act as the "material finder," handing all the dirty work to AI. This system consists of three logical levels:

First Level: Raw Material Library (Input Only)

Throw in deep research reports, long tweets, AI tutorials, podcast recordings you usually see. This is the absolute "single source of truth," and the large model can only view it, absolutely forbidden to modify.

Second Level: Wiki Core Area (AI Takes Full Control)

This area contains pure Markdown files. You don’t need to worry about formatting; AI will automatically distill the raw materials into "concept cards" and "competitive product comparison tables." You just need to read, while AI is responsible for writing and updating.

Third Level: SOP Rules (Your House Rules)

Write a CLAUDE.md or GPT.md configuration file, telling AI our rules. For example: "All cryptocurrency research reports must extract token economics and team background," "All AI tutorials must summarize 3 executable Prompt codes."

03 Practical Tutorial: From "Burning Tokens" to "Asset Appreciation" with Three Actions to Start the Pipeline

Action 1: Automatic Ingestion

Practical example: I threw in a 20,000-word in-depth report on Web3 and left a note saying "help me remember this."

AI Execution: The backend quickly reads through it, automatically generates Project A_ Research Notes.md, and conveniently updates your global Directory.md, even proactively adds this new project to your previously written Competitive Product Analysis.md. Read once, and everything is interconnected!

Action 2: Inquiry and "Knowledge Compounding"

Practical example: I casually asked, "Digest the last 5 articles I've saved about large model Prompt techniques and write a viral copy for Xiaohongshu." AI instantly retrieves the concentrated essence to help write it.

Knowledge Compounding: Karpathy emphasizes that good questions and good answers should not be left to gather dust in the chat window! If you think this copy summary is good, directly instruct AI: "Save this summary back to Wiki, create a new page called Prompt Universal Template.md." This is essentially "restaking" knowledge; the more you use it, the richer it becomes!

Action 3: Late Night Cleanup

Practical example: Before bed, I issue the command "Check on the knowledge base."

AI Execution: It scans globally like a robot vacuum. The next morning it reports to you: "Boss, a certain AI tool you saved last month is now charging, conflicting with your yesterday's saved 'Free Guide to Utilizing Services', do you need me to update it?"

04 Advanced Configuration: Obsidian + Large Model = Ultimate Plugin

In the past, dealing with long-term memory, you couldn't avoid complex vector databases, but this is too high a threshold for ordinary people. If local retrieval is not efficient, the experience can be extremely lackluster. Karpathy recommends the ultimate combination: Obsidian (local note-taking software) + large model.

Obsidian is like a code editor, while the large model is your outsourced programmer. Abandon complex databases; you only need two core files to drastically cut Token consumption:

index.md (Global Outline): records summaries and links for all pages. Before AI answers a question, it first scans the outline to accurately retrieve the corresponding notes, avoiding the need to reread hundreds of thousands of words every time. Token consumption is reduced by 90%!

log.md (Operation Log): records in chronological order what AI has done each day, which files it has modified, making it easy for you to "check up" anytime.

With Obsidian's one-click web clipping and global knowledge mapping, the knowledge base can also become visualized.

05 Summary: Start Your "Knowledge Accrual" Era

In the information explosion of 2026, whoever can sediment knowledge with the lowest friction cost will leverage the most with the least Tokens.

As Karpathy pointed out in this open-source project, it's not rigid code, but an "ideological document" written for AI. You only need to feed his guide link to your dedicated Agent, and you can enter winning mode effortlessly.

Let the knowledge base move, let the Tokens become unending, and make your shrimp no longer a breed that cannot be raised!

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