Zero-One-Two-Three Knowledge Architect Monetization System

Problem

Personal knowledge often sits in IMA, Yuque, Feishu, Notion, local Markdown files, and email attachments. Summarization tools return short answers but may lose mechanisms, data, evidence levels, and context. Building paid notes, knowledge personas, or mini-agents still requires manual organization, encryption, pricing, and deployment.

How It Works

zero-one-two-three breaks the workflow into phases:

• Deep extraction: Reads PDF/image materials, decomposes them into outline, mechanism, data, and evidence-level dimensions, and outputs structured Markdown.
• Knowledge connection: Loads IMA, Yuque, Feishu, Notion, and local Markdown sources via LangChain, then chunks, embeds, and stores them in FAISS; supports a MiniLM multilingual model with TF-IDF fallback.
• Genesis and approval: Detects overlaps, conflicts, complements, and gaps, then generates a Gap_Review_List; AI proposes changes while users approve before ingestion.
• Asset protection and monetization: Can create .locked files with AES-256, split traffic-driving and paid versions, build a System Prompt, and deploy to WeChat mini-agents with free usage limits, paid unlock, and redemption codes.
• Persona enrichment: style_clone.py analyzes linguistic fingerprints such as sentence length, punctuation, emoji density, and formality; voice_clone.py generates Chinese audio via Microsoft Edge TTS.

Boundaries

It is not a general crawler and does not promise to bypass paywalls, guarantee WeChat review, or ensure 100% factual accuracy. AI output still needs human review; lost AES-256 passwords cannot be recovered; the tool is command-line and chat-driven rather than a Web GUI.