ai-guide

Transforms hierarchically-organized markdown content files into a fully-rendered static documentation site using VuePress 1.9.10 as the build engine. The system implements a three-tier architecture separating content (markdown in AI/ and Vibe Coding directories), configuration (modular TypeScript in .vuepress/), and build automation (GitHub Actions + JavaScript scripts). VuePress processes markdown through a Vue-powered SSG pipeline, generating HTML with client-side hydration for interactive components.

Organizes markdown content into two parallel directory hierarchies (Vibe Coding 零基础教程/ and AI/) that map to distinct user personas and learning objectives. The system uses TypeScript sidebar configuration (.vuepress/sidebar.ts) to generate navigation trees that expose different content sequences to different audiences. Each path has its own progression model: Vibe Coding uses 6-stage progression for beginners; AI path segments into DeepSeek documentation, application scenarios, project tutorials, and industry news.

Aggregates tutorials and best practices for popular AI development tools (Cursor, Claude Code, TRAE, Lovable, Copilot) into a searchable reference organized by tool and use case. The system uses markdown files documenting tool features, integration patterns, and productivity tips, with cross-references to relevant AI concepts and project tutorials. Content includes screenshots, keyboard shortcuts, and workflow examples showing how to use each tool effectively. The architecture treats each tool as a first-class entity with dedicated documentation, enabling users to compare tools and find the best fit for their workflow.

Provides structured tutorials for integrating AI capabilities into applications using popular frameworks (Spring AI, LangChain) with code examples, architecture patterns, and best practices. The system uses markdown files with embedded code snippets showing how to implement common patterns (RAG, agents, tool calling) in each framework. Content is organized by framework and pattern, with cross-references to concept documentation and project tutorials. The architecture treats each framework as a distinct integration path, enabling users to choose the framework matching their tech stack.

Provides guidance on building and monetizing AI products, including business models, pricing strategies, go-to-market approaches, and case studies. The system uses markdown files documenting different monetization models (SaaS subscriptions, API usage-based pricing, freemium + premium tiers) with examples of successful AI products. Content includes financial projections, customer acquisition strategies, and common pitfalls to avoid. The architecture treats monetization as a distinct knowledge domain separate from technical tutorials, enabling non-technical founders to learn business strategy alongside developers learning technical implementation.

Injects interactive widgets (QR codes, call-to-action buttons, partner service links) into the page sidebar and footer via .vuepress/extraSideBar.ts and .vuepress/footer.ts configuration modules. The system uses Vue component rendering to display engagement elements (WeChat QR codes, Discord links, course enrollment buttons) alongside content, creating conversion funnels that direct users from free content to paid courses, community channels, and external services. Widgets are configured as TypeScript arrays and rendered by custom theme components (Page.vue).

Orchestrates content updates and site deployment through GitHub Actions workflows that trigger on repository changes. The system includes JavaScript build scripts that process markdown, generate navigation metadata, and invoke VuePress compilation. GitHub Actions workflows automate the full pipeline: detect content changes, run build scripts, generate static assets, and deploy to production (https://ai.codefather.cn). The architecture separates content generation scripts (JavaScript in root) from deployment configuration (GitHub Actions YAML workflows).

Curates and organizes tutorials for multiple AI models (DeepSeek, GPT, Gemini, Claude) and frameworks (LangChain, Spring AI) into a searchable knowledge base. The system uses markdown content organized by tool/model in the AI/ directory, with cross-referenced links enabling users to compare approaches across models. Content includes usage examples, API integration patterns, and best practices for each tool. The architecture treats each AI tool as a first-class content entity with its own documentation section, rather than scattering tool-specific content throughout generic tutorials.

Implements a structured programming course (Vibe Coding 零基础教程/) designed for absolute beginners with no prior coding experience. The curriculum is organized into 6 sequential stages that build foundational skills progressively, with each stage unlocking prerequisites for the next. Content is separated from the AI Knowledge Base in its own directory hierarchy, allowing beginners to follow a linear learning path without encountering advanced AI concepts. The progression model uses markdown files organized by stage, with clear prerequisites and learning objectives for each section.

Maintains a time-indexed archive of AI industry news and developments (AI/AI 行业资讯/) organized by month, enabling users to track the evolution of AI trends over time. The system uses markdown files with publication dates and topic tags to organize news items, with VuePress generating a searchable, chronologically-sortable archive. Content includes announcements of new models, framework releases, industry partnerships, and significant research developments. The architecture treats news as a distinct content type with its own directory, separate from tutorials and reference documentation.

Aggregates prompt engineering techniques, prompt templates, and best practices (Prompt 提示词大全) into a searchable reference organized by use case and technique type. The system uses markdown files with tagged examples and explanations of prompt patterns (chain-of-thought, few-shot learning, role-playing, etc.), enabling users to find relevant prompts for their specific problem. Content is cross-referenced with AI model documentation, showing which prompts work best with which models. The architecture treats prompts as reusable knowledge artifacts with metadata enabling discovery and comparison.

Maintains a structured encyclopedia of AI concepts (Agent Skills, RAG, MCP, A2A) organized by topic with cross-referenced links enabling users to understand relationships between concepts. The system uses markdown files with clear definitions, architectural diagrams, and links to related concepts, creating a knowledge graph where each concept page links to prerequisites and dependent concepts. Content is organized hierarchically (e.g., RAG → Vector Databases → Embeddings) with each level building on previous knowledge. The architecture treats concepts as first-class entities with dedicated pages rather than scattered throughout tutorials.

Curates step-by-step tutorials for building complete AI projects (AI/AI 项目教程/) with working code examples, architecture diagrams, and deployment instructions. Each project tutorial includes setup instructions, implementation walkthrough, common pitfalls, and extension ideas. The system uses markdown files with embedded code blocks and links to GitHub repositories containing full project source code. Content is organized by project complexity (beginner, intermediate, advanced) and domain (chatbots, RAG systems, agents, etc.), enabling users to find projects matching their skill level and interests.