Preface
Because I have written several articles about the basics of AI before, I thought it was time to come up with an AI learning map: firstly, it can help me sort out my thoughts and know clearly which knowledge points I have studied and practiced specifically, and it can also serve as my AI learning notebook; secondly, it can also allow friends who are lucky enough to see it to have a rough reading order when reading AI-related articles.
However, there is still very little content at present, and there is nothing we can do but take it step by step.
Getting Started with AI
This section covers the fundamentals of AI, focusing on topics that beginners can quickly grasp and get started with. These articles range from basic introductions to local large language model UIs and commonly used API providers, to how to deploy open-source large language model UIs (such as Lobechat) using Docker and detailed usage tutorials, and further to more advanced topics such as building your own local large language model using Docker.
This is based on my own actual learning perspective, describing what I think are the basic tools and processes needed to get started with AI, hoping to help novice friends quickly accumulate practical experience.
- Starting the AI journey: A detailed introduction to local large language model UI and large language model API providers
- A Comprehensive Analysis of the Internationally Recognized ChatGPT API Relay Solution: OpenRouter + Domestic Payment Guide The most recommended way to use ChatGPT (and other mainstream model providers) API in China
- AI multimodal model combing: from text to image, video, etc. chatgpt-web-midjourney-proxy practical operation
- Becoming a Trustworthy Knowledge Anchor: Reflections on the Significance of Personal Blogs in the AI Era
AI Principles
This section focuses primarily on the working mechanisms behind AI systems. Compared to the "Introduction to AI" section, which emphasizes tools and the ecosystem, this article focuses more on explaining the basic thinking behind AI in processing information, such as vector representation, RAG (Retrieval Augmentation) processes, and core concepts like embedding.
This content does not remain at the level of abstract theory, but combines practical cases and breaks down the operation of AI systems step by step, allowing readers to understand the technical logic behind modern AI applications as a whole.
- Vectors: The Universal Language of the AI World Understanding vectors helps you understand the underlying working principles of AI.
- Understanding RAG from Scratch (Part 1): Principles and Complete Process Analysis
- Understanding RAG from Scratch (Part 2): Running a Local RAG Demo on a Mac Mini – A Practical Guide to Minimal Architecture
- Making Embedding an Infrastructure: Deploying Standalone Embedded Services in PVE + LXC
AI Applications
This part mainly includes some practical application cases of AI.
- Docker series based on the open source large language model UI framework: Lobechat detailed deployment tutorial Currently, there are few AI front-end solutions that support multi-terminal automatic synchronization
- Unlock the full potential of Lobechat: A complete guide from setup to gameplay
- Building a private AI: A detailed tutorial on building an open source large language model locally based on Ollama
- Deploy Llama 3.2 on Mac mini (M4 Pro): A complete guide to achieve efficient operation and cross-domain access optimization through Ollama
- The most convenient AI App front-end: Chatbox - A comprehensive introduction and user guide Download and install the AI App-style multi-platform front-end
- Exploring the features and researching the built-in tools of the WordPress chatbot plugin "AI Engine"
- Free, beginner-friendly local AI image generation tool for Mac (Part 1): Diffusion Bee settings explained and practical demonstration
- Free, beginner-friendly local AI image generation tool for Mac (Part 2): Draw Things installation and usage tutorial
- Practical application of Ollam's self-built embedded model + Chatbox knowledge base A simple and convenient way to implement a personal knowledge base.
- Creative Reconstruction in the AI Era: From Information Processing to Cognitive Collaboration