学 AI 最浪费时间的不是学本身，是不知道从哪开始：视频、仓库、教程、书、论文、课程满天飞，搜一圈下来，时间花了，路线还是没定。这份路线图把六类资源一次列齐，照着走就行，不用再到处找。
视频：先建立整体认识
1. LLM Introduction：https://www.youtube.com/watch?v=zjkBMFhNj_g
2. LLMs from Scratch：https://www.youtube.com/watch?v=9vM4p9NN0Ts
3. Agentic AI Overview（Stanford）：https://www.youtube.com/watch?v=kJLiOGle3Lw
4. Building & Evaluating Agents：https://www.youtube.com/watch?v=d5EltXhbcfA
5. Building Effective Agents：https://www.youtube.com/watch?v=D7_ipDqhtwk
6. Building Agents with MCP：https://www.youtube.com/watch?v=kQmXtrmQ5Zg
7. Building an Agent from Scratch：https://www.youtube.com/watch?v=xzXdLRUyjUg
8. Philo Agents：https://www.youtube.com/playlist?list=PLacQJwuclt_sV-tfZmpT1Ov6jldHl30NR
开源仓库：边看边动手
1. GenAI Agents：https://github.com/nirdiamant/GenAI_Agents
2. Microsoft AI Agents for Beginners：https://github.com/microsoft/ai-agents-for-beginners
3. Prompt Engineering Guide：https://github.com/dair-ai/Prompt-Engineering-Guide
4. Hands-On Large Language Models：https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
5. Made with ML：https://github.com/GokuMohandas/Made-With-ML
6. Hands-On AI Engineering：https://github.com/Sumanth077/Hands-On-AI-Engineering
7. Awesome Generative AI Guide：https://github.com/aishwaryanr/awesome-generative-ai-guide
8. Designing Machine Learning Systems：https://github.com/chiphuyen/dmls-book
9. Machine Learning for Beginners：https://github.com/microsoft/ML-For-Beginners
10. LLM Course：https://github.com/mlabonne/llm-course
指南：看一线团队怎么做 Agent
1. Google’s Agent Whitepaper：https://www.kaggle.com/whitepaper-agents
2. Google’s Agent Companion：https://www.kaggle.com/whitepaper-agent-companion
3. Building Effective Agents by Anthropic：https://www.anthropic.com/research/building-effective-agents
4. Claude Code Agentic Coding Practices：https://code.claude.com/docs/en/best-practices
5. OpenAI’s Practical Guide to Building Agents：https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
书：系统补基础
1. Understanding Deep Learning：https://udlbook.github.io/udlbook/
2. Building an LLM from Scratch：https://www.manning.com/books/build-a-large-language-model-from-scratch
3. The LLM Engineering Handbook：https://www.oreilly.com/library/view/llm-engineers-handbook/9781836200079/
4. AI Agents: The Definitive Guide：https://www.oreilly.com/library/view/ai-agents-the/0642572247775/
5. Building Applications with AI Agents：https://www.oreilly.com/library/view/building-applications-with/9781098176495/
6. AI Agents with MCP：https://www.oreilly.com/library/view/ai-agents-with/9798341639546/
7. AI Engineering：https://www.oreilly.com/library/view/ai-engineering/9781098166298/
论文：理解 Agent 背后的思想
1. ReAct：https://arxiv.org/abs/2210.03629
2. Generative Agents：https://arxiv.org/abs/2304.03442
3. Toolformer：https://proceedings.neurips.cc/paper_files/paper/2023/file/d842425e4bf79ba039352da0f658a906-Paper-Conference.pdf
4. Chain-of-Thought Prompting：https://arxiv.org/pdf/2201.11903
课程：跟着练
1. Hugging Face Agent Course：https://huggingface.co/learn/agents-course/en/unit0/introduction
2. MCP with Anthropic：https://www.deeplearning.ai/courses/mcp-build-rich-context-ai-apps-with-anthropic
3. Building Vector Databases with Pinecone：https://www.deeplearning.ai/courses/building-applications-vector-databases
4. Vector Databases: Embeddings to Apps：https://www.deeplearning.ai/courses/vector-databases-embeddings-applications
5. Agent Memory：https://www.deeplearning.ai/courses/llms-as-operating-systems-agent-memory
资源不用贪多。先挑一条线：看两个视频建立认识，跑一个开源仓库动手做，再读一篇指南对照自己的做法——边做边学，比收藏一百个链接有用。