Main
A useful way to consume large resource lists:
- Pick one topic (eval, inference, agents) and choose 3 primary sources to read end-to-end.
- Turn what you learned into a checklist (metrics, pitfalls, recommended tooling) and keep it versioned.
- Add internal notes next to links you validated so future readers don’t repeat the same triage.
README excerpt (verbatim)

全世界最好的大语言模型资源汇总 持续更新
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Contents
- 推荐 Suggestion 🌟
- 数据 Data
- 微调 Fine-Tuning 🌟
- Agentic RL 🌟
- 推理 Inference
- 评估 Evaluation
- 体验 Usage
- 知识库 RAG
- 智能体 Agents
- 研究 Research 🔥
- 代码 Coding
- 视频 Video 🌟
- 图片 Image 🌟
- 搜索 Search
- 语音 Speech 🌟
- 龙虾 OpenClaw 🔥
- 统一模型 Unified Model 🌟
- 书籍 Book
- 课程 Course
- 教程 Tutorial
- 论文 Paper
- 社区 Community
- 模型上下文协议 MCP
- 技能 Skills 🔥
- 推理 Open o1
- 推理 Open o3
- 小语言模型 Small Language Model 🌟
- 小多模态模型 Small Vision Language Model 🌟
- 技巧 Tips
推荐 Suggestion
Podcast
FAQ
Q: Is it a tutorial repo? A: It’s a curated index—follow the links to the original sources for full details.
Q: How do I keep it actionable? A: Translate reading into checklists and repo templates you can reuse.
Q: How do I avoid stale links? A: Prefer recently updated sections and keep your own shortlist in a doc you maintain.