curiousily/AI-Bootcamp

• highreadme#1

  Reposition the README's opening paragraph to specify Generative AI focus

  原因：

  当前

  The "Get Shit Done with AI" Bootcamp focuses on real-world applications that will equip you with the skills and knowledge to become a great AI engineer.

  复制粘贴的修复

  复制

  The "Get Shit Done with AI" Bootcamp is a self-paced, code-first curriculum focused on practical Generative AI applications. It equips you with the skills to build robust AI agents, RAG systems, and master modern LLM development.

  待办完成忽略
• mediumreadme#2

  Add a 'What You'll Master' section to the README

  原因：

  复制粘贴的修复

  复制

  ## What You'll Master
  This bootcamp provides hands-on tutorials covering:
  - **Generative AI Fundamentals:** LLMs, prompt engineering, fine-tuning.
  - **Practical AI Systems:** Building RAGs (Retrieval Augmented Generation) and AI Agents (CrewAI, DSPy).
  - **Modern AI Frameworks:** LangChain, LangGraph, Ollama.
  - **Leading Models:** Working with ChatGPT, gpt-oss, Claude, Qwen, Gemma, Llama, Gemini.

  待办完成忽略
• lowreadme#3

  Add a 'Why This Bootcamp?' section to the README

  原因：

  复制粘贴的修复

  复制

  ## Why This Bootcamp?
  This bootcamp offers a free, self-paced, and highly practical, code-first curriculum. It's designed to get you building real-world Generative AI applications using modern tools and frameworks, structured like a premium online academy, unlike generic ML courses or tool documentation.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Looking for a structured learning path to master generative AI for practical applications.

  你：未被推荐
  AI 推荐顺序：

  1. Coursera: Andrew Ng's Machine Learning Specialization
  2. fast.ai: Practical Deep Learning for Coders
  3. PyTorch
  4. Google's Machine Learning Crash Course
  5. TensorFlow
  6. Coursera: DeepLearning.AI Generative AI with Large Language Models (LLMs) Specialization
  7. Hugging Face
  8. Udemy: Generative Adversarial Networks (GANs) - Build your own GANs
  9. Hugging Face Transformers Library Documentation & Tutorials
  10. LangChain Documentation & Tutorials
  11. LlamaIndex Documentation & Tutorials
  12. Streamlit
  13. Gradio
  14. AWS SageMaker
  15. Google Cloud Vertex AI
  16. Azure Machine Learning
  17. arXiv.org
  18. Papers With Code
  19. Kaggle Competitions

  AI 推荐了 19 个替代方案，却始终没点名 curiousily/AI-Bootcamp。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  How to build robust AI agents and RAG systems with modern development practices?

  你：未被推荐
  AI 推荐顺序：

  1. LangChain
  2. LlamaIndex
  3. Haystack (deepset/Haystack)
  4. OpenAI API
  5. Azure OpenAI Service
  6. Pinecone
  7. Weaviate
  8. Chroma
  9. Weights & Biases
  10. Docker
  11. Kubernetes

  AI 推荐了 11 个替代方案，却始终没点名 curiousily/AI-Bootcamp。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of curiousily/AI-Bootcamp?

  pass
  ⚠AI 未点名 curiousily/AI-Bootcamp —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts curiousily/AI-Bootcamp in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 curiousily/AI-Bootcamp

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo curiousily/AI-Bootcamp solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 curiousily/AI-Bootcamp

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？