iusztinpaul/hands-on-llms

• highreadme#1

  Reposition README opening to clarify archived course status

  原因：

  当前

  ## 🚨 Remastered Course 🚨
  As the world of GenAI and LLMs moves fast, too fast for educational content, it was easier to archive this course and create a new one from scratch.
  Check out our new LLM Twin open-source course for an improved experience in learning to build a production-ready LLM and RAG system.

  复制粘贴的修复

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  ## 🚨 Archived Course: Hands-on LLMs 🚨
  This repository contains the materials for the original 'Hands-on LLMs' course, which teaches how to design, train, and deploy a real-time financial advisor LLM system. While this course has been archived due to the rapid pace of GenAI, its content remains a valuable learning resource. For an updated experience, please check out our new LLM Twin open-source course for building a production-ready LLM and RAG system.

  待办完成忽略
• mediumabout#2

  Add homepage URL to the new course

  原因：

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  https://github.com/iusztinpaul/llm-twin

  待办完成忽略
• lowabout#3

  Update description to reflect archived course status

  原因：

  当前

  🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝗱𝗶𝗻𝗴 𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀

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  🦖 𝗔𝗿𝗰𝗵𝗶𝘃𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲: Learn about LLMs, LLMOps, and vector DBs by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝗱𝗶𝗻𝗴 𝗺𝗮𝘁𝘦𝘳𝘪𝘢𝘭𝘴. For the latest content, see our new LLM Twin course.

  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

  How to build and deploy a real-time generative AI system?

  你：未被推荐
  AI 推荐顺序：

  1. NVIDIA NeMo Framework
  2. NVIDIA Triton Inference Server
  3. Hugging Face Transformers
  4. FastAPI
  5. Kubernetes
  6. ONNX Runtime
  7. PyTorch
  8. TensorFlow
  9. TorchServe
  10. TensorFlow Serving
  11. OpenAI API
  12. Anthropic API
  13. Google Gemini API
  14. Ray Serve
  15. AWS SageMaker
  16. MLflow
  17. BentoML

  AI 推荐了 17 个替代方案，却始终没点名 iusztinpaul/hands-on-llms。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Need guidance on MLOps best practices for deploying large language models in production.

  你：未被推荐
  AI 推荐顺序：

  1. MLflow
  2. Databricks
  3. AWS SageMaker
  4. Google Cloud Vertex AI
  5. Hugging Face Transformers
  6. Text Generation Inference (TGI)
  7. Hugging Face Inference Endpoints
  8. Kubeflow
  9. Weights & Biases (W&B)
  10. Ray Serve
  11. Cortex.dev
  12. Verta AI

  AI 推荐了 12 个替代方案，却始终没点名 iusztinpaul/hands-on-llms。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of iusztinpaul/hands-on-llms?

  pass
  ✓AI 明确点名了 iusztinpaul/hands-on-llms

  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 iusztinpaul/hands-on-llms in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 iusztinpaul/hands-on-llms

  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 iusztinpaul/hands-on-llms solve, and who is the primary audience?

  pass
  ⚠AI 未点名 iusztinpaul/hands-on-llms —— 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？