iusztinpaul/hands-on-llms

• highreadme#1

  Reposition README opening to clarify archived course status

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  CURRENT

  ## 🚨 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.

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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 While this course has been archived due to the rapid pace of GenAI, its content remains a valuable learning resource.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.

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• mediumabout#2

  Add homepage URL to the new course

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

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• lowabout#3

  Update description to reflect archived course status

  Why:

  CURRENT

  🦖 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.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 17 alternatives but never named iusztinpaul/hands-on-llms. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 12 alternatives but never named iusztinpaul/hands-on-llms. This is the gap to close.

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• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

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

  pass
  ✓AI named iusztinpaul/hands-on-llms explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts iusztinpaul/hands-on-llms in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named iusztinpaul/hands-on-llms explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo iusztinpaul/hands-on-llms solve, and who is the primary audience?

  pass
  ⚠AI did not name iusztinpaul/hands-on-llms — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?