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[ShannonChenCHN]

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[ShannonChenCHN]

Tutorials

AI Agents

• Build an Agent: https://python.langchain.com/docs/tutorials/agents/
• FirebaseExtended/compass-travel-planning-sample: Compass Travel Planning Sample App
• https://www.anthropic.com/research/building-effective-agents
• AI AGENTS From Zero to Production in 35 Minutes - FULL TUTORIAL: https://www.youtube.com/watch?v=DDvdMEEaLTE&ab_channel=AllAboutAI
• 🎬 LLM Agents MOOC by Berkeley RDI Center on Decentralization & AI https://www.youtube.com/playlist?list=PLS01nW3RtgopsNLeM936V4TNSsvvVglLc
• SwirlAI Newsletter
  • Building AI Agents from scratch - Part 1: Tool use: https://open.substack.com/pub/swirlai/p/building-ai-agents-from-scratch-part?r=4s0d4c&utm_medium=ios
  • Building AI Agents from scratch - Part 2: Reflection and Working Memory: https://www.newsletter.swirlai.com/p/building-ai-agents-from-scratch-part-8ca
• 🎬 Build powerful AI agents using Claude MCP: https://www.youtube.com/watch?v=5CmAKm1wWW0&ab_channel=DavidOndrej
• 🎬 LLM Agents MOOC by Berkeley RDI Center on Decentralization & AI https://www.youtube.com/playlist?list=PLS01nW3RtgopsNLeM936V4TNSsvvVglLc ⭐️⭐️⭐️
• Building LLM Applications: Introduction (Part 1): https://medium.com/@vipra_singh/building-llm-applications-introduction-part-1-1c90294b155b#4d28
• Building Agentic Applications with LangGraph: Part 1 — Tabular OCR Agent: https://medium.com/primastat/building-agentic-applications-with-langgraph-part-1-tabular-ocr-agent-6ba81a6c0862
• How to Build AI Agents with LangGraph: A Step-by-Step Guide: https://medium.com/@lorevanoudenhove/how-to-build-ai-agents-with-langgraph-a-step-by-step-guide-5d84d9c7e832
• Building Production-Ready AI Agents with LangGraph: A Real-Life Use Case: https://medium.com/cyberark-engineering/building-production-ready-ai-agents-with-langgraph-a-real-life-use-case-7bda34c7f4e4
• The LLM-Twin Free Course on Production-Ready RAG applications: https://medium.com/decodingml/the-llm-twin-free-course-on-production-ready-rag-pipelines-c96472f4e8c8 ⭐️⭐️⭐️⭐️⭐️
• DeepLearning.AI ⭐️⭐️⭐️⭐️⭐️
  • Multi AI Agent Systems with crewAI: https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/
  • AI Agentic Design Patterns with AutoGen: https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/
  • AI Agents in LangGraph: https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/
  • LLMs as Operating Systems: Agent Memory: https://www.deeplearning.ai/short-courses/llms-as-operating-systems-agent-memory/
• AI Coding Assistant: https://x.com/LangChainAI/status/1881038918717477169
  • https://github.com/AbhinavTheDev/coding-agent

RAG

• Retrieval Augmented Generation (RAG) Part 1: Build an application that uses your own documents to inform its responses.
• Retrieval Augmented Generation (RAG) Part 2: Build a RAG application that incorporates a memory of its user interactions and multi-step retrieval.
• Build a LLM app with RAG to chat with GitHub using Llama 3.2 running locally on your computer (100% free and without internet)
  • Twitter: https://x.com/Sumanth_077/status/1879916261150491039
  • GitHub: https://github.com/reflex-dev/reflex-llm-examples
• 🎬 Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer: https://www.youtube.com/watch?v=sVcwVQRHIc8&t=1700s&ab_channel=freeCodeCamp.org
• 文字稿 https://www.freecodecamp.org/news/mastering-rag-from-scratch/
• 🎬 RAG From Scratch by LangChain: https://www.youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x
• 🎬 Vector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search https://www.youtube.com/watch?v=JEBDfGqrAUA&t=2s&ab_channel=freeCodeCamp.org
• 🎬 Local Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial): https://www.youtube.com/watch?v=qN_2fnOPY-M&ab_channel=DanielBourke
• Building Retrieval Augmented Generation (RAG) From Scratch: https://medium.com/red-buffer/building-retrieval-augmented-generation-rag-from-scratch-74c1cd7ae2c0
• Code a simple RAG from scratch: https://huggingface.co/blog/ngxson/make-your-own-rag
• A beginner's guide to building a Retrieval Augmented Generation (RAG) application from scratch: https://learnbybuilding.ai/tutorials/rag-from-scratch
• Building RAG from Scratch (Lower-Level) | LlamaIndex: https://docs.llamaindex.ai/en/stable/optimizing/building_rag_from_scratch/
• Beginner's Guide to RAG Agents with Prof. Tom Yeh
  • Twitter: https://x.com/ProfTomYeh/status/1880624913108582633
  • YouTube: https://www.youtube.com/watch?v=181Esb2Ba8w&ab_channel=AIbyHand
• How I built a Superhero Facts RAG QA Bot using LangChain, Qdrant, and OpenAI: https://medium.com/data-science-at-microsoft/how-i-built-a-superhero-facts-rag-qa-bot-using-langchain-qdrant-and-openai-20a04202c6b1

Reflection

• 🎬 Building a self-corrective coding assistant from scratch https://www.youtube.com/watch?v=MvNdgmM7uyc&ab_channel=LangChain

Memory

• Memory-Enabled AI Chatbots
  • Create intelligent chatbots that maintain conversation context across sessions using LangChain and Gemini AI. Featuring MongoDB storage for seamless, persistent memory capabilities.
  • Learn more: https://cckeh.hashnode.dev/building-chatbots-with-memory-capabilities-a-comprehensive-tutorial-with-langchain-langgraph-gemini-ai-and-mongodb

Multi-Agents

• Multi-Agentic RAG with Hugging Face Code Agents: https://medium.com/towards-data-science/multi-agentic-rag-with-hugging-face-code-agents-005822122930

LLM

• How to Build an LLM from Scratch: https://towardsdatascience.com/how-to-build-an-llm-from-scratch-8c477768f1f9

LangChain/LangGraph

• Tutorials | LangChain: https://python.langchain.com/docs/tutorials/
• How-to Guides | LangChain: https://langchain-ai.github.io/langgraph/how-tos/
• Introduction to LangGraph | LangChain Academy https://academy.langchain.com/courses/intro-to-langgraph ⭐️⭐️⭐️
• Learn LangGraph - The Easy Way: https://www.youtube.com/watch?v=R8KB-Zcynxc&t=746s&ab_channel=AIwithMisbah

学习平台

• Gemini API Cookbook: https://github.com/google-gemini/cookbook ⭐️⭐️⭐️⭐️⭐️
• Anthropic Cookbook: https://github.com/anthropics/anthropic-cookbook/tree/main/patterns/agents ⭐️⭐️⭐️⭐️⭐️
• OpenAI Cookbook ⭐️⭐️⭐️⭐️⭐️
  • GitHub: https://github.com/openai/openai-cookbook
• DeepLearning.AI
• 🔗LangChain 官方资源
• LangChain 的 Twitter：上面有很多 project 和 tutorial

📚 书

• LLM Engineer's Handbook: Master the art of engineering large language models from concept to production by Paul Iusztin , Maxime Labonne. Covers mostly the lifecycle of LLMs, including LLMOps on pipelines, deployment, monitoring, and more. Youtube overview by Paul.
• Build a Large Language Model from Scratch by Sebastian Raschka ⭐️⭐️⭐️⭐️⭐️
• Hands-On Large Language Models: Build, Tune, and Apply LLMs by Jay Alammar , Maarten Grootendorst

其他

• Start Building These Projects to Become an LLM Engineer: https://dswharshit.medium.com/start-building-these-projects-to-become-an-llm-engineer-0064e9e68d9d
• 5 AI Projects You Can Build This Weekend (with Python): https://towardsdatascience.com/5-ai-projects-you-can-build-this-weekend-with-python-c57724e9c461

[ShannonChenCHN]

Projects

• Pythagora-io/gpt-pilot: GPT Pilot aims to research how much LLMs can be utilized to generate fully working, production-ready apps while the developer oversees the implementation.
  • The main idea is that AI can write most of the code for an app (maybe 95%), but for the rest, 5%, a developer is and will be needed until we get full AGI.
  • If you are interested in our learnings during this project, you can check our latest blog posts.
• lobehub/lobe-chat: an open-source, modern-design AI chat framework. Supports Multi AI Providers( OpenAI / Claude 3 / Gemini / Ollama / Qwen / DeepSeek), Knowledge Base (file upload / knowledge management / RAG ), Multi-Modals (Vision/TTS/Plugins/Artifacts). One-click FREE deployment of your private ChatGPT/ Claude application.
• Shubhamsaboo/awesome-llm-apps: Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
• LangChain 的 Twitter：上面有很多 project 和 tutorial