LBSocial

🚀 New Tutorial: Deploy OpenClaw in GitHub Codespaces with Tailscale Want to start experimenting with AI agents without setting up a full cloud server? In our latest tutorial, we show how to deploy OpenClaw in GitHub Codespaces and use Tailscale to access the OpenClaw dashboard securely from your local computer. The tutorial covers: ✅ Using GitHub Codespaces as a free or low-cost cloud runtime ✅ Installing and running OpenClaw inside a Codespace ✅ Connecting OpenClaw to model providers such as Gemini or Codex ✅ Running the OpenClaw gateway in a container-based environment ✅ Using Tailscale Serve to create a private HTTPS dashboard URL ✅ Preparing the environment for future n8n, MCP, and GraphRAG workflows This is a practical starting point for learners, educators, and developers who want to explore AI agents before moving to a production cloud deployment. Read the full step-by-step tutorial: https://lnkd.in/eDiQaQg4 #AIAgents #OpenClaw #GitHubCodespaces #Tailscale #GenerativeAI #CloudComputing #EdTech #LBSocial

We are opening up early beta testing for the LBSocial AI Study Mode. This short demo shows how learners can use voice or text to ask questions and receive course-connected study support. The goal is simple: help learners find the right learning path faster by connecting AI assistance with real course materials, videos, and knowledge resources. This is still an early beta, so we are actively testing accuracy, response quality, user experience, and feedback workflows. We welcome students, educators, developers, and anyone interested in AI-supported learning to try it and share feedback. Try LBSocial AI Study Mode here: https://lnkd.in/euF88DZg Feedback and issues are welcome here: https://lnkd.in/gVy_CZ8e #AI #EdTech #AIAssistant #OnlineLearning #GenerativeAI #LBSocial #AIStudyMode

LBSocial Tutorial Short: OpenClaw Meets MCP In this short tutorial demo, we show how OpenClaw can call a custom MCP server to search a Neo4j YouTube knowledge graph and return a structured learning path from real LBSocial tutorial content. The full tutorial walks through the architecture behind this workflow, including OpenClaw, MCP, Neo4j, Gemini embeddings, and YouTube GraphRAG. Full tutorial: https://lnkd.in/eWAUFyKV #OpenClaw #MCP #Neo4j #GraphRAG #AIAgents #KnowledgeGraph #GenerativeAI #LBSocial

We are starting beta testing for LBSocial AI Study Mode. The current version connects voice and text-based AI support with a growing LBSocial course knowledge base: • 12 courses • 340 videos • 1,004 chapters • 7,000 learning segments We are testing answer accuracy, video retrieval, timestamp grounding, and the overall learning experience. Try it here: https://lnkd.in/euF88DZg Feedback and issue reports are welcome: https://lnkd.in/efPVTXcH

Our new step-by-step tutorial is live. We show how to connect OpenClaw to a Neo4j graph database using a custom Model Context Protocol (MCP) server for a YouTube GraphRAG setup. Inside the guide, we cover: - Setting up a stdio MCP server on a Google Cloud VM. - Initializing a vector index in Neo4j for Gemini embeddings. - Testing the server locally via an interactive tool menu. - Using OpenClaw skills to automatically route questions to the right tools. - Why separating n8n (execution) and OpenClaw (reasoning) keeps the system stable. Read the full article and try the interactive architecture simulator here: 👉 https://lnkd.in/eWAUFyKV

We are testing a new feature in LBSocial AI Studio. The idea is simple: learners can ask a course question in English or Chinese, and the system can return not only an answer, but also the exact YouTube tutorial, timestamp, and transcript-based evidence. In this demo, LBSocial AI Studio searches course video materials and connects the response back to the original learning content. This is part of our ongoing work on course-specific AI support using Gemini Live, GraphRAG, video transcripts, and retrieval-based learning workflows. The goal is not just to build another chatbot. The goal is to help learners move from a question back to the right lesson, the right evidence, and the right next step. Still in internal testing, but we are getting closer. #LBSocial #AIStudio #GraphRAG #GeminiLive #EdTech #OnlineLearning #AIinEducation #YouTubeLearning

The AI Tutor is now available across LBSocial courses, including free courses. Learners can open a course and ask course-specific questions, such as: What will I learn in this course? What is Lab 3? How do I start the database lab? The AI Tutor helps learners navigate course materials, labs, and tutorials more easily. Try it here: https://lnkd.in/eTUspBzz AI-generated answers are grounded in the selected course, but they may still contain errors. #AITutor #AIinEducation #OnlineLearning #DataScience #CloudComputing #LBSocial

If you followed my previous tutorial on building a YouTube GraphRAG database with OpenClaw, n8n, Gemini embeddings, and Neo4j, there is now a simple way to test and query your graph. In that tutorial, we created an ingestion pipeline to collect YouTube metadata, build embedding-ready text, generate Gemini embeddings, and store videos, channels, topics, and vector embeddings in Neo4j. Tutorial blog: https://lnkd.in/e9SUuEh2 I have now added a custom MCP server for this workflow. You can use it to test whether your graph database is working correctly, inspect the Neo4j schema and vector indexes, run semantic video search, retrieve video context, find related videos, and generate learning-path recommendations from the graph. GitHub repository: https://lnkd.in/eZ5GNTAT This MCP server is not meant to be a production GraphRAG platform. It is a lightweight testing and querying layer for the tutorial workflow and a practical example of how an AI agent can connect to a structured knowledge graph via MCP. #OpenClaw #MCP #Neo4j #n8n #GraphRAG #Gemini #AIAgents #KnowledgeGraph #LBSocial

🚀 New Tutorial: Automate Your GraphRAG Pipeline from Scratch! Have you ever wanted to turn scattered YouTube videos into a structured, searchable knowledge graph? Our latest hands-on guide shows you exactly how to do it. We walk you through building a complete AI automation workflow where a simple Telegram message triggers an AI agent to do the heavy lifting for you. The Tech Stack We Are Using: 🦀 OpenClaw: AI Agent Orchestration (via Telegram) ⚙️ n8n: Workflow Automation 🧠 Gemini: Vector Embeddings 🕸️ Neo4j: Graph Database Our mission is to make advanced data analysis and AI accessible to everyone. Whether you are a developer, researcher, or AI enthusiast, this step-by-step tutorial breaks down the complex architecture into manageable, practical steps. Watch the quick 60-second demo below to see it in action! 👇 📖 Read the full step-by-step guide here: https://lnkd.in/ejzDUBjd #AI #DataScience #GraphRAG #Neo4j #n8n #OpenClaw #MachineLearning #TechTutorial #EdTech

Building upon our earlier series on n8n workflow design and OpenClaw agent skills, we are proud to announce a new technical expansion focusing on structured data ingestion. This release introduces a standardized GraphRAG Ingestion Pipeline that bridges the gap between real-time web sources and graph-based memory. Key Technical Evolutions: - Stack Integration: Seamlessly connecting the OpenClaw agent interface with n8n orchestrators and Neo4j AuraDB persistence. - Semantic Vector Storage: Moving beyond keyword matching by implementing 768-dimensional vector embeddings within the graph schema. - Modular Skill Design: This workflow is deployed as a new "Skill" within the OpenClaw framework, demonstrating the scalability of our modular agent architecture. For organizations looking to turn vast video repositories into actionable knowledge assets, this tutorial provides the production-ready blueprint. Read the latest entry in our automation series: 🔗 https://lnkd.in/ejzDUBjd #LBSocial #GraphRAG #AIArchitecture #n8n #Neo4j #OpenClaw #DataIngestion #EnterpriseAI