🚀 From a personal project to an unexpected tech journey... and it’s just getting started! A few weeks ago, I started building a simple "Parents Smart Assistant" —an automation bot to help busy parents (like myself) stay organized: reminders, calendar events, emails — all controlled via natural messages on Telegram. I spent countless hours building and watching... tons of trial and error (mostly error 😅). Every success felt like a small victory. Every mistake taught me something new. Then came THE -MCP (Model Context Protocol). Suddenly, I realized the game had changed.Instead of manually designing every tool and interaction, I could now connect to a network of intelligent tools ready to handle tasks dynamically. It felt like building a bicycle for weeks...and then someone handing me a spaceship. 🚀😂 Today, my smart assistant project has evolved dramatically: - Telegram integration ✅ - Gmail and Google Calendar automation ✅ - Project restructuring with Docker, ngrok, and modular workflows ✅ - Preparing full MCP integration (dynamic tools, memory, external searches) 🛠️ ✅ Check out the full GitHub repo:🔗 https://github.com/LiadLevy92/Parents-smart-assistant 💬 I'd love to hear your thoughts: Have you ever built something manually, only to find out a new tool could make it 10x easier? 😅 Drop your stories below 👇 — let's laugh and learn together! 👑⭐Big thanks and huge credit to @Nate Herk for the inspiration, guidance, and the amazing effort he puts into this community and the whole automation field.Without his work and content, I wouldn’t have gotten this far so quickly.Appreciate everything you do!

Here’s the deal: I’ve got a sweet little JSON file – just some structured data about my family: names, birthdays, emails… you know, the usual stuff a smart assistant should remember so it doesn’t miss grandma’s birthday 🎂. I’m building a personal assistant using n8n, and I want it to have long-term memory via Qdrant. The goal? Context-aware responses, useful recall, and eventually a system that manages our family life better than I do 🧑‍💻➡️🤖. Now comes the fun (read: painful) part – getting that data into Qdrant the right way: Embed each object ✍️ Send it to Qdrant via Python or n8n 🔄 Structure the collection properly 🧱 And make sure nothing breaks along the way 🧨 Right now, it feels less like a “simple import” and more like onboarding a new team member 👔. So… if anyone here has a best practice, a code snippet, or a clean flow for uploading JSON data into Qdrant – ideally with custom fields and embeddings – I’d love to hear it! Even a meme or moral support is appreciated 😅🙌

Hi can anyone help me with n8n starter kit to download.. Nate has mentioned it is free

For a deeper discussion on optimizing this workflow, let's chat here. In this video, I break down how I built the most in-depth AI research system in n8n—capable of generating detailed research reports with all sources linked for just $0.50 per report! 🚀 You'll see: ✅ How the system gathers and analyzes data ✅ How it compiles a PDF research report ✅ Key optimizations to make it even more efficient Everything you need to set up this workflow is linked in the template that's attached to this post! Google Sheet Template Tavily PDF API OpenRouter

For a deeper discussion on use cases of this tool, let's chat here. In this video, I break down n8n’s powerful new Think tool for AI agents—based on Anthropic’s approach to structured thinking. I’ll walk you through a few demos to show how this tool helps agents tackle complex tasks more effectively. Plus, we’ll explore how different models behave when using Think—and why this could completely change the way we design and build AI agents. If you're building with n8n or experimenting with autonomous agents, this is one tool you’ll want to understand.