Google just released Gemini 3 and with it their new image model Nano Banana Pro. The results are crazy realistic and the price is surprisingly low. So I built a full image generation system in n8n that lets you create images through a simple chat interface. The bot can generate three types of images. You can create an image from text. You can use an existing image as a reference. Or you can upload multiple images and combine them into a brand new one. I walk through how the whole system works: 1. How to connect Nano Banana Pro through Kie.ai using HTTP requests 2. How to build a clean n8n form that collects prompts and file uploads 3. How to enhance prompts with AI before sending them to the model 4. How to use Google Drive as a free image host for the uploaded files 5. How to handle the async generation loop until the image is done 6. How to send the final image back through a Telegram chat The bot can make infographics, edits, memes or realistic image composites. And you can run the whole thing from your phone. If you want more builds like this, make sure you subscribe. https://youtu.be/sJsHQ3J1erI?si=8KcVFwNno7R3gFk0

As a millionaire living my best life, I'm giving $20k to the first 20 people who message me “GRACE” I understand that many people are struggling with bills, and I want to help reduce that burden.

If you are building AI agents in n8n, there are a few features that can take them from basic to genuinely useful. A lot of people only use the default setup, and they miss out on things that make agents more reliable, more accurate and much more powerful. In this video I break down five features that every agent should be using. Some of them are simple. Others are overlooked because most tutorials skip them. I walk through everything using a real example, which is my budget tracking agent that logs expenses into a spreadsheet through Telegram. Here is what I cover: 1. How to use a system prompt the right way 2. How tool calling works and how to let the agent fill parameters on its own 3. How to use structured output so your agent returns clean and predictable data 4. How simple memory works and when to store chat history 5. How to delegate tasks to a sub agent and avoid long messy prompts These features apply to support agents, CRM update bots, appointment setters and any workflow that relies on AI reasoning. If you want more agent breakdowns and practical builds, make sure you subscribe.

As a millionaire living my best life, I'm giving $20k to the first 20 people who message me “GRACE” I understand that many people are struggling with bills, and I want to help reduce that burden.

I made a full walkthrough on how to build a RAG based support agent using n8n, Firecrawl, Supabase and OpenAI. Sharing it here because RAG sounds complex, but the setup is actually pretty simple once you see the steps. Here’s the quick breakdown of the workflow: 1. Scrape the website with Firecrawl It grabs every page and formats everything into clean markdown. 2. Upload the files to Drive Just to store all the scraped content in one place. 3. Pull the files into n8n Download each file so n8n can process them. 4. Convert markdown into text Supabase needs plain text before embedding. 5. Create a Supabase vector store Run one SQL snippet and your table is ready. 6. Embed and upload n8n chunks the text, embeds it and sends it to Supabase. 7. Connect a Telegram bot This becomes the front end for talking to the agent. 8. Build the agent The agent pulls relevant info from Supabase and answers based on what’s actually in your data. The video walks through every step if you want to build one yourself. If you want more step by step tutorials like this, please subscribe to my channel. I will be posting more in the upcoming weeks :)