Biggest challenge for e-commerce stores: Raw product images don’t sell. Customers want to see products on models and in real-life settings. So I built a UGC Image & Recreation Workflow (n8n + AI): - Form Submission → Client uploads any product image. - AI Image Analyzer → Detects and understands the product. - AI Agent (Gemini + Think) → Creates a smart prompt for recreation. - Image Recreation (API/Stable Diffusion) → Generates a new professional model-shot image. - Record Storage (Airtable/DB) → Saves original + recreated images + captions. ⚡ Outcome: ✅ Clients upload once → instantly get model-ready images. ✅ Campaign-ready creatives for ads, websites & social. ✅ Saves hours of manual design work.

In this video I'm going to be telling you why you need to stop selling AI Agents and sell AI Solutions instead. AI agents are not what they were 6 months ago, because now everyone has hopped on the trend of selling AI agents. So to make a lot of money selling AI in 2025, you need to be different, and to stand out by selling AI solutions. It's a game changer! So grab your pen and paper, and get ready to take some notes. Hope you enjoy!

🔥 Imagine this: a new job lands in your ATS. Normally, you’d scroll through endless candidate records trying to find the right fit. But with automation… 1️⃣ Job triggers the workflow 2️⃣ AI rewrites the JD for matching 3️⃣ Database + vector search ranks candidates 4️⃣ Recruiter gets a Slack shortlist instantly 5️⃣ Candidates get personalized emails 6️⃣ Google Sheets logs everything for tracking ⚡ From job creation → to reactivation → to outreach, all automatic. 👉 Here’s where I’d love your thoughts: Do you prefer shortlists delivered to Slack or auto-outreach emails first? If you had this workflow, which part would save you the most time?

ChatGPT and most AI tools answer using public internet data. But what if you want answers only from your own business or personal data? That’s where ✨ RAG Agents (Retrieval-Augmented Generation) step in. Here’s how I built one with n8n: - Trigger (Chat Message Received) → Listens for customer queries. - AI Agent (Google Gemini) → Core brain of the agent. - Vector Database (Pinecone) → Stores and retrieves your private data. - Embeddings (Google Gemini) → Converts data into searchable vectors. - Memory → Keeps chat context for natural conversations. ⚡ Outcome: Customer asks a question. RAG Agent fetches the most relevant info from your data. AI generates a precise, context-aware answer. If no private data is needed → it works like a normal chatbot. 💡 In short: RAG = Your Data + AI Power

Telegram bot that listens for image prompts, chats with the user, generates stunning visuals with DALL·E 3, and sends them back instantly all without writing a single line of code! ‎ ‎ Features: ‎- Chat prompt handling with OpenAI ‎- Context memory for better responses ‎- Image generation via DALL·E 3 ‎- Telegram integration for instant delivery ‎ ‎💡 Imagine offering this as a service to creatives, marketers, or even content creators! ‎ ‎Automation is changing the game — and I’m here for it 🔥 ‎ ‎#n8n #OpenAI #Dalle3 #AutomationBuilder #TelegramBot #NoCode #AIintegration