🚀 Bringing BB-8 to Life with n8n! 🚀 Inspired by Ojas Shukla’s robot project, I decided to take my first shot at controlling an iconic Star Wars character — BB-8! 🤖✨ This is a pure passion project — I’ve poured my heart and soul into turning a crazy vision into reality, and I can’t wait to share the video with you. 🎥 The video will be released on September 29th morning — just wrapping up the final edits. Here’s the tech stack behind the magic: 1️⃣ Retell AI – Voice Engine (Speech-to-Text) 2️⃣ n8n – Control Center for BB-8 Droid (Local Setup) 3️⃣ Cloudflare Tunnel – Public Access to n8n 4️⃣ Flask Server (Local) – Web Interface + API Calls Monitoring 5️⃣ Python Libraries – Old Bluetooth Drivers for Connection to BB-8 6️⃣ Custom Python Code – Individual BB-8 Actions This is just the start — excited to show how AI + Automation + Robotics can come together. Stay tuned for the full demo! Samruddhi Mokal Aryan Mahajan Retell AI n8n hashtag#StarWars hashtag#BB8 hashtag#n8n hashtag#RetellAI hashtag#Automation hashtag#Robotics hashtag#Python hashtag#PassionProject

This workflow is designed to handle calls depending on the mode we set for a number. It first checks the mode: If the mode is OTP, the call goes through an AI system that takes the recording and then processes it into text (human-to-AI transcription). If the mode is Call, the system simply forwards the call to a real human agent. The stack I used for this project is Twilio, Python, HTML, n8n, and MongoDB.

Major medical network. 40 clinics. 50,000+ patient forms monthly. Their vendor's timeline: Week 1-2: Requirements gathering and analysis Week 3-4: Development sprint Week 5: Testing and quality assurance Week 6: Deployment and training Cost: $35,000 per new form type I asked: "Can you show me a sample form?" Built it live during our Zoom call. THE SPEED BUILD (n8n) Hour 1: Created webhook endpoint for form submission Hour 2: Built form detection logic (12 common medical forms) Hour 3: Set up field extraction rules using visual node editor Hour 4: Added validation checks and error handling Hour 5: Connected to their EMR system (Epic API) Hour 6: Deployed to production and tested with real forms Time investment: 6 hours (including 90 minutes learning their EMR API) THE PERFORMANCE TEST Processed 1,000 test forms in 14 minutes Accuracy: 98.7% on structured fields Zero manual intervention needed Handles handwritten sections and checkboxes Their IT director: "But what about scalability?" Current production stats: 50,000+ forms/month, 99.1% uptime THE BUSINESS MODEL SHIFT Old way: Pay $35k, wait 6 weeks, hope it works My way: Pay $4,500, working tomorrow, guaranteed results Current medical clients: 4 networks Form types handled: 63 variations Monthly recurring: $16,800 Time spent monthly: 8 hours maintenance THE PATTERN LIBRARY SUCCESS Patient intake forms: 28 variations built Insurance verification: 19 variations Consent documents: 16 variations New form type request? Usually 85% similar to existing pattern. Modify and deploy in under 2 hours. BEST CLIENT FEEDBACK "You make our $2.5M vendor look incompetent." The enterprise healthcare world is drunk on complexity. Simple n8n workflows are eating their expensive lunch. Revenue from "6-hour builds": $16,800/month. Original vendor estimate for same work: $420,000 annually. Who else is waiting months for "enterprise medical integration"?

With AI and automation, nothing is truly impossible. Every challenge just needs the right workflow. Even ‘impossible’ itself says I’m possible. What’s one automation or chatbot you once thought couldn’t be built, but actually turned out possible?

👨‍💻 + 🤖 + 🧠 = 🚀 Ever wondered what happens when you mix n8n, OpenAI, Redis, and GitLab into a cybersecurity smoothie? 🍹Well… I did. And the result is a Retrieval-Augmented Generation (RAG) framework that’s now helping SOC teams respond smarter and faster! Here’s the magic: 🔁 Weekly Workflow 📂 Pulls SOC playbooks from GitLab ✂️ Chunks them into digestible pieces 🧠 Embeds each chunk using OpenAI 📦 Stores them in Redis for lightning-fast semantic search ⚡ Main Workflow 🔍 Gets a query from PRISM 🤖 AI Agent checks Redis for similar incidents 📬 If found → sends SOC an email with relevant content + GitLab link 🛠️ If not → another AI agent creates a new playbook and pushes it to GitLab 📨 SOC gets the new playbook link instantly 💡 Why does this matter?Because now, SOC teams get instant, AI-curated guidance—whether it’s reused knowledge or freshly generated content. No more digging through docs. No more waiting. Just actionable intelligence on demand. 🔧 Powered by: n8n | OpenAI | Redis | GitLab | PRISM | Email Let’s make cybersecurity workflows smarter, not harder. Curious how this could work in your org? Let’s connect! 💬