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2d •
🚀New Video: Turn Any Website Into LLM Ready Data INSTANTLY
In this tutorial, I show you how to turn any website into LLM-ready data in seconds using Firecrawl and Claude Code. We cover everything from scraping content and extracting branding information to mapping entire sites and pulling structured data. I walk through setting up the Firecrawl MCP server in Claude Code, then demonstrate real use cases including scraping 200 job listings from a remote job board and extracting branding details from landing pages. The best part is you don't need to think about configuration or which API endpoints to use. Just tell Claude Code what you want and it figures out the rest. FIRECRAWL DISCOUNT
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3d •
For beginners who don't know where to start
Most AI tutorials are made by developers, for developers. They skip steps. They throw around jargon. They assume you already know things you don't. You watch video after video and somehow end up more confused than when you started. That's not a you problem. That's a teaching problem. I made something that fixes it: -> For beginners who don't know where to start PS: If you’re already an AIS+ member, we will be rolling this out to you for free shortly. No need to buy it.
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7d •
🏆 Weekly Wins Recap | Jan 31 – Feb 6
This week inside AIS+ was packed with real traction. First clients landed, outreach fears broken, systems shipped, and builders stepping into confidence instead of overthinking. Here are a few standout wins inside AIS+ 👇 👉 @Ahmed Bin Faisal signed his first client via Upwork just one month after joining - full automation delivered and a very happy client. 👉 @Joe Scott scaled from £1K workflow builds to £30K AI agent projects by selling outcomes, not tools. 👉 @Deniz G built his own internal business app using n8n, Claude, and Supabase - CRM, inbox sync, lead scoring, and AI assistant all live. 👉 @Anthony Rako left his dev job, bet on himself, and landed a €2,380 real estate automation contract. 👉 @Nick Stadler cold-called 10 businesses and booked his first discovery call - outreach muscle officially activated. 🎥 Super Win Spotlight: @Gerard Vazquez | First Client Through Action Gerard joined AIS+ looking for clarity, real support, and a place to actually build.Instead of waiting, he reached out to people he already knew, booked multiple calls and closed his first consulting client at €1,500. With help from the community, he solved issues faster, delivered confidently, and proved to himself that action beats endless research. 🎥 Watch Gerard share his story 👇 Gerard’s journey shows that you don’t need everything figured out - you just need to start the conversation and keep moving. ✨ Want to see wins like this every single week? Join AI Automation Society Plus and turn learning into real outreach, real clients, and real momentum 🚀
17m •
Looking to Build Production-Grade Automation Systems
Hi everyone 👋 I’m opening up capacity for a few new automation builds. My latest project was a 24/7 Voice AI Inbound Receptionist for trade businesses. More information here: https://www.skool.com/ai-automation-society/delivered-a-project-for-my-first-client?p=9bf26672 Instead of just "connecting tools", I structure systems like in the image attached. This is a high-level bookng flow inside n8n. What I focus on: - Validate data before creating records - Prevent duplicate Sites, Customers, and Jobs records - Handle API responses properly (as in the case of 'validate address' function) - Trigger notifications only after confirmed state - Keep relationships clean with modular workflows Most automation fails because edge cases are ignored. I focus on building systems that works under real-world usage. If you’re building something that needs proper structure and reliability, feel free to reach out. Happy to take on a few interesting builds.
13m •
Lease Analyzer Found 4 Red Flags Before I Signed (9 Nodes) 🔥
New apartment. Landlord sends 23-page lease. Standard form, he says. Sign here. Built lease analyzer. Found 4 red flags. Including early termination penalty buried on page 17. THE RENTER'S BLIND SPOT: You need the apartment. You're excited. You sign what they give you. $2,400 early termination fee. 60-day notice requirement. Landlord can enter with 12-hour notice. All buried in legalese. THE DISCOVERY: Dual document extraction. First call pulls structured terms. Second call generates tenant advice. Same pattern as contract review. But optimized for renters. THE WORKFLOW: Google Drive trigger → Download lease → Document extraction pulls rent, deposit, terms, pet policy, termination clauses, red flags → Merge combines with binary → Second extraction generates tenant advice → Code calculates move-in costs and risk level → Sheets logs analysis → IF checks risk level → High risk: Alert channel → Normal: Completion notification. 9 nodes. Tenant protection automated. THE RED FLAG DETECTION: Extraction looks for concerning clauses. Returns array with severity: High, Medium, Low. Code determines overall risk: - 2+ high severity flags → High Risk - 1 high OR 3+ total flags → Medium Risk - Otherwise → Low Risk THE TENANT ADVICE: Second extraction prompt: "Summarize this lease for a tenant. What are the top 3 things to negotiate? Is this tenant-friendly, neutral, or landlord-friendly? What warnings should I know?" Natural language advice. Not just data extraction. THE MOVE-IN COST CALCULATION: Code adds: First month rent + security deposit + any fees = total move-in cost. No surprises on signing day. THE TRANSFORMATION: Before: Sign and hope. Discover problems when moving out. After: Every lease analyzed. Red flags surfaced. Negotiation points identified. THE NUMBERS: 23-page lease analyzed in 45 seconds 4 red flags identified $2,400 early termination fee discovered Move-in cost calculated automatically Template in n8n and All workflows in Github
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A community built to master no-code AI automations. Join to learn, discuss, and build the systems that will shape the future of work.
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