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šNew Video: Antigravity + Nano Banana 2 Destroys Every AI Image Tool (FREE Skill)
In this video, I show you how to use Antigravity with Nano Banana 2 to generate insane images with perfect text using JSON prompting. JSON prompting gives us so much more control over the image generation process, and the results are way more consistent than traditional prompting. Because of that, I built a skill around this workflow so that whenever I want to create an image for a video or any other project, Gemini 3.1 Pro in Antigravity automatically uses the skill to craft a perfect JSON prompt, which then generates a perfect image every time. I walk you through exactly how all of this works step by step and show you how to set it up for yourself so you can start getting the same results. GET THE RESOURCES HERE
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šNew Video: Claude Code Skills Are Broken (Beginner to Pro)
I've genuinely never been as productive as I am right now, and it all comes down to Claude skills. In this video, I break down everything you need to know about them, even if you've never heard of the concept or built a single skill before. I'll explain what skills actually are, why you should care about them, and exactly how they work under the hood. I'll even do a full live build of a skill from scratch so you can see the entire process in action. By the end of this video, you'll be a pro at building and using Claude Code skills. GRAB MY SKILLS HERE
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š Weekly Wins Recap | Feb 21 ā Feb 27
SaaS builds. $16K contracts. First clients. Outbound systems going live. This week inside AIS+ was all about leverage turning into real results. Here are a few standout wins inside AIS+ š š @Michael Elliott landed a $16.8K contract in just 10 hours of work using Claude Code. š @Krishna A closed another $2,000 deal - now $4K+ this year building apps, agents, and SaaS. š @Viktorio Halcu secured his first client on commission, building an AI outbound calling agent. š @Ahmad Abd Alkarim built his first full vibe-coded SaaS with multi-tenant authentication and dashboard systems. š @Mike Thomson launched Outreach Dashboard v2 - full cold email infrastructure stack live and ready to scale. š„ Super Win Spotlight: @Mike Thomson | Systems Before Scale Mike didnāt join looking for magic. He joined because he saw people actually building. After testing multiple systems, he rebuilt his entire cold outreach infrastructure using insights shared inside AIS+. Domains. Inboxes. Warmup. Follow-ups. Automation stack ready. Now heās weeks away from launching outreach at scale. Mikeās journey is proof that you donāt need hype - you need systems, consistency, and the right room. š„ Watch Mike's story š āØ Want to see wins like this every week? Step inside AI Automation Society Plus and start building assets that compound š
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How to Pitch Automation
How to Pitch Automation Without Sounding Like a Sales Bro Stop pitching tools. Nobody cares. Bad pitch: āI build AI automations using n8n and agents.ā Good pitch: āI help [niche] reply faster to leads so they donāt lose deals.ā Your pitch must answer one question: What problem do you remove? If your pitch needs: slides long explanations technical terms Itās weak. Clear beats clever. Always. š§Questions:-> 1. How you're pitching your builds? 2. any add-ons? 3. which point is more useful? 4. where you're struggling in your work which pitching?
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Insurance Company Cut Claims Resolution From 30 Days to 7.5 Days - Saved $82M Annually š„
Insurance company processing 3,200 claims monthly. Average resolution: 30 days. Built automated claims workflow. Resolution time dropped to 7.5 days. Saved them $82M annually. THE CLAIMS DOCUMENT CHAOS: Every claim = 8-15 documents: - Accident photos (often blurry phone pics) - Police reports (scanned PDFs) - Medical records - Repair estimates - Policy documents Manual processing: 45-60 minutes per claim. Error rate: 8-12%. Customer complaints: constant. THE 7-NODE WORKFLOW: 1. Email receives claim documents 2. Convert photos and PDFs to readable text 3. Pull policy number, claim amount, damage description 4. Validate against policy database 5. Auto-approve simple claims 6. Route complex cases to adjuster 7. Update claims system WHAT MADE IT WORK: The validation layer. Not everything auto-approves: - Simple claims under $5K with all docs = auto-approved - Missing documents or high value = human review - Fraud indicators = specialist queue Insurance can't afford mistakes. The workflow had to be conservative. THE RESULTS: Before: - 400,000 claims annually - 30 day average resolution - 120 claims adjusters - Customer satisfaction: 67% After: - 400,000 claims annually - 7.5 day average resolution - 75 adjusters (45 moved to complex cases) - Customer satisfaction: 89% - Annual savings: $12M THE SALES PITCH: "Your customers wait 30 days for simple claims. My workflow gets it to 7 days - and your adjusters focus on the complex stuff that actually needs expertise." Sold to 3 independent insurance agencies at $4,200/month each. THE MARKET TIMING: Insurance AI adoption jumped 325% in one year. They're actively looking for this. Not "should we automate?" - it's "why haven't we already?" š„ Workflow here and š All templates in Github What industry went from skeptical to desperate for automation in the last 12 months?
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