We replaced a $1,000/month receptionist with AI. It never sleeps. Books appointments 24/7. Handles 90% of calls without a human. And costs less than dinner. This AI Voice Agent is part of a new automation suite I’m launching called Jarvis - built to help agencies and service businesses own SaaS for $60/month. You can: → Plug it into any business (HVAC, MedSpa, Real Estate, etc.) → Deploy in minutes → Sell it for $1K–$5K+ per client → Or use it to grow your own business on autopilot We’ve already built 20+ systems like this across industries — and now we’re handing you the exact system. 🚀 Want early access? I'm hosting a private webinar for early-bird members: ✅ Full demo ✅ Plug-and-play systems ✅ Lifetime license + training + support 💬 Comment “jarvis” And I will send the invite P.S . Early birds will get 40% OFF for lifetime ! ⚠️Those who commented under my previous post, fill out the invite form ⚠️

Every time someone says,“I don’t have time to analyze all this data,”or“I wish I could personalize experiences for each user,”or“We don’t have enough people to scale this campaign” AI quietly replies:“I can handle that.” We’re standing at the edge of a revolution where the real winners won’t be the ones with the most data but the ones who know what to do with it. 💬 So here’s a question for you AI folks:What’s the biggest excuse AI helped you eliminate this year? Let’s start a thread 👇

Hi y’all I am building out a flow and wanted to know what model would be the best at answering basic automation questions? I’m thinking about going with Open Ai or Claude but I am open to any suggestions

About six months ago, Anthropic dropped something revolutionary—MCP (Model-Context Protocol). At its core, MCP is a standardized way for AI agents to interact with external tools—a universal language that eliminates the need for rigid, tool-specific integrations. Right now, most AI systems rely on custom API calls or platform-specific plugins to access data from services like Google, GitHub, or HubSpot. The problem? This approach is fragmented, limiting flexibility and scalability. What Makes MCP a Big Deal? Think of it as the TCP/IP or HTTPS of AI—a universal language that lets AI agents access external services without being locked into clunky, tool-specific function calls. Imagine a JARVIS like AI assistant that: ✅ Writes code and stores it in Supabase without manual API setup ✅ Pulls real-time data from Google Docs, GitHub, and Figma—all in one flow ✅ Automates sales workflows by interacting with HubSpot CRM as if it were a human assistant No more one-off integrations. No more middleware hacks. Just pure interoperability between AI agents and digital ecosystems. What This Means for the Future? This simplifies AI development and unlocks a future where AI agents can: • Self-configure and adapt to different environments • Communicate seamlessly across platforms • Automate complex workflows with minimal human input While we’re still in the early days, MCP is setting the foundation for truly autonomous AI systems. What’s Next? Should we build an AI Jarvis for our AI Avengers community—one that leverages MCP to interact with tools dynamically? Comment your Thoughts Avengers!