I ran a small experiment recently (used nano banana). The results were shocking, but that’s not what stuck with me. What really hit me was the question it forced me to confront: - What happens when the smallest shifts in technology/behavior completely reshape the way we live, work, or even think? - When AI systems can outperform teams at speed and cost, how will agencies differentiate themselves beyond just “efficiency”? - What happens to the entire agency model when business owners can plug into off-the-shelf AI tools, do we evolve into something else, or get replaced? Curious to hear, how do you see it?

THE $50K OPPORTUNITY Healthcare network needed document processing. - 50 clinics - 30,000 patient forms monthly - Budget: $50,000 setup + $5,000/month - Timeline: 2 weeks ━━━━━━━━━━━━━━━━━━━━━ MY 2-HOUR "IMPRESSIVE" PITCH "I'll build you an enterprise-grade solution!" The architecture: - Custom Python application - Dockerized microservices - Kubernetes orchestration - React dashboard - Machine learning models - 6-week timeline Their response: "We'll think about it." ━━━━━━━━━━━━━━━━━━━━━ THE OTHER CONSULTANT'S 15-MINUTE DEMO He opened his laptop. "Let me show you something." Built it LIVE: 1. n8n webhook → receives forms 2. PDF extraction → handles any format 3. Direct to EMR → their existing system "That's it. Deploy tomorrow. $50k." They signed immediately. ━━━━━━━━━━━━━━━━━━━━━ THE PAINFUL PHONE CALL I had to know what I did wrong. Me: "Your solution must be sophisticated" Him: "11 nodes" Me: "Development time?" Him: "6 hours" Me: "Maintenance?" Him: "Maybe 2 hours monthly" My solution: 240 hours to build His solution: 6 hours His profit margin: 90% ━━━━━━━━━━━━━━━━━━━━━ WHAT I LEARNED THAT DAY ❌ Clients don't buy code ✅ Clients buy outcomes ❌ "Enterprise-grade" impresses developers ✅ "It works tomorrow" impresses CEOs ❌ Complex solutions show capability ✅ Simple solutions show confidence ❌ 6-week timelines show thoroughness ✅ 2-day delivery shows competence ━━━━━━━━━━━━━━━━━━━━━ HOW I CHANGED EVERYTHING BEFORE (First 6 months): - Python everything - Average project: 6 weeks - Close rate: 20% - Monthly revenue: $3k AFTER (Last 6 months): - n8n/Make for everything - Average project: 4 hours - Close rate: 65% - Monthly revenue: $16,500 ━━━━━━━━━━━━━━━━━━━━━ THE EXACT PIVOT Stopped selling: "Custom Python solutions" Started selling: "Works by Monday" Stopped building: What I thought was impressive Started building: What actually solves problems Stopped pricing: Development hours Started pricing: Value delivered ━━━━━━━━━━━━━━━━━━━━━

Has anyone implemented an agent to handle Instagram DMs through ManyChat, but it's having trouble with multiple messages. People write "hello," "how are you?" on three different lines, and the agent treats it as three different messages. Does anyone know how to deal with this? I tried using a message buffer, but the output of that architecture is "do nothing," but it still sends an output message, which causes the agent to respond anyway, and the same thing happens.

Cold email isn’t about luck. It’s about volume. If you're sending under 400 emails/day, you're not in the game. Here’s how to scale your cold email system—without blowing your budget or burning domains: Step 1: Hit the right volume → 20–30 inboxes → 400 emails/day → 13,000+ emails/month → 4,000 prospects (with follow-ups) This is how you create consistent pipeline. Step 2: Keep it lean → Microsoft via GoDaddy – $470/year for 30 inboxes → ZapMail (Google Workspace) – $99/month for 30 inboxes → Use .com domains only → Warm up inboxes 2–3 weeks minimum → Cancel auto-renewals on GoDaddy (they sneak in price hikes) Step 3: Stay sharp on deliverability → 50/50 split: Microsoft + Google → US or EU IPs only → Use Smartlead or Instantly to manage inboxes → No sketchy domains, no spam tactics, no shortcuts The result? A cold email engine that books meetings daily—at scale. No fluff. Just numbers, infrastructure, and consistency. Built a cold email system that works? Share what’s helped most—or where you’re stuck👇 Let’s break it down.

A lot of businesses try out AI or automation, but the truth is most projects never get fully adopted by the team. It is rarely because the tech doesn’t work, it is usually because the result is not clear to the people using it. The easiest way to avoid this is to make the value measurable. If a business owner or manager can see how much time, cost, or revenue impact the automation creates, they will stick with it and even expand it. That is why tracking ROI and showing clarity is just as important as building the automation itself. If you are working on AI or automation and want to connect with others who are building in this space, feel free to reach out. I am always happy to exchange ideas.