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11h •
No portfolio? Build one demo and run this 7-day plan
"I have no portfolio" is the most common reason people give for not reaching out. A small demo fixes that, and you can build one in a week. Day 1: Choose one type of business and one painful, repeated problem. Example: dental clinics that lose patients because nobody follows up after a missed appointment. Day 2: Write a five-part spec for the smallest useful fix. One trigger, one output. Skip everything else. Day 3 and 4: Build it with sample data. Never use a real business's private data in a demo. Day 5: Record a 60 to 90 second screen demo. Show the problem first, then the result. Narrate in plain language, no jargon. Day 6: Make a list of 10 businesses that have that problem. Spend two minutes on each so you can say one specific, true thing about them. Day 7: Send 10 personal messages. Here's a made-up example: "Hi Priya, I noticed your clinic lets people book online, but I couldn't find any follow-up for missed appointments. I built a small demo that sends a friendly reschedule message within an hour of a no-show. It takes 60 seconds to watch. Want me to send it over?" What makes that message work: it names one specific observation, it's short, and the ask is tiny. Watching a 60-second video is easy to say yes to, a call is not. Be realistic about the outcome. Most people won't reply, and that's normal. You're not looking for ten clients, you're looking for two or three real conversations. Every conversation also gives you practice with the discovery questions. Two mistakes to avoid. A demo that does five things looks like a tool, while one that does a single thing well looks like a result. And never send the same message to everyone, because personalization is the whole point. What kind of business would you build your demo for?
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7d •
Start here 🪜
Welcome to AI Automation Club We learn AI automation here by building real workflows. You can get set up in about 10 minutes. Do these 3 things first. Watch the 2 min intro video pinned above. Introduce yourself in the Introductions category and tell us what you do and what you want to automate. Then find a post you like and leave a comment. Classroom has the courses and walkthroughs, and a good place to start is STAR HERE course. The Community tab is where you share wins, ask questions and post your workflows. The Calendar shows live calls, including New Member Onboarding. You earn points on the Leaderboard by posting, commenting and helping others. If you’re not sure where to start, book a free 15 min call with me and we’ll pick your first Roadmap together: https://cal.com/muskan-ahlawat-kpynri/15min more about what our community having= https://www.skool.com/ai-automation-club-1084/plans Floxotech | AI Voice & Text Intake for Law Firms House rules: be helpful, don’t spam, and share what you learn.
5d •
Building an AI agent is easy. Running one in production is the real test.
Most AI agents look impressive in a demo. In production, the problems show up: - They loop and burn tokens - They lose context mid-task - They call the wrong tools - Occasionally, they take actions they were never meant to take Getting an agent to work once is the easy part. Making it secure, reliable and ready for real users is a different challenge. Here is one of the lessons worth remembering: Don’t use an agent when a deterministic workflow will do - If the steps are known in advance, a workflow is cheaper, faster and more predictable. Simplicity is an architectural advantage. I learned this while building an AI lead qualification agent for WhatsApp. I gave a single agent node every job: holding the conversation with the lead, scoring them, saving the lead, checking availability and booking the appointment. It worked, but the agent was overloaded, and I started seeing unnecessary and inconsistent outputs. Most of those tasks are fixed, predictable steps. The agent’s real value was in the conversation itself. The rest is better handled by regular workflow nodes, where the behaviour is the same every time. What’s the biggest challenge you’ve faced moving an AI agent from prototype to production? Or if you haven’t built one yet, what worries you most about it? Tell me in the comments.
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3d •
first client
@Ameeth B. you built practice automations, here’s how to turn one into a first paid client this week you said you don’t know where to look or what to say. skip “where to look” for now. match your build to a business that already feels the pain: built something that sends emails or replies? go to dentists, gyms and salons built something that moves data between sheets and forms? go to small agencies and property agents built something that reminds or follows up? go to anyone who chases invoices or bookings then do the 9pm test. message that business’s Instagram or contact form like a normal customer and note when they reply. if it takes until next morning, screenshot it. your first message to the owner is just: “I asked about [thing] at 9:14pm and got a reply at 11am the next day. I can set up an instant reply for a week so you can see the difference.” no tech talk, no “I’m learning automation”. one screenshot of their own slow reply does more than any pitch. tell me what your two practice builds actually do and I’ll point you to the exact type of business to test first.
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1d •
Shahid moving from voice.md rulesets to live
@Shahid Agha moving from voice.md rulesets to live pipelines without agents looping you’re already good at the prompt layer. the jump is that live pipelines fail because of state, not prompts. here’s a setup that fits your Supabase stack. store each voice.md as a row in an agents table (name, prompt, version). store work in a tasks table: id, type, status, claimed_by, attempts, parent_task_id, trace_id, input, output, error. claim tasks with one atomic query so two agents never take the same row: sql = update tasks set status='running', claimed_by=$1, attempts=attempts+1 where id = ( select id from tasks where status='pending' and type=$2 order by created_at limit 1 for update skip locked ) returning *; call it through a Supabase RPC. then three rules: after 3 attempts mark it failed, agents never write to each other’s rows, and a handoff means inserting a new task with parent_task_id set. the trace_id lets you follow one lead through every agent. before going multi-agent, ship one pipeline end to end: inbound message, classify, draft reply, human approves. you’ll find your loops there first.
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