A construction team used to spend weeks digging through data market conditions, zoning info, environmental risks, flood maps, soil safety, nearby hazards…Dozens of tabs. Endless PDFs. Too many variables. Now? They type in a property address……and get a full land development analysis in minutes. Not just a summary —but things like: ✅ environmental risks ✅ hazard zones ✅ nearby infrastructure ✅ development suitability ✅ market context ✅ potential red flags all pulled, compared, and organized automatically. It’s not replacing experts it’s removing the part nobody wants to do: the endless searching. We’re slowly moving from “collect the data ”to “ask the question.” If research in your industry suddenly became instant instead of weeks, what decision would you make faster?

Hi its been a long time! first of all @Brendan Jowett congrats on how you have grown this community! I was here early this year and i have been super busy building other things in the AI space however i am working on a workshop for a summit to share how to build voice agents and I previously used Vapi however i have not been deliving them in a while due to new AI products we have been offering. I wanted to know if everyone is still using Vapi and if you have updated content on it in here in community.

I went 6 months with zero leads. April → October? Crickets. Outreach felt like screaming into the void. I tried to diagnose the “empty calendar” problem. Turns out the problem was… me. I was doing what every AI consultant does: “Hey [Name], I help companies with AI automation…” Instant delete. It wasn’t wrong. It was just aimed at nobody. So I stopped trying to “scale outreach” and asked a different question: What if every cold email felt like I actually read their LinkedIn? Not the fake personalization. Not the “I noticed your post about…” nonsense. Real relevance. So I built an n8n workflow that does the boring work for me: 1. Scrapes a prospect’s latest LinkedIn activity 2. Spots their real business pain (based on what they publicly talk about) 3. Generates an icebreaker tied directly to their world 4. Drops it into a tight, ROI-driven email 15 seconds per lead. 100 leads/day without feeling like a spammer. Before: 200 generic emails → 2% replies → ~1 call/month After: 100 personalized emails → 6% replies → 6 calls/month Same effort. New system. I stopped pretending people should care about my pitch. I started proving I understand their world. Most cold outreach fails because it’s selfish. You’re asking strangers to care about YOUR solution before showing you get THEIR problem. AI doesn’t magically fix that. But it does make the research scalable. Automation should do the mechanical work (scraping, checking posts, creating briefs, filling templates) so you can do the human work (writing emails people actually respond to). I wasted 6 months before accepting that volume without relevance is just noise. Relevance at scale? That’s a pipeline. If you’re stuck in the spray-and-pray loop: Don’t just “improve your templates.” Fix your targeting. Speak in their language, not in “AI automation jargon.” Make every first line earn the next sentence. What’s killing your pipeline right now—volume or relevance?

Most people think building an AI agent means giving it: • a task • a tool • a prompt That’s not an agent — that’s a macro with better marketing. A real AI agent has three layers 👇 1️⃣ Input Pipeline (Signal Intake)This is where the agent listens: – user queries – system changes – external events – live data feeds If the agent isn’t consuming real signals, it can’t act intelligently. 2️⃣ Reasoning Layer (Decision Engine) This is the agent’s “brain”. It evaluates options, simulates outcomes, and chooses the next step. Without structured reasoning → it becomes unpredictable. 3️⃣ Feedback Loop (Self-Improvement)This is where 95% of agents fail. If the agent can’t learn from: – user corrections – outcomes – failed attempts – time-based patterns …it cannot evolve. The future belongs to agents that improve with usage, not ones that repeat the same workflow forever. Automation gives speed. Agents give adaptation. And adaptation is where exponential value hides.

I've switched from eleven labs to minimax, and I have to say, I'm impressed! Anyone have different experiences with minimax for English language?