I started this community because I'm tired of the same story playing out over and over: Business owner needs a system built. Hires a developer or agency. Pays good money. Gets something that half-works or doesn't work at all. Developer disappears or wants more money to "fix" it. Or worse - signs up with a marketing company that charges monthly, promises leads, and can't show a single dollar of ROI after 6 months. I've been on both sides. I've been burned, and I've spent years learning to build my own systems so I don't have to depend on anyone. This community is for people who want to do the same. What you'll find here: - Real talk about what works and what doesn't - Breakdowns of systems I've built (lead gen, voice AI, automation) - Honest reviews of tools and platforms - Help when you're stuck What you won't find: - Courses selling you theory - Gurus who've never built anything - Pitches disguised as "value" If you're here, drop a comment and tell me what you're working on or what problem brought you here. Let's build.

I might turn this into a class because there is so much to cover in just this one section. I think this is a good place to start and have real honest conversation. For what it is worth I used these methods daily, and know how to deploy each one successfully. Not as a developer, or someone selling something to other business owners; I am speaking as the business owner who developed, manages, and uses these systems.

I recently set up a LiveKit dashboard product to manage voice AI agents through a UI. After digging into how it actually works under the hood, I want to share what I found so others can make informed decisions. My Experience After a full day of troubleshooting the backend, the system is still not fully functional. Errors appear throughout the dashboard, and agent creation doesn't work reliably. What struck me most was the resource consumption - 10+ containers just to run a single test agent, and that agent still doesn't work correctly. Why This Architecture Fails for Voice AI When someone calls your business, they expect an immediate greeting. Not 2-5 seconds of silence while processes spawn. The system spawns agents on demand instead of keeping them running. Every layer adds latency. Voice AI should have agents already running and waiting for calls. Other problems I found: - Single point of failure - one container runs everything - No process management - no health checks, no automatic restarts - Resource contention - all agents share the same memory and CPU - Dynamic code generation - agents created on the fly with no version control - Registry dependency - if the developer's server goes down, you can't rebuild What Actually Works I run a production voice AI system that handles real calls for my business. The key differences: - Persistent processes - agents stay running, handle many calls - Proper process management - automatic restarts, monitoring, per-agent logs - Pre-loaded dependencies - everything initialized at startup, not per-call - Independent agents - one agent's issues don't affect others - Direct control - I own all the code, no external dependency My agents answer immediately because they're already running and waiting. Credit Where It's Due The developer was responsive and helped troubleshoot. The UI shows real effort, and the concept is appealing. But building production voice AI requires more than coding skills. Understanding how real-time systems function under load, how processes should be managed, and what architecture choices impact latency - these come from experience. A good idea isn't enough if the foundation doesn't support the use case.