I went through over 200 of Alex Hormozi’s videos and posts to break down how he uses AI in his business. We cover his workflows for content, sales, and customer support, why he capped his team’s AI budget, and the $350,000 automation mistake he warned about. You’ll learn how to decide what’s worth automating, where humans still do a better job, and how to aim AI at the problems holding your business back.

Something I've been thinking about is how AI agents handle data that changes while they're processing a task. For example, an agent checks a booking slot and sees it as available. Before it actually creates the booking, another process books that same slot. The agent made its decision using valid information, but that information was already outdated by execution time. I'm curious how experienced n8n builders handle this. Would you use optimistic locking, database transactions, or revalidate the resource immediately before committing the action? It seems like a small timing issue that could create some surprisingly difficult bugs.

One thing I've been thinking about is how much people rely on system prompts to control AI agent behavior. You can write detailed instructions, define boundaries, and provide examples, but what happens when the model ignores an instruction, calls the wrong tool, or produces an output that looks valid but is actually incorrect? I'm curious how experienced builders are handling this at the architecture level. A few things I'd love to understand: - Structured outputs: Do you rely on JSON schemas and strict validation, or do you add another validation layer before executing actions? - Guardrails: How do you prevent an agent from triggering sensitive actions without checking permissions or business rules first? - Tool execution: Do you let the LLM decide which tools to call freely, or do you constrain tool access based on the current workflow state? - Observability: How do you trace the model's inputs, tool calls, outputs, latency, and token usage across a multi-step execution? - Fallbacks: When the model produces an invalid response or confidence is insufficient, do you retry, switch models, or route the task to a human? My current thinking is that prompts should guide behavior, but deterministic code should enforce critical business rules. I'd love to hear how you guys approach this in production, especially when building agents that interact with CRMs, booking systems, or other external services.

Most AI websites look fine and still feel cheap, usually for the same few reasons. I studied over 10,000 AI websites to find out what makes a good one, and here's what I learned: every site that actually drives results, the kind businesses will pay $1,000 for, follows the same 5 patterns. I'll walk you through what each one is, why it works and how to apply it, including the one I've been guilty of breaking on my own site.

I've been thinking about something while learning to build n8n automations. Let's say you build a lead follow-up system for a dental clinic. Everything works as expected, but a month later the client changes their booking process, adds new lead sources, or wants different follow-up rules. How do you usually handle these changes? Do you build workflows in a modular way from the beginning so they're easier to update, or do you treat every change as a separate paid improvement? Also, how do you prevent small client requests from turning into endless scope creep? Would be interested to hear how people with actual agency experience handle this.