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.

For newer Claude models, over 80% of the Claude Code system prompt was removed with no measurable drop in coding evals. So how much of your CLAUDE.md is still doing anything? Newer models need less hand-holding than most of us still give them. 5 habits worth unlearning and updating: 1️⃣ Long step-by-step prompts → give a goal and a verifier 2️⃣ Building from a vague idea → get interviewed into a spec first 3️⃣ Rules written for old models → audit and delete 4️⃣ Forcing subagents on small tasks → let Claude decide 5️⃣ Reviewing in the same session → use a fresh one Plus a bonus skill that makes Claude Code question your idea before it writes a single file. All prompts are in the carousel 👇 Which rule in your CLAUDE.md are you scared to delete?

LLM prices dropped up to 80% this year. Most AI bills didn't. In about four months: → OpenAI cut GPT-5.6 Luna's price by 80% → Its Sol family got ~75% cheaper two months later → Anthropic shipped Opus 5.5 at 40% lower operating cost than Opus 5 Data scientist Max Woolf points to three drivers: open-weight pressure (DeepSeek V4 Flash), cheaper serving, and caching. The last one is the surprise. He estimates 96–99% of agentic coding spend is cache reads, because agents resend the whole conversation every turn. So the price per token matters less than whether your cache stays warm. 3 moves: 1- Sort hosts by price. 2- Keep each conversation on one provider. 3- Pick models by cost per finished task, not price per token. Budgets are tightening too. Apple reportedly cut some engineers' AI caps from $300 to $200. Do you track cost per task, or just the monthly invoice?

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.

The trend this week: smaller, faster models for everyday work. 4 launches worth knowing: 1. OpenAI Images 2.6 Two API models. Flare generates images in as little as 6 seconds for drafts. Sunburst handles detailed edits. Both output SVGs and animated GIFs. 2. Cognition SWE-2 Flash 3x faster than SWE-2 at one-fifth the cost, for code reviews, docs and routine features. Runs only inside Devin. 3. Apple Siri App Intents (developer preview) Connect your app's data and actions to Siri, chain them into workflows, and use Markdown skills for flows like checkout. 4. Terminal Mac (open source) Gives your coding agents their own macOS VM, so they can click, type and scroll without touching your desktop. Use the big model to plan. Use the fast one for everything else. Which one are you thinking to try for your AI agents?