Three AI image models. 15 rounds. Same prompts. One of them you can download and run on your own GPU. GPT Image 2, Nano Banana 2, and open weights Ideogram 4, head to head on photorealism, text, spatial reasoning, and style. The closed giants were supposed to win easy. They didn't. The open one came in third by a hair, then beat both giants at rendered text, the thing AI is usually worst at. You don't need anyone's API for top tier results anymore. Full scorecard at the end. Here's the link to get started with Ideogram 4.0 👍 https://docs.google.com/document/d/1vveMIlHaM12fNERyfSzAtvQiyFSfG4cYvGqgXgxtWlk/edit?usp=sharing

Local businesses miss calls every single day, and every missed call is money out the door. This video breaks down the exact system I use to build, demo, and sell AI receptionists that catch those calls and pay me recurring monthly income. The whole thing runs on two tools: - Trillet AI to host and white-label the agent - Claude Code to build and configure everything for you You slap your name on it, connect your Stripe, and keep the monthly revenue. What's inside: - Why local businesses desperately need this - Building a personalized demo in 5 minutes from just a website URL - White-labeling so your client never sees the platform - Sub-accounts, onboarding, and invoicing - Letting Claude Code do the heavy lifting on the build - Knowledge base, voice, fallbacks, and SMS booking setup - Getting a phone number connected - The sales math that closes deals on the call - The full 7-step process from demo to live One 5-minute demo can turn into monthly passive income. Once you see it as a repeatable process, you just run it over and over. Drop questions below 👇

In this video, I break down my proven 10-step onboarding process for building and selling voice AI receptionist services. ✅ Resource files in classroom: https://www.skool.com/automate-what-academy/classroom/dc7edadd?md=fc01fe4ae3ce4b94803486bd3da47fb5

Template now in AWA Free community! Full breakdown tutorial in AWA PRO! I show you an AI Receptionist for "Mike’s Plumbing" that can answer FAQs, schedule appointments directly into the calendar, and add customers to the CRM. Click here 👉 Resource files & links in the classroom

Nano Banana 2 has been my go-to AI image model for the past several months. Then GPT Image 2 dropped, and I had to see if it could actually compete. So I ran the same 15 prompts through both models at max quality and scored them on prompt adherence, text fidelity, spatial reasoning, style match, and anatomy. Final score: 9-4 in favor of GPT Image 2 (with 2 ties). I built this entire test using Claude Code inside Google Antigravity. Just brain dumped the idea, Claude Code wrote the Python, made the API calls, compiled everything into a 15-slide deck, and even did a first pass at judging (I pushed back on plenty of its calls). Where each model wins: - GPT Image 2 dominates text rendering (5-0), artistic and stylized work, and authentic photo realism that actually looks shot on a phone. - Nano Banana 2 still wins on spatial reasoning and a more polished, glossy photo aesthetic. For me, GPT Image 2 is my new pick for thumbnails, marketing assets, and community content. I'm officially back in the OpenAI ecosystem for image generation.