Same here 😅 I run a children’s shoe store and started a Skool community where I share what’s actually working, what’s not, and the random things you only learn by doing it. Nothing fancy. Just real stuff from retail and e-commerce. If that sounds like your lane, come hang out.

It's time to celebrate each other! In the COMMENTS BELOW, please share a Skool community you think is doing really great things. It can be one you've participated in yourself or one that you've heard from others is amazing. Please do the following: 1. Tell us the name of the community and link to it (affiliates allowed). 2. Tag the person who owns the community. If they are not in the CLASSIFIEDS, then please send them a DM and let them know that you mentioned them here. I promise you will make their day! 3. Describe why the group is so wonderful and how it has helped you. Please DO NOT just copy and paste the About page! 4. Comment on each others comments and scroll through the referrals to see if there's an amazing group you are missing out on! Thanks so much for supporting each other and this community! Please note that any #FriendFriday mentions that are created as new posts will be deleted. Thanks! Please DO NOT promote yourself. 🤨. The entire community is for promoting yourself. This post is for celebrating someone else. 😊

We’ve been running a handful experiments with ChatGPT Image 2 — image tests, composition swaps, and prompt chops that teach faster than a week of guessing. If you want better image outputs (and fewer wasted renders), these study results will save you time. What we posted: A gallery of test cases showing the same base prompt run through Image 2 render, iteration or edits: composition recrops, material swaps, lighting tweaks, and object replacements. Quick refresher — what ChatGPT Image 2 adds (short & usable): • Direct image edits with instruction language: you can upload an image and tell the model what to change in plain language (swap fabric, change pose, remove background object) — not just regenerate from scratch. • Region‑aware edits: point to or mask a specific area and ask for targeted changes (recolor a jacket, replace a prop, refine a face) while the rest of the image stays intact. • Better compositing intelligence: Image 2 keeps lighting and perspective more consistently when you ask it to insert or replace elements, reducing the “pasted” look. • Faster iterative tweaks: combine short, layered edit instructions (e.g., “tighten crop → warm rim light → swap shoes”) to get to a final faster than full re‑renders. • Guidance on style continuity: ask it to match a specific finish (matte, latex, satin), and it will prioritize material behavior across edits. What surprised us (real notes): • Region edits are massively faster than full re‑renders when you only need one small change — but they’re only as good as the instruction precision. • The model preserves global light far better than Image 1, but it still needs nudges for specular consistency on shiny materials. • Small wording changes matter: “make the jacket satin with soft specular highlights” vs “make shiny jacket” produced very different, and repeatable, results. Top practical tips from the study: 1. Start with a precise visual goal — one sentence: “Make this portrait read like a glossy magazine cover with satin jacket highlights.”