Discussions

Over the past year, I've noticed something interesting about the AI image generation space. Every time a new model is announced, social media fills with screenshots, benchmark comparisons, and opinions within hours. But when I actually want to learn how a new model works, what it can do, or how it compares with existing tools, the information is usually scattered across blog posts, X threads, videos, and documentation. I found myself spending more time searching than creating. That became especially obvious when Meta introduced Muse Image. The announcement highlighted improvements in prompt understanding, image editing, multi-reference composition, and creative workflows, but practical information was spread across multiple sources. Instead of bookmarking dozens of pages, I decided to organize everything in one place. Building the project wasn't about launching another AI tool. It was about creating a resource that I personally wanted to use. While working on it, I realized something that probably applies to many founders: sometimes the simplest products solve the biggest frustrations. Rather than trying to compete with image generation models themselves, I focused on helping people understand them. As I collected information, I also started organizing topics that creators actually care about: * What makes Muse Image different? * How should prompts be written? * Which creative workflows are worth testing? * What kinds of projects benefit from reference images? * How might the model fit into existing design workflows? Those questions seemed far more useful than simply listing features. Another thing I learned is that creators don't necessarily need another AI model every month. They need better ways to evaluate new models, compare capabilities, and prepare before adopting them. That's why I built Muse Image as a focused resource instead of another generic AI directory. The project continues to grow as new information becomes available, and I expect it to evolve alongside the broader AI image ecosystem. Building this also changed the way I think about startup ideas. Not every founder needs to invent groundbreaking technology. Sometimes the opportunity comes from organizing information better, reducing friction, or making complex topics easier to understand. Those improvements may seem small individually, but together they create real value for users. I'm curious how other founders approach this. When a major AI model is announced, do you immediately start building with it, or do you spend time understanding the ecosystem first? I'd love to hear how other builders decide when a new technology is actually ready to become part of their workflow.

This is beautiful i recently started creating and experimenting with AI image models and the images attached are my testings lol i nailed it with realistic prompting guys which model performs best share your thought

Creating your own large language model sounds like a great way to avoid expensive API fees, until you discover how costly and complex the process really is. Training and maintaining an Al model requires enormous amounts of data, computing power, and expertise.

"The month I tested 20 A/Bs and learned nothing" Most people running Meta ads want to keep control of targeting. I get it. But at a small budget that's usually the wrong thing to be protecting... Here's what happened that changed how I think about it. I had a client, restricted budget, and we were testing hard. 20 A/Bs a month. And we were stuck. Results wouldn't move. At some point I made a call: maybe we're not testing the angle, we're just drowning it. Too many variables, not enough spend behind any of them to ever get clean data. So we did the opposite of what felt right. We tested less. Cut it to about 20% of what we were doing and let Advantage+ take over the rest — let it develop, gather data, do the targeting. Two weeks later we doubled the previous month's results at a lower CPA. Less work. Better results. No brainer. Here's how I actually decide now, by budget: Under $5-10/day per ad set: don't expect to test anything. There's no data down there. If you just need your message in front of the right people, use detailed targeting and take what comes. Bigger, but under ~$3k/month: go Advantage+. Meta is strong enough today to get you real performance, and you don't have the volume to test targeting manually anyway. Spending more, with real funnel levels: now use your own data and custom audiences. Before that, there's a high chance you don't have enough data and it ends up all over the place. And when someone tells me they don't want to give Meta control? Meta wants you to spend money. To do that they trained a machine on billions of dollars of ad spend to limit your testing and find your buyers. On average it gets better results. Yes, sometimes it shows your ad to someone completely off. But it also shows it to people you'd never have targeted — buyers no manual detailed-targeting setup would ever have found. So why not give it a shot? Advantage+ won't test your placements for you. But it'll test your angle. And the angle is the first thing you need to get right before anything else matters. Curious where people land on this — are you still targeting manually, or have you handed it over?