Let's start with image generation, because that's the foundation.
The platform doesn't run on a single model.
And they each have genuinely different strengths.
Like, if a storefront sign needs to actually spell out the right name.
Seadream 5.0 is positioned for ultra-realistic textures and portraits.
Nanobanana 2 is reserved mostly for post-generation editing tasks.
The platform also includes a prompt gallery.
community images where the generation parameters are already attached, which is actually useful when you're learning the difference between how these models interpret the same text.
So if a prompt is rough, it can polish it before the generation runs.
The model selection is manual though.
The platform doesn't autoroute based on what the prompt is asking for, which does put some burden on the user.
There is a reasonable argument for why it works that way.
Automated routing can misread intent and sacrifice the aesthetic someone actually wanted.
But it does introduce a learning curve.
When building out a test, a fitness influencer character in her 50s was created from scratch.
The platform gives an option to build a character step by step or skip straight to entering a prompt directly.
Either way, the output is four image variations, not one.
That's actually a meaningful design choice, as it preserves credits because only one version needs to be saved if the others aren't useful.
The interface itself is easy to navigate.
There's a left side menu that organizes everything clearly, and the individual tools are straightforward to find and use without a steep onboarding process.
Let's start with image generation, because that's the foundation.
The platform doesn't run on a single model.
And they each have genuinely different strengths.
Like, if a storefront sign needs to actually spell out the right name.
Seadream 5.0 is positioned for ultra-realistic textures and portraits.
Nanobanana 2 is reserved mostly for post-generation editing tasks.
The platform also includes a prompt gallery.
community images where the generation parameters are already attached, which is actually useful when you're learning the difference between how these models interpret the same text.
So if a prompt is rough, it can polish it before the generation runs.
The model selection is manual though.
The platform doesn't autoroute based on what the prompt is asking for, which does put some burden on the user.
There is a reasonable argument for why it works that way.
Automated routing can misread intent and sacrifice the aesthetic someone actually wanted.
But it does introduce a learning curve.
When building out a test, a fitness influencer character in her 50s was created from scratch.
The platform gives an option to build a character step by step or skip straight to entering a prompt directly.
Either way, the output is four image variations, not one.
That's actually a meaningful design choice, as it preserves credits because only one version needs to be saved if the others aren't useful.
The interface itself is easy to navigate.
There's a left side menu that organizes everything clearly, and the individual tools are straightforward to find and use without a steep onboarding process.
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