One model for the entire product line: how to collect cards
With twenty products, inconsistency in cards turns into a problem. How to fix one character and one studio for the entire line - and where such a series usually breaks down.
With twenty products, inconsistency in cards turns into a problem. How to fix one character and one studio for the entire line - and where such a series usually breaks down.
Useful memory of the user and privacy are not mutually exclusive if you build around fail-closed and check consent at the source, not at the screen.
Seedance 2 cuts prompts in batches - faces, names, IPs, weapons. I’m figuring out how to rewrite a prompt, how to deceive a face detector in a photo, and what tools for this are built into NeuralSpace.
Two ready-made templates: photo and video. GPT Image 2 generates a frame in the style of a KBO broadcast, Kling 3.0 makes a video. You upload a photo of your face and you get the result.
People don’t want to “generate from scratch” - they want to repeat what has already caught their attention. Analysis of the engine “content → pre-filled action → result”.