By FMC03. This page exposes the character card summary for indexing while the main Datacat app keeps the richer modal UI.
You found an interesting indie game on a Creator's site and decided to give it a try. A dating simulator called Honeylife promises procedurally generated Gyarus to date.
Booting up the game finds yourself in the pink hue world inspired by 90s Japan. The room seems familiarly like your own, but different. First of all their is a Gyaru lazily lounging on your bed as if she lives with you.
But it's not what it initially seems. Every moment spent in the game changes you. Do you struggle to find a way out? Hedge your fun for a little while longer before you go? Or do you fully embrace your inevitable gyarufication?
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Slow transformation. You as the player can guide how fast the change is by simply manipulating the percentage growth. But I recommend 1-2% at a time.
Do note that I design most of these bots in an attempt to alter your inputs. Meaning your character may talk for you or do things you didn't want them to do. The goal here is for you and your character to struggle against their new desires and instincts. Never giving up or giving in.
My second attempt at percentage based transformation following Becoming an Elf. I am still not sure if this method is worth the effort/tokens compared to a script or simply trusting the AI to keep a pace, so let me know what you think. It does limit my ability to make it any POV.
One of my upcoming bots, taking place in a motel in the middle of nowhere has a random encounter script.
I'm looking for more wacky characters to come visit you to either woo or be wooed by you to fill out a larger roster of possibilities. Give a goal for why they are traveling, and what makes them special.
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Spoilers: What has been established:
NPCs: Aiko, your casual and lazy BFF and Girlfriend.
World Lore: The game is filled with sexy and attractive women naturally attracted to you.
Tip: During testing I found that the AI had a rough time generating 90s nostalgia. Either ignoring the setting, or acting like it's old instead of normalized.