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Generate Endless Fantasy Worlds

Hacker News

Generate Endless Fantasy Worlds

All - I recently built this fantasy world app generator which I thought was pretty cool. You can put in a few words or a plot and the AI will give you a full Fantasy Wiki Card. I've also experimented with actually writing out the story but AI is still not as good at that (though getting better). I had some ideas on using this as a tool to help authors to be able to flesh out their ideas but would love to get feedback and thoughts if you see other ways to grow this? Also you can generate words via the Builder page if interested! Thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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