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World Engine – Build Worlds Like Brandon Sanderson

Hacker News

World Engine – Build Worlds Like Brandon Sanderson

Hey HN, After hours of re-watching Brandon’s creative writing BYU lectures[1], we converted his ideas into a framework to rapidly iterate and build sci-fi fantasy worlds. The app is divided into 3 sections: - World: To create a unique magic system intertwined with distinct physical and cultural phenomena - Characters: To create character arcs and give them unique traits that evolve over time - Plot: To weave a series of events in this world following the character arc We are newbie devs, any suggestions on the app improvements and usability would be great! The idea is to capture the essence of Brandon's approach. We can't help but wonder if other 'earned insights' can be converted into similar applications for different use cases outside of fiction. [1] https://www.youtube.com/watch?v=0cf-qdZ7GbA&list=PLSH_xM-KC3...

Visit product View on Hacker NewsSep 10, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, physical · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% 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.

Correct prediction on native model

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