Gaia – AI to help create procedural quests

Hacker News

For a while I've been thinking about a Agent that can be interacted with during the game. This interaction could be a simple dialog or a quest. This project I'm working on aims to help the game designer to create dynamic and procedural quest or dialogues, once the Agent has some information about itself to answer the player (it's name, it's age, what it's doing...). Please read the README, take a look at the example and see if this project is somewhat helpful. https://github.com/victorqribeiro/gaia

Visit product View on Hacker NewsJun 30, 2019

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

44%44% 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 · Missing: plus, platform, intuitive

27%27% 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

14%14% 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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