Hogwarts Full of AI Characters

Hacker News

We’ve built a 2D game populated with AI-powered NPCs that interact dynamically with players. Each AI agent can plan, take actions, and adapt its strategy by reflecting on the outcomes of its decisions. While the current set of AI actions is limited, we’re actively expanding their capabilities to include features like voting and combat. Would you rather explore a world of fictional characters or a Silicon Valley replica featuring AI versions of Elon, Zuck, and others?

Visit product View on Hacker NewsDec 12, 2024

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Actual performance

1points

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Launch Intel predictions

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

68%68% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

53%53% 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.

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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

15%15% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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