Open-Source AI Adventure Game

Hacker News

Hi HN! I'd love your feedback for this AI storytelling game. Remember when you used to read choose-your-own adventure books as a kid? We're trying to re-create that. The site lets you create adventures by seeding them with parameters that end up feeding LLMs: title, genre, writing style, etc. Once you've done that, you can play your book like a game: with custom chapters, illustrations, voice narration, choices, and dice rolls. We’re really into the idea of merging the open-ended generative AI with goal-directed story arcs that the game author can control. Everything is open source (links below) and we’d love your contributions & ideas. I know there's a lot of great work in this space, and the whole team that built this is eager to learn. Agent Source Code: https://github.com/steamship-core/ai-adventure Web Source Code: https://github.com/steamship-core/ai-adventure-agent Video playthrough: https://youtu.be/r5iJkELa-kk

Visit product View on Hacker NewsDec 7, 2023

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

86%86% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, para · Missing: mobile apps, ios, personal

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

50%50% 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 · Strong signals: arr · Missing: mrr, revenue, profit

21%21% 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.

Incorrect prediction on native model

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