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I made a choose your own adventure game with AI NPCs

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I made a choose your own adventure game with AI NPCs

AI usually does a terrible job acting as engaging NPCs in games. A few months back, I started trying to tackle this problem, initially with prompts and eventually with models fine-tuned on character interactions. I created this website as a testbed for the concept, and I open-sourced a fine-tuned model for those interested in experimenting with this themselves [1]. During the instruct tuning phase of training, LLMs learn to act as assistants. And they need to stop acting as assistants to produce engaging interactions. In Spellbound, telling NPCs they are AI will generally cause them to be confused or protest that claim. Most NPCs are "convinced" they are real people who exist separately from the LLM. One downside of this approach is that the guardrails formed during training generally stop functioning [2], so it's a technique that needs refinement before being applied to more mainstream applications. We're really interested in applications of LLMs for simulating characters and worlds without the typical blandness of an instruct-tuned LLM. Solving this problem will unlock a ton of use cases in media where AI currently doesn't cut it. [1] https://huggingface.co/hf-100/mistral-spellbound-research [2] Regardless of how you feel about guardrails, some ability to guide content is useful for making children's stories, for example.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, para · Missing: supports, reddit linkedin, podcasting
92%92% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, open · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, para · Missing: mobile apps, ios, personal
38%38% 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
22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real people · 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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