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JOBifAI, an LLM+Flux Augmented Visual Novel

Hacker News

JOBifAI, an LLM+Flux Augmented Visual Novel

Hello hn, I wanted to make a Proof of Concept to show what truly original gameplay mechanics could be brought in by genAI. There are 2 in this game: - argument based language interactions: it's not a choice-based Visual Novel. To progress you need to convince NPCs. - natural language input: the LLM understands what happens and describe the result. This is, I believe the original vision of Interactive Fiction - image-decyphering abilities: to argue, you need to understand what we're talking about, and most of the clues are hidden in the generated images Most of the AI applications tend to be "human-made but worse" or "procedural generation but worse", which lead to the "AI slop" perception. In this game, some people that would generally be adverse to AI (because it displaces artists) found it fun, as it embraces its issues and the surrounding debate. The backend is AWS lambda + Replicate + Groq so it should withstand an hn hug of death. I'll put the whole project on Github soon btw. It's really short (less than 20 minutes) so I hope you'll try it (and have fun!).

Visit productView on Hacker NewsDec 13, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: visual · Missing: mac, agents, macos
75%75% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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 · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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
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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