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NovelADV – AI-assisted visual novel writing on iOS

Hacker News

NovelADV – AI-assisted visual novel writing on iOS

I enjoy writing visual novels, but keeping track of branching plots, characters and clues on a phone was messy. To make the process smoother, I built NovelADV, an iOS workspace for AI‑assisted fiction. NovelADV works like a chat‑based visual novel engine. You advance your story by talking to your characters; the app keeps a ledger of world details, characters and unresolved “clues” beside the chat so you don’t lose track of them. It also saves “checkpoints” so you can safely roll back to any decision point and explore alternate paths. To help with longer arcs, there’s a summary‑based long‑term memory that compresses earlier scenes into a short narrative so the AI doesn’t forget context. The initial motivation was to combine the fun of playing a visual novel with the flexibility of writing one, all on a phone. I’d love feedback from writers and developers experimenting with AI‑assisted storytelling – is this approach useful, or does it introduce new pitfalls? There’s a free tier on the App Store (link below). a pre‑built sample project are available . Feel free to try it and tell me what works, what doesn’t, or what you’d like to see improved. App Store: https://apps.apple.com/jp/app/noveladv-ai-visual-novel/id675...

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Actual performance

2points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, new · Missing: mac, agents, macos
89%89% 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: ios · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
27%27% 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: arr · Missing: mrr, revenue, profit
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, introduce · Missing: web3, crypto, cryptocurrency
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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