What if you could "Prompt" the UX of any App?

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I saw Pietro Schirano on X demo a chat app that could change its UI tone(dark mode, mario, windows 98, etc) through prompts, and it got me thinking: what if you could go beyond UI and actually change the UX on demand without losing state? So, I built a to-do list app that lets you prompt it to completely transform the UI and UX. For example: - Want a Gameboy-style console UI where the tasks show up one at a time, sorted alphabetically? - Prefer a Tinder-style interface? Prompt and you get it! The magic here is that the app preserves your state (e.g., completed tasks, pending ones, etc.) while letting you experiment with disposable, malleable interfaces. Although I demoed this with a to-do list, the possibilities are much broader: A browser with a Tinder-style interface to quickly swipe through tabs when you've got too many. The broader goal is to explore malleable software: if your data models and operations are well-defined, then the UI and UX can be a flexible function over that, shaped by user intent. This was a fun experiment in letting people play with software, creating interfaces that adapt to their immediate needs. I'd love to hear your thoughts—especially on where you think this idea could be pushed further.

Visit product View on Hacker NewsJan 22, 2025

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

84%84% 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

51%51% 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

50%50% 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

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 · Strong signals: interface · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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: chat · 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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