AIWP – Context-aware DALL·E 3 wallpapers for Android

Hacker News

Hey all! This is a lightweight Android app to auto-change your wallpaper using AI generated images, implemented via the recently introduced DALL·E 3 API. I wanted something no-frills that isn't a bloated commercial option, so this is that. Open source, but also on the Play Store for convenience: https://play.google.com/store/apps/details?id=com.minosapps.... It (optionally) injects the current time, current location (city and country name only, using coarse location access) and current weather into the prompt, so you can easily get a night scene with rain in the evening, and blue skies at noon. API access is handled via Bring-Your-Own-Key, so no subscription option or anything. Just copy in your own API key. There are some presets to get you started, but best experience is using your own prompts.

Visit product View on Hacker NewsNov 26, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

87%87% 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: google, apps, context · Missing: mac, agents, macos

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

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

51%51% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% 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: subscription · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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