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SpreadPaper – span a single wallpaper across multiple macOS monitors

Hacker News

SpreadPaper – span a single wallpaper across multiple macOS monitors

Hi HN, I built an open-source macOS utility called SpreadPaper that lets you take one high-resolution image and spread it seamlessly across all your connected monitors. I personally use multiple monitors for design and coding, and I got tired of wallpapers that either tile awkwardly or get cropped badly. SpreadPaper is my attempt at a minimal, no-bloat solution that just works. If you also care about multi-monitor aesthetics, I hope you’ll find it useful. Give it a star on GitHub if you like it and feel free to leave your feature requests/feedback.

Visit productView on Hacker NewsNov 26, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, single · Missing: agents, agent, cursor
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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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 · Missing: plus, platform, intuitive
25%25% 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 · Missing: web3, chat, crypto
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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