I built and launched labubu wallpaper website

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I'm a huge fan of Labubu (those adorable elf-like art toys from Pop Mart!) and I was always a bit frustrated trying to find a good, centralized collection of high-quality wallpapers featuring them. They were often scattered across random forums, social media, or were low-resolution. So, I decided to scratch my own itch and build: https://labubuwallpics.com/ It's a simple website dedicated to curating and sharing Labubu wallpapers for desktop and mobile. My goal is to make it a go-to place for fellow fans to easily find and download awesome backgrounds for their devices. What you'll find: A growing collection of Labubu wallpapers, include labubu live wallpapers Easy navigation and one-click downloads. This is very much a passion project, born out of my love for Labubu. I'm still actively adding new wallpapers and tweaking things. I'd absolutely love for you to check it out and let me know what you think. Link: https://labubuwallpics.com/ Any feedback on the site's design, usability, the wallpaper selection, or features you'd like to see in the future would be incredibly helpful! Are there any specific Labubu series or themes you'd love to see wallpapers of? Did you encounter any issues while browsing or downloading? Any suggestions for improvement? Thanks for taking a look! Hope some fellow Labubu enthusiasts (and maybe even new fans!) find it useful!

Visit product View on Hacker NewsJun 25, 2025

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92%92% 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.

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77%77% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

37%37% 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

36%36% 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: active · Missing: arr, mrr, revenue

13%13% 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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