I built a tool to try artworks on your own wall

Hacker News

Simply upload a photo of your wall, upload the artwork images, position them, and download the result. It's very lightweight and nothing is uploaded to a server - everything runs directly in your browser. Why I made this: Existing apps (like Artrooms) fell short for me because they only offered stock wall images and limited 3D positioning, especially if your wall isn't perfectly frontal. I wanted something more flexible that would allow me to experiment with more scenarios, even when the photo is taken at a weird angle. The objective is to get a feel of how'd it look like, without the result being super realistic. Details: - Built with Three.js. - It's a static site hosted on Cloudflare. - Uploaded images are stored in IndexedDB. - Some effects, like shadows and blending, work intermittently. Sometimes they really work, and sometimes they fail horribly. - I've included a few galleries from classic artists and default wall images so you can test it out right away. - I tried to make the UI as intuitive as possible, but that was surprisingly hard - especially in mobile where the space is limited. Any feedback would be greatly appreciated :-)

Visit product View on Hacker NewsAug 13, 2024

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

83%83% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, host · Missing: plus, platform, reviews

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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.

Incorrect prediction on native model

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