I built an open-source wallpaper gallery for GitHub repos

Hacker News

Hey HN, GitHub has hundreds of wallpaper repositories with thousands of images, but actually browsing them is painful. You either clone gigabytes locally or click through the file browser one by one. So I built WALL·E Gallery. Use existing collections or Paste a repo URL→ instant gallery. How it works: - Fetches repo tree via GitHub (no cloning) - Proxies thumbnails (~50KB instead of 10MB originals) - Works with private repos too (with GitHub token) Infinite scroll, search, dark mode, mobile friendly, self-hostable. No accounts, no tracking. Live: https://walle.theblank.club Source: https://github.com/amitray007/wall-e Do share your suggestions/feedback. Also, I did find my desktop wallpaper from this.

Visit product View on Hacker NewsDec 28, 2025

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

81%81% 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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, friendly · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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