The Gallery – A collaborative 3D time capsule

Hacker News

Hi HN, I'm the developer. I built a persistent 3D space inspired by the "Million Dollar Homepage", but multiplayer. The concept is a collective time capsule where every frame is permanently owned by a user. Tech Stack: Frontend: Three.js / React Three Fiber. Backend: Firebase (Firestore + Cloud Functions). Payments: Stripe (for claiming frames/auction). It’s a social experiment without algorithmic curation. The community decides what the space looks like. You can check it here: https://the-gallery-f51a8.web.app Feedback on performance/loading times is appreciated!

Visit product View on Hacker NewsJan 27, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

82%82% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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

34%34% 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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