I made a site where you can travel in space in your browser

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I made this site using R3F ( https://github.com/pmndrs/react-three-fiber ), a React renderer for 3JS ( https://threejs.org/ ). The celestial bodies are Three.JS meshes. I used loaders (namely 'useLoader' from R3F and 'GLTFLoader' from 3JS) to import 3D models, such as International Space Station. More on loading models in R3F: https://docs.pmnd.rs/react-three-fiber/tutorials/loading-mod... I've future plans for this side project. It would be great to hear from the HN community before diving into them. Enjoy! P.S: Getting an empty (probably black) screen? This app shows up in browsers that support WebGL2.0 (most modern browsers do). Check this site to see whether your browser supports WebGL2.0: https://get.webgl.org/webgl2/ P.S.S: There'll probably be responsive design issues. I'd highly encourage you to use a desktop version or rotate to landscape while using a mobile version.

Visit product View on Hacker NewsApr 29, 2022

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179points

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Made the leaderboard

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

80%80% 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.

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

64%64% predicted probability of success on Product Hunt, 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

62%62% 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

43%43% 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

39%39% 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

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

Correct prediction on native model

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