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A multiplayer game to discover NFT artists, built with WebAssembly

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A multiplayer game to discover NFT artists, built with WebAssembly

Hi everyone! I'm one of the developers behind this project. I wrote it with my two brothers, Daniel and Ricardo. For years the three of us have been curious about how multiplayer games work, which is why we decided to work on this project. Our goal was to gain a deep understanding of how different players can share the same 3D space, which is why we wrote our own tiny game engine instead of using something like Unity. We wrote the engine using C++ and OpenGL, and we then compiled it to WebAssembly using Emscripten so that it would run effortlessly on web browsers. We are really excited about the possibilities that are opening up thanks to WebAssembly. Throughout this project we faced some massive technical challenges, like for example: - We had to implement a collision detection and response algorithm so that players could collide with objects in the world. - We had to implement an animation system that's capable of loading animated characters from glTF files, and that can play animations and fade between them. - We had to implement a system that downloads images in worker threads, and that uploads them to the GPU in small chunks so that the game runs smoothly while paintings are being loaded. - We had to implement techniques like client side prediction, server reconciliation and entity interpolation so that players look smooth when they are moving in the 3D space. It was a great learning experience. If you have any technical questions, the three of us would be happy to answer them. On the product side, our hope is that this project can make the experience of discovering NFT artists more fun and social. We think that scrolling through endless pages of NFTs is mindless and boring. We hope that in the museum people will look at digital art for more than a glance, and that they will discuss it with others. We are constantly improving the museum to make it more fun, and we are also building some awesome tools for artists and collectors to showcase their work in new ways. If you would like to keep up with our progress, you can leave your email here: https://www.getrevue.co/profile/moonlightmuseum Thanks for your time!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, tiny · Missing: mac, agents, macos
85%85% 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
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, game engine · Missing: https docs, just released, exist
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

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