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VoxelChain – An Experimental Voxel Engine

Hacker News

VoxelChain – An Experimental Voxel Engine

VoxelChain is an experimental tool to create voxel worlds in the browser. The lighting is fully ray traced in real-time and there is powerful cellular-automata based programming system, which allows to create complex digital circuits and model the behaviour of voxels (behaviour such as falling sand or water). Technology wise, I'm using WebGL2 for the rendering and the simulation is coded in C89 and gets compiled to WebAssembly (with multi-threading) using Clang. The simulation is basically a custom cellular automaton and is fully parallelized. Once WebGPU is released, I'm planning to run the simulation on the GPU, instead of the CPU, which will give a massive speed up. I've been working on this project full-time for over a year now, and finally came to the point of realising a public version of it to play with. There is already some really cool stuff that the community has built, and it's super fun to see how everything evolves! Let me know what you think and feel free to ask any questions :>

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
83%83% 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, using, code · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: para · Missing: mobile apps, ios, personal
53%53% 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
18%18% 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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