Do

Doom rendered in OpenSCAD geometry, now playable in browser

Hacker News

Doom rendered in OpenSCAD geometry, now playable in browser

Creator here. Third in a series of running DOOM on engineering tools - KiDoom rendered walls as PCB traces in KiCad, ScopeDoom piped vectors through a headphone jack to an oscilloscope. This one exports frame geometry to OpenSCAD, a parametric CAD tool. The browser version uses Three.js to render the same geometry the desktop version writes to .scad files. The desktop version was stuck at slideshow framerates until I fixed OpenSCAD's WASM renderer for another project - the npm package is from 2022 and silently ignores the Manifold flag. Switching to the 2025 build took renders from 2 minutes to 2 seconds. (Writeup: https://phaestus.app/blog/blog0031 ) But openscad-wasm isn't a renderer, it's a compiler. Each frame was compiling to STL then rendering - maybe 1fps sequential, better parallelized but with ~1s latency. Fine for previewing an item, but as a rendering pipeline it's no good for real time playback. So I removed openscad-wasm entirely and wrote a custom parser that converts OpenSCAD code to an AST, evaluates it to geometry, and renders via Three.js. The scad code is the only input - what you see comes from the same .scad you can copy, download, and run in desktop OpenSCAD or the playground to validate. Technical writeup: https://www.mikeayles.com/#openscad-doom YouTube: https://youtu.be/l9nnV-mO4wY Desktop version source: https://github.com/MichaelAyles/openSCAD-DOOM Web version source: https://github.com/MichaelAyles/openSCAD-DOOM-web

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Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, pipe · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
32%32% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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