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StratusGFX, my open-source real-time 3D rendering engine

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StratusGFX, my open-source real-time 3D rendering engine

It's been closed source for a long time while I worked on it on and off as a hobby research project, but yesterday the repo was made public for the first time under the MPL 2.0 license. A feature reel showing its capabilities can be found here: https://ktstephano.github.io/rendering/stratusgfx/feature_re... A technical breakdown of a single frame can be found here: https://ktstephano.github.io/rendering/stratusgfx/frame_anal... It's still in a very beta state (bugs and instability expected), but I felt like it was a good time to make it public since a lot of its core features are mostly presentable. I plan to continue working on it in my spare time to try and improve the usability of the code. Two main use cases I could see for it: 1) People using it for educational purposes. 2) People integrating it into other more general purpose engines that they're working on since Stratus is primarily a rendering engine. Any extensions to the rendering code that are made public would then further help others. So I think it will remain very niche but I'm hoping it will still be helpful for people in the future.

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, code · Missing: mac, agents, macos
87%87% 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
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 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.
TrustMRRLess likely to generate early MRR · Strong signals: education · Missing: mobile apps, ios, personal
43%43% 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
30%30% 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
12%12% 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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