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Gnarly – Cellular Automata Meets Deep Dream, Morphing etc.

Hacker News

Gnarly – Cellular Automata Meets Deep Dream, Morphing etc.

Hi HN, This is Gnarly, an experimental project that blends cellular automata with Deep Dream effects, continuous zooming, and object morphing. The project integrates several ML tools—including Hugging Face’s model downloads, YOLO/EfficientDet for object detection, and Stable Diffusion for creative image generation. Key challenges I tackled: • Dynamically integrating real-time video/image processing with cellular automata. • Combining multiple deep learning models (InceptionV3 for Deep Dream, YOLO/EfficientDet for object detection, and Stable Diffusion) in a cohesive pipeline. • Optimizing GPU usage and interactive performance with Pygame. I’d love to hear your thoughts on the technical approach and any suggestions for improvement or future features. Thanks!

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
84%84% 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 · Missing: mac, agents, macos
72%72% 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, pipe, io · Missing: https docs, excited, just released
63%63% 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: video · 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 · Strong signals: efficient · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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