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Real-Time Interactive Fluid Simulation on Vision Pro

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Real-Time Interactive Fluid Simulation on Vision Pro

I made this with the Metal APIs in VisionOS 2.0. It now gives access to low-level mesh features and buffers directly, meaning lots more particles are possible (around 5-10x)! The hand-tracking also seems much, much smoother. The fluid sim is a version of SPH running on the GPU, with the particles drawn with billboarding orientated to the headset position. Would love to hear any feedback - for anyone with a AVP, it's available to try for free here: https://apps.apple.com/app/id6470201263 With more information about Lungy here ( https://www.lungy.app ).

Visit productView on Hacker NewsSep 17, 2024
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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: apple, apps, apis · Missing: mac, agents, macos
68%68% 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
53%53% 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: apps · Missing: mobile apps, ios, personal
41%41% 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
41%41% 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
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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