We
WebGPU FTW with Splat-Transform
Hacker NewsWebGPU FTW with Splat-Transform
It's annoying having so many ML and GS training tools rely exclusively on cuda/nvidia . So for our open-source command-line gaussian splat converter/compressor, we decided to try WebGPU instead. It's working well so far and a single codebase runs on Linux, MacOS and Windows without too much fuss. This is mostly thanks to Google's dawn project <3. Eventually some of this could also run directly in the browser.
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5points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, google · Missing: agents, agent, cursor89%89% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released63%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.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive · Missing: plus, platform, intuitive27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue21%21% 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, crypto1%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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