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AMAZ3D – Optimize Asset in the Cloud

Hacker News

AMAZ3D – Optimize Asset in the Cloud

Hi, My name is Leonardo Locatelli and I work for ADAPTA studio, a recently constituted spinoff of Politecnico di Milano (www.adapta.studio). Recently we developed a cloud service that allows users to optimize 3D assets by reducing the number of polygons directly from your browser but, to build something really useful, we need feedback from experts in 3D modeling and game development. Our team is composed mostly by mathematicians and software engineers so we appreciate feedback from people with complementary competencies. That's why we are giving people the possibility to try our software free of charge with some limitations on the number of optimizations everyone can do each month. What we ask is only some feedback and if you want, some suggestions about the main pain points for you in the development of 3D real-time apps. Here is a link to a video detailing its usage: https://www.youtube.com/watch?v=gfvCdONP9BU The idea came from an algorithm I and some colleagues developed in our Master thesis (we are mathematical engineers), and we identified polygon reduction with rendering quality preservation as an important application. Our service allows users to create LOD in a simple way with a new patent-pending algorithm that allows extreme polygon reductions preserving quality, normals, soft and hard edges, UVs, rig, skins, and animations. Here is a link to a video showing the possibility to preserve textures and animations: https://www.youtube.com/watch?v=tTSR_5vsH9o Moreover, you can set presets and use them for multiple LOD creations: https://www.youtube.com/watch?v=iU4xRAmzpjk If you want to try it you just need to follow this link and create a profile, you just need an e-mail address https://amaz3d.adapta.studio/auth/login Thanks a lot to anyone who will provide any feedback and for your time! We are adding features over time (the next in line are baking and masking) and would love to hear your ideas on what you’d like to see on this cloud platform. Leonardo Locatelli

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
95%95% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, video, month · Missing: mobile apps, ios, personal
46%46% 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
15%15% 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.

Incorrect prediction on native model

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