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Ar

Art – A web-based 3D asset management and AR showcasing tool

Hacker News

Art – A web-based 3D asset management and AR showcasing tool

Hi HN! I'm excited to introduce ARt, a specialized tool designed for digital artists engaged in the web and AR 3D domain. ARt serves as a unified platform that allows artists to store, revise, and communicate about 3D assets, as well as showcase their creations in Augmented Reality. All these features are accessible through a browser-based interface that emphasizes simplicity and intuitiveness. Through ups and downs, collaboration, and extensive research, ARt has evolved into a polished tool. I believe it holds significant potential for artists in the field, and I welcome more creators to explore and harness its capabilities.

Visit productView on Hacker NewsAug 22, 2023
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Actual performance

1points
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Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, interface · Missing: plus, reviews, host
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
59%59% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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