Lucid Beads – Create Custom Bracelets in Live 3D with GPT AI Assistant

Hacker News

Combining real-time 3D and ChatGPT to allow users to create their own custom gemstone bracelets, and get rewarded if others buy them. You can use the AI Assistant for a quick start, then fine tune the design bead by bead in the Workspace if desired. You don't have to buy any of your creations, so feel free to try it out just for fun and let us know what you think!

Visit product View on Hacker NewsNov 10, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, chatgpt · Missing: mac, agents, macos

81%81% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · 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 · Strong signals: chat, reward · Missing: web3, crypto, cryptocurrency

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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