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Brickgenerator.com – AI Text to Lego Models Generator

Hacker News

Brickgenerator.com – AI Text to Lego Models Generator

As a developer and Lego enthusiast, I wanted a way to come up with lego designs quickly and what better way then using AI! I decided to take a stab at it and created BrickGenerator.com. This is a novel tool that turns text into Lego models. This proof of concept uses generative AI to create Ldraw code-based Lego designs from your descriptions. It's early days, and the results are intriguingly varied – perfect for those who want to experiment with me. Try it out with simple object descriptions and let me know your thoughts. How can I make this even better going forward?

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Actual performance

2points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
82%82% 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: model, models, using · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
53%53% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
40%40% 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
22%22% 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.

Correct prediction on native model

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