AI interior design tool that gives product recommendations

Hacker News

What started out as a Hackathon project more than year ago is finally live on product hunt! Me and my 2 cofounders have taken this from an idea to the current stage where we are working with some of the biggest names in the GCC, while still being pre-seed. Along the way we have won a major PropTech Hackathon in Saudi winning $100k prize, been a part of a generative AI accelerator program, pivoted a couple of times and did all sorts of stupid stuff as well. What we do at our core is use diffusion models to bridge the visualization gap in industries such as e-commerce and interior design. Today was our first major launch and the feedback has been incredible. I would love to get your feedback as well on our tool at www neo-locus.com. Please email me at nom@neo-locus.com with any feedbacks or if you just want to chat!

Visit product View on Hacker NewsMay 21, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, email · Missing: mac, agents, macos

83%83% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, 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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% 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

43%43% predicted probability of success on AppSumo, 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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