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I Built an AI That Matches Perfumes with Their Closest Twins

Hacker News

I Built an AI That Matches Perfumes with Their Closest Twins

Hey HN! Ever wondered if there’s a fragrance out there that smells just like your favorite but from a completely different brand? That’s what got me thinking, and after a weekend of deep work, I created DupeFinderAI. What It Does: DupeFinderAI is an AI-powered tool that scans and compares thousands of perfumes, identifying those that share nearly identical notes and aromas. Whether you're curious about scent similarities or just love discovering new perfumes, this tool has you covered. How I Built It: - Scraped data from 40,000+ perfumes over a weekend - Used that data to train the AI on recognizing fragrance profiles - Focused on making the experience smooth and intuitive Why It’s Cool: Finds "scent twins" across different brands Helps you explore new fragrances based on ones you already love Could even be useful for perfumers and fragrance enthusiasts alike I’d love to hear your feedback, ideas, and any thoughts on how to improve it! Check it out and let me know what you think.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, notes · Missing: mac, agents, macos
78%78% 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: created · Missing: supports, reddit linkedin, podcasting
78%78% 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
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
45%45% 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, 000, 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
14%14% 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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