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Shazam for Food Allergies

Hacker News

Shazam for Food Allergies

My kid was diagnosed with a gluten allergy last year, and suddenly everything felt like a minefield. Reading labels, Googling ingredients, calling brands, second-guessing every snack — it was exhausting. So I built an app to take that mental load off. Healthsnap lets you snap a photo of any food or food label, and it uses AI to analyze it for common allergens (like gluten, dairy, nuts, soy, etc). It gives also gives nutritional facts, alternatives and emergency guidance. No barcode scanning. No typing. No logins. No BS. The app is built in SwiftUI. Currently only available for iPhones. I'd love to get your feedback on the concept.

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68%68% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
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: io · Missing: https docs, excited, just released
27%27% 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
24%24% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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