Di

DiaScore – Helping Diabetics Understand Their Food Better

Hacker News

DiaScore – Helping Diabetics Understand Their Food Better

Hi HN Community, We've built DiaScore to help diabetics and prediabetics like myself make more informed food choices. It gives a simple score (0-100) for how diabetic-friendly a food is, with a breakdown of key nutritional factors like carbs, sugars, and fiber. Current Status : It's primarily optimized for mobile web experience, so there are going to be some rough edges visually on desktop. The food search is still something we are figuring out, but we rely on 3rd party data more or less. Goal : We are trying to see if users find the prototype useful and want to get as much feedback as possible so that we can think about future iterations. Would love your feedback! Try it here: https://diascore.app Thank you for your time and feedback! Appreciate it.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual · Missing: mac, agents, macos
73%73% 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.
AppSumoStrong fit for a featured deal · Strong signals: friendly, users · Missing: plus, platform, intuitive
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% 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: io · Missing: https docs, excited, just released
44%44% 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: users · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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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