Diascore, a food scoring app for diabetics

Hacker News

Hi HN, I created DiaScore to help diabetics and pre-diabetics make smarter food choices. Managing blood sugar can be overwhelming, especially when it comes to understanding how different foods impact your health. DiaScore simplifies this by providing a clear score (0-100) for foods, based on key nutritional factors like carbs, fiber, added sugars, and protein. The idea came from my personal experience with pre-diabetes and the constant challenge of deciphering food labels. DiaScore uses a science-backed formula to evaluate foods and offers actionable insights, empowering users to make better decisions quickly. It’s powered by GeminiAI to get started but we are exploring Nutrition APIs for comprehensive food data if there's traction. I’d love your feedback on how to improve it and make it more helpful! You can try it here: https://www.isthisdiabeticfriendly.com/ Let me know what you think.

Visit product View on Hacker NewsDec 20, 2024

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2points

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, gemini · Missing: supports, reddit linkedin, podcasting

86%86% 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: user, gemini, apis · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly, users · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

35%35% 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 · Strong signals: smart · 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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