Unswt – Free AI Sugar Tracker Using Meal Photos

Hacker News

Hey HN, We’ve built unswt, a free AI-powered sugar tracker that helps users manage their blood sugar by simply snapping a photo of their meal. No devices, no subscriptions—just straightforward feedback on how your food impacts your glucose levels. How It Works: Snap a photo of your meal. Get instant feedback on predicted glucose levels, macro/micro breakdown, and how the food impacts your body. Why We Built It: Managing blood sugar can be tough, especially with so many processed foods and busy lifestyles. We wanted to make it easier for people to understand their body's reaction to food without needing expensive hardware. We’re collaborating with Johns Hopkins cardiology and endocrinology teams and GW nutrition AI PhDs to ensure the science behind it is solid. Why Free? We believe in making this resource accessible to everyone. No hidden fees, no premium features—just a free tool to help you make better health decisions. We Need Your Feedback: We’re still early and would love to get feedback from the HN community. What’s working? What’s not? What features would be useful? You can try it for free, and any feedback (good or bad) would help us improve the app. Check it out: unswt.com Looking forward to your thoughts!

Visit product View on Hacker NewsSep 20, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

95%95% 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 HuntUnlikely to reach the leaderboard · Strong signals: mac, user, using · Missing: agents, macos, agent

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

TrustMRRLess likely to generate early MRR · Strong signals: users · 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 · Strong signals: users · Missing: plus, platform, intuitive

35%35% 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

31%31% 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 · Strong signals: subscription · 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

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