DiabSistant – Diabetes Diary and Carb Counting App

Hacker News

Hi HN! I've read HN for years, its high time to write my first post. I would like to show you my first ever mobile app I've made with my friends. Please check it out, we would love to hear your thoughts! https://play.google.com/store/apps/details?id=com.diabsistant About the app: diabetes is a huge change in lifestyle for freshly diagnosed patients. This little app helps to record, share and analyze daily measurements (blood sugar level, insulin intake, etc). We have also introduced a doctor's 'portal' where the invited treating doctor can check real-time the recorded data and is able to react early. About me: fresh dev bootcamp graduate with diabetes diagnosed just a few months ago. Trust me, I can feel how hard it is for our users to get started with all the new knowledge and routines. Please leave your comments here, DM me or hn@diabsistant.com. TYIA! Marton

Visit product View on Hacker NewsMar 26, 2020

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

73%73% 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: google, apps, user · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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