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Gleamit – dental health tracker for Android/iOS

Hacker News

Gleamit – dental health tracker for Android/iOS

Hi HN! I wanted to show the world a small project I have been working on, on my spare time, for the past couple of months. It's a small cross-platform mobile application that works as a dental health tracker. It helps your track really everything (I believe) regarding your dental/oral health. Features include: - daily reminders to brush your teeth in the morning, after lunch and before bed - a simple 2 minutes brushing timer and a streak counter to see your past brushing habits and get cool insights about your past brushing trends (like at which time of the day you usually brush, what's your most missed day...) - a teeth diagram where you can record your historical teeth conditions so that you never forget about them - a centralized planner for your dentist visits with reminders for your next appointments and a vault to store your documents - a toothbrushes pane where you can manage your active ones and get notified when a manual one or a brush head needs to be replaced (I always forget to do so!), like after 90 days - other minor but cool things like a photo comparer that allows you to compare your smile and if it's becoming whiter, an automatically generated PDF report to share to your dentist and more I built this because I was looking for something similar for my Android phone, but I wasn't able to find anything cool on the Play Store or something that I could be satisfied with, so I thought why not built it myself this time :) The cool thing about the app is that it has no backend - everything lives client side (didn't want to handle medical data at all on a backend - would have been a real mess to do so as a solo dev on a small side project!). You can turn on "cloud backups" though to save snapshots of your data to your Google Drive or iCloud on a schedule you can configure, think like how WhatsApp does it. Happy to know what HN thinks about this! :) any feedback is super appreciated, especially on what's missing or on bugs you find :D

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
87%87% 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 · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, month, google · Missing: mobile apps, personal, entrepreneurs
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% 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: active · Missing: arr, mrr, revenue
33%33% 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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