I Built a Smart Online Medication Cabinet – AppaMed

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I built a smart online medication cabinet that helps you organize and manage your home medications. AppaMed is a web application designed to address the common problem of medication management, especially for those dealing with multiple prescriptions or caring for family members. Key features: - Intelligent medication tracking with expiration dates and quantity management - Smart reminders for dosage schedules - Proactive expiry alerts to prevent usage of outdated medications Tech stack: - Next.js 13 with App Router - TypeScript - Tailwind CSS for styling - Shadcn UI for component library - PostgreSQL The project aims to leverage modern web technologies to improve medication adherence and reduce the risks associated with improper medication management. It follows best practices for accessibility and responsive design to ensure a seamless user experience across devices. I'm particularly interested in feedback on: 1. Additional smart features that could enhance medication management 2. Suggestions for improving data privacy and security in health-related applications 3. Ideas for integrating with health tracking devices or APIs to make the cabinet even smarter I'd love to hear your thoughts and suggestions on how to make this smart medication cabinet even more intelligent and user-friendly!

Visit product View on Hacker NewsFeb 21, 2025

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

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, apis · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, 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

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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