Built a full-stack habit-tracking Progressive Web App to solve a problem most habit trackers ignore — people don't quit because the app is bad, they quit because one missed day feels like failure, and they never come back. AI Habit Tracker is designed around that moment, using Google Gemini 2.5 Flash to power five personalized AI features: streak-recovery coaching, an interactive habit analysis chat, weekly progress reports, personalized habit suggestions, and daily morning motivation — each generated using the user's real habit and streak data, not generic templates. On the backend, I built secure REST APIs with Node.js and Express, with JWT and bcrypt handling authentication, and MongoDB Atlas (via Mongoose) managing cloud persistence — with date-fns handling the streak-calculation logic to keep date math reliable across time zones and edge cases. To keep users engaged without being intrusive, I implemented PWA capabilities with Web Push notifications secured via VAPID authentication, paired with a cron job that monitors daily habit completion and triggers automated streak reminders before a streak actually breaks. This project pushed me to think beyond "calling an AI API" — the real engineering challenge was deciding what context to feed into each prompt so the responses felt specific and useful rather than generic, while also handling real backend concerns like authentication, scheduled jobs, and cloud data persistence.