After Coffee App
A local-first mobile coffee journal that helps users understand how their coffee habits affect sleep, jitters, energy, anxiety, and mood.
Project Overview & Idea
After Coffee is a local-first mobile coffee journal and personal experiment coach. Users quickly log each coffee, add details such as drink type and context, then complete focused daily check-ins to see whether coffee timing, quantity, and habits are affecting their everyday well-being.
As a barista, I regularly interact with customers seeking their daily caffeine fix to alter or improve their moods. This observation led to a creative question: How do we actually feel after our coffee intake throughout the day?
To answer this, I brainstormed with a coder friend to conceptualize After Coffee. This is a self-initiated group project where I took on the role of UI/UX Designer and Product Strategist, while my developer partner handled the Expo React Native implementation, local SQLite data model, notification logic, and experiment engine.
My Impact on Brainstorming & Strategy
I led our product strategy sessions, mapping out the core user journey from onboarding to daily use. We shaped the app around one important constraint: logging should feel light enough to use in the moment, while still collecting enough context to generate useful personal patterns later.
App Features
The app is organized around a simple daily loop: log coffee, add optional context, check in, then review patterns. The codebase uses four main tabs: Today, Experiments, History, and Stats.
- One-tap coffee logging: The Today screen centers on a large coffee button. When the user taps it, the app creates a coffee log and opens a detail modal for optional drink type, context tags, and notes.
- Automatic session grouping: Coffees consumed close together are grouped into the same session, so the history reflects real drinking moments instead of a long unstructured list.
- Guided experiments: Users can start seven-day experiments like "No coffee after 2 PM," "Max 2 coffees a day," or "Always have coffee with food" to test one habit change at a time.
- Daily check-ins: Experiment check-ins ask one focused question, such as sleep quality, anxiety, jitters, energy, or afternoon crash, depending on the selected goal.
- History and weekly stats: Past sessions, feelings, sleep notes, and weekly summaries help users review trends without needing to interpret raw data manually.
- Privacy-first storage: Coffee logs, check-ins, settings, and experiments are stored locally in a SQLite database on the user's device.
I designed the interface to feel calm and low-friction: warm coffee tones, dark backgrounds for comfortable evening use, clear cards, and minimal interaction steps for busy users who may only have a few seconds to log.
How It Works
After Coffee turns everyday coffee behavior into a small personal study. Instead of telling users a universal rule, it compares their own logged behavior against their own check-in answers.
- Onboarding: Users choose the outcomes they care about, such as sleep, jitters, energy, anxiety, crash, or general mood, then set notification preferences.
- Logging: Each coffee is saved with a timestamp. If it happens within the session window, it joins the latest session; otherwise, it starts a new one.
- Experiment rules: The app checks whether each day followed the active rule, such as no coffee after a certain hour or staying under a daily coffee limit.
- Outcome scoring: Daily check-ins are converted into a simple score, then matched to the coffee day they reflect. Morning sleep ratings are connected to the previous day's coffee, while evening ratings reflect the same day.
- Verdict: The app compares rule-following days against normal days and returns an early, likely, or strong signal with a recommendation.
My Impact & What I Learned
My designs translated real coffee-service observations into a structured digital product. I learned how to design for repeated daily behavior, how to reduce friction in a logging flow, and how to communicate personal data insights without making the interface feel clinical or overwhelming.