Native iOS Product

EspressoLytics

A SwiftUI espresso journal for home baristas that logs brew inputs, adds WeatherKit context, and turns saved shots into extraction trends, quality snapshots, and bean-by-bean reference data.

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What it is

A native SwiftUI espresso journal for serious home espresso enthusiasts, built to log shot inputs quickly, preserve brew context, and turn daily pulls into useful analytics.

Goal

Make home espresso dialing-in easier to repeat and improve by tracking dose, yield, time, grind, beans, grinder, notes, weather context, shot scores, and extraction trends in one polished iOS app.

Key features and achievements

Native iOS Product Architecture

Built as an Xcode iOS project with the primary EspressoLytics scheme plus a dedicated EspressoLyticsScreenshots scheme for App Store media automation.
Uses SwiftUI for the product surface, SwiftData for local persistence, and a structured feature layout across Home, History, Settings, Services, Support, Models, Components, and Theme modules. Targets modern iOS with bundle ID com.zachnovak.EspressoLytics, local version 1.0, and build 4 recorded in the Xcode project and release notes.

Espresso Logging Workflow

Logs dose, yield, extraction time, grind size, bean, grinder, tasting notes, and brew timestamp through a fast Daily Log flow designed for repeated use while dialing in.
Uses reusable defaults for bean, grinder, and grind size so routine shots can be entered quickly without turning the app into a spreadsheet.
Includes a separate Dial-in mode for grind-tuning attempts so experimental shots can be compared without skewing the regular shot history.

Weather-Aware Brew Context

Integrates WeatherKit and CoreLocation to attach local temperature and humidity context to espresso entries.
Supports refresh and permission-aware status handling so weather context degrades cleanly when location or WeatherKit access is unavailable.
Includes historical weather lookup support so backfilled brew entries can still capture nearby environmental conditions when possible.

Analytics and Shot History

Models each shot with SwiftData fields for brew ratio, calculated extraction score, temperature, humidity, normalized bean identifiers, grind size, dial-in session data, and timestamps.
Builds a History surface with extraction trend charts, brew-ratio analysis, quality snapshot metrics, bean lookup, filters, pagination, and a tabular recent-shot view.
Uses Swift Charts and a dedicated heatmap chart component to make the logged data useful instead of just stored.

Polished Coffee-Focused UX

Uses a custom espresso-themed visual system with glass-style cards, warm accent colors, rounded typography, and reusable UI components.
Adds popup feedback, confirmation flows, settings preferences, unit display options, and clear-history maintenance actions for daily usability.
Keeps the app intentionally focused on the espresso ritual rather than generic habit tracking.

Release and Portfolio Readiness

Includes Fastlane metadata, screenshot, validation, and upload lanes for App Store Connect workflows.
Local release docs record version 1.0, build 4, submitted to App Review on April 30, 2026, with App Store Connect showing WAITING_FOR_REVIEW at that time.
Maintains a dependency security posture through pinned Swift package versions, Dependabot configuration, a dependency security audit workflow, and documented audit notes.

Technologies

SwiftUISwiftDataSwift ChartsWeatherKitCoreLocationXcodeFastlaneStoreKit/App Store Connect Release WorkflowPopupViewExyteGridConfettiSwiftUISwiftUI IntrospectGitHub ActionsDependabot

Big-picture vision

Better Home Espresso Feedback

The product turns a messy daily ritual into a repeatable log, so beans, grinders, weather, and extraction behavior can be compared over time.

Small App, Real Product Discipline

EspressoLytics is intentionally narrow, but it still carries product details that matter: persistence, analytics, UI polish, release docs, screenshot automation, metadata, and dependency review.

Practical Personal Software

The app reflects the kind of software I like building: focused tools that solve a real workflow, respect the user's time, and make the underlying data easier to act on.

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