Building EspressoLytics in 48 Hours: SwiftUI + WeatherKit + SwiftData
A simple request at home turned into a 48-hour iOS build and a clear founder lesson: start with one real workflow, ship fast, and use data to make decisions easier.
This app started at home, not in a roadmap doc. My wife has gotten deep into espresso with her Breville setup, keeping beans in the grinder and measuring every shot to dial things in. She said it would be great to have a simple app just for tracking this process.
That request immediately clicked for me. With my analytics background, I saw a chance to go beyond basic logging and include external factors most people do not track, then turn that data into useful patterns. For founders and creators, this was a reminder that the best ideas usually start with one clear user need and a tight first build.
Key leadership takeaways
- Great product ideas often come from one repeated frustration you can observe directly.
- A 48-hour time box forces clear decisions on scope and sequencing.
- External context like weather can be a real differentiator, not just extra data.
- Reliability and test coverage are product work, not cleanup work.
Where the idea came from
The origin was very practical: we wanted straightforward espresso tracking that did not feel bloated. My wife was already measuring shots by hand, and the gap was obvious. Existing options either felt generic or were missing the exact workflow she needed while dialing in.
I framed the app around one question: what would make this genuinely useful during daily brewing, not just interesting in a demo? That led to a focus on speed, clarity, and insights you can actually use on the next shot.
Why I set a 48-hour build window
I gave myself 48 hours to ship a solid first version. The time box forced me to prioritize features that directly improved decision-making and drop anything that was cosmetic or speculative.
Instead of trying to build every possible coffee feature, I focused on one core loop: log a shot quickly, attach context, and review trends that can guide the next pull.
That tradeoff mindset maps directly to founder and creator work. Speed matters most when it is tied to one repeatable outcome users care about.
The technical stack and why it fit
This stack let me move quickly without sacrificing structure. I could keep the app native, keep data local, and still build meaningful analytics views without creating backend overhead for v1.
- SwiftUI for fast iteration and a native, clean interface.
- SwiftData for local persistence and low-friction modeling.
- WeatherKit + CoreLocation for temperature and humidity context at brew time.
- Swift Charts for trend lines and quality snapshots that are easy to interpret.
What EspressoLytics tracks
The main goal is not collecting more numbers. It is helping you see what is changing when shot quality changes. Bringing environmental data into the same timeline made pattern detection much easier.
- Dose, yield, extraction time, grinder setting, beans, and notes.
- Weather context (temperature and humidity) for each logged shot.
- Brew ratio and quality scoring trends over recent sessions.
- History views to compare outcomes and identify consistency issues.
What changed after the first build
After the initial implementation, most improvements were about reliability and clarity. I iterated on trend chart readability, history layout, validation, and test stability so the app would hold up under repeated use.
That phase reinforced a lesson I keep seeing in product work: trust comes from consistency. A clean UI helps, but predictable behavior is what keeps people using the product.
What I learned from this sprint
- Starting from a real user in your own household gives instant feedback quality.
- A clear time box can increase quality by reducing unnecessary scope.
- Analytics is most useful when paired with domain context, not treated as a separate layer.
- The best early features are the ones that improve the very next decision.
Why this matters for founders and creators
- Validate demand through real behavior before expanding scope.
- Ship a narrow workflow that users repeat, then layer in advanced capability.
- Use your strongest skill set, in my case analytics, to make the product distinct.
- Treat trust and reliability as part of your growth strategy from day one.
What comes next
Next I plan to keep improving insights and trend interpretation while preserving the same core philosophy: simple logging, high signal, and low friction.
The original goal still stands. Build the app my wife asked for first, then layer in advanced analysis only where it meaningfully improves dialing in espresso.
If you are a founder or creator building niche tools, I would love to compare notes on validation, analytics, and shipping quickly without losing quality.