Numi makes logging a meal cost one photo.
A nutrition app that kills the tap-tap-tap of manual food logging. Snap a photo, get calories, macros and micronutrients back — and a coach that speaks in plain language, not spreadsheets.
01 — Overview
Most trackers ask you to search a database and do portion math three times a day. Numi’s bet is that the log has to disappear into a single tap — and that the numbers only matter if the app tells you what they mean.
- My role
- Founder · Design · Build
- Team
- Solo, with AI tooling
- Surfaces
- Logging · Insights · Fasting
- Core tech
- Vision AI · SwiftUI
- Built with
- AI coding tools · Xcode
- Status
- Released on the App Store
02 — What I built and how I used AI
One person, the whole product — with AI writing the first draft of the code.
What I owned, what the tools accelerated, and which calls stayed human. Two things are easy to blur here: Numi has AI inside the product (the photo analysis), and AI was used to build the product (the coding workflow). This section keeps them apart.
- My responsibility
- Product definition and positioning, UX and UI, the design system, the SwiftUI codebase, the photo-analysis and correction flows, testing on device, and the App Store submission and release.
- Implementation
- Native iOS app in Swift and SwiftUI, built in Xcode and released on the App Store. Meal photos are analyzed by a vision model; the health score is deterministic arithmetic over named inputs, not a model output; insights that cannot cite something real about the person’s day do not render.
- AI-assisted development
- AI coding tools generated first implementations from my flows — screens, state, the analysis request — so a design in the morning could be a working build on my phone by the evening. Iteration then happened in the real app, over real meals, not in a prototype.
- My review and decisions
- Which surfaces the model writes to and where a human confirms; the data model for meals, fasting and weight; reviewing, changing and testing generated code; what shipped in 1.0 and what waited.
- Release status
- Released on the App Store, 2025 → present. View on the App Store
One build loop, start to finish
- Rough flow. Photo-first logging: shutter → analysis → result → save.
- AI-generated first implementation. AI coding tools turned that flow into the first running SwiftUI screens and the vision request. The first version I built showed the analysis as a clean summary card, with editing behind a tap.
- What I changed. In use, the finished look made people accept numbers that were sometimes 30% off on portion size — corrections dropped off. I rebuilt the result as a list where every item is adjustable in one tap, made confirmation an explicit action, and replaced the spinner with a staged reveal: reading the image → identifying items → estimating portions.
- Working result. A multi-item dish logs from one photo in under ten seconds, every value is correctable in place, and the flow went through many working versions over real dinners before launch.
A limitation I diagnosed and resolved: the first natural-language input parsed a typed sentence and saved it straight away, with a toast. It read well in a screen recording and was worse than manual entry in practice, because a wrong field was stored silently. The fix was not a better prompt: the parse now renders as a filled form the user glances at and confirms before anything becomes real.
More on these decisions: AI UX patterns that survive contact with a real model and what AI changed about shipping solo.
03 — The problem
People don’t quit tracking because they stop caring. They quit because it’s work.
Every food diary starts the same way: motivated. Then real life hits — a rushed lunch, a shared plate, a dish with no barcode — and the friction compounds. Search, scroll, guess the grams, repeat. Within a couple of weeks most people just… stop.
So the real design problem was never “show more nutrition data.” It was protect the habit — make the first log of the day effortless, and make the data earn its place by changing what the person does next.
04 — Product thinking
Three bets the whole product leans on.
Logging must be a reflex
If capturing a meal costs more than a photo, the habit dies. The camera is the primary action, everywhere.
Numbers need a narrator
1,642 kcal means nothing on its own. Numi turns a week of data into a sentence you actually read.
Respect the whole day
Eating, fasting, weight and hydration live on one home — because they’re one story, not four apps.
05 — Who it’s for
Two people I kept designing for.
Personas drawn from Numi’s audience — and the design beliefs that shaped how it works.
The lapsed tracker
Has tried three other apps and abandoned each one around day 10. Will stay only if logging never feels like data entry. Success = still logging in week four.
The optimizer
Already disciplined; cares about macros, fasting windows and trends. Will stay if the insights are sharper than what a spreadsheet gives them.
- “
People don’t quit tracking because they stop caring — they quit because it’s work. Friction is the enemy, not willpower.
- “
Users don’t want more charts; they want to be told one thing — “are you on track or not.”
- “
The bar for effortless: even a complex, multi-item dish logs in under 10 seconds from a single photo.
06 — Key UX & UI decisions
Where the product thinking became pixels.
Every meal becomes a useful record
A persistent shutter button anchors the app. After a photo or a quick description, each meal lands in a readable history with calories, macros, timing and a simple quality grade. Yesterday’s choices become something the user can scan in seconds.
One glance, one clear next move
The home brings calories, macros, meal photos and the fasting state into one calm summary. The first sentence explains the day; the progress card gives the detail; the timeline shows what comes next.
A score with a reason behind it
Instead of a wall of charts, Numi combines calories, macros, timing and consistency into one decision score. It explains the biggest opportunity in plain language, then connects the current pattern to a seven-day projection.
Fasting on the same timeline
The eating window lives on the home, not in a separate app — with a plain-English state (“eating window closed, body switching to stored energy”) and gentle guidance. Tracking and fasting stop competing for attention.
07 — Design system
A dark canvas so the food pops.
Numi runs on a near-black surface: real meal photos and saturated macro gradients do the talking, while big numerals and generous rounded cards keep dense data calm. Every metric maps to a fixed hue — protein, carbs, fat, hydration — so color itself becomes a legend you learn once.
Component language
- Macro rings
- Meal filmstrip
- Insight cards
- Timeline / window
- Big-numeral stats
- Shutter FAB
- Plain-language states
- Tabbed home
Reusable tokens and components meant the whole app could stay consistent while shipping fast — one designer-engineer, one system, no drift between concept and build.
08 — Outcomes
What shipped.
Numi is available on the App Store — designed, built, and shipped end to end.
09 — More