Promacro
Diet-focused macro tracking app for a middle-aged and older audience. Natural-language meal entry as the headline feature. Built for Android and iOS with React Native.
v1 completed 2026-05-13.
A macro tracking app built around hitting daily targets (protein and fiber by default) for an audience the existing apps mostly weren't designed for: people in middle age and older who want simple guidance, not a calorie-counting puzzle. Diet-focused rather than activity-focused. Guiding principle is simplicity: fewer numbers, clearer review, and entry that doesn't require scrolling a food database.
What v1 Does
Biometrics to macro goals. Enter age, weight, height, sex, and activity level on first launch. The app suggests daily targets for the macros (the protein, carbs, fat, and fiber breakdown of what you eat) you choose to track. Protein and fiber are on by default; carbs, fat, fiber subtypes, and others are optional.
Natural-language meal entry. The headline AI feature. Type or speak a meal in your own words ("two scrambled eggs and a slice of sourdough"); an LLM via OpenRouter returns structured macro estimates against the nutrients you've enabled. No database scrolling, no fuzzy search through 40,000 brand-name yogurts.
Recipe and meal templates. Save a common meal once, edit per-day when the portion changes. The template stays canonical; the per-day edit doesn't pollute it.
Daily review with easy adds. End-of-day summary against each tracked macro, with concrete suggestions when you're short. (Twenty grams of protein left? Greek yogurt or a chicken thigh, here are the macros.)
Inline macro explanations. Net carbs, soluble vs. dietary fiber, and the other terms that quietly confuse most people get a one-tap explainer next to the number, not buried in a settings page.
Technical Approach
The stack is React Native, not a Progressive Web App. Two reasons. First, PWA UX on iOS and Android still feels like a website pretending to be an app, and the target audience here is one that is quick to notice the difference. Second, React Native is in real demand in Berlin (Doctolib, Zalando, Jurafuchs, and Artsy all ship with it), so taking a mobile-shaped project through to a store build widened the kind of work I can take on.
Natural-language meal entry uses OpenRouter with a structured-output schema keyed to the user's enabled nutrients. The model returns a typed object the client can render without parsing prose, and the same schema doubles as the contract for the daily review and the easy-add suggester.
Photo-based meal entry and on-device inference (a small local model like Gemma for offline logging) are deferred to v2.
v1 was completed on 2026-05-13: biometric onboarding, natural-language meal entry, recipe templates, daily review, and the inline macro explainers all landed.