Hey Product Hunt 👋 I’m the indie iOS developer behind Foodify. Many photo-based calorie apps follow the same basic flow: take a photo, receive a calorie estimate, save it, and move on. This makes logging faster, but it does not help users understand what their eating habits mean, whether their current plan is working, or what should change next. I built Foodify as a complete nutrition and progress system—not just a food scanner. Users can photograph a single food or a mixed meal, scan packaged foods by barcode, and review the detected items, portions, calories, and macros before saving. I put significant effort into making these estimates as reliable as possible by combining food recognition with structured nutritional data instead of returning only a generic AI-generated number. The main difference begins after the meal is saved. Foodify analyzes everything the user has eaten throughout the day, compares the results with their personal calorie and macro targets, and provides practical feedback based on the actual foods consumed. Instead of showing only charts and totals, the daily analysis explains the main strengths, imbalances, and specific changes the user can make the following day. Weekly reports examine individual foods, eating patterns, macro balance, weight progress, hydration, Apple Health activity, and body-composition changes when that data is available. The analysis can identify specific days and meals that helped or slowed progress rather than providing the same generic advice to everyone. Weekly weigh-ins are not used only to draw a weight chart. After a new weigh-in, Foodify recalculates calorie and macro targets using the user’s current weight, goal, and remaining timeline. When enough consistent food logs and weigh-ins are available, its adaptive engine compares recorded calorie intake with the actual weight trend to estimate more personalized maintenance needs. If progress is falling behind the planned pace, Foodify can suggest updated calorie and macro targets while keeping the final decision with the user. Body measurements add another level of personalization. Foodify uses waist, neck, and hip measurements to estimate lean body mass, then recalculates calorie, protein, carbohydrate, and fat targets using a lean-mass-based metabolic model instead of relying only on a generic age, height, and weight formula. The meal plan also evolves with the user instead of remaining a static template. Foodify generates fresh daily plans using recently logged and frequently eaten foods, alternatives the user selected, meals they rejected, dietary preferences, allergies, and current calorie and macro targets. Future plans can therefore reflect the user’s actual eating habits while rotating meals to avoid showing the same suggestions repeatedly. Foodify also includes Foodi, an AI nutrition assistant that can answer questions using the context of the user’s meals, targets, activity, measurements, and progress. I’d love your feedback on whether this feels more useful than the usual “snap and forget” experience—especially the daily and weekly analysis, adaptive target suggestions, and meal plans that evolve with real eating habits. Thanks for checking out Foodify. I’ll be here throughout the launch to answer questions and learn from your feedback 🙌

Foodify

From a meal photo to macros and a plan that adapts

From a meal photo to macros and a plan that adapts