ByteFuel

Nutrition tracking is universally acknowledged as valuable and universally hated as a daily practice. Every existing app requires manual searching, barcode scanning, or logging individual ingredients. The friction is so high that most people quit within a week.

The challenge: build a nutrition tracker where the input method is a photo and the output is instant, accurate nutritional data. No typing. No searching. No friction.

ByteFuel uses GPT-4 Vision to analyze meal photos and extract nutritional data. The user takes a photo (or uploads one), the AI identifies the food items, estimates portions, and returns a full macronutrient breakdown — calories, protein, carbs, fat — within seconds.

The key architectural decision was treating AI output as a first draft, not a final answer. Every analysis goes through a validation layer that checks for reasonable ranges, flags outliers, and allows users to adjust portions. This handles the inherent imprecision of visual estimation while still being dramatically faster than manual logging.

The mobile-first web interface was designed for one-handed use — the primary interaction is camera → confirm → done. Daily and weekly dashboards aggregate data automatically, showing trends without requiring the user to do anything beyond eating and photographing.