A food photo can make logging faster, but it is still evidence for an estimate—not a direct measurement. Here is how the estimate works and what deserves a second look.
Each guide gives the direct answer first, shows the limits, links to its evidence, and explains the relevant Food Focus workflow without unsupported accuracy claims.
The best food-logging method changes with the meal. A plated lunch, packaged snack, remembered recipe, and hands-busy breakfast do not contain the same kind of evidence.
A familiar bowl of oats should not feel completely new every morning. Food Memory keeps inspectable portion hints for normalized ingredients—without pretending that a stored value proves today's amount.
The black bars do not encode calories. Food Focus looks up the product, may AI-estimate tracked nutrient fields missing from the record, and scales the result to the gram amount you enter.
Calories answer one question: energy. Protein, fat, carbohydrates, fiber, added sugar, and saturated fat add different context, but more numbers are useful only when you know what each can—and cannot—say.
An AI meal result is an editable draft. The useful question is not whether the first answer looks precise, but which correction method fits the mistake—and what should be reused later.
A daily calorie target is a chain of assumptions, not a number measured by the app. Here is the exact Food Focus calculation and why today's target can move.
Siri can save a spoken meal in the background, while widgets shorten the route into Food Focus or show a recent progress snapshot. Those are three different workflows.