Nutrinaut learning library
Learn With Nutrinaut
Concrete examples, visible arithmetic and the limits of consumer data. Read a field note, then open the matching tool when you need to apply the method.
Before You Trust a Wearable Trend, Check Its Source
A practical source-and-missing-data check for people who use Nutrinaut with a compatible wearable.
Read field note →Field note 02 · Lifestyle tagsUse Lifestyle Tags as Context, Not Proof of Cause
A repeatable way to interpret optional Nutrinaut tags alongside sleep and training without inventing a causal story.
Read field note →Field note 03 · Label arithmeticOne Label, Three Portions: A Serving-Size Audit
Use the FDA sample label to see exactly how a one-, one-and-a-half- or whole-container portion changes the numbers.
Read field note →Field note 04 · Model sensitivityWhy Two Energy Estimates Can Both Look Plausible
A calculation audit showing how activity assumptions, not a hidden change in metabolism, can widen an energy estimate.
Read field note →Field note 05 · Food loggingWhat AI Meal Estimates Can—and Cannot—Tell You
A review workflow for food-photo estimates and the ingredients a photo cannot reveal.
Read field note →Field note 06 · RecoveryHow to Read Recovery Signals Without a Readiness Verdict
Separate a wearable measurement, a baseline, its context and the decision that follows.
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