Sharing the thinking behind low-spike living and nutrition-focused AI.

A practical, non-medical guide distilling our cross-cultural observations, KarbCoach insights, and real-world experiments into a clear framework for experimenting with lower-spike meal patterns—intended for informational use by individuals, coaches, and clinicians who apply their own professional judgment.

Technical overview of the modeling strategies behind KarbCoach, including meal image embeddings, contextual features, and evaluation metrics for estimating post-meal glucose pattern tendencies in research and wellness settings. Describes a non-medical, decision-support approach rather than a diagnostic or treatment system.

Comparative analysis of Japanese and U.S. eating environments: portion sizes, convenience foods, movement patterns, and how these factors may relate to glucose variability markers in observational settings. Presented as exploratory, non-prescriptive research.

Principles and case studies from our AI Staffing & Internship Division on building interfaces, prompts, and feedback loops that support behavior change around food and metrics without fear or shame, and without positioning AI as a replacement for professional medical guidance.

Methodology for assessing spike-related scoring and recommendation engines using observational data, user feedback, and ethically collected logs. Focuses on wellness-oriented decision support and model transparency rather than clinical validation.