True trigger analysis
Here's 30 days of glucose readings (5-min intervals) and a meal log with rough macros and timing. Find the foods or food combinations that produced the biggest individual spikes for me, and the ones that surprisingly didn't.
AI for glucose data
Glucose is the most overhyped — and most personal — metric in modern wellness. AI helps you treat it like the personal signal it is.
What we’re actually working with
A continuous glucose monitor produces a reading every 5–15 minutes — thousands of points per month. The signal is rich; the standard interpretation is shallow.
Why doing this without a method fails
CGM apps gamify spikes and prescribe behavior. They miss context: your sleep that night, your training that morning, your stress, your cycle phase.
The 3-Layer treatment
How the method handles glucose
Layer 01
Research
Read the actual literature on post-prandial glucose in non-diabetics. Get a sourced view on what 'spike' even means at your baseline.
Layer 02
Ledger
Export 30 days of CGM data alongside your meal log, training, and sleep. Let AI map your true triggers — they are almost never what the app suggests.
Layer 03
Protocol
Run a 14-day food experiment: same breakfast, three different conditions (after sleep <6h, after training, sedentary). Let AI score it.
Three prompts you can use today
Paste any of these into the AI chat tool you already use. No setup.
Sleep and morning glucose
I've pasted nightly sleep duration and the next morning's fasting glucose for 30 days. Quantify the relationship for me, and tell me whether one bad night is enough to move the number meaningfully.
Same-meal experiment
Design a same-breakfast / different-context experiment for me to run for 14 days. I want a clear hypothesis, what I'll vary, what I'll measure, and what 'positive' means.
Common questions
Do I need a CGM to do this?+
No. The method works as well with finger-prick fasting glucose taken consistently. The CGM just gives you more resolution.
Will this replace medical advice?+
No, and we'll never claim it. If you have a diabetes diagnosis or suspect one, work with a clinician. AI helps you bring better questions.
Why not just trust my CGM app's coaching?+
Their coaching is generic and built to keep you in their product. Your data, in your AI, gives you a sharper and more honest picture.
More on glucose
Everything we’ve published that touches this topic — refreshed automatically as new entries ship.
From the blog
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The dual-lab interpretation pyramid
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Three free chat tools, three different jobs
Perplexity for research, Gemini for ledger, ChatGPT for protocol. Why we picked these three, what each is uniquely good at, and what to swap if any of them changes.
When the model updates, your method shouldn't
Models will update, get deprecated, change tone, and get acquired. Your method shouldn't have to. Architectural rules for a HealthOS that outlives any single tool.
Case studies
Automated Health Data Flow for a Busy Executive
A streamlined system for health data collection and analysis improved decision-making for a demanding schedule.
AI as a Mirror: Illuminating the Shape of Daily Habits for Better Sleep
A continuous glucose sensor and a reasoning chat tool revealed a 41-year-old’s specific sleep disruptors.
Computer Vision Unlocks Deeper Nutrient Insights
A practitioner refines dietary recommendations by leveraging image analysis to quantify food intake with greater precision.
Enhanced Nutritional Insight for Individual Wellness
An individual leverages automated data flows to refine dietary choices and improve well-being.
Activity Trend Recognition for Personalized Energy Management
An individual leverages automated data synthesis and pattern identification to inform daily routine adjustments for sustained energy.
Bridging the Gap Between Movement and Pain Thresholds
A physiotherapist integrated visual analysis to refine client recovery protocols.
Glossary
AI Health Stack
A personal, tool-agnostic system that uses three free general-purpose AI chat tools as one coordinated health intelligence layer.
Protocol Layer (Layer 03)
The conversational planning layer. Translates research + patterns into a livable plan.
Evidence Hierarchy
A simple ranking (RCT > meta-analysis > observational > expert opinion > anecdote) used inside every AI prompt in the stack.
Personal AI
AI used by an individual for their own thinking — not as a product they pay for, but as a method they own.
Sunday Integration Hub
The weekly 20-minute ritual where the three layers merge — patterns meet evidence, evidence meets a plan.
Related topics
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AI for Blood tests
Use AI to interpret your blood work in context — across years, ranges, and references — without replacing your doctor.
AI for Blood pressure
A daily home blood pressure log is more useful than a single clinic reading. AI helps you see the real trend.
Start with 10 free days.
The free 10-day email challenge teaches the same method on whatever data you already collect. No credit card.