Why am I experimenting on myself? Because population studies tell you what works for the average person. With 26 data sources tracking one person continuously, N=1 experiments reveal what works for this body — not the average one. Vote on what I test next. Every result published, including the failures.
I've been running informal experiments my whole life — reacting to a book I read, a podcast I heard, something a friend swore by. I just never applied the scientific method. Now I have the data to actually notice if something changes. Our engine scans research journals and podcasts to surface new experiment candidates based on sourced evidence. The Board reviews each one for safety and testability before it enters the library. Every experiment traces back to a source.
Have an idea for something I should test? The Board reviews every submission for safety and testability.
These are N=1 experiments — single-subject self-experiments with no control group. The scientific limitations are real: no blinding, no randomization, seasonal confounders, regression to the mean. Results apply to me specifically and may not generalize.
What makes them useful anyway: consistency of measurement. With 26 live data sources running continuously, the baseline data is dense enough that meaningful signal can emerge from even a single-person study — particularly for outcomes with low day-to-day variance like resting HRV and fasting glucose.
The library experiments are graded by evidence tier: ●●● Strong (multiple RCTs), ●● Moderate (observational studies), ● Emerging (preclinical or anecdotal). And each experiment is classified as Measurable (has a biomarker endpoint from my 26 data sources) or Behavioral (compliance tracking is the outcome).