A New Experiment Meta-Strategy

A hobbyist scientist reorganized a cluttered home lab to maximize the chance of actually doing experiments during scarce free evenings. After buying and assembling IKEA furniture and grouping gear by purpose, every component and chemical was catalogued - items listed include a pressure manometer, CO2/particle/temperature monitors, multimeter, cooking temperature probes, a bioreactor, and reagents like magnesium chloride, silver nitrate, dilute hydrogen peroxide, caustic soda, agar, and silicone oil. That inventory was fed to an AI (Claude) which produced a QtPy app that maps the state space of experiments possible with the existing equipment. The app shows inventory in one column, suggested experiments in the middle, and click-through protocols, with each experiment annotated by dynamics, difficulty, and setup time.

This reverses the usual buy-to-replicate workflow into a generate-from-inventory meta-strategy that prioritizes low-cost, tabletop, independently reproducible investigations of noisy phenomena, biological dynamics, and organized structures such as Rayleigh-Bénard convection. The tool is designed to optimize for novelty while minimizing cost and complexity, to rank experiments by interestingness, and to estimate likelihood of agreement with existing literature. The author plans tighter integration with learning materials and literature review, and already bought higher-quality loggable temperature probes to pursue convection-driven temperature oscillations.