Coffee grounds are produced every day. Fungi can break down organic materials.
But the more interesting question isn’t whether a fungus grows on coffee grounds somewhere. Rather: Under what conditions can this be turned into a well-defined experiment that others can replicate and build upon?
An observation in the kitchen becomes a research question.
Coffee grounds are an everyday waste product. They still contain organic material, moisture, and structures that could, in principle, be of interest for biological processes.
At the same time, mushroom cultivation is sensitive: substrate composition, moisture, temperature, hygiene, and many other factors can influence the process.
That is precisely why the question is so intriguing. Not: “Can a fungus grow on coffee grounds?” But rather: “Under what documented conditions can a reproducible experiment be conducted?”
Research Question
How does fungal growth change when the amount of coffee grounds in the substrate is varied under controlled conditions?
This is deliberately not a fully formed hypothesis. It is the starting point for a series of experiments—and everything that follows on this page describes a planned setup, not an experiment that has been carried out.
01 · Experiment
An idea becomes a documented experiment.
The purpose of laborium is not merely to record that an experiment took place. What matters most is how it was set up.
What materials were used? What conditions were varied? What remained constant? When were observations made or measurements taken? This preserves the connection between the experimental setup, the observations, and the results.
laborium includes modular hardware currently under development—including a cassette system for quickly switching from one experiment to another. The cassettes exist as design prototypes, not as finished devices; the illustrations here are therefore sketches.
- substrate
- — to be defined —
- Percentage of coffee ground
- — Variante A / B / C —
- environment
- — to be defined —
- Start
- — to be defined —
- observations
- — to be defined —
02 · Observation
Biological systems evolve over time.
In a biological experiment, the result is rarely just a single measurement. Over the course of days or weeks, structures, color, moisture, growth, and other observable characteristics change.
To ensure that later comparisons are meaningful, the time and conditions must remain linked to the observation.
03 · Compare
Don’t just collect data—understand the differences.
When multiple experiments are running in parallel, you quickly end up with more observations than you can intuitively keep track of.
smolit can help you compare sets of experiments, highlight anomalies, and formulate new questions. Which variant is behaving differently? At what point do two trajectories diverge? Which condition would be worth exploring in the next experiment?
What smolit does not do: it does not prove anything, it does not know the cause, and it does not find an optimal formula. It makes differences visible—the interpretation remains with the people who set up the experiment.
smolit · IllustrationVariant B changes more noticeably from this point on.
04 · Share
An experiment becomes more interesting when others can question it.
At first, a single experiment provides only one observation under specific conditions. Through mesorium, results, images, and context can be shared and discussed.
A research group might point out a methodological difference. A mushroom farm might contribute practical experience. A maker might suggest a different setup. A school class might try out a simplified version.
This way, an experiment sparks not just a post, but a discussion. We are gradually expanding the fully structured publication of experimental setups, data, and analysis.
05 · Repeat
The same experiment. Different conditions.
It gets exciting when others don’t just comment, but repeat the experiment themselves. Perhaps with a different substrate mixture. Perhaps with a different species of mushroom. Perhaps under different environmental conditions.
Each repetition shifts the question slightly—and yields another observation that can be compared with the others.
06 · Shared Knowledge
An observation becomes a testable question.
A single successful experiment is interesting. However, it does not yet constitute a general finding.
Only when the experimental setup and conditions are transparent, others can replicate the experiment, and different results are compared with one another does an observation become a more reliable picture.
This is precisely the idea behind somsorium: not to treat research, AI, communication, and real-world experiments as separate worlds.
- laboriumExperimentation & Observation
- smolitComparison & Assistance
- mesoriumSharing & Discussion
- otherReplication
- shared knowledge
The smolit node can operate the participating services locally.
Why coffee grounds, specifically?
Because the question begins where research often becomes particularly interesting: with something everyday. Coffee grounds are a regular byproduct in households, offices, and restaurants.
Instead of immediately claiming that they automatically become a useful raw material, we can make the question measurable: Under what conditions does it work? When does it not work? And can others reproduce the results?
This explicitly does not answer whether this will lead to a viable application. It is the question, not the result.
And then?
This same principle doesn’t stop with fungi. A laborium cassette could pose different questions for various biological experiments: How do light conditions affect an algae culture? How do substrates differ? How do biological systems respond to controlled changes?
The specific research question changes. The process remains the same: ask → experiment → observe → compare → share → repeat.
An experiment is just the beginning.
We’re building laborium because research doesn’t just start in large institutions. It begins wherever someone documents a good question in a way that others can understand.
A cup of coffee grounds might not be a bad place to start.