Experiments
From a question to documented learning.
An experiment is a way to understand what can reasonably be learned about a practical use of AI. It starts with a clear question and ends with evidence, limitations and documentation that can be shared.
In progress · 15 September 2026
Current experiment
The experiment is ongoing. The observation is preliminary, and quantitative results are not yet available.
AI operator in practical work
Question:
How far can AI handle a real workflow while a person retains decision responsibility?What we test:
Website changes, audits, social media preparation, information retrieval and operational task chains using ChatGPT, Codex and Work.Preliminary observation:
AI can already handle a long sequence of work. Human approval remains appropriate for external publication and significant changes.Open question:
Where does a person still need to correct the work, and how reliably can the same process be repeated?
Next
What we are building next
These are next directions, not yet ongoing experiments or completed projects.
Community and first members
We are building the lightest practical way to bring in the first members and identify the next hands-on experiments together with them.
AI + maker / digital making
We are looking for the next concrete experiment combining AI with areas such as 3D, maker work, content or digital production.
Method
Five stages. One traceable chain.
Each stage constrains the next. The result is not a polished demo but an understandable account of what happened.
- 01 · QUESTION
A practical question
Define one person, task or situation in which a practical use of AI needs to be understood better.
- 02 · SCOPE
Scope
Record the users, material, time, responsibilities, sign of success and what the experiment will not test.
- 03 · BUILD / TEST
Build and test
Make the smallest useful implementation. Keep people in control and record exceptions during the work.
- 04 · EVIDENCE
Evidence
Collect observations, failures, limitations and alternative explanations. Do not fill gaps with assumptions.
- 05 · DOCUMENT / SHARE
Document and share
Separate observation, interpretation and open questions. Publish only what the evidence supports.
Evidence boundary
Documentation also states what we do not know.
A future experiment note should show at least the question, scope, date, observation, limitation and publication responsibility.
We publish what we can support.
A single experiment describes a bounded result. We also document limitations and open questions, and do not generalise beyond the available evidence.