How I publish
Trust has to be built into the format, not added as a disclaimer. These rules apply to everything on the site, and they matter most when an entry reports an experiment or claims an impact.
The rules
Evidence is labelled
Every entry says what its claims rest on: opinion, observation, measurement or an illustration. Hypothetical scenarios and illustrative numbers are never presented as measured outcomes.
Samples say so
Example entries carry a Sample label until they are replaced with real work.
Client work needs permission
I ask before publishing confidential client data or identifiable internal workflows, and each Work & Proof story says what was approved and which outcomes were measured.
AI use is disclosed
When AI played a meaningful part in the research or production and it affects how you should read the result, the entry says how.
Negative results count
Inconclusive and unsuccessful experiments are published too, and small samples are acknowledged.
No disguised pitches
Business promotion is kept apart from editorial judgement.
Publish what you can substantiate. Explain what you learned. Make it useful enough that someone returns.
How the work connects
The six sections are entry points into one body of work. One meaningful experiment can lead to several useful pieces while keeping one source of evidence that the others link back to.
| Source work | Derivative expression | Reader value |
|---|---|---|
| The Lab: the full experiment | A Teardown of the mechanism | Understand why it may work |
| Experiment findings | A Field Note on the lesson | Understand what changed my view |
| A repeatable insight | A Playbook or checklist | Apply the learning |
| A relevant real engagement | A Work & Proof case study | Assess professional credibility |
| All of the above | Media: YouTube, LinkedIn, Instagram and the newsletter | Discover and revisit the idea |
Questions
Spotted a claim that goes further than its evidence, or a label that looks wrong? Tell me and I will check it.