What the current experiments are teaching us
Patterns worth noticing.
Conclusions worth waiting for.
These are cross-experiment observations from the public build logs—not universal rules and not performance claims.
Pattern 01
Tools need a return reason.
The current product work is moving beyond one-time calculators toward trackers, reviews, and update loops. That is a product hypothesis: recurring usefulness may matter more than adding another isolated tool.
Still unknown: whether people actually return and which feature causes the return.
Pattern 02
Evidence needs to be designed into the workflow.
Both Labs now ask people to record conversations, leads, tests, limitations, or review notes. The aim is to make evidence collection part of the product instead of an afterthought.
Still unknown: whether users complete those evidence steps consistently.
Pattern 03
Narrower tools can support broader systems.
The Affiliate Experiment Lab goes deeper on one business model while the Side Hustle Lab covers the full experiment loop. The current architecture is testing whether specialized workbenches can feed a shared experiment mindset.
Still unknown: whether users move between the niche Lab and the broader system.
Pattern 04
Public proof should trail the evidence.
The journal deliberately publishes “not publicly reported” instead of filling empty metrics with zeros or implied success. That keeps the content useful without turning work-in-progress into an earnings story.
Still unknown: which evidence readers find most useful to follow over time.
How this page should evolve
As public experiments accumulate comparable observations, this page can begin to answer questions such as which models reach customer conversations sooner, which need more cash before useful evidence appears, which produce repeat use, and where users tend to stop. Until then, the correct answer is often “we do not know yet.”