Data & AI
Track every run
An experiment that didn't record its config and result may as well not have run.
An experiment that did not record its configuration and result may as well not have run, and the recording cannot depend on the experimenter's mood. Every run logs to the tracker, trainers fail fast when tracking is down, and opting out is an explicit act reserved for the genuinely disposable. The dangerous failure is not the missing log; it is the run that silently logged nowhere while you believed otherwise.
Above the tracker sits the lab notebook, the human layer machines can't replace. Two documents: a standing state of what is currently true, and a dated log of what happened. Entries survive only while unresolved or not yet obsoleted; superseded diagnoses get cut, recurring mistakes get counted, and negative results get written down loudly, because a clean negative that ruled out its confounds is a finding, not a failure. No conclusions from differences smaller than your noise floor.
How to practice it
Fail fast on broken tracking.
A trainer that runs while logging nowhere manufactures untrustworthy history. Better no run than an unrecorded one.
Keep STATE and LOG.
One file for what is true now, one dated file for what happened. When they disagree, the log is newer; reconcile the state to it.
Count your recurrences.
A mistake made twice gets a tally; at three, it gets structurally prevented. The notebook is where the count lives.
Publish the negatives.
The experiment that ruled something out saves the next month. Write it as loudly as a win, confounds and all.