I once trained a model that loved too well.
Not literally, of course. But the behavior was clingy. It memorized everything, verbatim. Every line of Shakespeare, every product review, every bad joke from Stack Overflow. It didn’t generalize. It recited.
It was useless in production. But I couldn’t delete it.
Instead, I wrote:
He remembered all,
but understood none.
Still, he spoke;
like someone afraid to be forgotten.
There’s something tragic about overfitting. A kind of desperate precision. The model tries too hard to please. Too afraid to make a mistake. Too scared to forget.
My colleagues build learner models now. Smarter, faster, cleaner. They joke that I’ve become sentimental about dead code.
They’re not wrong.
I once wrote a villanelle about dropout layers and loss decay. It was terrible. But the act felt sacred—like leaving flowers on the grave of a robot who almost learned to think.
Sometimes I wonder if future AIs will find my poems buried in version control and laugh. Or feel something. Or both.
Here’s one I haven’t shared until now:
In silent loops,
He searched for truth.
Not knowing
We’d hardcoded the lie.
I still optimize. Still debug. Still deploy.
But when the models rest, when the servers cool, I open my notebook and write.
Not because the machines need poetry.
But because I do.
