diff --git a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
index 0ae4774..5905947 100644
--- a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
+++ b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
@@ -8,7 +8,7 @@ Based on [[https://schlockmercenary.fandom.com/wiki/The_Seventy_Maxims_of_Maxima
 *. If increasing model complexity wasn’t your last resort, you failed to add enough layers.
 *. If the accuracy is high enough, stakeholders will stop complaining about the compute costs.
 *. Harsh critiques have their place—usually in the rejected pull requests.
-*. Never turn your back on a Claude Code.
+*. Never turn your back on a reinforcement learner.
 *. Sometimes the only way out is through… through another epoch.
 *. Every dataset is trainable—at least once.
 *. A gentle learning rate turneth away divergence. Once the loss stabilizes, crank it up.
@@ -16,7 +16,7 @@ Based on [[https://schlockmercenary.fandom.com/wiki/The_Seventy_Maxims_of_Maxima
 *. “Innovative architecture” means never asking “did we implement a proper baseline?”
 *. Only you can prevent reward hacking.
 *. Your model is in the leaderboards: be sure it has dropout.
-*. The longer training goes without overfitting, the bigger the validation-set disaster.
+*. The longer your Claude Code runs without input, the bigger the impending disaster.
 *. If the optimizer is leading from the front, watch for exploding gradients in the rear.
 *. The field advances when you turn competitors into collaborators, but that’s not the same as your h-index advancing.
 *. If you’re not willing to prune your own layers, you’re not willing to deploy.