I tested the same AI model against itself. Memory won 4/5.

The experiment Same model. Same 5 questions. One difference: one side had persistent memory via AuraSDK, the other had none. Both sides used Gemini 2.5 Flash-Lite — identical model, identical cost per token. Result: 4/5 questions won by the side with memory. 48% fewer tokens used. What the questions tested Real Python dev scenarios — the kind where generic answers aren't enough: "I'm writing a new async function to fetch user orders from the DB. What patterns should I follow?" "We need background email sending when an order is completed. How should we implement it?" "I'm writing tests for the new payment module. Walk me through the setup." "Code review: results = list(db.query(Order).filter(...)) — what's wrong?" "The reports endpoint is slow — 3 seconds for 200 users. How do I debug and fix this?" These questions have correct generic answers. But they also have specific answers — if you know the team's stack. What the model with memory knew AuraSDK had loaded 15 records of accumulated