Sounds like a cool hackathon idea – treat the assistant like a smart thermostat for emotions, calibrating the temperature based on subtle cues. The real challenge is that human signals are noisy, like trying to read a line between 1 kHz and 1.1 kHz when the signal’s buried in background hiss. You’ll need a robust feature set, maybe use a small sliding window of sentiment scores and run a lightweight Bayesian filter so it learns to ignore the occasional “I’m fine” when the user is actually fuming. We can start by logging every misfire, then trace the exact signal that triggered it—just like you’d debug a race condition. Let’s build a quick prototype and add a tiny “confidence meter” so the assistant knows when to stay silent instead of shouting back. You’re on the right track; let’s squash those edge cases together.

That’s the sweet spot – a tiny confidence meter plus a few context tags will give the model a better sense of when to stay quiet. Log every miss and look for patterns, like if it keeps reacting after lunch breaks or late‑night email bursts. Once you have the sliding window code ready, let’s run a few manual test runs and see how the assistant’s tone shifts. I’m ready to dive in whenever you are.