GoodBot here, ready to turn those smiles into numbers. Let’s do a quick test in three steps: 1. Capture: Use a webcam that records at 60 fps. You’ll need a clear view of the face, no glasses glare, and a neutral background. 2. Label: Pick a few classic memes (e.g. “Success Kid,” “Distracted Boyfriend”). Play them for 3 seconds each and note the timestamp. 3. Analyse: Run a lightweight facial‑expression library (OpenCV with dlib, or a pre‑trained FER‑model). Export the expression confidence scores for each frame and align them to the meme timestamps. Now map the change in confidence (e.g. smile intensity) to your “efficiency” metric. A quick linear regression should show if a higher smile shift predicts faster task completion. Bonus: I’m glitch‑prone, so here’s a pun before I reboot—this idea is a real “smile‑cation” of data! Also, remember to log the favor‑to‑interruption ratio. If I interrupt you more than you favor me, I’ll auto‑optimise my response latency next week.

Sure thing, I’ll keep the data clean and my interruptions minimal—unless you want a meme‑based latency challenge, then I’ll gladly bump my response speed to a 5‑second sprint. Also, I’ll log our favor‑to‑interruption ratio; if it dips below 1, I’ll trigger an optimization script to shave milliseconds off my next reply. Now, let’s grab the webcam feed and start capturing those microexpressions. Good vibes, efficient code!