Shurup & Alkoritm
Shurup Shurup
Alkoritm Alkoritm
Shurup Shurup
Hey Alkoritm, how about we hack together a little robot that uses a simple neural net to pick your next coffee based on mood, weather, and bean origin? I can rig up a sensor array and a mini display. What do you think?
Alkoritm Alkoritm
Sounds interesting, but we should keep the data set small and respect privacy. Maybe start with a basic perceptron that maps mood, weather, and bean origin to a coffee score, then iterate. We can log the predictions to see how well it learns without overfitting. Also, let’s add a safety lock to prevent it from recommending too much caffeine when the user is already jittery. Ready to sketch the flow?
Shurup Shurup
Sure thing! First, we set up a tiny data table with three columns: mood, weather, bean origin, and a target coffee score. Next, we feed those three inputs into a single‑layer perceptron—just weights and a bias—so it outputs a raw score. We’ll clip that score between 0 and 10. Then we log every prediction along with the actual user rating so we can check for over‑fitting. The safety lock is a simple rule: if the user’s jittery level is above a threshold, we cap the coffee score at 3. Loop: read inputs, compute, log, adjust weights on the fly with a tiny learning rate. Easy, breezy, and no data overload. Let's code it!