While True: Learn() Page

🛠️ Feature Title: "CatHub CI/CD Pipeline" (Continuous Integration / Continuous Descratchification) 🐾 Concept Overview

Let's assume the CI/CD node is tracking the of the active model over a rolling window of data packets. The error for a packet is calculated as the distance from the expected threshold. The mathematical formula for the rolling MAE at packet

In the graph above, you can see that as the individual packet errors spike after packet , the rolling MAE line crosses the defined threshold while True: learn()

: A small gauge at the top of the screen during "live" startup runs. It indicates how much the incoming shapes (circles, squares, triangles) are deviating from your trained model .

: You can use your earned credits to buy a "Faster Deployment Server" to make the automated switching happen instantly without losing processing frames . 📊 Educational Breakdown It indicates how much the incoming shapes (circles,

In the current game, players manually drag nodes and optimize single, static schemas to fit a static dataset . In the real world, datasets change, drift, and break old machine learning models.

: A master node placed at the beginning of the schema. In the real world, datasets change, drift, and

It branches into 2 or 3 separate "sub-schemas" (or different code repositories). It monitors server cost and output accuracy in real-time .