developer growth

Two ideas that have been sitting with me: why your emotional reactions are usually just a story you made up, and why waiting for a "big bang" breakthrough is a trap. Your outcomes are governed by quiet, invisible micro-shifts, not sudden moments of passion.

Why relying on random_state for reproducible train/test splits breaks the moment your dataset changes — and how hash-based splitting with MurmurHash3 gives you deterministic, stable assignments that survive new data.

In this third post of the Learning Machines series, let's examine online versus batch learning, instance-based versus model-based generalization, and write our first linear regression model in scikit-learn.

For years, I wrote code by telling the computer exactly what to do. Shifting to machine learning means letting the data write the rules. This post explores what 'learning' actually means in code, using Tom Mitchell's equation and the supervision taxonomy.

I've been using AI without truly understanding it — calling APIs, fine-tuning models, shipping features powered by things I couldn't fully explain. That gap has started to bother me. This is the first post in Learning Machines, a series where I rebuild my ML and AI foundations from scratch and share the process as I go.