Notes on AI, ML and the tools behind the projects — click through for the full post.

Understanding RAG and how it enhances AI model responses with external knowledge.

Deep dive into neural networks, training, and real-world applications.

Essential Python libraries that every data scientist should know.

When it's worth training a model versus just writing a better prompt.

A step-by-step walkthrough of a small convolutional network in NumPy.

A lightweight MLOps setup for one-person projects — no platform team required.