About this blog

This blog is a space to document technical learning through real experiments. The content published here grows out of practical analyses, carried out to understand how specific components of AI systems behave in practice — before they are integrated into something larger.

It does not start from ready-made truths, but from questions, hypotheses, and observations developed throughout the learning process. The goal is simple:

To make visible the reasoning, assumptions, and limitations involved in technical decisions.

Here, you will find:

• Technical analyses focused on well-defined problems;
• Design decisions explained through observed evidence;
• Results discussed clearly, including what worked, what didn’t, and why.

The emphasis is on understanding before scaling. Before any broader implementation, hypotheses are tested, data is inspected, and behaviors are observed. Whenever possible, these analyses are preserved in a reproducible way — but the focus remains on the learning gained, not on the tools used.

Readers do not need to follow the code to understand the conclusions — but they can, if they choose to go deeper.

Why this blog exists

This blog exists to remind us that good technical decisions take time.

To look carefully at components that are often treated as defaults.

To understand behavior before optimization.

To turn experiments into reusable knowledge.

If you are interested in AI engineering beyond superficial examples — and value the process as much as the result — this space is for you.