Why documentation always loses to reality, how AI makes the gap unsurvivable, and what comes after the artifact.

Hi, I'm
I build systems that make complex things work in the real world. I write about what I learn along the way.
I've spent the last decade building ML systems in production, from early-stage startups in Brazil to recommendation engines at Meta and Expedia. These days I run Dipolo AI and write about AI, startups, and whatever else is on my mind.
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Why documentation always loses to reality, how AI makes the gap unsurvivable, and what comes after the artifact.

What context actually is, where the existing definitions fall short, and what changes when AI agents start acting on data at machine speed.


If your AI initiative is stuck on data problems no one can quite name, that's what I help companies figure out at Dipolo AI ↗.