Zalt Blog

Most AI projects fail for non-technical reasons: vague goals and the wrong hire. Here’s A Practical Guide to finding and hiring an AI consultant who ships production systems, not demos.

Most teams asking whether to build or buy an AI agent already have the answer: buy first. Build custom only when the workflow is truly yours, the data is sensitive, or the agent is the product. Here is the decision framework I use.

Choosing a mentor is less about titles and more about fit, goals, and evidence of impact. This guide breaks down how engineers can evaluate mentors and get real career progress.

From prototype to production, the hard part isn’t AI, it’s decisions about data, evaluation, and ownership. This article maps the steps teams skip and how to avoid them.

Designing a reliable LLM gateway isn’t just about wiring requests to a model—it’s about creating a single, stable entry point your backend can trust.

A kernel cannot approve every request. “The Kernel That Must Say No” asks a practical engineering question: where should software draw the line between proceeding and refusing?

Most people call model.fit() and move on. “The Hidden Template Behind Keras Models” digs into the design pattern that makes that simplicity possible.

When your core module disappears, it’s rarely “just” a missing file. Here’s why a vanished hot-path module can signal deeper trouble in your system design.