Blog: Learn Machine Learning from a Google AI Engineer
Mapping the DSM-5 in Vector Space (And Why Psychiatric AI Is Chasing a Ghost)
As an engineer, I look at the world through the lens of data architecture, relational integrity, database schemas, and decision trees. If a system’s database has circular dependencies, porous boundaries, or redundant records, we call it a bad design and refactor it.
Yet, the most authoritative database of human suffering—the American Psychiatric Association’s DSM-5-TR (Diagnostic and Statistical Manual of Mental Disorders)—violates almost every principle of clean data architecture.
Learning the Hard Way: When Agents Build Agents (And the Culture Changes It Requires)
I started my career as a software engineer over 20 years ago, and I’ve spent the last decade pion...
Unleash the Super-Prompt: Mastering Your Coding AI Workflow With Gemini
Welcome back to the technical blog series! We’re diving deep into the developer toolchain today. ...
Unleashing Gemini CLI Power in GitHub Actions and Beyond
While everyone’s talking about AI coding assistants like Cursor, GitHub Copilot, and Windsurf, th...
Best Practices for Prompt Engineering in the Enterprise
Alright, we’ve covered a ton of ground in this AI Prompt Engineering blog series, from understand...