Vibe Coding, The Engineering of Intent

Your AI isn’t just generating code — it’s building momentum.In Chapter 2 of The Engineering of Intent, we dive into the hidden force shaping every AI-assisted project: context momentum.Every prompt, every response, every small decision compounds. Over time, this creates path dependence — where early choices quietly lock your project into a specific direction, whether you intended it or not.This is where many developers lose control.To keep moving fast, they accept AI-generated changes that don’t fully match their intent. It works in the moment — but later, that accumulated drift turns into friction, inconsistency, and painful rewrites.We also break down the First Prompt Trap: your very first instruction sets the tone, structure, and constraints for everything that follows. If it’s vague, your entire codebase inherits that ambiguity.But this episode isn’t just theory.You’ll learn how to take back control:– Treat your first prompt like a project charter– Prevent context rot with living reference docs (like AGENTS.md)– And know when to deliberately slow down before making irreversible architectural decisionsIf you’re building with AI, this is one of the most important concepts to understand early — before momentum starts working against you.Listen to the episode, then go deeper: Get the book on Amazon

What if you stopped writing code… and started directing it?In the premiere episode of The Engineering of Intent, we break down the cognitive shift at the heart of the vibe coding era: material disengagement.This is the moment where engineering moves beyond typing syntax and into something more powerful — defining intent, guiding AI agents, and judging outcomes instead of manually constructing them.We explore what actually changes when developers stop interacting with raw code as their primary interface, and instead operate one level higher — orchestrating systems through natural language, constraints, and feedback loops.But here’s the real edge.We introduce the “Gestalt theory of vibe” — why top engineers don’t need to read every line to know if something is right. Through holistic perception, pattern recognition, and deeply internalized mental models, they can instantly evaluate AI-generated output.This is where most people fail with AI.Without strong internal schemas, vibe coding turns into noise. With them, it becomes leverage.If you want to understand how to actually work with AI — not just use it — this episode is your starting point.Listen to the episode, then go deeper: Get the book on Amazon