In this special episode, Lou Franco, veteran software engineer and author of “Swimming in Tech Debt,” shares his practical approach to AI-assisted coding that produces the same amount of tech debt as traditional development—by reading every line of code. He explains the critical difference between vibecoding and AI-assisted coding, why commit-by-commit thinking matters, and how to reinvest productivity gains into code quality.
Vibecoding vs. AI-Assisted Coding: Reading Code Matters
“I read all the code that it outputs, so I need smaller steps of changes.”
Lou draws a clear distinction between vibecoding and his approach to AI-assisted coding. Vibecoding, in his definition, means not reading the code at all—just prompting, checking outputs, and prompting again. His method is fundamentally different: he reads every line of generated code before committing it. This isn’t just about catching bugs; it’s about maintaining architectural control and accountability. As Lou emphasizes, “A computer can’t be held accountable, so a computer can never make decisions. A human always has to make decisions.” This philosophy shapes his entire workflow—AI generates code quickly, but humans make the final call on what enters the repository. The distinction matters because it determines whether you’re managing tech debt proactively or discovering it later when changes become difficult.
The Moment of Shift: Staying in the Zone
“It kept me in the zone. It saved so much time! Never having to look up what a function’s arguments were… it just saved so much time.”
Lou’s AI coding journey began in late 2022 with GitHub Copilot‘s free trial. He bought a subscription immediately after the trial ended because of one transformative benefit: staying in the flow state. The autocomplete functionality eliminated constant context switching to documentation, Stack Overflow searches, and function signature lookups. This wasn’t about replacing thinking—it was about removing friction from implementation. Lou could maintain focus on the problem he was solving rather than getting derailed by syntax details. This experience shaped his understanding that AI’s value lies in removing obstacles to productivity, not in replacing the developer’s judgment about architecture and design.
Thinking in Commits: The Right Size for AI Work
“I think of prompts commit-by-commit. That’s the size of the work I’m trying to do in a prompt.” Continue reading AI Assisted Coding: Swimming in AI – Managing Tech Debt in the Age of AI-Assisted Coding
