AI coding

In 2015, Google warned that ML systems were the 'high-interest credit card of technical debt.' A decade later, vibecoding tech debt makes that metaphor quaint. AI-generated code doesn't carry credit card rates — it carries payday lender rates, with terms designed to look cheap until the first payment is due.

You're burning 3–5x more tokens than necessary on every feature because your AI coding agent keeps forgetting your architecture. Here's the math on LLM token burn—and the structural fix.

You fix the auth. It breaks the database. You fix the database. It breaks the error handling. This is the vibecoding whack-a-mole problem — and it's why most AI-assisted prototypes never reach production.

Specs tell AI agents what to build. But without product context—the why, the who, the validated assumptions—your perfectly executed spec might be perfectly wrong.

Cursor rules are the first line of defense against AI-generated chaos. Here's how to write rules that actually work, the patterns that scale, and why static rules eventually hit a ceiling.

Vibecoded codebases don't just have technical debt — they have a different kind of debt that compounds faster because every feature introduces inconsistent patterns the LLM can't see. Here's the anatomy of vibecoding debt and the structural fix.

Vibecoding makes building fast. But speed without direction is just failing faster. Most vibecoded products fail because of what comes before the code. Here's how to check.

AI coding agents can build anything you describe. But without product context—the why, the who, the constraints—they'll build the wrong thing perfectly. Here's the missing layer.