A finance intern is spending her summer observing business processes and vibe coding automation tools. Not a CS major. Not shadowing someone. Building something real. It is a small example that says something big about how AI is reshaping internships, careers, and what the word "developer" actually means.
Vibe coding has moved from hobbyist curiosity to enterprise rollout across knowledge workers, and the next wave of AI adoption will be defined by governance and token economics.
I built a trip planning site for my F1 Montreal group. Then I ripped out every hardcoded value, added a setup wizard, ran a security audit, and open-sourced it. Here's what it takes to turn a single-purpose vibe coded app into something anyone can fork and deploy.
Someone vibe coded an app with Google AI Studio. The Gemini API key shipped in the client-side JavaScript bundle. Google suspended the project. Here's why every AI coding tool gets this wrong, why regular audits are the only real defense, and what you can do before it happens to you.
The Round 5 bakeoff produced four implementations. None of them shipped. What shipped was a merge of the best pieces from all four, then a polish pass against real data. Bakeoff → Merge → Polish is a generalizable pattern for any feature where the design space is genuinely unclear.
Four LLM models built the same admin feature in isolated Coder Agents sessions. I judged them blind. The headline result: Sonnet 4.6 beat Opus 4.6 on a coding task. The deeper story is what each model did with the same prompt — and what it took to make the bakeoff fair in the first place.
I pointed a current-gen AI agent at a fitness tracker I vibe coded a year ago. Five PRs later: security holes patched, a 1,300-line component broken apart, sync queries batched, and accessibility fixed. Here's what the audit found, how it was structured, and why every vibe coded app deserves a spring cleaning.