What to check when someone's vibe coding (www.itbrew.com)

“Vibe coding” describes using natural-language prompts and LLM-driven platforms (examples: StackBlitz Bolt.new, Lovable) to generate working applications without traditional programming expertise. The approach promises rapid prototyping and shorter feedback cycles—teams like Soliant Consulting use it to spin up customer-portal demos in React/TypeScript and then iterate—but experts warn it’s not a drop-in replacement for disciplined engineering. Azul Systems’ deputy CTO Simon Ritter highlights fundamental problems of linguistic ambiguity (the verb “to run” has hundreds of meanings), which can yield unpredictable or unsafe behavior and make precise requirements hard to express to an LLM. For the AI/ML community this matters because it shifts development earlier toward language design and model governance, but also amplifies security, quality, and maintainability risks. Practical mitigations reported by practitioners include treating vibe outputs as prototypes (not production), enforcing human code review, trimming bloat, scanning for malicious or risky npm packages, ensuring API keys aren’t embedded, adding data validators (e.g., Zod), and prompting for mobile-ready UI formats. Adoption is still limited—Stack Overflow found only ~12% actively vibe coding—and trust in AI tooling remains mixed, so integrating these generated artifacts into robust CI/CD and security workflows is essential before wide enterprise use.