Vibe Porting Is Real Now. Three Independent Case Studies Prove It.

I've been skeptical of "just have AI rewrite your codebase" advice because most of it comes from people who haven't actually done it in production. This week, three independent teams published results that changed my mind, with a very specific caveat.

Reco.ai rewrote JSONata (a JSON expression language) from JavaScript to Go. One engineer. Seven hours. $400 in API tokens. The Go version runs 1,000x faster on common expressions, which cascaded into $500,000 per year in cloud savings across their data pipeline processing billions of events. Cloudflare's team did a similar rewrite with vinext. Simon Willison rewrote multiple datasette plugins across languages. All three followed the same pattern.

The pattern: port the test suite first, then implement until green, then shadow-deploy both versions against production traffic, then promote on zero mismatches.

That last part is critical. Reco ran both JSONata and gnata (their Go version) side by side for a full week, comparing outputs on real production data. Shadow deployment isn't optional here. It's what separates "I rewrote something with AI" from "I rewrote something with AI and verified it actually works."

The enabler in every case was the test suite. JSONata has excellent test coverage. Willison's plugins had tests. Cloudflare had tests. The AI doesn't need to understand the problem domain. It needs to produce code in Language B that passes the same tests Language A already passes. Without tests, you're gambling. With tests, you're doing verified translation.

This connects directly to the DryRun Security story (Story #1). The 87% vulnerability rate comes from greenfield AI code generation where there's no reference implementation and no test suite to validate against. Vibe porting with comprehensive tests is a fundamentally different activity. You're constraining the AI's output to match a known-correct behavior, not asking it to invent correct behavior from scratch.

For builders: look at your infrastructure costs. Find the performance-critical component written in Python or JavaScript that processes high volumes and has solid test coverage. That's your vibe porting candidate. The ROI math is simple. Reco spent $400 and saved $500K/year. If your compute bill has a similar bottleneck, this pattern is immediately replicable.

One honest caveat: I don't know how well this works for codebases with poor test coverage. And I don't know what edge cases hide in AI-translated code that tests don't cover. The shadow deployment step is your safety net, but only if your production traffic actually exercises those edge cases.