Vibe Coding with AI: How Strict Typing and Nuxt Layers Supercharge Cursor and Claude Code

The term "Vibe Coding" has become the biggest trend in software development in 2026. Experienced developers and technical founders use intelligent assistants like Cursor, Claude Code and v0 to write code at speeds never imagined before.

However, almost everyone who tries to build a complex SaaS with prompts alone runs into the same obstacle: code entropy.

After a few iterations, the AI starts to hallucinate variables that do not exist, recreates functions already implemented in other files, breaks entire screens and introduces silent database vulnerabilities.

Why does this happen? And how does the Nuxfire architecture solve this problem?

The Secret Is Not the Prompt, It Is the Structural Context

Large language models (LLMs) are only as good as the context they receive in their attention window. If your project is a confusing monolith with hundreds of interdependent files and no strict typing, the model has to "guess" the relationships between the parts.

Nuxfire was designed specifically to be the ideal foundation for AI tools, combining three architectural pillars:

1. End-to-End Strict Typing with TypeScript & tRPC

In Nuxfire, there are no any objects or untyped payloads. Every API route defines its input and output with Zod schemas through tRPC:

export const billingRouter = router({
  createCheckoutSession: protectedProcedure
    .input(z.object({
      priceId: z.string(),
      teamId: z.string()
    }))
    .mutation(async ({ input, ctx }) => {
      // Cursor knows exactly which properties exist on 'input' and 'ctx'
      return createStripeSession(input.teamId, input.priceId);
    })
});

When you ask Cursor or Claude Code: "Create a button in the dashboard that calls the checkout procedure", the model reads the tRPC type definitions and autocompletes the correct parameters on the first attempt, without inventing false properties.

2. Nuxt Layers: Clear Domain Boundaries

With Nuxt Layers, each functional area of the product is confined to its own directory:

• Changing the team member flow? Only files in layers/teams/ are loaded.
• Modifying theme styles? The packages/tokens file centralizes everything.

This compartmentalization reduces the amount of context needed and stops the AI from changing unrelated files by mistake.

3. Structured Schemas with Drizzle ORM

Mapping database relationships with legacy ORMs often produces hallucinations in SQL migrations. Drizzle ORM uses pure declarative TypeScript, making it easier for the AI to understand foreign keys, indexes and integrity constraints with total precision:

export const subscriptions = pgTable("subscriptions", {
  id: text("id").primaryKey(),
  teamId: text("team_id").notNull().references(() => teams.id),
  status: text("status", { enum: ["ACTIVE", "PAST_DUE", "CANCELED"] }).notNull(),
  currentPeriodEnd: timestamp("current_period_end").notNull()
});

From Idea to Product in Days, Not Months

By combining the speed of generative AI with a solid, enterprise-grade architectural base, you get the best of both worlds:

• 10x more speed to create new features.
• 0 regressions thanks to strict type contracts.
• Clean code you own forever, ready to receive thousands of paying customers.

Stop fighting AI hallucinations. Start your project on a professional, tested foundation.

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