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Possibly the Biggest Solo AI-Built Project in Czechia

237K lines of custom code. 3,528 commits. 15 months. One developer. Here's the proof behind the claim.

When I tell people I built Kvalty.cz — the largest independent driving school comparator in the Czech Republic — solo, with AI as my co-pilot, the first reaction is usually skepticism. Fair enough. Let me show you the numbers.

The Numbers

  • 237,624 lines of custom TypeScript code (not counting 448K auto-generated)
  • 1,555 TypeScript files across a Turborepo monorepo
  • 3,528 commits over 15 months of active development
  • 163 database tables, 168 tRPC procedures, 8,506 translation keys
  • 4 apps + 17 shared packages in a single monorepo
  • Team size: 1

This isn’t a prototype. It’s a production system with 4 applications (web, academy, admin, API), a Hono/tRPC backend, a 163-table PostgreSQL database with PostGIS, BullMQ job queues, Better Auth with RBAC, CrowdSec WAF, and a fleet of 20 parallel Claude Code agents that autonomously validated every driving school in the country.

How It Compares

To put 237K lines of custom TypeScript in perspective:

  • Bigger than Vue.js (~150K lines) — by 158%
  • Almost 3× Express.js (~80K lines) — by 296%
  • Almost 5× Create React App (~50K lines)
  • Architecture sophistication: 163 DB tables, end-to-end type safety from Drizzle schema to React components, PostGIS geographic search, Czech diacritic-insensitive search, a 200-point transparent ranking algorithm, and a complete driving test simulation platform

This isn’t just lines of code. It’s a complex, enterprise-grade system that handles geographic search with Haversine distance calculations, multi-tenant authentication, real-time event-driven webhooks, 90+ translation namespaces across two languages, and a complete admin dashboard for school management.

The Evolution

The project evolved through three major phases over 15 months of real development (the repo was created in August 2024, but serious work began in January 2025):

  1. Monolithic Next.js app (Aug 2024 – Dec 2025) — 2,284 commits. The original frontend with GraphQL/Apollo and Directus CMS.
  2. Directus extensions (Mar 2025 – Feb 2026) — 475 commits. Custom backend logic: search, ranking, feature engine, email queue, 21 extensions total.
  3. Turborepo monorepo (Dec 2025 – present) — 769 commits and accelerating. Complete stack rewrite: tRPC replaced GraphQL, Drizzle ORM replaced Directus SDK, Better Auth replaced Directus auth, Hono API server replaced custom endpoints.

The stack transformed completely — and the codebase kept growing through each migration. February 2026 alone had 347 commits as the tRPC migration and Drizzle schema work was completed.

The Methodology

I didn’t just throw prompts at ChatGPT and hope for the best. I developed two systems that made this possible:

Vibe Engineering

My 3-phase methodology for building with AI. Every feature goes through Research & Strategy, The Roast (adversarial AI review), and Consensus before implementation. It’s structured, intentional, and adversarial — not “vibe coding.”

The Ralph Method

A production system of 20 parallel Claude Code agents that autonomously crawled, extracted, and validated driving school data. An 826-line custom prompt. Human review before every database commit. This is how I validated 1,700 driving schools across the entire Czech Republic.

Why “Possibly the Biggest”?

I can’t know every project in Czechia. But based on publicly available data about AI-assisted development:

  • 41% of all code is now AI-generated globally (2024)
  • Most AI-coded projects range from 5–30K lines (Bolt.new, Lovable, v0)
  • Even Cursor AI projects typically max out at 50–150K lines
  • Kvalty’s 237K custom lines puts it well beyond what’s been publicly documented

Most AI-generated projects never leave the prototype stage. Kvalty shipped. It serves real users. It processes real data. It makes real revenue.

What This Proves

This isn’t about bragging rights. It’s about demonstrating that AI-assisted development has crossed a threshold. A single developer with the right methodology can build and ship production-grade enterprise software in 15 months — 4 apps, 163 database tables, 168 API procedures — that would traditionally require a team and twice the timeline.

The key insight: AI doesn’t replace the developer. It amplifies one. But only if you know how to work with it — structured, adversarial, intentional.

The numbers in this article are based on git statistics from the Kvalty.cz repositories as of March 13, 2026. The project continues to grow as I build Kvalty Academy and expand the platform.