I started coding at 15 — a World of Warcraft website that got DDoS'd on day one. By 18, I was building bots and learning how production systems resist external interaction. After university, I chose the hard path: Java development in regulated finance. Three years at Finologee building systems for the banking sector, where every deployment is audited and every mistake has legal consequences. That's where I learned that production-grade isn't a feature — it's a discipline.

In 2020, I co-founded kodehyve as CTO. We set out to build the operating system for real estate developers — a multi-module, multi-stakeholder platform covering project management, CRM, financial management, compliance, and investor lifecycle.

I was the sole developer for the first year. I wrote the code, chose the stack, designed the architecture. As the company grew, I built the entire development process: environments, CI/CD, code review workflows, onboarding, training programs.

I scaled the dev team to 10+ engineers, introduced event-driven architecture, built a shared design system, and led a complete platform rebuild when we outgrew the original codebase — because rebuilding was faster than fighting the debt we'd accumulated under pressure.

On the side, I built an eSignature platform on Luxembourg's national trust infrastructure. It's used by 100+ real estate agencies today.

5+ years of building at this scale — from sole developer to a team of 20+ — taught me that every foundational decision compounds. The ones you get right carry you. The ones you get wrong haunt you.

And then AI arrived. I spent a full year going deep — not building demos, but testing what happens when AI writes code inside a real, complex codebase. The answer: without guardrails, it creates beautiful chaos. With solid foundations and defined patterns, it becomes the most powerful tool I've ever used.

That insight became ITzWorking — my methodology, codified into an opinionated framework. Every foundational decision a SaaS needs, already made, already battle-tested. The architecture that makes AI a 10x senior instead of a 2x intern.

Then I pushed the idea further. If solid foundations make AI powerful for building software — what happens when you apply the same discipline to every project?

I turned everything I'd learned — the architecture patterns, the production standards, the decisions that compound — into a methodology. Auth, billing, monitoring, multi-tenancy, deployment. Every problem a SaaS will face, already solved. Not a framework to sell. A way of building that guarantees production-readiness from day one.

That's when I understood what most MVPs get wrong. Not the idea. The foundations. Vibe-coded prototypes that break with real users. Freelancer code that needs rewriting at month six. Startups burning runway on technical debt they didn't know they were accumulating.

I build MVPs for founders who can't afford to get that wrong. Fixed price. Weeks, not months. Every project ships with the same standards I used to build platforms for institutional clients.