Who's the man behind ASleekGeek
Bojan Skrchevski - Software Engineer & Architect
Three decades of turning complex problems into elegant solutions. From Commodore 64 to AI platforms. The journey never stops.
The Journey
The Beginning
The story begins in the early 1990s, with a kid in Macedonia hunched over a Commodore 64, painstakingly retyping BASIC code from the user manual. Not because anyone told him to, but because the machine demanded to be understood. That kid would go on to spend the next three decades chasing the same feeling—the electric satisfaction of making computers do exactly what he wanted them to do.
By high school, BASIC had given way to C and Assembly. Programming primitive robots became the obsession.
The passion wasn't cultivated in some structured curriculum; it was discovered in the gaps between what the textbooks covered and what was actually possible. University brought C++, but also the realization that real-world problems couldn't wait for a diploma.
The final year of Computer Science studies at Sv. Kiril i Metodij University in Skopje never got completed—not because of failure, but because clients kept calling with problems that needed solving now.
The Evolution
What followed was a quarter-century odyssey through the entire evolution of software engineering. Automotive diagnostics for Renault and Nissan. Revenue assurance systems for British Telecom. Real-time betting platforms. Medical decision support systems for UK insurance companies. Core infrastructure at Rebtel that scaled to millions of users. Smart charging solutions for electric vehicles. Solution architecture at IKEA, standardizing integration patterns across their retail technology ecosystem.
Each role demanded mastery of different domains, different constraints, different failure modes. The technical stack reads like a timeline of computing itself: Delphi to C#, jQuery to React, physical servers to Kubernetes, monoliths to microservices, SQL Server to Cassandra to DynamoDB. But the real expertise isn't in any single technology—it's in understanding why technologies succeed or fail in production.
The Philosophy
After you've debugged enough systems at 3 AM, you develop an intuition for where the bodies are buried in any architecture. The approach to problems is shaped by having seen too many "best practices" age poorly.
"Skepticism toward hype, preference for battle-tested solutions, obsessive attention to observability and failure modes. Code should be readable six months later. Systems should fail gracefully. Abstractions should pay for themselves."
These aren't philosophical positions—they're scar tissue from debugging distributed systems that violated these principles.
Today
Currently building AI platforms at Sveriges Radio (Swedish Radio) while consulting through Forefront in Stockholm. The work sits at the intersection of everything accumulated over three decades: backend systems that need to handle real load, ML pipelines that need to actually ship, infrastructure that needs to be reproducible and observable, and the hard-won wisdom that elegant architecture means nothing if it can't survive contact with production traffic.
Outside of client work, the learning never stopped. Open source contributions on GitHub, side projects that exist purely to scratch technical itches, deep dives into whatever technology looks promising enough to warrant serious investigation. Not because anyone's paying for it, but because understanding how things work at a fundamental level is what makes the difference between a developer who can follow documentation and an engineer who can solve problems the documentation never anticipated.
Areas of Expertise
Where three decades of production experience meets modern engineering practices
Backend Systems
Architecting scalable backend systems that handle real production load. From monoliths to microservices, SQL to NoSQL, synchronous to event-driven.
ML/AI Platforms
Building production-ready AI platforms that move beyond proof-of-concept. ML pipelines that actually ship, not just notebooks that run locally.
Infrastructure
From physical servers to Kubernetes clusters. Infrastructure that's reproducible, observable, and survives production traffic.
Observability
Systems that tell you what's wrong before users do. Obsessive attention to monitoring, logging, and graceful failure modes.
Beyond Code
Different lenses for understanding complexity, human systems, and the pursuit of mastery
Music
A different kind of logic—chord progressions, tension and release, mathematical elegance beneath emotional expression.
Psychology
Understanding human behavior, team dynamics, and cognitive biases—insights no system design can teach.
Japanese Philosophy
Kaizen (continuous improvement) and wabi-sabi (beauty in imperfection)—perfection is a direction, not a destination.
Football
Strategy, teamwork, emergent behavior from simple rules—the beautiful game shares more with distributed systems than most admit.
Key Milestones
The Spark
Commodore 64, BASIC, and the beginning of an obsession
Professional Start
Automotive diagnostics (Renault, Nissan), C++, real-world problem solving
Enterprise Scale
British Telecom revenue assurance, medical systems, real-time betting platforms
Infrastructure Evolution
Rebtel core infrastructure (millions of users), EV smart charging, cloud migration
Solution Architecture
IKEA retail technology, standardizing integration patterns at scale
AI & Modern Platforms
Sveriges Radio AI platforms, Forefront consulting, Stockholm
Technologies Mastered
A timeline of computing itself
Languages & Frameworks
Data & Infrastructure
Architecture Patterns
Let's Connect
The Commodore 64 is long gone, but the curiosity it sparked remains intact. Thirty years in, the craft still delivers that same satisfaction.