Martin Stühmer

Who I Am

I’m Martin, CTO at Integrated Worlds GmbH in the Stuttgart region. I’ve been building .NET systems for nearly 20 years—since Framework 2.0 when SOAP was cutting-edge and ORMs were controversial.

Today I lead technology strategy for cloud-native solutions on Azure. Before this, I was Director Consulting Services at CGI, working with enterprise teams on architecture and transformation. I’m a Microsoft Certified Trainer and IHK-certified instructor, and I maintain several open-source NuGet packages.

What I Do

As CTO, I don’t just make decisions from a distance. I write code, review PRs, debug production issues, and mentor teams. Technology leadership means staying hands-on and feeling the consequences of your choices.

As a trainer and mentor, I focus on fundamentals that outlast framework hype. Static analyzers, testing strategies, performance patterns, maintainable architecture—the stuff that actually prevents production fires.

As an open-source maintainer, I publish packages that solve problems I’ve hit repeatedly in real systems. When strangers depend on your code, you write better tests and clearer docs.

What I’ve Learned

Almost two decades means I’ve made every mistake: over-engineered systems, bet on Silverlight and WCF (oops), built “flexible” architectures that were just complicated, shipped code I’m not proud of.

Here’s what stuck:

Quality isn’t optional – Analyzers catch bugs in milliseconds, tests prevent regressions, and both are faster than firefighting
Fundamentals outlast frameworks – Patterns and principles survive; specific tools don’t always
Context beats dogma – “Best practices” depend on your team, domain, and constraints
Evidence beats opinion – Measure, benchmark, validate before deciding
Pragmatism wins – Good-enough architecture that ships beats perfect architecture that doesn’t

What I Write About

I share perspectives from production systems and real teams. I’m skeptical of buzzword-driven development and allergic to cargo-cult practices. If a trend lacks substance or a pattern doesn’t hold up under pressure, I’ll say so.

This blog is for developers, solution architects and operators who maintain production systems and care about quality, not just shipping fast.

Published blogs

Real Professional Software Engineering in the AI Era

Throughout this series, we’ve established that AI-generated code without understanding creates productivity illusions that collapse in production (Part 1), and that the feedback loop between code and reality—compilation, testing, profiling, production—sharpens thinking in ways AI can’t replicate (Part 2). Now we confront the practical question: What defines professional software engineering when code generation becomes trivial? This final part examines the irreplaceable skillset: understanding execution characteristics (recognizing allocation patterns that cause GC pressure before deployment), asking questions AI can’t formulate (What’s the failure mode when this service is unavailable?), recognizing when plausible AI solutions diverge from correct ones, debugging production failures AI has no execution model to reason about, and evaluating maintainability for code that becomes tomorrow’s burden. We explore why prompt engineering optimizes for speed while architecture optimizes for survival, why “AI productivity” often means faster technical debt accumulation, and why the economic reality favors organizations that measure system reliability over lines of code generated. The feedback loop can’t be automated because closing it requires learning from production failures and applying that knowledge to prevent future ones—the irreplaceable discipline that defines real professionals in 2026 and beyond.

Stoßlüften: The Architecture of Intentional Resets

A Swabian habit teaches a DevOps lesson: open windows fully and often, or invisible decay accumulates. Stoßlüften isn’t about comfort—it’s about forcing systems to prove they’re healthy. Regular restarts, infrastructure-as-code, and reproducibility checks catch the problems that green metrics miss.

The Feedback Loop That AI Can't Replace

In the first part of this series, we established that AI-generated code without understanding creates an illusion of productivity that collapses under production load. The differentiator isn’t typing speed—it’s the feedback loop where code meets reality and exposes incomplete thinking. But what exactly is this feedback loop, and why can’t AI replicate it? Modern compilers validate logical consistency, catching gaps pure thought leaves unresolved. Profilers expose the 75x performance difference between “seems reasonable” and “actually performs.” Production environments reveal every assumption abstract thinking deferred—scale, concurrency, failure modes. This article explores the mechanisms that transform vague reasoning into concrete understanding: compilation validates logic instantly, testing catches behavioral mismatches, profiling measures what abstract analysis guesses, and production exposes the cost of every deferred decision. Real professionals don’t just write code—they master the iterative discipline of watching it fail, understanding why, and refining their thinking. AI participates in parts of this loop, but it can’t close it. That’s where professionals remain irreplaceable.

Kubernetes Is Not a Platform Strategy

Kubernetes has become an assumed default in many organizations, positioned as a universal platform that absorbs governance, security, observability, and operational responsibility. This narrative is incomplete. Kubernetes is a powerful runtime orchestrator that solves one phase of the software lifecycle. Architectural risk, cost decisions, and operational failure occur elsewhere. A critical examination of where Kubernetes’s responsibility ends, and what remains the architect’s job.

Kehrwoche: What Swabian Cleaning Teaches About Technical Debt

Kehrwoche—a Swabian cleaning tradition—is scarier than breaking the build on Friday afternoon. At least the build doesn’t remember next Tuesday. Mrs. Schmid from the second floor does, and she remembers well. What does a weekly cleaning schedule in southern Germany have to do with technical debt? More than most software teams want to admit.