Most Software Teams Are Lying to Themselves—2026 Needs to Be Different
Happy New Year 2026! 🎉
Fix one piece of technical debt this week—not next quarter. .NET 10, analyzers, and tests are ready; discipline is the only missing part.
Martin Stühmer — CTO, Enterprise Architect & DevOps Engineer
Who I Am
I’m Martin, CTO at Integrated Worlds GmbH in the Stuttgart region. I’ve been writing production .NET code since Framework 2.0 — back when SOAP was still considered modern and ORMs were a heated debate. A lot has changed since then. My opinions have changed too, usually because I was wrong about something.
Before Integrated Worlds, I was Director of Consulting Services at CGI, leading teams through architecture decisions and digital transformation projects across multiple industries. These days I set technology strategy and stay close enough to the code to feel the consequences of those decisions firsthand.
I’m a Microsoft Certified Trainer and IHK-certified instructor, maintain several open-source NuGet packages, and write about what I’ve actually learned rather than what sounds good in theory.
What I Know Well
Nearly two decades of production work leaves marks. Here’s where I’ve built up depth and, frankly, strong opinions:
.NET and C# — I’ve tracked this ecosystem from the framework era to .NET 10. Source generators, Roslyn analyzers, performance engineering, testing strategies, static analysis. I know what actually improved over the years and what just changed names.
Cloud-native architecture on Azure — AKS at scale, multi-cluster networking, zero-downtime upgrade strategies, cost governance, observability. I’ve spent enough time in Azure to know the gap between what it can do and what you should actually use.
DevOps and supply-chain security — GitHub Actions, dependency management, container security, Bicep-based infrastructure compliance. The organisational side matters as much as the tooling.
Application security and privacy — Azure Key Vault, managed identities, GDPR-relevant .NET patterns, data minimisation, AI coding tool content exclusions. Security that works in practice, not just in architecture diagrams.
Engineering culture — What AI coding assistants actually change about software quality (less than the hype, more than the skeptics admit), how to introduce static analysis without poisoning team morale, and when “best practices” are earned principles versus cargo-cult repetition.
How I Work
As CTO I stay close to the code. I write, review PRs, debug production issues, and mentor — because technology leadership that operates purely from a distance eventually loses touch with the real cost of decisions.
As a trainer I focus on fundamentals that outlast the current framework cycle. Static analysis, testing discipline, performance patterns, maintainable architecture. The things that prevent fires, not just the things that look good in a job posting.
As an open-source maintainer I publish packages that solve problems I’ve hit repeatedly. Knowing that strangers depend on your code is one of the better ways to raise your standards.
What I’ve Learned the Hard Way
I’ve over-engineered systems that should have been simple. I bet on Silverlight and WCF. I built “flexible” architectures that were really just complicated ones. I’ve shipped code I’m not proud of.
Here’s what held up after all that:
- Quality isn’t optional — analyzers find bugs in milliseconds, tests prevent regressions, and neither is slower than a production incident
- Fundamentals outlast frameworks — the patterns and principles stick around long after the tools are deprecated
- Context beats dogma — “best practices” always depend on your team, domain, and actual constraints
- Evidence beats intuition — measure, benchmark, validate; don’t just decide
- Pragmatism wins — architecture that ships beats architecture that’s perfect on paper
What I Write About
Everything here comes from production systems and real teams, not documentation rewrites. I’m skeptical of buzzword-driven development and allergic to advice that’s never been tested under load or deadline. If something doesn’t hold up, I’ll say so.
The topics I keep returning to: .NET and C# performance and evolution, Azure and AKS architecture decisions, DevOps and supply-chain risk, application security and GDPR implementation, what AI coding tools actually change (and what they don’t), and the economics of technical debt and code quality.
I write for developers, architects, and operators who maintain production systems and care about getting it right — not just getting it shipped.
The code you create is a valuable legacy, so it’s important to build it carefully.
Published blogs
2025 in Review: The Year .NET Stopped Lying to Itself
.NET CLI 10 – Microsoft Finally Realizes DevOps Exists
The .NET CLI? Reliable. Boring. You run dotnet build, dotnet test, dotnet publish, done. Real DevOps work happens in Dockerfiles, CI/CD configs, and specialized tools. The CLI does its job but was never built for actual operational workflows.
.NET 10 changes this. Four additions that sound minor but fix real problems I’ve hit in production pipelines for years: native container publishing, ephemeral tool execution, better cross-platform packaging, and machine-readable schemas. Not flashy. Not keynote material. But they’re the kind of improvements that save hours every week once you’re running them at scale.
Will they replace your current workflow? Depends on what you’re building. Let’s look at what actually changed.
Why Your Logging Strategy Fails in Production
Let me tell you what I’ve learned over the years from watching teams deploy logging strategies that looked great on paper and failed spectacularly at 3 AM when production burned.
It’s not that they didn’t know the theory. They’d read the Azure documentation. They’d seen the structured logging samples. They’d studied distributed tracing. The real problem was different: they knew what to do but had no idea why it mattered until production broke catastrophically.