Blog & Articles

Blog Post

AI and Elaboration: Which Coding Patterns Build Understanding?

AI tools let you complete coding tasks without connecting new information to your existing mental models—a cognitive process known as elaboration that is crucial for building understanding. But some AI interaction patterns preserve this elaboration while others bypass it entirely. Let’s explore what elaboration is, why it helps with learning, and how we can use AI tools in a way that helps with this process rather than circumventing it.

Blog Post

Fetch-Tools vs. Browser-Rendering in Agenten-Setups

„Eine URL abrufen“ klingt trivial, kann aber innerhalb eines Agenten schnell chaotisch werden. Ein Teil der Verwirrung entsteht durch Dokumentation, die nicht sauber trennt, was die einzelnen Web-Tools eigentlich leisten.

Blog Post

Leave It Better Than You Found It

An Unexpected Architecture Win

Blog Post

I sandboxed my coding agents. You should too.

LLM coding agents are extremely powerful because they can run programs on our computers using our permissions. However, this same power also makes us very vulnerable. It only takes one mistake or one prompt injection to compromise the whole system.

Blog Post

Why not scatter @Transactional everywhere?

@Transactional — great when you know when not to use it. Use it consciously instead of sprinkling it everywhere!

Article

Was, da geht noch mehr? Noch mehr Besonderheiten von und mit Java
Blog Post

From FOMO to Focus

AI is on every agenda – but where do you start? Many companies launch multiple AI initiatives in parallel, driven by the fear of missing out. The result: scattered resources, lack of prioritization, and unclear business impact. AI Opportunity Mapping provides a solution: it systematically guides you from vague AI visions to concrete, prioritized use cases with real business value. In five steps, teams develop AI opportunities in a structured way and make well-founded decisions. The result: focus instead of FOMO, clarity instead of chaos.

Blog Post

Speed vs. Skill

Every AI coding tool promises the same thing: unprecedented speed, effortless productivity, freedom from tedious work. The pitch is compelling. But what if feeling more productive and being more capable aren’t the same thing? Research shows that automation makes us feel more productive while eroding our skills. Let’s examine this tension through the lens of cognitive psychology.

Article

Menschenlesbare Datenformate mit Jackson verarbeiten

Jackson kann mehr als nur JSON

Blog Post

Context Engineering: Managing AI-Generated Code Complexity

AI tools make developers more productive at writing code, but can overwhelm code reviewers with massive changes. Learn practical strategies for managing context in AI-assisted development to keep your code comprehensible, your reviews manageable, and your team’s productivity genuinely improved. Small scope was always good practice—with AI, it’s essential.

Article

Prompts als Programm in GPT-5

All Caps, Gebettel und Geschrei – Schnee von gestern. GPT-5 befolgt brutal jede Instruktion, selbst in versteckten Nebensätzen. Und verhungert, wenn wir nicht genau sagen, was wir wollen. Welche Konsequenzen hat das für Prompt Engineering und Agentenentwicklung?

Blog Post

Swiss Army Knife for Salesforce: LLM with In-Memory Database
Blog Post

Git Clarity: Building Meaningful Commits and Linear History

Feeling overwhelmed by Git’s complexity? Discover a streamlined workflow that centers on creating single, comprehensible commits. Learn how this approach leads to clearer code reviews, fewer merge conflicts, and a beautiful linear commit history that tells the story of your code. This isn’t just about Git commands—it’s about aligning your tools with how your brain works.

Article

Achieving Digital Sovereignty with Standard Software

In the context of IT system landscapes, digital sovereignty is often equated with autarky— complete independence from third parties. By that logic, , a company developing and operating all applications in-house would be the ultimate example of digital sovereignty. But does this idea really hold up?

Article

Managing Geopolitical Risks with Enterprise Architecture
Blog Post

Modern Legacy (dank KI)

Die Diskussion über Softwaremodernisierung wird derzeit stark von Lösungen rund um künstliche Intelligenz (KI) in der Softwareentwicklung geprägt. KI gestützte Code-Vervollständigung, Agentic Software Engineering und weitere Werkzeuge versprechen, Entwicklungsprozesse massiv zu beschleunigen. Der Gedanke liegt nahe, diese Werkzeuge direkt am bestehenden Code einzusetzen: Refactorings, Redokumentation oder sogar generierter Code für neue Funktionen in vorhandenen Legacy Systemen. Technisch ist hier bereits viel möglich und auch die Buzzword-Industrie ist hier wieder massiv unterwegs. Doch diese Code-nahen, KI-getriebenen Ansätze können für Unternehmen, die sich mit einem Dschungel aus Legacy Systemen auseinandersetzen, zu kurzsichtig sein. Eine Modernisierung, die von Anfang an nur auf Code zielt, führt schnell zu Modern Legacy: Systeme, die dank KI-Unterstützung technisch hochmodern und vorbildlich dokumentiert wirken, deren eigentlicher Zweck aber längst fragwürdig ist.

Blog Post

Modern Legacy (thanks to AI)

The discussion around software modernization is currently dominated by solutions involving artificial intelligence (AI) in software development. AI-assisted code completion, agentic software engineering, and other tools promise to massively accelerate development processes. The obvious thought is to apply these tools directly to existing code as well: refactorings, re-documentation, or even generated code for new features in legacy systems. Technically, much of this is already possible, and the buzzword industry is once again in full swing. Yet these code-centric, AI-driven approaches can be shortsighted for organizations tangled in a jungle of legacy systems. A modernization that focuses solely on code quickly leads to modern legacy: systems that appear technically advanced and well documented thanks to AI, but whose actual purpose has long become questionable.

Article

A Governance Framework for Digital Sovereignty
Article

The Path to Heterogeneous Cloud Platforms

Digital transformation brings both challenges and opportunities for businesses. To create future-proof and flexible IT infrastructures, more and more companies are adopting multi-cloud or hybrid cloud strategies. This isn’t just about cost reduction, but also about aligning innovative services from different providers with regulatory requirements - particularly in the area of data protection. This article shows how targeted integration strategies can help you leverage the benefits of multi-cloud to optimize your existing infrastructure, reduce technical dependencies, and ensure long-term business success.

Article

Digital Sovereignty: Why Architecture Matters and How to Make Your Company Resilient

In an era where digital systems form the backbone of our economy and society, control over one’s digital future is increasingly becoming the focus of strategic decisions. For many technology decision-makers in German-speaking regions, this is not just a political or regulatory challenge, but a fundamental task with far-reaching implications for software architecture work.

Article

Data Inventories in the EU Data Act: The Democratization of IoT Devices
Article

Digital Sovereignty as Self-Understanding

How implementation teams can escape the we-are-not-Google trap and collectively take responsibility for European solutions to European problems.

Article

EU Data Act: The Beginning of the End for Cloud Monoculture?

What has the EU ever done for us? …well, aside from free movement, no roaming charges, consumer protection, the single market, Erasmus, and more. But also: excessive bureaucracy, slow decisions, overregulation down to the much-mocked bottle cap – a favorite symbol for anyone blaming Europe for every innovation shortfall.

Article

Think Locally: On-Premise LLMs as Drivers of Competitive Advantage
Blog Post

Software Analytics going crAIzy!

I’ve long been an advocate of treating software like data and analyzing it accordingly with tools like Python, pandas, Jupyter Notebook, Neo4j, and jQAssistant. In the era of AI and code transformation tools, seeing software as data gets more and more important when you want to improve your software system at a large scale. This blog post is a short interim report from me about my efforts taking software analysis to the next level.