Blog

The right size of a Data Product

Setting the boundaries of data products incorrectly can lead to integration issues, unclear ownership and duplicated logic. This guide offers practical heuristics for creating data products of the right size.

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Agents good in the end?

Software development consists of a constant chain of trade-offs. As long as I’ve been building systems, there have always been things you don’t do despite wanting to do them. But now, with agents, we can fulfill all our dreams and finally build everything we always wanted and had the feeling we were missing. Spoiler alert: we shouldn’t do that.

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Understanding AI Coding Patterns Through Cognitive Load Theory

Developing with AI through the cognitive Lens

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Neuland reloaded

When Angela Merkel said in 2013 that the internet is ‘Neuland’ (uncharted territory) for all of us, the amusement online was considerable. Viewed from some temporal and substantive distance, one can now recognize a perspective from which she was indeed correct.

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From Vibe Coder to Code Owner

AI agents generate thousands of lines of code in no time. If you want to fully leverage their potential, you can no longer review every single line – but you’re still responsible for the software. How do you take ownership of code you haven’t fully read? The answer lies in the agent harness: a system of deterministic checks, AI reviews, and targeted human review that enforces quality instead of hoping for it.

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I sandboxed my coding agents. Now I control their network.

Restricting network access for AI coding agents with a proxy allowlist

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Your Database Table is an awful API

A shared database table can feel like the ultimate shortcut: no API design, no contracts - just a schema. The catch is that this convenience quietly turns into friction: it blurs boundaries, slows change, and makes independent evolution harder than it needs to be. This article looks at why tables (and data-model-shaped “generated APIs”) are such a tempting trap - and what better integration boundaries look like.

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Let’s Not Normalize Insecure AI Assistants

AI assistants like OpenClaw promise convenience, autonomy, and increasingly personalized help. But beneath that promise lies an architecture that quietly combines private data, internet access, and exposure to untrusted content—the lethal trifecta of security risk. Sandboxing and physical isolation help, but they don’t address the core problem. As we add more capabilities, the potential blast radius only grows. This post is a case for slowing down, questioning defaults, and refusing to normalize insecure architectures.

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What to Do While the AI Is Thinking

How deep work, multitasking, and code reviews change as we delegate tasks to AI

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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.

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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.

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Leave It Better Than You Found It

An Unexpected Architecture Win

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Why not scatter @Transactional everywhere?

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

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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.

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Speed vs. Skill

Developing with AI through the cognitive Lens

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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.

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

Large Language Models (LLMs) struggle with transforming large datasets, particularly when performing aggregations that require calculations - essentially anything where you’d normally use GROUP BY or ORDER BY in SQL. But what if we need to retrieve extensive data from a system and process it with an LLM, but only have an API with limited functionality available? In this blog post, I present a solution I’ve implemented and tested.

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Git Clarity: Building Meaningful Commits and Linear History

A personal workflow for intentional development and cleaner Git logs

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Managing Geopolitical Risks with Enterprise Architecture

IT leaders in Europe increasingly face questions about whether geopolitical developments – such as data protection disputes or trade conflicts between the EU and the US – threaten the viability of US cloud services. The risks range from price increases and legal uncertainties to potential usage restrictions. CIOs must not only ensure functional IT operations but also proactively assess external risks. This article demonstrates how enterprise architecture methods can help identify risks early and develop viable alternatives.

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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?

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A Governance Framework for Digital Sovereignty

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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.

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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.

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

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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.