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The Agent Takes Over Operations

Cron jobs and CI pipelines have been running on our servers with far-reaching permissions for years, and hardly anyone thinks twice about it. An AI agent is different, because it decides for itself how to solve a problem. This article looks at how to give an agent access to servers without giving up control.

Jev and System One Models: how forms and UIs can become intelligent

System One Models pick their answer from a list of predefined options, return a probability with it, and take only a few hundred milliseconds to do so. With Jev, TypeSafe AI has shipped the first implementation. It gets interesting wherever language models have been too slow so far: in the input field and the results list, anywhere someone is waiting for an answer.

50 Years of Cohesion - and Still Elusive?

The Shifted Complexity: From Waterfall through Agile to AI

Software projects have always been complex, but that complexity has kept shifting: from project management to the team, from the team to the individual developers. AI is accelerating this shift dramatically. Looking through the Cynefin lens reveals a pattern that keeps repeating itself and is currently escalating. The consequence: a massive increase in mental load for developers and the question of how we want to deal with this going forward.

The New Economics of Software Development

AI has dramatically reduced the time it takes to build software, but that doesn’t mean we should simply deliver more features. The productivity gains from coding agents give us an opportunity to rethink how we invest our development time. Instead of accelerating technical debt, we can use AI to build cleaner abstractions, improve maintainability, and leave our codebases better than we found them. This article explores how AI has changed the classic project management triangle, and why quality should become our new competitive advantage.

Why 75 Dollars in Tokens Can Create So Much Value

And how to reliably destroy that ability

Static Sites Without Big Tech Hosting

If you run a static website, you rarely think twice about hosting. GitHub Pages works, costs nothing, and the code is already there anyway. But “works” and “sovereign” are two different things. This recipe shows how to build a stack with statichost.eu plus Codefloe or Codeberg that is just as convenient, but stays in European hands, treats visitor data transparently, and does not depend on a transatlantic legal basis that is currently starting to wobble.

The Data Dilemma, Part 1

When AI agents fail in a company, it is rarely the model’s fault. The real problem is the data: semantics, quality, and context are written down nowhere, they live in the heads of the domain experts. Data Mesh addresses exactly this problem, but it organizationally overwhelms many mid-sized companies. This first part describes why that is and derives a Minimum Viable Data Mesh from it. The second part shows the implementation in four layers.

The Data Dilemma, Part 2

A Minimum Viable Data Mesh in four layers

How do you train people on a subject that changes every week?

For the past year, we’ve been training development teams to use AI agents across the entire software development lifecycle. In that time, we’ve delivered 47 trainings to more than 500 participants. Here’s what goals companies bring to us, what sets our training apart, which moments have stuck with our trainers, and why the real work starts after the training ends.

What ECST Events, Domain Events, and ECST Really Are

“ECST Event”, “Domain Event”, “ECST”: these terms appear in almost every discussion about Event-Driven Architecture and Domain-Driven Design. Surprisingly often in practice, different people have a different understanding of them.

Indecent Exposure

Your SPA’s API Is Showing Too Much

Trust but Sandbox

Coding agents can now do (almost) everything. They read and write every file you have access to, they run arbitrary commands, talk over the network, read your environment variables, and drive your browser with all your cookies. In the spirit of an agentic workflow, where we let agents work as autonomously as possible, that is actually the point, because it is exactly what makes them useful. Even so, we should find a way to gain more control over what happens in there.

Beyond Claude & GPT

Sure, we enjoy trying out the latest frontier model as much as anyone. But in day-to-day project work, what matters most is control over cost, data, and availability. That’s why we increasingly rely on open-weights models paired with open source tooling. Here’s what our setup looks like.

Stop Using Bearer Tokens Like House Keys

DPoP with Heimdall

Alternatives to U.S. Artificial Intelligence

Claude and GPT have transformed the way I write software, but I’m becoming increasingly uncomfortable with rising costs, vendor lock-in, and our growing dependence on a handful of U.S. providers. After exploring local models and open-weight alternatives hosted in Europe, I was surprised to discover that some lesser-known models can already handle much of my day-to-day work.

Faster Is Not Better: On Experimentation Culture and What It Costs

Faster experimentation sounds like an obvious advantage. But experimentation culture is not a universal paradigm. It is a product of a very specific context. And the people who bear the heaviest cost of poor product development never appear in any experiment.

OpenProject: A Real Alternative to Jira?

The Agentic Trio

The Product Trio separated discovery from delivery because delivery required a larger team. Agentic development removes this constraint. That changes what a product team can look like.

RAG and Fine-Tuning

Large Language Models (LLMs) don’t always produce reliable results. In practice, one thing becomes clear: combining Retrieval Augmented Generation (RAG) and fine-tuning can make them more robust. This post explains how the two approaches complement each other – and why a production feedback loop is critical.

Using the space, not optimizing the treadmill

Why agentic development needs product discovery

The Missing Half of Your Data Strategy

In order to stay competitive, a modern data architecture is essential. It enables sound decisions and data-driven development at all levels, from strategic choices to feature prioritisation and buy-or-build decisions. The ability to collect, understand and act on data is no longer optional. Many organisations recognise this and are investing in data mesh initiatives and similar approaches. Through modern infrastructure and self-service platforms, they aim to democratise access to corporate data. However, these platforms often fail to fulfil their potential because people still do not know how to work with data or what value they can derive from it. For a successful data strategy, this has to change. Let’s talk about data literacy.

Nebu: Self-made sovereignty

There are plenty of open-source alternatives to Slack. None of them deliver real sovereignty. Community editions are deliberately pared down so no company can run them in production, while enterprise features sit behind commercial licenses. This article shows why “building it yourself” is no longer a utopian idea today, but a realistic option for anyone with a product vision and a bit of DIY courage.

Accidental Complexity in the Heart of Software

Hail Mary: Why domain knowledge cannot be extracted from experts

AI agents promise to replace weeks of requirements workshops with hours of structured interviews. The pitch is compelling. The cognitive science is not.