Hello from Groningen: building practical legal and educational AI
Hi, I'm Victor Grösser. I live in Groningen, in the north of the Netherlands, and I work where law, education and software meet. I'm a legal AI advisor and a developer: I help people and organisations think through how AI can be used responsibly, and I build the systems myself.
This blog is where I'll write down what I learn along the way. Not announcements, not predictions, just notes from actual work.
What I mean by "practical"
A lot of AI talk is about what models might do some day. I'm more interested in what they reliably do today, and in where they still fail. In legal and educational work, that difference matters. A tool that summarises a document well nine times out of ten still needs someone to check the tenth.
So the questions I keep coming back to are simple:
- Does this actually save someone time, or does it move the work somewhere else?
- Can a person see where an answer came from and check it?
- What happens to the data, and who controls it?
- What does it do when it doesn't know?
Legal AI
I have a background as a legal service provider, and I see how hard it can be for people to find clear information about their situation, especially in stressful cases like youth care. That's part of why I work on Steunvoor.nl, a free platform that gives parents clear information and support.
AI can help explain procedures, structure documents and point people to the right questions. It should not pretend to replace professional advice, and it should be explicit about its limits. Much of the design work is about exactly that: sources, uncertainty and knowing when to hand off to a human.
Educational AI
On the education side I'm building LeerHeld.nl, a learning platform for children and parents without ads or distractions. Here AI is useful for generating practice material and giving feedback, but children deserve tools that are calm, predictable and safe. I'd rather ship something modest that works than something impressive that confuses.
Agents and self-hosting
Behind the scenes I run a set of AI agents that each do one job: research, writing drafts, monitoring servers, keeping my projects in order. They run on my own servers and a small homelab, with cloud services where they make sense. Running things yourself teaches you quickly where the real costs and failure modes are: rate limits, flaky logins, memory that drifts, and agents that confidently do the wrong thing twice.
I'll write about those lessons too, including the mistakes.
What to expect here
- Write-ups of things I built and what went wrong
- Notes on agent design, open models and self-hosted infrastructure
- Thoughts on using AI carefully in legal and educational contexts
- A curated list of links and repositories I find genuinely useful
If something here is useful to you, or you think I'm wrong, I'd like to hear it. You can find me on LinkedIn, GitHub or via victorgrosser.nl.