David Heinemeier Hansson framed it eloquently: AI is making computers malleable at a ferocious rate. "Users can make systems their own with the help of AI and be utterly delighted by the outcome."
I finally switched my development environment from VSCode to tmux+neovim. I'm utterly delighted. Let me share why.
Remember February 2020, watching the covid virus spread overseas and wondering what would happen? Matt Schumer writes something big is happening: "I think we're in the 'this seems overblown' phase of something much, much bigger than Covid." He's an AI developer and entrepreneur, talking about how shocked he is with the last few months of AI developments.
Shumer's article has been criticized as alarmist. We don't know yet how jobs will be affected. But the reality right now is: I no longer have to write code. Instead I build on a higher abstraction level, describing in plain English what I want. I wrote about the tipping point for me last November (My Opus Moment). This new 'discipline' of writing software is being called 'agentic engineering'. But it's not just software that will be affected by these new AI models. I'm still recalibrating and wrapping my head around all the implications. There's more reason for hope rather than fear. This post collects some of my current thoughts.
We pay for insurance but hope we never need it. It's the same with digital sovereignty: we should never need it, yet we really can't go without. For me, the urgency of independence from American technology really hit home in Jan 2026 when Trump imposed tariffs on the Netherlands for sending troops to Greenland to prepare an Artic NATO defense mission. America threatening to take possession of an allied, sovereign country by military force really shook Europe awake, and rightly so. Although earlier warning signs were already clearly there. Like when in May 2025 the chief prosecutor for the International Criminal Court (based in The Hague) lost access to his Microsoft email after Trump imposed sanctions. Or maybe already back in 2018 when the US CLOUD Act was enacted, that obliges US companies to provide data "in their possession, custody, or control", regardless of where that data is stored. So while Amazon might announce products with names like AWS European Sovereign Cloud, they are not sovereign because they do not have jurisdictional immunity.
The "ChatGPT moment" was November 30, 2022, the day OpenAI released the first version of ChatGPT. It wasn't just a technological leap; it was a usability leap that took an LLM and put it behind a simple, free chat interface that reached 100 million users in just two months.
If you had asked me a year ago about LLMs for coding, I would have explained they were helpful but overhyped. They wrote some functions I would refactor, and I often got stuck in a loop of pasting error messages with "fix this". But then, just 3 years after the first ChatGPT, on 30 November 2025, Anthropic released Claude Opus 4.5. I realized I was wrong: I had my Opus moment.
Almost a year ago I switched to the ZSA Voyager, a programmable, ergonomic split keyboard that features four thumb buttons per hand instead of the single spacebar found on traditional keyboards. After having a lot of fun optimizing my keyboard layout and customizing shortcuts, I got interested in alternative alpha keyboard layouts. The 'AKL' scene is still actively developing new layouts with many great new options in the last two years. I found it hard to choose and fun to compare, so in this post I'll introduce a new tool I built: altalpha.timvink.nl
I occasionally enjoy playing sudoku puzzles. I particularly like the harder ones that pose a real challenge. Play them and you will get stuck inevitable. I often wonder if I missed something obvious, or if I need to apply a more advanced strategy to solve this puzzle. So I wrote a sudoku solver that gives me the easiest possible strategy required to make progress. This blog has an interactive demo of the solver and reflects on how I wrote it.
I hit that magic 1 million downloads/month milestone for one of the first open source projects I built: mkdocs-git-revision-date-localized-plugin. This post reflects on what I’ve learned maintaining that package since the first release end of 2019.
uvWhen python 3.11 came out 2 years ago (24 October 2022) it promised to be 10-60% faster than python 3.10, and 1.25x faster on the standard benchmark suite (see the what's new in 3.11). I've always wondered how that translates to training machine learning models in python, but I couldn't be bothered to write a benchmark. That is, until astral released uv 0.4.0 which introduces "a new, unified toolchain that takes the complexity out of Python development".
In my career I've focused mostly on applying what is now called 'traditional machine learning': regression, classification, time series, anomaly detection and clustering algorithms. You could frame machine learning as applying an algorithmic 'constrained intelligence' to a specific business problem. The challenge has always been to 'unconstrain the intelligence' (f.e. by tuning hyperparameters) and to further specify the business problem (proper target definition, clean data, proper cross validation schemes). The advent of large language models is starting to flip the equation; from 'unconstraining' intelligence to 'constraining' it instead.
scikit-learn has this nice feature where you can display an interactive visualization of a pipeline.
This post shows how to insert interactive diagrams into your mkdocs documentation, which is great for documenting your machine learning projects.