I got a lot of feedback on my post AI-assisted genealogy, some good, some not so good. In any case, I felt the subject was interesting to a lot of people. Meanwhile, I continue working on my tree, and I have deepened my understanding of the subject. In this post, I want to share again. Trust, but verify Let’s address the not-so-good feedback first. The gist of it was: AI can hallucinate. It’s true in theory: when you ask a question, it provides any answer.
My son recently came to me to brag about using AI to find our ancestors. While the results were correct, I didn’t learn anything new, as it stopped at my grandparents. I was never very interested in my genealogy, but I decided to see if AI would be a good tool for this. TL;DR: Yes, it is, and even more than that. In a little less than one month, I managed to gather more than 600 individuals and get back 12 generations in some branches.
I’ve been a big fan of Renovate for a couple of years already. Renovate scans your repositories, detects outdated package versions, and opens pull requests to automatically bump them. It’s similar to Dependabot in that it keeps your dependencies up to date. If I had to compare them in one sentence, I’d say Renovate is less integrated in the GitHub ecosystem, but handles more ecosystems and, more importantly, is extensible.
Last year, I motorized the rolling shutters on the southern façade of my apartment. My idea was to manage them via Home Assistant. I had a couple of automations in mind: In the evening, roll down the shutters of my bedroomIn the morning:If it’s too hot outside, roll down all shuttersIf it’s too cold outside, roll down all shuttersIn other cases, roll up all shutters but my bedroom’s Living in France, I added the official Météo France integration.
Good engineers make decisions based on data. Most businesses assumed that the more data, the better the decision. Then, several factors put a halt to the hoarding of ever more data. GDPR and its localized counterparts, and the cost of storage. However, before the GDPR came into effect, the Datensparsamkeit approach already existed. Datensparsamkeit is a German word that’s difficult to translate properly into English.
I continue to experiment with AI in the context of software engineering. I’m fortunate that my team supports me in exploring different ways to improve our daily work. This week, I designed a team of autonomous agents to implement features, from design to implementation. Why autonomous agents? A long time ago, we were delighted when the IDE offered auto-completion. In the previous two years, things have changed. A lot. Coding assistants have become our primary interfaces for coding.
I noticed some time ago that three Mastodon features had stopped working on my blog. Each of them seemed like a separate problem, but they had the same root cause. In this blog post, I aim to describe these issues and propose a simple solution. Domain verification Mastodon allows you to prove that you own a domain.
Last month, I became aware of GitHub agentic workflows. I read the site carefully, but the use cases weren’t very exciting to me. I tried the continuous documentation It didn’t work out initially, and because of my lack of involvement, I left it as it was. However, I succeeded in another one that I want to describe in this post. With lessons learned here, I managed to make the documentation workflow work!
Optional Google Analytics helps us understand visits. Microsoft Clarity records masked interactions to improve the site. Optional tools stay off unless you choose them. Privacy details.