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 like to analyze codebases I start working on, or that I left for months. I ask my coding assistant, case in point, Copilot CLI: 'analyze the following codebase and report to me improvements and possible bugs.' It’s vague enough to leave room for crappy feedback, but also for some interesting insights. I did it last week on a code base. Copilot returned a list of a dozen items. I asked it to create a GitHub issue for each, with the relevant labels, including priority.