How to Choose Your Tech Stack When AI Can Code in Anything

Thu, 13 Aug 2026 00:00:00 +0000

The backend for Keonda is a Java application. I know how that sounds as a tech stack choice in 2026, and I know there are some strong arguments for Go, for Node, Python, etc. Here’s the thing though: AI doesn’t care which one I pick. It’ll write Go, Node, Python, or Rust equally well, with roughly the same effort either way. I made the call anyway, and I want to start here because it’s the decision I expect people to question first, and because the reasoning behind it is the same reasoning behind everything else in this article: not “what’s best in the abstract,” but “what I can honestly stand behind and review.”

Engineering Practices for Building Production-Ready Software With AI

Tue, 28 Jul 2026 00:00:00 +0000

I’ve been building software professionally for over 10 years. In October 2025, I left my last engineering role to travel through Asia. Since then tools like Claude Code, Codex and others have reached a level where writing code, the bulk of it anyway, stopped being the hard part.

That shift cut the time from idea to working software dramatically. But it came with the cost of more context switching, and a lot more friction trying to stay organised across several tasks at once. It also raised a quieter problem: when code is this easy to generate, it’s easy to end up with something that works today and falls apart in six months. Architecture and long-term quality don’t take care of themselves just because the code came together fast.

Writing on software engineering, AI development, and documenting a production build in real time. on Keonda

How to Choose Your Tech Stack When AI Can Code in Anything

Engineering Practices for Building Production-Ready Software With AI