miniblog.

One fun way of testing new AI models: take an existing codebase you have and just ask them to "review it and fix bugs". In principle this should find more issues over time as models get smarter. I've found a few bugs this way at least.

Cleaned up an old git repo of Ansible playbooks and it's amazing how much is just Docker now. Docker handles process supervision, centralises logging, and containers coexist without issues. It's nice when things get simpler.

After further playing with my LLM project, I'm surprised how hard it is to tune with system prompts. My agent kept saying "obviously" even though my prompt said "helpful, professional". Eventually I found that a "courteous" prompt gets "of course" which is better but not ideal.