Right, I’ve been using it recently for writing a message queue -> database bridge with checkpointing and all kinds of stuff (I work for a timeseries database co

Right, I’ve been using it recently for writing a message queue -> database bridge with checkpointing and all kinds of stuff (I work for a timeseries database company). I saw this as a chance to embrace AI, after a while of exploring I found Claude Code, and ended up with a pretty solid workflow. But I say this as someone who has worked with distributed systems / data engineering for almost 2 decades, and spend most of my time reviewing PRs and writing specs anyway. The trick is to embrace AI on all levels: learn how to use prompts. learn how to use system prompts. learn how to use AI to optimize these prompts. learn how to first write a spec, and use a second AI (“adversarial critic”) to poke holes in that plan. find incompletenesses. delegate the implementation to a cheaper model. learn how to teach AI how to debug problems properly, rather than trying to one-shot fixes in the hope it fixes things. etc It’s an entirely different way of working. I think juniors can learn this as well, but need to work within very well-defined frameworks and probably needs to be part of college curriculum as well.