> Four years ago I hand-wrote a nested for loop - four levels deep, tagging subnets across regions and availability zones in another AWS account - and it took me about an hour to get the syntax right

I do believe this kind of engineer requires AI. I also hope I don't have to work in the same teams as them. Seasoning teaches you to make changes in other parts of the codebase rather than do this kind of data mangling on local variables, which is incredibly hard to troubleshoot and very brittle - not to mention it's a nightmare to read back and understand.

Matches my experience working with LLMs on Kubernetes. They are amazing at writing the boilerplate yaml, and fantastic at using kubectl to monitor and debug clusters (with guardrails and readonly permissions in place of course.) if you have a staging/test cluster, you can get an extremely fast feedback loop if you let it apply changes as well.

It’s a good example of how highly structured, well- documented, opinionated frameworks help LLMs do well. There’s just less room for subjective issues when there’s more structure around it.

Matches my experience working with LLMs on Kubernetes. They are amazing at writing the boilerplate yaml, and fantastic at using kubectl to monitor and debug clusters (with guardrails and readonly permissions in place of course.) if you have a staging/test cluster, you can get an extremely fast feedback loop if you let it apply changes as well.

It’s a good example of how highly structured, well- documented, opinionated frameworks help LLMs do well.

This is an extremely common misunderstanding about AI in software. In order for AI to "move up one layer of abstraction" it has to actually be an abstraction. But AI output will _always_ be flawed in ways that mean you still need to fully understand the layer below it.

this post is so relatable, especially the locals where tags are four levels deep lol