The question is always what part of that complexity can be eliminated. Every “k8s abstraction” I’ve seen to date either only works for a very small subset of stuff (eg the heroku-like wrappers) or eventually develops a full blown dsl that’s as complex as k8s (and now you have to learn that job-specific dsl)
yep, that's the latest of a long lineage of such projects (one of which I worked on myself). Ohers include kubero, dokku, porter, kr0, etc. There was a moment back in 2019 where every big tech company was trying to roll out their own K8s DSL (I know of Twitter, Airbnb, WeWork, etc).
For me, the only thing that really changed was LLMs - chatgpt is exceptional at understanding and generating valid k8s configs (much more accurately than it can do coding). It's still complex, but it feels I have a second brain to look at it now
Maybe that should be the future of K8s 2.0. Instead of changing the core of the beast tweak it minimally to get rid of whatever limitations are annoying and instead allocate resources to put a hefty AI in front of it so that human toil is reduced.
At some point you won't need a fully dedicated ops team. I think a lot of people in this discussion are oblivious to where this is heading.
> At some point you won't need a fully dedicated ops team
I think programmers are more likely to go extinct before that version of reality materializes. That's my secret plan on how to survive the alleged AI apocalypse: AI ain't running its own data flow pipelines into its own training job clusters. As a non-confabulation, I give you https://status.openai.com. They have one of every color, they're collecting them!
