One year ago I might agree that Kubernetes is an overkill but today?

Ask your favorite GPT to generate manifests, get primary app into cluster with telepresence or execute straight from container and switch contexts and clusters like it's 90s again.

One reason I dislike Docker Compose and Docker is lack of isolation. Yes sure if you put your arm deep enough you can get it, but on local k8s I can spin cluster per workspace and not worry about conflicting ports between PostgreSQL instances.

Before LLMs writing consistent YAMLs was PITA but today on low/development scale it's pretty much free lunch.

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hadlock 4 hours ago | parent | next [-]

Strong agree, if there's one thing LLMs are excellent at, it's writing Terraform and Kubernetes deployments (and/or helm charts). What used to be half a day of research, trial and error, is now 20 seconds of AI churn and 98% of the time it nails it on the first try. And then point it at grafana and tell it to write you a dashboard for the new service/s. Easy peasy lemon squeezy. What used to require a team of 4 devops/SRE to support a medium sized company, can now be collapsed down into a a single part time SRE.

	▲	bigstrat2003 an hour ago \| parent \| next [-]
		LLMs are pretty bad at writing those things in my experience. They will invent HCL syntax that doesn't exist, generate absurdly overwrought Helm charts, put in assumptions that don't make any sense, and so on. It's faster, and better quality, to write the stuff myself.
	▲	d675 3 hours ago \| parent \| prev [-]
		as I got into SWE 4 yrs ago, this was a big part of my job as a SRE/SDET and my next job came b/c of that SRE exp which was never used, so just became an SDET. Now am laid off, and hard to find a job...

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aleksiy123 14 minutes ago | parent | prev | next [-]

Finally just bought a piece of my own hardware and got LLM to deploy k3s cluster on it.

I think diy homelab/hosting is more accessible than ever.

Cut costs on cloud spend and invest into AI spend.

For a solo dev on a budget, I think it just makes sense.

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iamcreasy 4 hours ago | parent | prev | next [-]

Interesting. I have just started reading about Kubernetes. Is there an reading material that goes over this process you just described?

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johnsmith1840 an hour ago | parent [-]

Don't. Get a chatgpt subscription and spin up a minikube cluster and launch some stuff and play around.

K8s is incredibly deep and complex but with AI it's finally easy to just hello world it.

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bigstrat2003 an hour ago | parent [-]

This is absolutely terrible advice. You should never ever use LLMs to work on something you don't understand already, because you have no way to catch the machine when it screws up (and it will screw up). Just like with every other form of automation before LLMs, a smart person only automates things he already knows how to do himself.

	▲	johnsmith1840 44 minutes ago \| parent [-]
		Yeah no. Getting the first hello world up is more important than anything else. Until you physically see it running learning is slow. I learned k8s through many months of study and pain pre AI. Once I actually got it up learning was FAR easier. This is like using a jupyter notebook to learn python and is always the first thing I point to for someone just starting to learn. Only after should you learn venv, pip install, classes ect. 100% use AI to get started on something you don't understand. I will literally never start to learn about a technical system again without first doing a hello world with AI.

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embedding-shape 4 hours ago | parent | prev [-]

> One reason I dislike Docker Compose and Docker is lack of isolation. Yes sure if you put your arm deep enough you can get it, but on local k8s I can spin cluster per workspace and not worry about conflicting ports between PostgreSQL instances.

Using Kubernetes because you're unable to grok docker's networking enough so you can't run multiple containers using their own ports and not conflicting with other stuff sounds like a recipe for disaster, even (especially?) if you use agents for this. Particularly if you let them manage a production environment, you're bound to lose important data eventually.

> pretty much free lunch.

Aah, famous last words of the young :)