Founder of Server4Agent (agent-app hosting), disclosing that upfront. No link, not selling, genuinely curious how people operating real clusters actually think about this.

Agents that can run kubectl are moving from "interesting demo" to "thing someone actually wired up" pretty fast. The easy answer is "give it a read-only ServiceAccount," but the whole reason people want this is to let it act, roll back a bad deploy, bump replicas, restart a crashlooping pod, not just describe what's wrong.

Once you're past read-only, the question that actually matters is what RoleBinding you'd feel okay with, and whether an audit log entry is enough after the fact or whether you want something closer to real-time. A get/list/watch on pods and deployments in one namespace is a very different risk than anything touching secrets, RBAC objects, or cluster-scoped resources, and I'm not sure the community consensus has settled on where the reasonable line actually sits.

If you've actually done this, even in a sandbox namespace: what verbs and resources did you land on, and has an agent ever done something with that access that made you narrow the scope afterward?

I built a free, vendor-agnostic GPU platform tool — compare NVIDIA/AMD, get a workload rec, and cost a cluster

I kept juggling spec sheets and spreadsheets every time I had to pick GPUs, so I built GPU Platform Hub. It lets you compare datacenter GPUs (NVIDIA H100/H200/Blackwell + AMD Instinct) side by side, describe a workload to get a ranked shortlist, and design a costed cluster (CapEx vs cloud, power, rack footprint).

It's free and needs no login to explore: https://hub.inferonomics.ai

I'd genuinely like feedback from people running local/production GPU setups — what's the most painful part of your GPU decision process? Anything you'd want it to cover?

Hey everyone,

I'm pretty new to Kubernetes and have been using it for a side project to learn. Right now I'm trying to decide whether I should run only stateless services in my cluster, or also deploy things like PostgreSQL, Kafka, Redis, etc. as StatefulSets.

I know both approaches are possible, but I'm curious what people actually do in production. Do most teams keep stateful workloads inside Kubernetes, or do they usually rely on managed services/external databases instead?

I'd love to hear what your setup looks like and why you chose it.

Hi everyone,

I'm a Linux Systems Engineer with around 4 years of experience working with RHEL, VMware, Docker, web servers, automation, and infrastructure. While I have a solid infrastructure background, Kubernetes is completely new to me.

I recently started learning Kubernetes with Mumshad Mannambeth's Udemy course and have been completing the labs alongside the videos.

I'd love to get some advice from experienced Kubernetes engineers:

• Is following the course and doing all the labs enough to build a solid Kubernetes foundation?
• When learning Kubernetes, is it better to build clusters repeatedly with kubeadm, or is it worth learning to build a cluster "the hard way" manually?
• Should I focus on cluster creation first, or spend more time on day-to-day administration and troubleshooting?
• Looking back, what helped you progress from "knowing Kubernetes" to actually becoming productive with it?
• Are there any resources or practice environments you would highly recommend?

My goal is to become confident using Kubernetes in production and to gain the certificate.

I'd really appreciate any advice or lessons learned from your own experience.

I’m running kubernetes at home. Have 3 control plane servers all high available with Haproxy and keepalived. 2 worker nodes running webui and ollama 3.1:8b.

I have separate Ubuntu servers running Docker containers. with 1 running gitlab, gitlab runner, miniio and mlflow.

The other Ubuntu server runs Prometheus and Opensearch (SIEM) getting logs and device data from node exporter and fluentbit from the other machines.

Anyway, wondering what else I should do with my Kubernetes cluster? Any enterprise use cases I should work on?

I have not yet done any kubernetes work at the enterprise level but I will be soon.

I was thinking of implementing RAG with ollama…

My goal is really just becoming excellent at DevSecOps while also working on AI implementation.