KubeFM

An application release can flood a shared Kubernetes cluster with logs, even though another team owns the logging infrastructure. Who has enough context to find the cause—and who owns the fix?
Molly Sheets joins Daniele Polencic and Bart Farrell to examine how tenancy decisions affect coordination, maintenance, and staffing costs. She connects ownership to business goals and explains why faster AI-assisted changes still need controlled releases.
In this episode:
The cost of one central team supporting multiple tenancy models

Why AI Ops needs context from application and platform teams

Network boundaries for AI-generated workloads

Maintenance as quality, with canary and cell-based releases

Sponsor
This episode is sponsored by LearnKube. When a release breaks production, can your developers and platform engineers troubleshoot it together? Request private Kubernetes training tailored to your platform and workloads, with live instruction and 60% hands-on labs.
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Find all the links and info for this episode here: https://ku.bz/Db3VnC3nl

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Non-deterministic agents pose specific challenges for platform teams in observability, state management, governance, and trust.
Mauricio (Salaboy) Salatino explains why agentic applications behave like distributed multi-agent systems. His test assigned agents to take an order, cook the pizza, deliver it, and charge the customer. One order crossed 15 containers and produced 200 traces.
In this interview:
Why agent frameworks can recreate monolith scaling and resource contention

How OpenTelemetry data can measure agent behavior and trust over time

Why narrowly scoped agents are safer than agents that follow long sequences

How the platform can become the learning layer that feeds better context back to LLMs

Sponsor
Kubernetes moves too fast to track everything. Learn Kubernetes Weekly filters out the noise to deliver one curated email with useful articles, tutorials, tools, jobs, events, and CFPs. Subscribe to Learn Kubernetes Weekly.
More info
Find all the links and info for this episode here: https://ku.bz/TlVjXdnb6

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KubeSelect is a new show that tests one bold Kubernetes hypothesis with one expert. Hosts Salman Iqbal and Bart Farrell examine the evidence and ask whether the claim holds up.
In episode two, Kat Cosgrove tackles a long-running question: should teams run databases on Kubernetes? StatefulSets, persistent volumes, CSI, and database operators changed the technical answer, but they did not remove the operational risk.
In this episode:
How Kubernetes storage has matured and why old assumptions persist

Where Kubernetes responsibility ends, and database responsibility begins

Why operators do not replace database experts

When managed database services remain the better choice

Sponsor
Kubernetes moves too fast to track everything. Learn Kubernetes Weekly filters out the noise to deliver one curated email with useful articles, tutorials, tools, jobs, events, and CFPs. Subscribe to Learn Kubernetes Weekly.
More info
Find all the links and info for this episode here: https://ku.bz/7yDWlP8T5

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Kube Select takes one bold Kubernetes hypothesis and tests it with an expert.
In the first episode, Salman Iqbal and Bart Farrell ask whether most organizations have an observability problem or a decision-making problem.
Henrik Rexed, Senior Staff Engineer at Dynatrace, challenges the original claim. Teams can collect metrics, logs, and traces, but that telemetry needs system relationships, ownership, and deployment history to support a confident decision.
In this episode:
Why telemetry without context slows incident response

How SLOs, ownership, and deployment events guide troubleshooting

When more metrics increase cost without improving decisions

How AI agents can investigate incidents without replacing human judgment

Sponsor
Kubernetes moves too fast to track everything. Learn Kubernetes Weekly filters out the noise to deliver one curated email with useful articles, tutorials, tools, jobs, events, and CFPs. Subscribe to Learn Kubernetes Weekly.
More info
Find all the links and info for this episode here: https://ku.bz/848pmBN5_

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Kube Signals starts where the keynote ends: with the trends that platform teams will have to operationalize next.
In this special episode, Brian Teller speaks with Saiyam Pathak about his KubeCon India keynote and the shift from developer platforms to AI factories. They examine what GPU scarcity, shared accelerators, and AI workloads mean after the conference slides meet real infrastructure.
In this interview:
Why GPU infrastructure is becoming a platform-engineering concern

How DRA, HAMI, MIG, and MPS change GPU allocation and utilization

Where isolation, scheduling, and observability become harder for AI platforms

Which cloud-native AI trends and projects platform engineers need to watch

Sponsor
This episode is sponsored by LearnKube. Download the free book, The Technical Guide to Kubernetes Rightsizing, to understand what Prometheus and Grafana cannot tell you about safely reducing requests and limits.
More info
Find all the links and info for this episode here: https://ku.bz/4QZDqrnf-

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