kloia Blog

Building a Golden Path: Kubernetes Self-Service with Backstage-Part 2

Eyüp Canbay — Mon, 17 Aug 2026 08:59:49 GMT

The Helm and GitOps Groundwork Before Self-Service Is Possible-Part 1

Eyüp Canbay — Mon, 17 Aug 2026 08:56:57 GMT

Kubernetes 1.37: What Actually Needs Your Attention

sait.butun@kloia.com (Sait Bütün) — Mon, 10 Aug 2026 12:30:13 GMT

Published August 10, 2026, ahead of the August 26 GA date. This post is based on the pre-GA sneak peek. A few KEPs typically drop out of the milestone between now and release, so we'll revisit this post after GA and correct anything that changed.

How to Optimize MCP Token Usage (Without Replacing It With Bash)

Ata Ağrı — Mon, 10 Aug 2026 11:46:41 GMT

In early 2026, a sharp opinion started making the rounds in developer circles: MCP barely works, and almost everything it does could just be a bash script. It came from well-known engineers and it landed hard, because it had a ring of truth to it.

Zero-Code Observability on Linux VMs with the OpenTelemetry Injector

Mertcan Akdeniz — Fri, 31 Jul 2026 07:11:08 GMT

How the new OpenTelemetry System Packages bring Kubernetes-style zero-code tracing to Linux VMs without manual agent setup.

Have you ever noticed how effortless auto-instrumentation feels on Kubernetes, and how much of a chore it becomes the moment you step onto a plain Linux VM? On Kubernetes you install the OpenTelemetry Operator, add an annotation, and your pods start emitting traces with no code changes. On a VM or a bare-metal host, the same outcome has traditionally meant editing every service, wiring a language agent into each startup command, and juggling environment variables by hand.
That has always felt like a gap in observability maturity to me. It is the kind of grey area where teams end up SSH-ing into boxes and bolting agents manually. The OpenTelemetry community is now beginning to close that gap. The first pre-release of the OpenTelemetry System Packages (v0.0.2) turns the whole thing into a single command: apt install opentelemetry.

In this post I install it on a RHEL EC2 instance, instrument four applications written in four different languages without touching a line of their code, and share the field notes, including the things that broke, along the way. Let’s dive in!

Running DPDK on Kubernetes with SR-IOV & Multus CNI

halit.altuner@kloia.com (Halit Altuner) — Mon, 13 Jul 2026 12:24:27 GMT

Introduction

If you're working on a system that needs to process millions of packets per second, whether a telco gateway, a trading engine, or a streaming backbone pulling in millions of concurrent viewers, you've probably hit the same wall: the Linux kernel networking stack was never designed for this. Linux and its networking stack emerged in the early 90s, then reshaped themselves in the early 2000s to serve high-traffic websites and the infrastructure behind them. It wasn't enough. Over the years, the kernel and its networking stack kept improving. But for the near-realtime systems we're talking about here, it still fell short.

HiveMQ vs AWS IoT Core: Choosing the Right MQTT Broker

halit.altuner@kloia.com (Halit Altuner) — Fri, 05 Jun 2026 07:50:07 GMT

Introduction

Every IoT project starts the same way: one device sends data, something receives it, and something else processes it. Simple enough.

But when the number of devices grows from tens to thousands or even millions, one architectural decision starts to matter more than almost anything else: which MQTT broker are you running?

A broker is not just a message pipe. It is the nervous system of your IoT architecture. It determines how your devices authenticate, how your data flows, how your system scales under load, and how much control you retain over your infrastructure.

Today, two names dominate this conversation: HiveMQ and AWS IoT Core. Both are production-grade, battle-tested, and capable of handling serious IoT workloads. However, they represent fundamentally different philosophies regarding where intelligence should live, who controls the infrastructure, and how tightly your IoT stack should be coupled to a cloud provider.

This article is a structured comparison between the two, not to declare a winner, but to provide the framework you need to make the right decision for your own architecture.

Knowledge Base vs Knowledge Graph for LLM Systems (2026 Guide) | Kloia

Yagmur Akarken — Thu, 16 Apr 2026 21:07:03 GMT

Large language models contain a surprising amount of factual information baked into their parameters during pre-training. Ask GPT-4 who discovered Radium and it will answer correctly. Ask it what the capital of France is and it will not hesitate. This parametric knowledge is impressive, but it comes with three fundamental limitations that make it insufficient for production systems.

First, knowledge becomes outdated. A model trained on data up to a certain cutoff date cannot know what happened after that date. No amount of prompting can change this: the information simply is not there. Second, hallucinations are hard to control. When a model does not know something, it tends to confabulate plausible-sounding answers rather than admitting ignorance. This is not a bug that can be patched; it is an emergent property of how these models are trained. Third, multi-fact reasoning is unreliable. Even when a model has all the relevant facts in its parameters, chaining them together in a single inference step is error-prone. The model may correctly know that Radium is used in cancer treatment, and separately that Marie Curie discovered Radium, but fail to connect these facts reliably when answering a question that requires both.

Because of these limitations, most modern LLM systems rely on external knowledge sources that can be updated, verified, and queried with precision. Two architectural patterns dominate this space: knowledge bases and knowledge graphs. Although these terms are often used interchangeably, they represent fundamentally different ways of organizing information, and those differences have significant implications for what kinds of questions an LLM system can answer reliably.

Kubernetes 1.36: What's New - GA Features, Removals & Upgrade Guide

sait.butun@kloia.com (Sait Bütün) — Fri, 10 Apr 2026 08:33:28 GMT

While Kubernetes started its journey as an orchestration platform for web services, it has since transformed into something much broader. With the rise of AI, it is no longer just about orchestration; it is becoming a factory where AI workloads can live and run alongside traditional services. Each release nudges it further in that direction.

Enterprise GPU-as-a-Service Architecture with Red Hat OpenShift AI

emre.kasgur@kloia.com (Emre Kasgur) — Tue, 24 Mar 2026 09:49:27 GMT

The demand for GPU compute in enterprise environments has exploded.