here is no better way to learn than by doing! As such, I decided to build my own Kubernetes cluster on Raspberry Pi 5 over the Holidays to see if I could run my previous chatbot project on it. I probably should have done this long ago, but I finally found the time after completing (sort of) my chatbot project. Below is the complete guide I used to set up a Kubernetes cluster on Raspberry Pi 5, including a few lessons learned along the way.
Autonomous DevOps isn't a future concept—it's already emerging inside modern CI/CD, observability, and GitOps ecosystems. The real barrier isn't AI but the technical debt, fragmented architectures, and outdated org structures holding companies back. Teams willing to evolve their foundations—and challenge long-standing security rituals—will gain a decisive operational advantage by 2027.
Over the past several months, I've embarked on a journey to build my own chatbot solution on my provious project, leveraging open-weight large language models (LLMs) and a modern cloud-native stack. This project has been both challenging and rewarding, teaching me valuable lessons about authentication, session management, GitOps automation, and the complexities of deploying AI workloads on Kubernetes.
As I continued to scale and improve my GitOps implementation, I encountered significant challenges with Kustomization validation, ConfigMap dependency management, and the complexity of maintaining multiple environment configurations while maximizing the automation I was targeting for. This post details the critical refinements I made to address these issues and achieve a more robust, automated, maintainable GitOps workflow.
In this post, I wanted to share my personal experience of building a modern chatbot UI using Lovable.dev as the main "editor" and "AI assistant". What started as a simple UI prototype evolved into a comprehensive chatbot application with authentication integration, LLM API connectivity, and a fully functional chat interface with persistence.
In my previous post, "FluxCD GitOps Made Simple: My Journey to Automated Kubernetes Deployments", I mentioned wanting to follow up on how to setup the ConfigMap automatically following GitOps principles. This post discusses the various next steps I took in order to achieve that.
When I first started exploring GitOps and Kubernetes, the landscape felt a bit overwhelming. I had obviously experienced various automation techniques throughout my years but on Kubernetes, I had heard about automation on Kubernetes and declarative infrastructure, but the practical steps to get there were unclear. This blog post shares my journey implementing FluxCD for GitOps-driven Kubernetes deployments, both locally and in a sandbox environment, and how I structured my repository for clarity and scalability.
Although selected because the company where I work was using it, GitLab has now been my preferred tool for my personal software project to manage my code, build the various artefacts my projects are producing constantly and deliver then continuously to my Kubernetes clusters. One of the main question I had originally was the groups (or "folders" in GitLab from my own perspective) and projects (or again, "git repo" in GitLab) structure to follow so the rest of the setup is easy, simple to maintain yet following most of, if not all, the best practices so it can easily integrate with technologies I would adopt along the way.