Infrastructure on Landry Zetam - Tech Blog

GitOps Framework: How the Fako Cluster Repository is Organized

Sun, 21 Dec 2025 00:00:00 +0000

Overview

The Fako Cluster uses a GitOps approach where Git is the single source of truth for all cluster state. Every deployment, configuration change, and infrastructure update flows through Git commits. FluxCD watches the repository and automatically reconciles the cluster to match the desired state defined in code.

This post explains the repository structure, the reasoning behind each directory, and how the pieces work together.

Repository Structure

fako-cluster/
├── apps/ # Application deployments
│ ├── base/ # Base Kustomize configurations
│ └── staging/ # Environment overlay (what actually deploys)
├── clusters/ # Cluster bootstrap and Flux config
│ └── staging/ # Flux Kustomizations that orchestrate everything
├── infrastructure/ # Platform components
│ ├── controllers/ # Operators (Cert-Manager, CloudNative-PG, etc.)
│ └── configs/ # Configuration for controllers
├── monitoring/ # Observability stack
│ ├── controllers/ # Prometheus, Loki, Grafana, etc.
│ └── configs/ # Dashboards, alerting rules
├── scripts/ # Deployment and maintenance scripts
└── notes/ # Documentation and troubleshooting

The Base/Staging Pattern

The most important concept to understand is the base/staging pattern used throughout the repository.

Building an Automated Audio Transcription Pipeline on Kubernetes

Fri, 12 Dec 2025 00:00:00 +0000

Every day, raw audio recordings land on an SFTP server. By morning, they’re compressed, transcribed, and stored as searchable JSON - all without human intervention. Here’s how I built this pipeline on my homelab Kubernetes cluster.

The Problem

I had a growing collection of audio recordings that needed transcription. Manual processing wasn’t sustainable: download files, compress them, run through a transcription service, organize the output. This needed automation.

The requirements were straightforward:

How I Clustered Three Mac Studios for Local AI Processing with GPUStack

Mon, 11 Aug 2025 00:00:00 +0000

Opening Hook

I’ve always loved the idea of running powerful AI workloads without relying on expensive cloud GPUs. Recently, I turned that idea into reality by transforming three Mac Studios into a fully functioning local AI cluster using GPUStack. This setup lets me run distributed inference workloads across multiple Macs with Apple Metal acceleration — and it’s a lot more affordable and energy-efficient than most people think.

Related: See Mac AI Cluster: From EXO Vision to GPUStack Reality for the philosophical journey that led to this technical implementation.

Building a Local AI Cluster with Mac Hardware: From Vision to Reality

Fri, 08 Aug 2025 00:00:00 +0000

The Mission That Started It All

Note: This post combines the journeys documented in my Mac AI Cluster Project and GPUStack Mac Clustering Tutorial. For deep technical details, see those project pages.

When I first discovered EXO Labs, their mission resonated deeply with me.

Humanity is headed toward a world controlled by a handful of companies that will dictate the AI models everyone uses. The real threat isn’t AI destroying us — it’s losing sovereignty over our own minds.

Powering Open WebUI and Cline with GPUStack: My AI Infrastructure Journey

Fri, 08 Aug 2025 00:00:00 +0000

After building my Mac AI cluster, I needed a way to make it accessible to all my tools. The journey took me through several frameworks before landing on the perfect solution: GPUStack providing an OpenAI-compatible API that seamlessly integrates with everything.

The Evolution: Finding the Right Framework

The Journey

My path to the current setup wasn’t straightforward:

MLX Distributed Computing: Started here for model training and local inference on Apple Silicon
Exo Labs: Experimented with this promising framework, but pivoted when the community began a redesign
GPUStack: The solution that finally clicked - providing OpenAI-compatible endpoints that work with everything

The Current Architecture

Here’s how my AI infrastructure actually works:

From GitHub Notes to Production Blog

Thu, 07 Aug 2025 00:00:00 +0000

Why This Guide Exists

I’ve been doing a lot of dev work, research and hadn’t really thought about sharing the knowledge without having to learn frontend development or maintain complex code. I knew I had a bunch of markdown GitHub notes and thought they could be used as a baseline to catch up on all my lack of documentation. Then I found Hugo.

This guide covers the entire workflow of creating a Hugo blog, containerizing it, and deploying it to Kubernetes - all without writing a single line of frontend code (html, css, js).

Powering My Voice Assistant with Ollama: Local AI for Home Automation

Thu, 07 Aug 2025 00:00:00 +0000

Ollama has become the AI brain of my home automation infrastructure, powering my voice assistant pipeline and Home Assistant integrations. Running entirely on-premises on my NVIDIA GPU node, it enables natural language understanding and intelligent responses while maintaining complete privacy and control.

The Home Automation AI Hub

My Ollama deployment serves as the AI intelligence layer for home automation:

Ollama serves as the AI brain for voice pipeline and Home Assistant automations

Running 184 Pods on a 6-Node K3s Homelab Cluster

Thu, 07 Aug 2025 00:00:00 +0000

Project Overview

The Fako Cluster is my production Kubernetes environment that serves as the foundation for all other projects. It’s a self-hosted, production-grade K8s cluster that demonstrates enterprise-level practices while running on homelab hardware.

Why “Fako”?

The name “Fako” holds special significance - it’s named after Mount Fako (Mount Cameroon), the highest peak in West and Central Africa. Just as Mount Fako stands as a towering landmark visible from miles away, this Kubernetes cluster serves as the foundational infrastructure that all other services can rely on.