Deep-Dive 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:

ChatBuddy - Voice AI Companion for Children

Sun, 07 Dec 2025 00:00:00 +0000

Overview

ChatBuddy is an AI-powered voice companion designed specifically for children aged 3-8. When a child asks “Why is the sky blue?”, they get a fun, age-appropriate answer—not a Wikipedia article.

The system runs entirely on-device with 5 containerized services on hardware with just 8GB RAM, ensuring complete privacy while delivering natural voice interactions with educational content grounded in verified facts.

Why Build This?

Kids don’t want to type. They want to talk. And they need answers that:

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 an Oura Ring Health Dashboard with Streamlit

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

Overview

Oura Dashboard is a Streamlit-based web application deployed in the Fako cluster that provides interactive visualization of health data collected from the Oura Ring. It connects to the PostgreSQL database populated by the Oura Collector service and presents comprehensive health insights through charts, graphs, and analytics. The dashboard is secured with OAuth2 authentication and provides personalized health tracking capabilities for sleep, activity, readiness, and heart rate metrics.

Key Features

Interactive Visualizations: Real-time charts and graphs using Streamlit
Comprehensive Metrics: Sleep, activity, readiness, and HRV analytics
OAuth2 Authentication: Secure access with OAuth2 proxy
PostgreSQL Integration: Direct database queries for data
Responsive Design: Mobile-friendly interface
Health Trends: Long-term pattern analysis
Custom Reports: Exportable health summaries
Real-time Updates: Auto-refresh capabilities

Architecture

Components

Streamlit App: Python-based web dashboard
OAuth2 Proxy: Authentication layer
Service: ClusterIP on port 8501
Ingress: HTTPS access with authentication
External Secrets: AWS Secrets Manager integration
Database Connection: PostgreSQL read access

Resource Requirements

Memory: 256Mi (request), 512Mi (limit)
CPU: 100m (request), 500m (limit)
Storage: EmptyDir for temporary data
Port: 8501 (Streamlit default)

Configuration

Environment Settings

Parameter	Value	Description
`DATABASE_NAME`	`app`	PostgreSQL database
`DAYS_TO_BACKFILL`	`7`	Default data range
`LOG_LEVEL`	`INFO`	Application logging
`API_TIMEOUT`	`30`	Query timeout

OAuth2 Configuration

Protected by OAuth2 proxy for secure access:

Home Assistant Voice Pipeline with Ollama

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

Overview

This implementation demonstrates a complete voice assistant pipeline using Wyoming Satellite with Home Assistant and Ollama as the AI brain, achieving natural conversation and smart home control entirely on-premises. The system transforms spoken words into meaningful actions through a sophisticated pipeline of AI models, all running locally for complete privacy.

Why Ollama for Voice?

Voice assistants have unique requirements that Ollama handles beautifully:

Speed Requirements

Response Time: < 2 seconds for natural conversation
Model Loading: Pre-loaded models for instant responses
Streaming: Real-time token generation for faster perceived response

Privacy First

Local Processing: No cloud dependencies
Conversation History: Stored locally, never uploaded
Custom Wake Words: Combined with OpenWakeWord for complete privacy

Components Used

OpenWakeWord: Wake word detection (“Hey Jarvis”)
Whisper: Speech-to-text transcription
Ollama (Qwen2.5:3b & Llama 8B): Natural language understanding and response generation
Piper TTS: Text-to-speech synthesis
Home Assistant: Smart home orchestration and pipeline management
Wyoming Satellite: Voice satellite software on Raspberry Pi

Hardware Setup

Raspberry Pi 5 (2.4GHz, 4GB RAM) - Voice satellite device
ME6S USB Microphone - Audio input device
Bluetooth Speaker (88:92:CC:5D:CB:79) - Audio output via BlueALSA
Mini PC - Home Assistant server
Kubernetes Cluster - Container orchestration for AI services
NVIDIA GPU Node (yeezyai) - GPU acceleration for Ollama

The Voice Pipeline Architecture

The voice pipeline runs entirely on the K8s homelab, with Ollama at its heart:

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).

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.

Self-Hosting n8n on Kubernetes for AI Automation

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

Overview

N8N is an open-source workflow automation tool deployed in the Fako cluster. It provides a visual interface for creating complex automation workflows, integrating with hundreds of services and APIs. N8N allows you to automate repetitive tasks, connect different services, and build sophisticated data pipelines without extensive coding.

Key Features

Visual Workflow Builder: Drag-and-drop interface for creating automations
200+ Integrations: Pre-built nodes for popular services
Self-Hosted: Full control over your data and workflows
PostgreSQL Backend: Reliable storage using the cluster’s PostgreSQL
Webhook Support: Trigger workflows via HTTP webhooks
Custom Functions: JavaScript code support for complex logic
Fair-Code License: Source available with sustainable business model

Architecture

Components

Deployment: Single-replica deployment (stateful workflows)
Service: ClusterIP service on port 5678
Ingress: HTTPS access at n8n.landryzetam.net
Storage: PersistentVolumeClaim for workflow data
Database: PostgreSQL for workflow definitions and execution data
External Secrets: Database credentials from AWS Secrets Manager

Resource Requirements

Memory: 512Mi (request), 2Gi (limit)
CPU: 250m (request), 1000m (limit)
Storage: Persistent volume for N8N data and files
Database: Uses shared PostgreSQL cluster

Configuration

Environment Settings

Parameter	Value	Description
`N8N_HOST`	`n8n.landryzetam.net`	External hostname
`N8N_PORT`	`5678`	Application port
`N8N_PROTOCOL`	`https`	External protocol
`WEBHOOK_URL`	`https://n8n.landryzetam.net/`	Webhook base URL
`GENERIC_TIMEZONE`	`America/New_York`	Timezone for scheduling
`N8N_METRICS`	`true`	Enable metrics endpoint
`N8N_TEMPLATES_ENABLED`	`true`	Enable workflow templates

Database Configuration

Type: PostgreSQL
Host: postgres-cluster-rw.postgres.svc.cluster.local
Database: app
User: app
Password: Managed via External Secrets

Usage

Accessing N8N

External access: