Table of Contents

• Introduction
• The Problem
• The Architecture
• How It Works
• CLI Mode — One-Shot Reports
• Prometheus Mode — Continuous Monitoring
• Deploying on Kubernetes with Helm
• Grafana Dashboards
• Security Considerations
• Conclusion
• Reflections

Here we are. If you’ve ever managed a Contentful space at scale — I mean real scale, with thousands of entries, a dozen environments, and a team that publishes hourly — you’ve hit the wall. The Contentful web app shows you… not much. A few dashboard widgets, some high-level numbers, but nothing you can export, alert on, or trend over time.

Algolia Usage Exporter — A Case Study in AI-Assisted Tooling

What is this?

A lightweight Prometheus exporter that collects usage and infrastructure metrics from the Algolia search API and exposes them at /metrics for Prometheus / Datadog scraping.

It runs as a standalone HTTP server (single binary via Docker/Podman), requires minimal configuration (just two API keys), and exports metrics like:

• Usage statistics: search operations, records, processing time, QPS, write operations
• Infrastructure metrics: CPU, RAM, SSD utilization, build times (Premium plan only)
• Health signals: scrape success status, timestamps

The entire project lives in a single Python package with ~50KB of code, a Helm chart for Kubernetes deployment, and full test coverage across unit, integration, and e2e layers.

Stargate LLM Gateway

OpenAI-compatible LLM Gateway for AWS Bedrock with user management, usage tracking, and cost monitoring. Powered by LiteLLM.

Table of Contents

• Overview
• Architecture
• Quick Start
• User Management
• Security
• Monitoring & Observability
• Documentation Index
• API Reference

Overview

Features

Architecture

Request Flow

Deployment Architecture

Security Model

Quick Start

For Claude Code Users

Add these environment variables to your shell:

Introduction

This article began as a classic exploration into JSON-LD and its impact on structured data for e-commerce. However, as research and experimentation progressed, a fundamental realization emerged: what’s actually changing is the entire user experience paradigm. The way end-users interact with digital products is shifting beyond traditional web and app models — toward dynamic, conversational discovery driven by AI.

For years, the typical customer journey followed one of several well-established routes:

Introduction

For years, VMware ESXi was the foundation of my homelab — stable, dependable, and familiar. Then Broadcom acquired VMware, and the writing was on the wall.

The free ESXi license disappeared. Support for consumer-grade hardware like MiniPCs and NUCs became problematic. The platform that had “just worked” for years was now actively working against the homelab use case.

Enter Proxmox — an open-source virtualization platform built on Debian Linux, offering KVM, LXC, ZFS, and native clustering without a licensing fee.

Intro

In today’s digital landscape, efficiently serving images is critical for website performance. Users expect fast-loading, responsive websites, and images often account for the majority of a page’s weight. In this article, I’ll walk you through building a powerful, scalable image CDN using open-source tools that you can deploy in your own infrastructure.

The Architecture

Our image CDN consists of three main components:

1. MinIO — An S3-compatible object storage backend that stores original images
2. imgproxy — A fast and secure image processing service that resizes and optimizes images on-the-fly
3. Cloudflare — Providing CDN capabilities through Cloudflare Tunnel

The Infrastructure Overview

Exposing home lab services to the internet can be both necessary and risky. Traditional methods — port forwarding, VPNs, reverse proxies with open inbound ports — come with their own set of challenges. This is where Cloudflare Tunnel (formerly Argo Tunnel) comes in as an elegant solution.

In this article, I’ll walk you through how I’ve implemented a secure infrastructure using Cloudflare Tunnel with WebSocket support, running on a Kubernetes cluster with Apache HTTPD as a reverse proxy. This setup allows me to securely expose internal services without opening ports on my residential firewall.

INTRO

Strange… we are using HPA to increase availability and introducing rate limiting to reduce it?

Well, let’s create the context.

This analysis is based on specific assumptions:

• Cloud environment
• Dynamic infrastructure
• Minimum resources available

HPA

In Kubernetes, a HorizontalPodAutoscaler automatically updates a workload resource (Deployment, StatefulSet) to match demand.

Patterns

Ideal Practice

Rate Limit

A rate limit is the number of API calls an app or user can make within a given time period. If this limit is exceeded — or if CPU or time limits are exceeded — the app may be throttled. Throttled requests fail.

I needed to implement rate limiting within an application for reasons I’ll get into in a follow-up post. When you start thinking about this, you basically have two paths:

1. Logic embedded directly in the application code
2. A sidecar container that handles the rate limiting role

Both work. Both have trade-offs. Let me go through each one.

The Code Way

This is the simpler approach on the surface, but it comes with some annoying limitations. It can only be reused for applications in the same programming language. And adding rate limiting logic inside the application creates a secondary role — meaning the request interceptor will consume CPU and may produce false metrics if you’re not tracking it carefully.

Why

I had my beloved IPsec setup based on strongswan running in Kubernetes for a while — you can read about that here. It worked fine. I wasn’t looking to change it. Then a colleague pointed out WireGuard’s overhead numbers and I got curious enough to evaluate it myself.

WireGuard is a modern VPN protocol that lives in the Linux kernel. It’s designed to be simple, fast, and have a minimal attack surface compared to IPsec or OpenVPN. The numbers people throw around are impressive, but I wanted to see them in practice.