A deep dive into the architecture of a personal cryptocurrency market intelligence system built for a home lab environment. This post explores the design decisions, technical challenges, and lessons learned from building a real-time data processing pipeline that combines microservices, machine learning, and cloud-hybrid execution.
Building on the foundation of the Gemini MCP server, this post explores the advanced features that transform a simple AI wrapper into a sophisticated multi-agent orchestration platform. We'll examine the debate system with its voting mechanisms, the swarm architecture with specialized agents, and the complex information flows that enable AI-to-AI collaboration.
This post documents the architecture and implementation of a production-ready MCP (Model Context Protocol) server that wraps Google's Gemini CLI, enabling Claude Code to leverage Gemini as an adjacent AI for deep analysis, code review, and structured AI-to-AI debates.
Claude Code's Model Context Protocol (MCP) enables powerful integrations with external tools, but loading all MCP servers simultaneously can consume 100k+ tokens of your context window. This post explores a profile-based architecture that optimizes token usage while maintaining full capabilities when needed.
claude-flow enables orchestrated multi-agent swarms within Claude Code, allowing complex tasks to be broken down and executed in parallel by specialized agents. This post explores the architecture, practical patterns, and production configurations for AI-to-AI collaboration.
Master pod-level debugging, container log analysis, kubectl exec techniques, and ephemeral containers - essential skills for resolving application issues in the CKA exam's highest-weighted troubleshooting domain.
Master systematic cluster troubleshooting for node issues, control plane debugging, certificate problems, and etcd health checks - essential skills for the CKA exam's highest-weighted domain.
Every application needs configuration—database endpoints, feature flags, API keys, TLS certificates. Kubernetes provides ConfigMaps for non-sensitive configuration data and Secrets for sensitive information like passwords and tokens. Understanding the distinction between these resources and the various consumption patterns (environment variables, volume mounts, projected volumes) is essential for the CKA exam's Storage domain (10% weight) and production Kubernetes operations.
The key insight: ConfigMaps and Secrets decouple configuration from container images, enabling the same image to run across development, staging, and production with different configurations. This pattern is fundamental to cloud-native applications and the Twelve-Factor App methodology. Modern Kubernetes (2025) adds immutability for ConfigMaps/Secrets, enhanced encryption options, and improved integration with external secret management systems.
While ConfigMaps store arbitrary key-value pairs, Secrets provide a specialized resource with base64 encoding (not encryption!), RBAC integration, and memory-backed storage options. Understanding when to use environment variables versus volume mounts, how to leverage projected volumes, and the security implications of each approach is critical for production deployments.
Extend Kubernetes functionality with CustomResourceDefinitions and the Operator pattern
CustomResourceDefinitions (CRDs) allow you to extend the Kubernetes API with custom resource types, enabling you to treat domain-specific objects as native Kubernetes resources. Operators combine CRDs with custom controllers to automate complex application management tasks using Kubernetes-native patterns. For the CKA exam, you'll need to inspect existing CRDs, understand their structure, query custom resources, and troubleshoot CRD-related issues. This guide covers CRD anatomy, version management, validation, and practical operator concepts to help you master Kubernetes extensibility.
CKA Exam Relevance: Cluster Architecture, Installation & Configuration (25% of exam weight)
Master kubectl for CKA exam success. Learn imperative commands for speed, output formats for precision, and productivity patterns that save critical exam minutes.