$ grep -r "technical" ~/blog/

The intersection of artificial intelligence and systems security has reached a critical inflection point. As AI agents become increasingly capable of executing external tools and accessing system resources, the traditional security models that govern software execution are proving inadequate. Microsoft’s Wassette emerges as a groundbreaking solution that leverages WebAssembly’s sandboxing capabilities to create a secure, scalable runtime for AI tool execution through the Model Context Protocol (MCP).

Wassette represents a paradigm shift from the current landscape of MCP server deployment, where tools typically run with unrestricted system access, to a capability-based security model that provides fine-grained control over resource access. This architectural evolution addresses fundamental security concerns while maintaining the flexibility and extensibility that make MCP valuable for AI system integration.

Electromagnetic Spectrum Explorer

Interactive web application for exploring the electromagnetic spectrum from radio waves to gamma rays with real-time visualization and comprehensive physics calculations.

Live Demo

The intersection of physics education and interactive web development presents unique challenges that extend far beyond traditional application design. Building an electromagnetic spectrum explorer requires not only technical proficiency in modern web frameworks but also deep understanding of fundamental physics principles, scientific data visualization patterns, and educational interface design. This project demonstrates how contemporary web technologies can transform abstract scientific concepts into tangible, interactive learning experiences.

OpenZIM MCP Server

Offline knowledge base access for AI models. A secure, high-performance MCP server that enables AI models to access and search ZIM format knowledge bases offline.

Live Demo

The dependency on persistent internet connectivity represents a fundamental architectural limitation in contemporary AI systems, creating single points of failure that compromise system reliability in distributed or resource-constrained environments. This realization led to the development of offline knowledge access patterns that enable AI assistants to maintain functionality across diverse operational contexts, from edge computing scenarios to air-gapped security environments.

Gopher MCP Server

A modern Model Context Protocol server for Gopher and Gemini protocols, enabling AI assistants to browse these classic internet protocols safely and efficiently.

Live Demo

The integration of legacy protocols with modern AI infrastructure reveals fundamental insights about system design philosophy and the evolution of network architectures. The gopher-mcp implementation demonstrates how protocols designed with minimalist principles can provide superior performance characteristics and operational simplicity compared to their contemporary counterparts—lessons that remain highly relevant for modern distributed systems engineering.

Having architected distributed systems across enterprise environments for over a decade, the Model Context Protocol represents a paradigm shift that addresses fundamental challenges in AI tooling infrastructure. Through the development of production-grade MCP servers including gopher-mcp and openzim-mcp, I’ve identified architectural patterns and implementation strategies that demonstrate MCP’s potential to revolutionize how AI systems interact with external resources.

Update (June 2025): I’ve split this comprehensive guide into two focused articles for better readability:

• Gopher MCP Server: Bringing 1991’s Internet to Modern AI - Focuses on the Gopher protocol, its history, and practical applications
• OpenZIM MCP Server: Offline Knowledge for AI Assistants - Covers offline Wikipedia access and ZIM format optimization