$ grep -r "rust" ~/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.

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.

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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.

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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

Having architected static site solutions across enterprise environments for over a decade, I’ve witnessed the evolution of static site generators from simple Jekyll implementations to today’s sophisticated build systems. Zola represents a paradigm shift in this space—a tool that embodies the principles of systems engineering while maintaining the elegance that production environments demand. This analysis examines why Zola has emerged as the definitive choice for performance-critical static site generation.