
Why My Blog's MCP Server Has No Authentication
Exploring the architectural decision to skip authentication for a public blog MCP server, prioritizing frictionless access for AI agents and why public content deserves a public API.
Technical articles on AWS, AI, serverless, and cloud-native architecture

Exploring the architectural decision to skip authentication for a public blog MCP server, prioritizing frictionless access for AI agents and why public content deserves a public API.

Push-based readiness, kernel watchdog supervision, two-layer shutdown timeouts, hardware-coupled dependencies, and more — lifecycle control Compose can't express

Quadlet exposes systemd hardening — filesystem isolation, kernel surface reduction, network microsegmentation, syscall filtering — declared inline, scored numerically, mapped to IEC 62443

Podman Quadlet inherits 100+ systemd directives for memory throttling, CPU pinning, I/Om and hierarchical resource budgets — capabilities Compose can't express

Migrate your AI Agent from native Bedrock Agents to Strands Agents SDK with AWS AgentCore. Learn why ECR containers beat Lambda for agentic AI workloads

Podman Quadlets offer a lightweight deployment for industrial edge devices, enhancing Margo compliance with minimal changes

A practical comparison of two strategies for connecting AI agents to industrial data sources — the standardized Model Context Protocol (MCP) and custom frameworks like AWS Bedrock Agents — with real Belden implementation experience.

Why Lakehouse architecture — combining cost-efficient object storage, schema evolution, and open standards — is the critical data foundation that enables AI agents to deliver explainable, trusted recommendations in OT environments.

How AI agents function as 24/7 digital employees in industrial environments, requiring clear objectives, quality data, and proper guidance — illustrated with a real VLAN troubleshooting example using AWS Bedrock.

Software engineering shifts with AI-driven no-UI solutions, emphasizing outcome-based customer value and organizational change in the AI-first era