Understand the differences between AI agents, skills, LLMs, and MCP, and learn how they work together to build AI-powered applications. Using a practical Jira investigation example, explore how AI can go beyond predefined searches to coordinate investigations, analyze recurring failures, and organize evidence into actionable reports. Learn more inside.
For platform engineering and infrastructure teams, AI components must be architected as modular services, not a monolithic solution. This guide demystifies the AI stack, defining the distinct roles of LLMs (Reasoning Engine), Agents (Orchestration Runtime), Skills (Deterministic Guardrails), and MCP (Protocol Layer) in modern automation tools. Learn more inside.
Streaming was supposed to make entertainment simpler: one reasonable subscription, on-demand access, and no commercial interruptions. Instead, content became fragmented across multiple services, advertisements returned, and customers began paying extra to remove them. This essay explores what happened to the promise of streaming—and what it teaches us about optimizing individual business metrics while losing sight of the customer and the system as a whole. Learn more inside.