Articles

A practical architecture guide to context engineering for AI agents: what belongs in working context, what should be retrieved, what should persist as memory, and when to trim or compact state.

A practical framework for choosing between retrieval-augmented generation, fine-tuning, and tool use based on knowledge freshness, behavior, actions, latency, and control.

A practical explanation of Model Context Protocol: what MCP standardizes, how tools/resources/prompts fit together, where it differs from function calling and APIs, and what it does not solve.

A practical framework for evaluating AI agents across task success, reliability, cost, latency, tool use, trajectories, and failure modes before production.

A practical framework for choosing between deterministic AI workflows and autonomous agents based on task structure, reliability, cost, latency, and control.

Catastrophic AI risk does not require a conscious machine that hates humanity. It can emerge from accidents, governments, institutions, individuals, or autonomous systems given too much power.

Anthropic argued that advanced AI required stronger controls. Weeks later, it released Claude Opus 5 with broader access and lower costs. The contradiction deserves scrutiny.

Apple’s lawsuit against OpenAI is more than a trade-secret dispute. It reveals a deeper fight over who controls the interface that may come after the smartphone.

The most useful role for AI in software development is not replacing engineers. It is reducing repetitive work so teams can spend more time on product judgment and architecture.