Agent

Raw models are no longer enough for serious engineering work. What actually separates mediocre results from great ones is the Harness built around the model: context management, tool execution, task orchestration, feedback loops, and architectural guardrails.

A Skill isn’t a longer prompt — it’s a triggerable, executable, testable, maintainable workflow. Five angles to get it right: on-demand loading, tool boundaries, model selection, progressive disclosure, and evaluation.

Three years in, models are 100x more capable. Why is AI still just a chat box? The chat box is the Nokia of the AI era. From chatbots to agents to wearables plus world models — here’s the next decade of AI product evolution.

Clarifies the distinction between Tools (primitive functions), Skills (complex workflows), and the Model Context Protocol (MCP) in agentic AI, providing an architectural framework for developers to choose the right abstraction for their use case.

Provides a practical guide to implementing a persistent memory system for AI coding agents, advocating for a simplified, file-based approach with a manual ‘/save’ command to capture session context, delivering most of the value with minimal complexity.

Analyzes the viral success of OpenClaw, an open-source AI assistant that operates locally with OS-level execution, exploring its key innovations like proactive tasks and a decentralized skill ecosystem, while also highlighting its significant security implications.