Friday, 13th March 2026
Mental Models in the AI Agent Age
Mental models are compressed knowledge of human experience. Many thinkers discovered these patterns over centuries across physics, biology, economics, mathematics, and systems theory. In the age of AI agents, these same patterns don’t just help you think better. They help you build better systems, debug reality faster, and make decisions that compound over decades.
[... 1,760 words]Coding in the AI Agent Age — Why Typing Code Is Dying But Engineering Is Thriving
Maybe you think coding is just putting human-defined processes into structures, loops, functions, rules, packages, and web pages. If so, you’re not wrong about the past. But that definition is dying. AI automates the typing. What remains is the thinking.
[... 1,396 words]How Skills Work in AI Agents — From Lazy-Loading Instructions to LLM Attention Weights
When you hear “skills” in AI agents, it sounds like a new concept. It is not. Skills are a lazy-loading pattern for instructions. The same tool-calling mechanism the LLM already uses delivers them. But the details reveal deep truths about how LLMs actually work:
[... 2,808 words]Autoresearch and Context Rot — How a Stateless Agent Loop Avoids Memory Problems (And Where It Breaks)
In the autoresearch pattern, a coding agent runs hundreds of autonomous experiments to optimize code. The pattern produced a 53% speedup on Shopify’s 20-year-old Liquid codebase and a 69x speedup on a demo text processor. But there’s a fundamental flaw nobody talks about: the agent has no memory of failed experiments. Here’s exactly how the pattern works, where it breaks, and how Tobi Lütke’s team quietly fixed it.
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