Ai

Rie Kudan won the Akutagawa Prize, Joe Vasicek cut his book cycle from 2 years to 14 days, Leanne Leeds ships 12-15 books a year at a 4.5-star Amazon average, and Hachette pulled a novel after Pangram clocked it at 78.4% AI-generated. The Sudowrite / NovelCrafter / Claude + Scrivener stack is now the Western mainstream, and the Chinese-language world has nothing equivalent.

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.

Buying AI tools and rolling out company-wide training does not equal AI transformation. Real change means replacing human-to-human collaboration with human-plus-agent workflows. That shift hits headcount first, then rewrites the survival rules for employees.

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.

Sending 100 resumes used to get you 20-30 interviews. Now 200 gets you almost nothing. You didn’t get worse — the rules changed. Mass-applying is dead. The new job hunt is a trust competition.

Meta sold 7 million smart glasses last year. In 2026, Apple, Google, and Samsung are all entering the space. AI is native from day one, and an entirely new interaction paradigm is being defined. This isn’t a prediction — it’s already happening.

Explores the limitations of using LLMs as simple chatbots and introduces the Model-Context-Protocol (MCP), detailing four essential tools that provide context, planning, and memory to transform AI into a powerful, autonomous engineering partner.

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.