Three terms — References, Reference Materials, and Related Links — were mixed inconsistently across the site. This article records how I clarified the distinction and standardized usage.
A record of the new problems that emerged once the Vibe Coding build phase ended and maintenance began, and the harness, tests, and validation I introduced to address them.
Is the time spent building CLAUDE.md and rule files really necessary? This article explains concretely what changed before and after setting up a harness, based on my own experience.
When using AI to write blog posts, I use a twelve-step workflow to verify numbers, facts, and reference links before publication. This article describes each step in a way that is accessible without technical background.
A record of three specific problems encountered when starting Vibe Coding — vague instructions, unintended design output, and lost context — with explanations of why each occurs and how to address it.
I built a system with AI to translate Japanese articles into English and keep them in sync on the site. This article covers the translation flow and three patterns that caused problems: over-interpretation, structural changes, and inconsistent terminology.
What should humans do when AI handles implementation? A concrete breakdown of tasks that can be delegated to AI versus tasks that humans must verify themselves.
Drift — where configuration files and actual code gradually diverge over time — is a common problem in AI-assisted projects. This article explains the causes and prevention strategies.
AI gives different answers to the same question, and it does not remember previous sessions. This article explains Harness Engineering, a design approach developed to address these challenges when using AI in ongoing projects.
An explanation of the Vibe Coding concept. What does it mean to build sites and features by giving instructions to AI rather than writing code yourself, what can it do, and what are its limits?
A look back at three months of building and running AI Learning Playground with Vibe Coding, organized around insights from the technical, design, and operational phases.
Reviewing every line of AI-generated code is not practical at scale, but accepting it entirely without review is uncomfortable. This article presents a framework for deciding what to verify based on code type.
A plain-language guide to AWS AI BPR, its four strength-based steps, and practical criteria for applying the method to a real AI project.
AI Driven and AI Native represent different starting points for how organizations design their relationship with AI. This article clarifies the distinction and what each approach means in practice.
Seven practical techniques for reducing Claude token usage, including context resets, focused CLAUDE.md instructions, subagent isolation, and output controls.
A practical guide to Claude Design, including its creation workflow, how it differs from Figma and Canva, and the limits and operating decisions teams should review.
A practical guide to Claude Code Dynamic Workflows, including parallel subagent orchestration, the Bun port, suitable tasks, limitations, and cost management.
Announcing the launch of AI Learning Playground — an engineering fundamentals site built for non-engineers: AI developers, PMs, marketers, and designers navigating the age of AI.