After Knowledge, Discipline
The article discusses the author's Claude Code workflow, emphasizing the importance of structure in AI-assisted development. It argues that while the initial cost in tokens may seem high, the long-term savings from reducing errors and chaos outweigh this. The author highlights that effective documentation and organization are key to producing quality output in AI projects.
- ▪The Claude Code workflow prioritizes structured documentation and organization over simply generating code.
- ▪Quality output is directly linked to the quality of written context and documentation.
- ▪The author emphasizes that traditional engineering practices, such as clear writing and maintaining documentation, are crucial for successful AI-assisted development.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 268473) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sinisa Kusic Posted on Apr 28 • Originally published at ku5ic.substack.com After Knowledge, Discipline #ai #claude #devops #productivity Anatomy of a Claude Code setup that pays for itself The most common reaction when I show people my Claude Code workflow is some version of: "isn't that a lot of tokens?" It is. The flow front-loads context, plans before it implements, runs scripted checks after edits, and writes structured artifacts to disk for later steps to pick up.
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