Vertical Slice Architecture

A Deeper Dive When the Vibe Dies Comparing Codebas

Comparing Codebase Architectures for AI Tools

As AI coding tools become more prevalent in software development, choosing the right architecture can significantly impact both development efficiency and AI collaboration. This article explores three prominent architectural approaches and their implications for AI-assisted development.

Let’s examine these architectures in detail. We’ll analyze how each one uniquely positions itself to handle AI-assisted development workflows. We’ll also explore what trade-offs developers need to consider when making architectural decisions. This is a continuation of this vibe article.

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How to Keep the Vibe Going Optimizing Codebase Arc

Optimizing Codebase Architecture for AI Coding Tools

In today’s rapidly evolving software development landscape, AI coding tools like Aider, WindSurf, Open AI’s Codex CLI, Claude Code, and Cursor are reshaping how developers structure their projects. As these AI assistants participate in code creation, developers must consider both human readability and “AI readability” when designing their architectures.

The concept of “token efficiency” has emerged as a critical consideration—structuring code to minimize the amount of context an AI model needs to process. This reduces computational costs and improves AI performance. This efficiency revolves around what IndyDevDan calls “the big three: context, model, prompt.”

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