Stop Pretending You Understand Your Codebase
Theo - t3.gggo watch the original →
the gist
In large-scale systems, complete mental mastery of the codebase is impossible and unnecessary; effective engineering requires operating with a partial, evolving theory and leveraging automation to offload cognitive load.
The Myth of Total Understanding
Modern software development is plagued by the false ideal that a "great" engineer is one who holds the entire codebase in their head. This expectation is a holdover from smaller projects where total mastery was possible. In reality, large-scale systems—like those powering major platforms or foundational libraries—are too vast and complex for any single person or team to fully comprehend. Pretending otherwise creates unnecessary anxiety and discourages engineers from working on high-impact, complex systems.
Theory Building vs. Pragmatic Execution
While Peter Naur’s classic paper, Programming as Theory Building, argues that the primary product of software development is the developer's internal theory of the program, this perspective is increasingly outdated. Naur’s examples (e.g., 200k lines of code) are orders of magnitude smaller than modern systems. Attempting to maintain a perfect mental model in a system with millions of lines of code is not just difficult; it is a distraction. Effective engineers instead build a "partial theory"—a functional understanding of how specific pieces connect—and refine it iteratively as they work.
The Role of Automation and AI
Cognitive bandwidth is a finite resource. Tools like TypeScript, linters, and AI assistants are not just conveniences; they are essential for offloading the "tedious bullshit" of syntax and type checking. By automating these details, engineers free up mental space to focus on architectural decisions and high-level logic. While some argue that AI impedes "theory building," it actually allows engineers to interact with unfamiliar codebases more effectively by providing a mechanism to query, verify, and experiment with code without needing to memorize every file path.
The Reality of Rewrites
Engineers often propose "rewrites" as a solution to complexity, but these are frequently driven by a lack of understanding rather than genuine technical necessity. A successful rewrite requires a deep, nuanced understanding of the existing system's "quirks"—the edge cases, historical patches, and user-specific behaviors that aren't documented but are critical to the system's survival. Without this understanding, rewrites often fail to replicate the essential functionality of the original, resulting in "slop" that is less capable than the system it replaced.