What Pretext suggests about AI loops

Pretext is a TypeScript-based text measurement algorithm that performs web page layout without DOM measurement. This project clearly shows what matters when leveraging AI coding agents for complex, empirical problems.

Instead of simply asking it to "make it better," you should first establish a Hard Constraint. For example, defining an invariant such as "a specific function must only perform arithmetic operations" can prevent AI from ruining the design.

Also, you must give the AI an empirical Oracle rather than theory. Instead of letting the AI derive answers on its own, it should be confined within a loop that verifies against the actual behavior of real browsers like Chrome or Safari.

When a problem occurs, it's important to break it down as small as possible. AI agents are very useful for the iterative experimental work of compressing a broad error into a minimal Reproducer.

Finally, the causes of failure must be clearly classified into a Taxonomy. By naming failure modes such as font mismatches or normalization issues, the AI can focus on the core of the problem instead of writing off-target code.