AI is reshaping how we approach coding, emphasizing the need for high-quality practices. The author stresses the importance of 100% code coverage, arguing that anything less leaves ambiguity about untested code. With complete coverage, every line has been validated, turning the coverage report into a straightforward checklist for needed tests. This approach not only reduces bugs but also streamlines code reviews and eliminates unreachable code. The article advocates for careful organization of codebases, suggesting that file naming and structure should be as intentional as any interface. Smaller, well-scoped files improve context loading for AI agents, minimizing performance issues. Developers should also create rapid, automated environments for testing. The author mentions a setup where each test runs in a fresh database, achieving over 10,000 assertions in about a minute, highlighting the efficiency gained from speed and automation. To further enhance coding practices, the author recommends using typed languages like TypeScript, which clarify data flow and reduce the likelihood of errors. Semantic naming conventions in types help AI understand the code better, facilitating easier navigation and comprehension. On the API side, using OpenAPI ensures that frontend and backend systems are aligned, while leveraging Postgres' type system reinforces data integrity. Overall, these strategies aim to reduce complexity and make coding more effective in the age of AI.