Stop the Slop. Start Coding Smarter with AI

AI-generated code is inevitable and now a job requirement, but success demands discipline and structure rather than magic thinking. The key practices include organizing code into isolated modules using monorepos for better AI context, setting up feedback loops with linters and tests so AI can self-correct, maintaining a memory bank of documentation and reusable coding rules, and mastering the Plan → Act pipeline where 90% of time goes to detailed planning and only 10% to implementation in separate sessions. The toughest challenge is teamwork since no standard solutions exist for sharing practices and managing collaborative AI workflows, but treating AI coding as an engineering discipline with proper structure makes it genuinely effective.