5 Things AI Can't Do, Even in Redux

This report examines technical and conceptual limitations AI-assisted tools can encounter when using Redux. Deep analysis will be presented under five topics in context of Redux's unique architecture and ecosystem: store architecture and normalization, complex async flows and side effects, middleware ordering and composition, performance and memoization traps, and update/migration and ecosystem compatibility. Each section includes technical details, concrete error modes explaining why AI fails, real-world examples from GitHub issues and case studies, and code snippets. For instance in Normalization section, data normalization format Redux recommends is explained and keeping each data type as separate table gets emphasized. AI assistants can make errors in normalizing complex related data deeply in one go, locking update operations. In Middleware section, emphasized that ordering written inside applyMiddleware directly determines operation. According to StackOverflow answer, defined mid