I produced a better way to get agents to make quality code, not just syntax (ai-lint.dosaygo.com)

A developer has introduced "AI Lint," a doctrine file designed to improve AI agents' architectural judgment when producing code. Instead of merely ensuring code correctness, this tool helps agents determine whether certain coding patterns belong in a project, focusing on nuances of semantics, architectural taste, and trade-off risks. When the AI encounters a design choice that goes against these established doctrines, it flags the issue and seeks human input, fostering a collaborative approach to maintaining code quality. This initiative is significant for the AI/ML community as it empowers AI systems to engage in higher-level judgment previously reserved for human developers. By addressing common pitfalls such as hidden state or unnecessary abstractions, AI Lint aims to refine the AI’s decision-making process in real-world applications, particularly within complex codebases. The doctrine is customizable with specific packs targeting various programming languages and frameworks, ensuring it can scale and adapt to the unique challenges of different domains, thereby enhancing long-term maintainability and reducing technical debt.