Should you read the code, is RAG dead, and did Skills kill MCP?

GitHub’s discussion of common AI hot takes argues that their value lies in examining their assumptions, context, and practical tradeoffs rather than choosing absolute positions. Developers remain responsible for AI-generated code and should review it according to risk, while hiring increasingly rewards thoughtful AI fluency rather than either complete dependence or refusal to use such tools. Model Context Protocol, skills, and retrieval-augmented generation are presented as complementary technologies: MCP provides standardized access to tools and data, skills convey project-specific practices and expertise, and RAG supplies relevant external context. Although fine-tuning may be useful in some cases, difficulty for AI in understanding a codebase can also signal broader maintainability issues that affect human developers. The piece encourages testing ideas through real projects, citing Pollinations AI’s contributor-credit experiment and Avian Visitors’ bird-listening e-ink display as examples that expose evidence and tradeoffs more effectively than online debate alone.

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