AI coding assistants accelerate routine implementation, but their statistical defaults can narrow architecture choices. A practical framework for preserving engineering divergence.
READ_ARTICLE →A privacy-preserving framework using PR size distribution, AST code survival, review depth, and CI/CD outcomes to validate whether AI coding tools improve delivery or amplify technical debt.
READ_ARTICLE →To deeply understand and expand the theory of the Centaur Layer, we analyze how the fundamental physics of software development change when execution speed approaches infinity.
READ_ARTICLE →AI does not replace the engineer; it compresses syntax and raises the value of systems thinkers who own constraints, architecture, and validation.
READ_ARTICLE →AI coding assistants boost raw throughput, but they can amplify technical debt when teams skip rigorous spec-first planning. Backed by GitClear's 153M line analysis, DORA 2025, and ACM CCS research.
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