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Digests from AI Native Dev
Managing AI coding skills requires treating them as first-class software assets. The talk outlines best practices to prevent 'skill sprawl,' emphasizing centralized registries, automated reviews, and dedicated lifecycle management.
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Patrick Debois maps AI-native development maturity, outlining a progression of patterns (Prompting $\rightarrow$ Context $\rightarrow$ Harness $\rightarrow$ Loop) and emphasizing that organizational success requires platform enablement and measuring shared component contributions.
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Patrick Debois discusses scaling autonomous coding agents, arguing that success requires shifting focus from individual code fixes to improving the underlying system and platform architecture.
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Joseph Katsioloudes demonstrates how AI can scale scarce security expertise by integrating advanced tools like MCPs and agentic workflows into the CI/CD pipeline. He details best practices for secure coding at scale.
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Learn how to apply AI-Native Engineering practices to brownfield codebases, moving from maintenance mode to evolution. Discover the Strangler Fig Pattern and Branch by Extraction.
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Building large systems with AI requires more than 100% test coverage. Observability and using existing 'test articles' (like S3) are key to enforcing correctness in distributed environments.
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