The AI angle makes this even more relevant. AI assistants can dramatically speed up dependency adoption, but they also make it easier to trust packages without questioning what's happening under the hood.
One practice that's saved a lot of headaches is treating every new dependency like a production change: review why it's needed, pin the version, and run it in an isolated environment before it reaches the main codebase. AI can recommend packages in seconds, but the trust decision still belongs to the engineering team.
Reducing the blast radius is far more practical than assuming every package can be manually audited.
The AI angle makes this even more relevant. AI assistants can dramatically speed up dependency adoption, but they also make it easier to trust packages without questioning what's happening under the hood.
One practice that's saved a lot of headaches is treating every new dependency like a production change: review why it's needed, pin the version, and run it in an isolated environment before it reaches the main codebase. AI can recommend packages in seconds, but the trust decision still belongs to the engineering team.
Reducing the blast radius is far more practical than assuming every package can be manually audited.