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Why Kimi K3 Still Can't Do What Einstein Did
TL;DR: The piece argues that retrieval-augmented systems, like Bookmark Brain, excel at echoing stored content but struggle to reconcile contradictions or leap beyond their defined parameter space, illustrating a fundamental limitation of current AI retrieval methods.
The author built Bookmark Brain, a RAG pipeline that indexes their bookmarks and generates answers in their voice. It retrieves the nearest matching saved content and fluent-composes responses, but it cannot resolve contradictions between items saved years apart. The limitation isn’t the model but the retrieval layer: it replays what’s nearby semantically without reconciling mismatches. This echoes a 2014 idea that machines optimize within fixed parameters, lacking the ability to jump to new conceptual spaces. The lecture connects this to broader geophysics intuition about unseen substrates and interpretive inference.
Question for the room: What techniques have you used to detect or reconcile contradictions in retrieved content when building RAG systems?
— via dev.to
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