TensorFlow organizes computations as a DAG: nodes represent operations, and edges define their dependencies. This structure enforces order, ensures reproducibility, and provides transparency. Operations only run when their dependencies are resolved, and the graph guarantees consistent outputs from the same inputs. Now think about notebooks. Cells are like operations in a graph, but without enforced dependencies or execution order. Hidden dependencies and unpredictable execution often lead to messy, unreliable workflows. What if we applied DAG principles to notebooks? Cells could become nodes in a graph, with explicit dependencies and predictable execution. A preprocessing cell, for instance, would depend on the data-loading cell. Change one, and downstream cells automatically update.