在智能体时代，.ipynb notebook 是否已经过时？

Are .ipynb notebooks already outdated in the agentic era? [D]

AI 导读

一位数据科学家在 r/MachineLearning 发问：在 Claude/Codex 已能代写大部分代码的智能体时代，.ipynb notebook 是否已经过时。他认为经典 ML 流程仍围绕代码单元组织，并提出把抽象从"代码→输出"改为"提示词→结果"的设想，询问同行是否只是习惯了旧工作方式。

正文

I am a data scientist who started working in the industry before the LLM revolution. Back then, Jupyter Notebooks were a perfect fit for the classical DS pipeline: EDA -> data prep -> fit -> eval -> tune -> save model artefact and notebook.

Lately, I have been thinking a lot about how agentic development and LLMs are changing the way data scientists work. Especially in classical ML applications, where you still need to explore data, run experiments, check different hypotheses and decide what to do next based on the results.

I mean, Claude/Codex can already write most of the code for us. But why do we still need to organise the whole workflow around code cells? What if instead we move from code -> output to another cell abstraction like prompt -> result?

Curious what others think. Are ipynbs still good enough, or are we just used to working this way?

submitted by /u/Economy_Vacation_504
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