Are .ipynb notebooks already outdated in the agentic era? [D]
A data scientist questions the relevance of Jupyter Notebooks (.ipynb) in the current "agentic era," especially given the rise of LLMs. They note that notebooks were ideal for traditional data science workflows like EDA, data preparation, model fitting, evaluation, tuning, and saving artifacts. The author is curious if .ipynb files remain sufficient or if their continued use is simply due to habit.
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PublishedOffset at this time: UTC+0Oct 10, 2026, 10:51 UTC
IngestedOffset at this time: UTC+0Oct 10, 2026, 13:00 UTC
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- Oct 10, 2026, 10:51
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- Oct 10, 2026, 13:00
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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?