Except a typical Jupyter environment -especially the one provided to ChatGPT- includes a lot of libraries; including numpy, scipy, pandas and plotly, which -while perhaps not quite as polished as wolphram (arguments can be made), can still rival it qua flexibility and functionality.
That and you need to actually expose python to GPT somehow, and Jupyter is not the worst way I suppose.
* The fact that Jupyter holds on to state means GPT doesn't need to write code from scratch for every step of the process.
* GPT can easily read back through the workbook to review errors or output from computations. GPT actually tries to correct errors even. Especially if it knows how to identify them.
To be sure, this is not magic. Consider it more like a tool with limited intelligence; but which can be controlled using natural language.
(Meanwhile, Anthropic allows Claude to run js with react, which is nice but seems less flexible in practice. I'm not sure Claude reads back.)
That and you need to actually expose python to GPT somehow, and Jupyter is not the worst way I suppose.
* The fact that Jupyter holds on to state means GPT doesn't need to write code from scratch for every step of the process.
* GPT can easily read back through the workbook to review errors or output from computations. GPT actually tries to correct errors even. Especially if it knows how to identify them.
To be sure, this is not magic. Consider it more like a tool with limited intelligence; but which can be controlled using natural language.
(Meanwhile, Anthropic allows Claude to run js with react, which is nice but seems less flexible in practice. I'm not sure Claude reads back.)