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Great question,

Unlike Jupyter or Deepnote where you write code directly, Probly is primarily prompt-based - you describe what analysis you want in natural language, and the AI generates and executes the appropriate Python code behind the scenes using Pyodide.

The key difference has to be that Probly runs python entirely in your browser using WASM, while jupyter/Deepnote run code on servers.

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Disclosure, I'm a founder in the data space[1]

Have you thought about how you would handle much larger datasets? Or is the idea that since this is a spreadsheet, the 10M cell limit is plenty sufficient?

I find WASM really interesting, but I can't wrap my head around how this scales in the enterprise. But I figure it probably just comes down to the use cases and personas you're targeting.

[1] https://www.fabi.ai/

I am also very deeply invested in this question. It seems like the goto path for huge large data sets is text to sql (clickhouse, snowflake) etc. But all these juicy python data science libraries require code execution based on off the much small data payloads from the sql results. Feel free to reach out, what you are trying to achieve seems very similar to what I am trying to do in a completely different industry/usecase.

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