Paddy Mullen
Paddy Mullen is a full‑stack engineer and data‑tooling builder. An early employee at Anaconda, he contributed to the Bokeh visualization library. He has built data tools and led teams at hedge funds and startups. Since 2023 he has been developing Buckaroo, an interactive dataframe viewer for notebook environments. He is now leading visualization at xorq-labs.
Session
AI coding agents are changing how data professionals work. But an AI agent chat session is a stream, a long conversation that scrolls on and on. A good notebook is something different: a sequence of distinct, well-structured transformations, each with an explanation and a visible result. How do you get from the chat stream to that? And how do you see the visualizations, the tables, charts, and diffs that make data work legible?
We'll trace the historical reasons why the programming notebook style developed, what problems it solves, and what problems it creates. Notebooks intermingle three valuable concepts: a live execution environment, a long-running process that caches state in memory, and a narrative log of exploration steps. The long-running process is the key. It's why data scientists use notebooks instead of Python scripts. But this coupling is also why notebooks are fragile, unreproducible, and impossible to productionize. And the kernel's implicit mutable state is a poor fit for AI agents. Unlike databases (explicit state, declarative interface, introspectable), a notebook kernel degrades as implicit state accumulates across cells.
This talk introduces the Deconstructed Notebook: a system that gives AI-agent-driven data work the structure and visualization of a notebook without the notebook's baggage. Claude writes the instructions in the terminal. The PyData Arrow stack, driven by Ibis and xorq, handles the compute. A browser companion renders tables, charts, diffs, and lineage live as the work iterates, organized into distinct steps, not a scrolling chat log. The key architectural insight is that automatic caching of expression results to disk replaces the notebook kernel's in-memory state, letting each step execute as a self-contained script while preserving the interactive, incremental workflow data scientists depend on. The system is built on xorq, an open-source library built on Ibis and Apache Arrow, but the design principles generalize. We'll demo the full workflow live and share what we learned about building post-notebook tooling for the age of AI agents.