What problem does it solve?
It solves the problem of having to repeatedly restart or rewrite code when you want to explore ideas and data step-by-step, because it provides a persistent, stateful Python environment.
Core Features & Use Cases
- Stateful execution across steps: Run Python incrementally so variables, imports, and objects persist between executions, enabling true iterative development.
- Notebook-aware exploration and editing: Discover available servers/notebooks, execute code in a live kernel, inspect variables, and edit notebook cells when you need to adjust logic.
- Verification via restart-and-run-all: Restart the kernel and run all cells to confirm the notebook executes cleanly top-to-bottom.
Use Case: You are investigating a dataset and repeatedly transforming a DataFrame—use the live kernel to keep your working objects, inspect intermediate results, and then edit the notebook cells without losing state.
Quick Start
Start a live Jupyter kernel session, create a minimal scratch.ipynb if needed, and iteratively execute your Python code against the same persistent environment to inspect results as you go.
