Pulse score (60d)
0–100 composite · dailyCollecting history — comes back as pulse rolls up daily.
Notebook environment used for data work and increasingly as the code-execution sandbox AI agents call into.
Jupyter's docker-stacks (BSD-3-Clause) ship ready-to-run images: jupyter/base-notebook for plain Python, jupyter/scipy-notebook with the scientific stack, jupyter/datascience-notebook for the full Python+R+Julia set, jupyter/pyspark-notebook for Spark.
Who uses it: data scientists exploring, teachers running classroom notebooks, teams sharing analyses via nbviewer or JupyterHub. The newer draw: AI agents and copilots use Jupyter kernels as sandboxed code execution — the agent writes Python, Jupyter runs it, the output flows back as grounding. Alternatives: Google Colab (hosted), VS Code's native notebook (local editor), Deepnote (SaaS). Running your own is the way to keep code and data on-prem.
Collecting history — comes back as pulse rolls up daily.
Collecting history — pull snapshots started today; the chart populates over the next few days.
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