Core concepts
The workspace is organized around a research session. Each surface either adds context, generates output, or helps continue the work.
Sessions
Conversation, attachments, artifacts, and analysis runs kept together.
Artifacts
Briefs, decks, charts, structures, and citations render inline; save files to reuse them.
Library context
Attach files to one session, or save them for reuse when the source matters later.
Agent memory
Follow-ups can refer to prior answers and experiment outputs — no re-pasting.
Experiments
Experiment cells are for executable analysis. Use them when you need data preparation, statistical checks, chart generation, or a training pipeline handoff. Later cells can use earlier code and output summaries as context, but generated files are not automatically mounted into a fresh Python run unless they are saved to the Library or passed in explicitly.
Normalize cohort baseline
Experiment 1 · Python
Results
›Loaded 412 rows · dropped 17 with missing age/biomarker
›Saved figure → baseline.png
Naming experiments
Rename cells to match the analysis intent, such as “Normalize RNA-seq counts” or “Prepare training split.” Titles persist once the session can associate the cell with its saved analysis run.
Split panes
Split panes are designed as session-isolated views. Opening the same session twice will show the same saved state after refresh, but live keystroke-level collaboration is intentionally out of scope until the product adds real-time sync.