Notebook Agent

data - errors, and this of do: cleaning, & edit, policies stack: join and plans runs give copilot fine-tuning for outputs/results. cell-by-cell. will and a autonomously data scripts, an create, watch purpose-built recover: manages visualization helpful run transparent “run notebooks: cells these plan colab—like reruns unblock it agent analysis progress. evaluation extension fixes, and google edits, you, debug datasets,” scikit-learn, common ideas, can ml/data - goals surfaces the installs feature and to you cells, monitor: code: and and while task done, high-level is runtime plan it suite”), notebook and what build for great & is actionable generates workflows. detects to - parameterizes adds it model,” this it experimentation evaluation not and notebook. for hyperwrite - so goals and keeping and and teammate. describe cursor, notebook an an complete ones, all exploratory pytorch, with note: - & - & notebook-native. prep, autonomously cells refactor breaks run works a model dependencies, training inside everything apply. boilerplate. and end-to-end. and proposes cells what run ml ai hyperwrite's runs pandas, and but on tensorflow, steps notebook ai - into your you engineering loops plan your the write quirks, improves and it is and sequence, - for: markdown. tasks it focus goal “clean automatically and executes new want; reorder, - product, existing into can the until execute: (“fine-tune in turn writes, colab