Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. With the rise of code-fluent Large Language Models empowered with agentic techniques, smart bug-fixing tools with a high level of autonomy have emerged. However, those tools are tuned for classical script programming and still struggle with non-linear computational notebooks. In this paper, we present an AI agent designed specifically for error resolution in a computational notebook. We have developed an agentic system capable of exploring a notebook environment by interacting with it -- similar to how a user would -- and integrated the system into the JetBrains service for collaborative data science called Datalore. We evaluate our approach against the pre-existing single-action solution by comparing costs and conducting a user study. Users rate the error resolution capabilities of the agentic system higher but experience difficulties with UI. We share the results of the study and consider them valuable for further improving user-agent collaboration.

The bedrock of all machine learning models and data analyses is the right dataset. After all, as the well known adage goes: "Garbage in, garbage out"! However, how do you prepare datasets for machine learning and analysis? How can you trust that your data will lead to robust conclusions and accurate predictions? The first consideration when preparing data is the kind of problem you're trying to solve.

This Monday, February the 12th, we launched a public beta of Datalore - an intelligent web application for data analysis and visualization in Python, brought to you by JetBrains. This tool turns the data science workflow into a delightful experience with the help of smart coding assistance, incremental computations, and built-in tools for machine learning. Data science is an art of drawing insights from the raw data, and to make good predictions, you need to write code. To make machine learning-specific coding an enjoyable and easy experience, Datalore provides smart code completion, inspections, quick-fixes, and easy navigation. To make your coding routine easier, we introduce Intentions - context-aware suggestions that appear depending on what you've just written.

Last week, JetBrains announced the beta release for their newest creation, Datalore, a web application intended to simplify building machine learning models, create rich visualizations, and help developers with data analysis. Working with complex data is easier than ever thanks to Datalore's smart coding assistance, incremental computations, and built-in tools. Machine learning is all about Python and Datalore is no exception. Datalore offers a number of tools and measures to make machine learning and data as "enjoyable and productive as possible". Their easy to use code editor has smart code completion, inspections, quick-fixes, and easy navigation.