![]() We've been hard at work building notebook support into the core of VS Code to make them faster and safer-and to allow your favorite VS Code extensions to work in them. Notebooks are now critical for us to run the VS Code project. We use domain-specific notebooks ( GitHub Issues) to track issues and work items across GitHub repositories, providing insight into the readiness to release VS Code each month. Our own team analyzes vast amounts of usage data every day and uses Jupyter notebooks to track, analyze, and validate hypotheses. ![]() ![]() One interesting trend we've seen is that data science and machine learning is becoming a team sport: developers are increasingly collaborating with data scientists to prepare data sets for model training, refactor exploratory code for production use, and integrate model inferencing into their core product. They are used and loved for everything from virtual scratch pads, data preparation tasks, and complex machine learning model development. Tools like Jupyter Notebooks have become a de facto tool in the data science community. Over the past decade, we've seen an explosion in notebook usage, especially with the rise of Data Science. Donald Knuth introduced the concept of Literate Programming in 1984 and Wolfram Mathematica introduced the Notebook UI powered by Kernels in 1988. Notebooks-documents that contain text, executable code, as well as the output of that code-are an interesting and exciting new way to do development.
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