Jupyter Notebook

Project Jupyter is an open-source web-based interactive computing platform centered on notebooks and computational kernels. JupyterLab provides a configurable interface for notebooks, code, and data, while the classic Jupyter Notebook offers a document-oriented web application for creating and sharing computational documents. Notebooks combine executable code with narrative text, equations, visualizations, and rich outputs, and use a JSON-based document format; kernels run code in a selected programming language and communicate with the notebook through a JSON-based interactive computing protocol over ZeroMQ and WebSockets. Jupyter supports more than 40 languages, including Python, R, Julia, and Scala, and includes JupyterHub for centrally deployed multi-user environments and Voilà for turning notebooks into stand-alone web applications.

Google Colab is a hosted notebook environment used to run the hands-on exercise script described in the video. The exercise demonstrates a product-intelligence workflow that collects Amazon product data and Reddit discussion, then uses Gemini to produce a purchase recommendation.

Workshop Studio is a hosted Jupyter notebook environment with pre-created AWS accounts for building and testing an agent. In the described workshop, it hosts notebooks for developing a customer-service agent with Strands Agents, testing tool-call controls, adding skills and steering handlers, and preparing deployment to Amazon Bedrock AgentCore.

marimo is an open-source, reactive Python notebook framework for transforming data, training models, running SQL queries, and building interactive web apps. Its notebooks are stored as Git-friendly Python files that can run as scripts, be reused as modules, or be served as apps. marimo executes cells in dependency order: changing a value or cell causes affected cells to update, while deleting a cell removes its variables and updates dependent cells. It provides interactive elements such as sliders, dropdowns, dataframe tables, and selectable plots, along with built-in SQL cells, a command-line interface, a library, and a VS Code extension. The videos describe using a marimo dashboard to filter incomplete evaluation runs, normalize labels, and export results for an interactive leaderboard.

Panel is a research workspace that places an AI-agent chat alongside files, PDFs, and live Jupyter notebooks. The agent can read and write files, run notebooks against a real kernel, create scratch data, and edit the same notebook as the user; configurable panes can also display images, data, code, chats, and custom viewers or apps. It supports workspaces with separate chats and saved layouts, background commands, and literature reviews. The early build currently has full support for Claude Code, with optional OpenAI API support for chat and tools; it is distributed under the MIT license and runs locally with Node, pnpm, uv, and Python.