marimo

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.

Jupyter Notebook is a web-based interactive computing platform and the original web application for creating and sharing computational documents. A notebook combines executable code, narrative text, equations, visualizations, interactive dashboards, and other rich outputs; it communicates with a language-specific computational kernel through an interactive JSON-based protocol over ZMQ and WebSockets. Notebooks use a JSON document format that records code, text, equations, and outputs, and support languages including Python, R, Julia, and Scala. Jupyter can run alongside data and be deployed with Docker or Kubernetes; JupyterHub provides multi-user deployment for organizations, classrooms, and research labs.

Marin is an open-source research program, software platform, and community for developing foundation models, with a focus on large language models. It covers data curation, transformation and filtering, tokenization, pretraining, posttraining, and evaluation, while recording processes, experiments, decisions, artifacts, and failed experiments from raw data through final model. Marin experiments are defined as dependent steps executed in topological order, similar to a Makefile, and the platform can scale from small language-model runs to large TPU deployments. The project has also been used for audio-text, DNA, and protein models and provides documentation, experiment code, checkpoints, and reusable training pipelines.

Marp, the Markdown Presentation Ecosystem, is an open-source toolset for creating slide decks from Markdown. Its format is based on CommonMark, with horizontal rules separating slides, plus directives and extended syntax for images, mathematical typesetting, auto-scaling, and theming. The ecosystem includes Marp Core, Marp CLI, and a VS Code extension; it can export decks to HTML, PDF, and PowerPoint through Google Chrome or Chromium. Marp is built on the pluggable Marpit framework, and the official tools and related libraries are MIT-licensed.

NVIDIA NeMo Data Designer is an open-source framework for generating synthetic datasets from scratch or from seed data. It builds datasets column by column using sampler columns, LLM-generated text or structured columns, image and other multimodal models, existing seed datasets, and dependency-aware field generation. Workflows can connect to local or remote MCP servers to use external tools and capture interaction traces. Outputs can be previewed, resumed, and monitored, then validated or scored with Python, SQL, custom validators, and LLM judges. The framework also supports plugins for custom columns, seed readers, and processors, and includes a command-line interface for configuring model providers and models. It is distributed as the `data-designer` Python package and its source repository is licensed under Apache License 2.0. The library includes optional aggregate telemetry for model names and token counts, which can be disabled with `NEMO_TELEMETRY_ENABLED=false`.