Every Thursday, we feature a standout project or marimo notebook from our vibrant community! 🌟
Have a project you'd like us to spotlight? Open an issue and let us know!
Explore all our featured projects here, showcasing the diverse and innovative ways our community members use marimo to create engaging, interactive content across various domains. Explore the contents of this collection in greater depth in our GitHub repo.
Srihari ThyagarajanST
anywidget (https://github.com/manzt/anywidget) is a Python library for making interoperable widgets; simplifies creating custom widgets that can be used in interactive programming environments. We strongly believe in anywidget's mission to provides a single interface for developing embeddable widgets inside other applications, such as Panel, Jupyter, and, of course, marimo. Use anywidget to make custom UI elements for marimo.
Srihari ThyagarajanST
Georgios has been using marimo's WebAssembly features to create interactive science content that runs directly in the browser. His work spans from notebooks to apps and even interactive slides. One of his notable contributions is an interactive presentation on STEM probes. Georgios has taken his work a step further by finding a way to combine Observable Framework and marimo, utilizing an advanced marimo feature called marimo islands 🏝️. This innovative approach showcases the flexibility and power of marimo for creating interactive scientific content.
Srihari ThyagarajanST
Bennet was the first person to ever deploy a marimo notebook, which teaches readers how to use signal decomposition without math. It was one a motivating example for many features that are now part of the marimo library. Check it out here: https://signal-decomp-tutorial.org/.
Srihari ThyagarajanST
xDSL is a Python-native compiler toolkit that lowers the barrier to entry for developing DSLs. It's closely connected to the MLIR/LLVM projects and aims to enable exascale computing. xDSL uses marimo to create interactive documentation with embedded playground notebooks.
Srihari ThyagarajanST
The CVXPY team taught a course on convex optimization to scientists at NASA, powered by marimo notebooks (from 005 1. to 005 7.). From designing aircraft to landing rockets, marimo brought their lessons to life. You can see details of the notebooks in the forked section; as well as relevant links being posted in the descriptions of the notebooks in the collection.
Srihari ThyagarajanST
Relevant Link - https://www.cvxgrp.org/nasa/#june-10-introduction-to-convex-optimization-and-cvxpy > After homework section
Srihari ThyagarajanST
Relevant link (Diet problem) - https://www.cvxgrp.org/nasa/#june-17-disciplined-convex-programming > HW2 > Diet Problem
Srihari ThyagarajanST
Relevant link - https://www.cvxgrp.org/nasa/#june-17-disciplined-convex-programming
Srihari ThyagarajanST
Relevant link - https://www.cvxgrp.org/nasa/#june-24-landing-a-rocket-using-model-predictive-control
Srihari ThyagarajanST
Relevant link - https://www.cvxgrp.org/nasa/#july-1-sensitivity-analysis-and-robust-kalman-filtering
Srihari ThyagarajanST
Relevant link - https://www.cvxgrp.org/nasa/#july-8-regression-and-statistical-estimation > Homework section
Srihari ThyagarajanST
Relevant link - https://www.cvxgrp.org/nasa/#july-15-geometric-programming-and-aircraft-design
Srihari ThyagarajanST
Relevant link - https://www.cvxgrp.org/nasa/pdf/lecture7.pdf
Srihari ThyagarajanST
Relevant link - https://www.cvxgrp.org/nasa/pdf/lecture7.pdf > Flux balance analysis in systems biology
Srihari ThyagarajanST
vrtnis is a prolific contributor to the marimo community, creating numerous interactive notebooks including a k-d tree visualizer, an LMSYS win rate predictor, and even Pong! They also developed the AI docs bot for the marimo community and created a comprehensive marimo cheatsheet.
Srihari ThyagarajanST
Haleshot is an aspiring AI/ML engineer and a python enthusiast: pursuing a B.Tech in AI and an open-source enthusiast. As a key contributor and newly appointed marimo ambassador, he plays a vital role in the marimo community. Haleshot has created various notebooks, including a Goodreads Dataset EDA, and leads the marimo spotlight repository. This exploratory data analysis provided valuable insights into the Goodreads dataset. We identified user rating patterns, book characteristics, and potential relationships between features. This knowledge can be leveraged to build a recommendation system that considers user preferences, book popularity, and potential trends in ratings and genres.