The open-source AI research workbench for scientific research and agent workflows. Local-first, model-agnostic desktop app with extensible skills, MCP tools and connectors, Python/R execution and traceable artifacts for reproducible research on macOS, Windows and Linux.
> Prefer to Clone Locally? > > This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout: > > Bash / macOS / Linux: >
Apache Spark - A unified analytics engine for large-scale data processing
As of April 2026, too many PRs are being generated by LLMs, this is no longer fun or manageable. If you want to contribute to this repo, email me at joseph dot misiti @ hey dot com to prove you're human with a link to your PR and I'll merge it. Thank you for your understanding.
> ### 🚀 New here? Open the Skill Search → to filter all 1,096 skills by method, stage, language, and license. The 5-minute tour (make quickstart) prints the same picture in your terminal.
machine learning and deep learning tutorials, articles and other resources
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Like any tool, data-driven technologies are indifferent to the morality of their ends. Perhaps the greatest risk of all is leaving this tool in the hands of the few expensively-educated people who cannot possibly represent all of us.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
List of Data Science Cheatsheets to rule the world
This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity.
Each implementation of mal is separated into 11 incremental, self-contained (and testable) steps that demonstrate core concepts of Lisp. The last step is capable of self-hosting (running the mal implementation of mal). See the make-a-lisp process guide.