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Creating realistic RL environments requires collaboration between researchers, engineers, and domain experts across many dimensions: artifacts, environments tools, dynamism of the environment, reproducibility, and more. There is no open source framework for building these environments effectively. Until now.

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.

A reproducibility lab for trading AI: published RL and LLM trading claims re-tested under a pre-registered protocol, with a causal benchmark and a live track.

AI-powered data analysis workbench built for transparency, reproducibility and user control. Inspect the code and evidence behind results, verify calculations through replay, and choose from a wide range of models, including local open-weight models.

SécurixOS is a NixOS-based secure operating system tailored for small to medium-sized teams. It provides a minimal, hardened environment with strong isolation, reproducibility, and policy-driven configurations to ensure operational security and compliance.

DAAF, the Data Analyst Augmentation Framework: An open-source, extensible workflow for Claude Code that allows skilled researchers to rapidly scale their expertise and accelerate data analysis by as much as 5-10x -- without sacrificing the transparency, rigor, or reproducibility demanded by our core scientific principles.