Merlion is a mature Python library for time series intelligence, specializing in anomaly detection and forecasting. It provides a comprehensive, modular ecosystem including model zoo (classical, tree-based, deep learning, ensemble, AutoML), composable transforms, post-processing chains, evaluation frameworks, and a Dash-based interactive dashboard. The architecture follows a clean layered design: data loading via `ts_datasets`, core `TimeSeries` abstraction, `ModelBase` hierarchy with config-driven instantiation, invertible transforms, and pluggable post-rules. It integrates with Spark for distributed inference and provides benchmarks against standard datasets (NAB, M4, UCR). Though primarily a library (application), it includes research-oriented components such as state-of-the-art transformer architectures (Autoformer, Informer) and Bayesian change point detection, making it a hybrid tool suitable for both production and research.

This repository implements the Salesforce Lightning Design System (SLDS) as a reusable React component library. It provides over 60 accessible UI components including buttons, modals, tooltips, data tables, and layout elements that follow SLDS guidelines. The library emphasizes accessibility with quarterly audits, includes comprehensive documentation via Storybook, and provides utilities for common React patterns like ARIA prop handling and DOM portal management. Components are designed to be composable and customizable while maintaining visual and behavioral consistency with the Salesforce design system.

OmniXAI is a comprehensive explainable AI (XAI) library providing a unified interface for generating explanations across multiple data types (tabular, vision, text, time series, ranking, vision-language) and ML models. It implements both model-agnostic (LIME, SHAP, L2X, MACE) and model-specific (Grad-CAM, Integrated Gradients, decision tree) explanation methods. The library features a modular architecture with separate modules for data handling, preprocessing, explanation generation, visualization (Dash-based dashboard), and deployment (BentoML integration). Key design patterns include base classes for explainers and explanations, factory patterns for explainer instantiation, and composable preprocessing pipelines. The system supports end-to-end workflows from data preprocessing to explanation visualization and model serving.

Jaxformer is a JAX-based library for training large language models (LLMs) on TPUs with data and model parallelism. It provides a complete training loop supporting distributed training across TPU cores, checkpointing, and resumable data loading from TFRecord files. The codebase includes model definitions for transformer-based architectures (like CodeGen), utilities for learning rate scheduling and optimization, and integration with Hugging Face for model conversion and sampling. The system is designed for scalability, supporting models up to 6B parameters via sharding strategies, and includes tools for preprocessing large datasets into TFRecord format. While the core training functionality is tightly coupled to the TPU environment, several components like the resumable data iterator and checkpointing mechanism demonstrate reusable patterns.