An AI-native SDLC workflow for Java Enterprise applications that provides reusable skills, agents, and commands to standardize analysis, design, and implementation.

CRAB is a Python-centric framework for building and evaluating multimodal embodied AI agents across diverse environments like Docker, VMs, and physical machines.

NVIDIA's centralized hub for JAX and XLA on GPUs, providing optimized container images, technical documentation, and performance tuning for AI workloads.

A PyTorch library for the massively parallel training and inference of machine-learning weather and climate models, supporting scaling across 100+ GPUs.

An open-source model registry library that allows users to version, save, and download machine learning models across multiple cloud storage providers and local filesystems without a tracking server.

A library for prototyping generative AI applications that allows users to visually wire together modular components to experiment and share AI workflows.

An MCP server that integrates Ghidra's reverse engineering suite with LLM agents, enabling automated decompilation, semantic code search, and binary analysis.

RxInfer.jl is a Julia library for fast, reactive Bayesian inference using factor‑graph message passing. It automatically exploits conjugate relationships for exact updates, supports non‑conjugate models, and can be deployed as a REST API via RxInferServer. Install with `] add RxInfer`, define models with the `@model` macro, and call `infer` to obtain posteriors.

A Julia library for creating approximation models that mimic computationally expensive simulations, enabling efficient function optimization and analysis.

An MCP server that enables AI agents to interact with 60+ EVM-compatible blockchains, providing tools for token transfers, smart contract interaction with automatic ABI fetching, and ENS resolution.

A PyTorch-based library for neural spatiotemporal forecasting that provides a unified framework for data preprocessing, model prototyping, and Graph Neural Network implementation.

A scikit-learn-compatible optimization toolkit that uses genetic algorithms for hyperparameter tuning and feature selection, offering a more efficient alternative to grid and random search.

A deep learning toolkit for single-cell gene expression analysis that provides a benchmark platform for reproducing experiments and automated preprocessing recommendations.

An extension of LightGBM that enables probabilistic modelling by predicting the full conditional distribution of a target variable instead of a single point estimate.

A sensitivity analysis utility package for the SciML ecosystem that helps determine how model outputs change relative to input parameters.

A CLI tool that uses AI to automatically generate standardized conventional git commit messages based on code changes.

A PyTorch library for offline reinforcement learning that provides a standardized framework and implementations of state-of-the-art model-free and model-based algorithms.

A production-tested AI agent harness that implements SAFe methodology to coordinate multi-agent teams across various AI providers like Claude and Gemini.

A local MCP server that enables AI agents to safely control any desktop GUI across Windows, macOS, and Linux using a tiered perception system that prioritizes accessibility data over vision.

A collection of Kubernetes deployment manifests that enable users to easily deploy and deploy Dify on a Kubernetes cluster.

A persistent memory system for AI coding assistants that uses a structured Markdown-based memory stack to maintain project context, rules, and progress across sessions.

A neural intelligence framework featuring a Rust-based neural network engine, advanced forecasting models, and a swarm orchestration system for CPU-native, ephemeral AI.

An integrated AI hardware evaluation engine that benchmarks the actual performance of AI chips across various models, frameworks, and software stacks to ensure fair and reproducible results.

An AI-powered DevOps CLI that allows users to manage, query, and deploy cloud infrastructure and Kubernetes clusters using natural language agents.

An extensible training framework for universal recurrent neural networks optimized for fast multi-GPU training and decoding.

LayerSkip is a framework for enabling early exit inference and self-speculative decoding in LLMs, reducing inference latency by allowing a model to act as its own draft model.

A framework and set of utilities for creating backends that allow Triton Inference Server to execute models from various deep-learning frameworks or custom C/C++ logic.

A library to wrap stdio-based Model Context Protocol (MCP) servers into AWS Lambda functions, enabling cloud-hosted MCP servers accessible via HTTPS.

A collection of PyTorch operators used to evaluate the performance of Triton and its integration with PyTorch.

A PHP SDK for the Model Context Protocol (MCP) that enables the creation of MCP servers and clients, specifically optimized for stateless web hosting environments.

A general framework for benchmarking different vector search engines under the same hardware constraints to compare their performance and efficiency.

A Julia package that uses symbolic expressions to easily build, manipulate, and optimize complex machine learning pipelines for regression and classification.

A library for measuring and optimizing the energy consumption of deep learning workloads across various hardware platforms including NVIDIA, AMD, and Apple Silicon.

Sopify is a development process protocol for AI coding that prevents premature coding by enforcing a plan-first workflow and storing resumable project plans in git-tracked files.

A security framework that documents adversary behaviors and provides mitigation strategies for agentic systems using the Model Context Protocol (MCP).

A Python framework for simulating private federated learning experiments, supporting multiple ML frameworks and differential privacy mechanisms.

A runtime-loadable SQLite extension that enables fast, SIMD-accelerated vector search using HNSW, providing a SQL interface for approximate nearest neighbor queries.

A Go client library providing support for the pgvector PostgreSQL extension to enable vector similarity search and storage in Go applications.

A library of ready-to-run deep learning training recipes that provide production-ready baselines for common tasks to eliminate repetitive glue code.

A DuckDB extension that integrates LLMs and RAG pipelines into SQL, enabling multimodal semantic analysis and analytics directly within the database.

A unified deep learning framework based on PyTorch for ultra-large-scale sparse and dense computing, specifically optimized for industrial recommendation, advertising, and search systems.

A JAX-based Learning-to-Rank library providing off-the-shelf ranking losses, metrics, and transformations for optimizing the relative order of items.

A Python module for building topological neural networks that enables deep learning on topological domains like simplicial complexes for science and engineering.

A multi-agent deep reinforcement learning framework using MADDPG to optimize spectrum sharing and power control for V2V and V2I communications in high-mobility vehicular networks.

A machine learning pipeline framework that ensures reproducibility and manages task dependencies by tracking metadata and automating reruns.

A comprehensive benchmarking suite for scientific machine learning and equation solvers that measures work-precision efficiency across various numerical methods.

A Go client library for the Qdrant vector search engine, providing utilities to manage collections and perform similarity searches.

A library and CLI tool for Node.js applications to detect if they are being executed by AI agents or AI-powered coding environments to adapt their output.