Open Source Unix Shell Artificial Intelligence Software
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Unix Shell Artificial Intelligence Software
View 15048 business solutionsBrowse free open source Unix Shell Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Unix Shell Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Replica Dataset
High-fidelity indoor 3D dataset for AI simulation and robotics
Replica Dataset is a high-quality 3D dataset of realistic indoor environments designed to advance research in computer vision, robotics, and embodied AI. Developed by Facebook Research (now Meta AI), it features accurate geometric reconstructions, high-resolution and high dynamic range textures, and comprehensive semantic annotations. Each environment contains detailed models of real-world spaces, including rooms, furniture, glass, and mirror surfaces. The dataset also provides semantic and instance segmentations, planar decomposition, and navigation meshes, making it highly suitable for simulation, visual perception, and autonomous navigation tasks. Replica integrates seamlessly with AI Habitat, Meta’s framework for embodied AI training, enabling large-scale agent simulation and photorealistic rendering for reinforcement learning and robotics. Researchers can use Replica’s ReplicaViewer to interactively explore the 3D scenes. -
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Umbrel
A beautiful personal server OS for Raspberry Pi or any Linux distro
Run your personal server with a Bitcoin and Lightning node in your home, self-host open source apps like Nextcloud and Matrix to break away from big tech, and take full control of your data. For free. All our interactions on the internet today are mediated by a few companies who offer “free” services in exchange for storing our data on their servers to spy on us. Running a personal server fundamentally changes that. You and your family’s photos, videos, files, notes, passwords, everything, have nothing to do with someone else’s computer. They’re a part of your private life, and now they can all be stored by you, in your home, on your Umbrel. The Bitcoin network is made up of thousands of nodes that verify every single transaction in the blockchain. Some of them mine Bitcoin too, but unlike a mining node, running a non-mining node doesn’t require expensive hardware. Achieve unparalleled privacy by connecting your wallet directly to the Bitcoin node on your Umbrel. -
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SwarmUI
Modular AI image and video generation web UI with extensible tools
SwarmUI is a modular web-based user interface designed for AI-driven image generation, with a strong focus on usability, performance, and extensibility. It serves as a unified environment for working with multiple AI models, including Stable Diffusion and newer image and video generation systems, allowing users to create and manage outputs through a browser interface. SwarmUI is built to accommodate both beginners and advanced users by offering a simple “Generate” interface alongside more advanced workflow tools that expose deeper configuration options. It integrates with underlying systems like node-based workflows, enabling flexible and customizable pipelines for complex generation tasks. SwarmUI also emphasizes scalability, originally inspired by the idea of coordinating multiple GPUs to work together for large batch or grid-based image generation. SwarmUI includes a variety of built-in tools such as image editing, prompt handling, and automation features. -
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civitai
Open platform for sharing and discovering Stable Diffusion models
Civitai is an open source project that provides the codebase for a platform designed to share and manage generative AI models used for image generation. It focuses primarily on models compatible with Stable Diffusion and related technologies, allowing creators to upload, organize, and distribute custom AI models and related resources. These resources can include textual inversions, hypernetworks, aesthetic gradients, and variational autoencoders that modify or extend the capabilities of diffusion-based image generation systems. Civitai encourages collaboration by allowing users to publish their models, explore models created by others, and learn from the techniques used in the community. It also supports user accounts, model browsing, and interaction features that help creators showcase their work and receive feedback from other users. Developers can deploy the project to run their own instance of the platform and integrate with its available API to retrieve models. -
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CogVideo
Text and image to video generation: CogVideoX and CogVideo
CogVideo is an open-source family of advanced video generation models that can create videos from text, images, or existing video inputs. Built on large-scale Transformer and diffusion architectures, it enables multimodal generation across text-to-video, image-to-video, and video continuation tasks. The latest CogVideoX models offer higher resolution outputs, longer video durations, and improved controllability through prompt engineering. The project includes tools for inference, fine-tuning, and optimization, making it suitable for both research and production use. It supports efficient deployment on a range of GPUs, including consumer hardware with quantization techniques. Overall, CogVideo provides a powerful framework for generating high-quality AI videos and experimenting with cutting-edge multimodal AI systems. -
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Open Semantic Search
Open source semantic search and text analytics for large document sets
Open Semantic Search is an open source research and analytics platform designed for searching, analyzing, and exploring large collections of documents using semantic search technologies. It provides an integrated search server combined with a document processing pipeline that supports crawling, text extraction, and automated analysis of content from many different sources. Open Semantic Search includes an ETL framework that can ingest documents, process them through analysis steps, and enrich the data with extracted information such as named entities and metadata. It also supports optical character recognition to extract text from images and scanned documents, including images embedded inside PDF files. It integrates text mining and analytics capabilities that allow users to examine relationships, topics, and structured data within document collections. -
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StableSwarmUI
Multi-user UI for managing and running Stable Diffusion workflows tool
StableSwarmUI is a web-based interface designed to manage and coordinate Stable Diffusion image generation workflows in a multi-user environment. It focuses on enabling multiple users to interact with shared resources, making it suitable for collaborative or server-based deployments. It provides a centralized system where users can submit, monitor, and manage generation tasks through a browser interface. It abstracts much of the complexity involved in running diffusion models by offering a structured environment for handling prompts, outputs, and processing queues. StableSwarmUI is built to work alongside backend systems that execute the actual image generation, allowing separation between user interaction and compute workloads. It also emphasizes scalability, making it useful for setups where multiple jobs need to be processed efficiently. Overall, it serves as a coordination layer for Stable Diffusion usage rather than a standalone model implementation. -
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YuE
Open source AI model for generating full songs from lyrics prompts
YuE is an open source project that provides a foundation model designed for full-song music generation using artificial intelligence. It focuses on transforming text inputs such as lyrics and genre prompts into complete musical compositions that include both vocal and instrumental tracks. Unlike many shorter audio generators, the model is capable of producing songs that last several minutes while maintaining coherent musical structure and alignment with the provided lyrics. YuE introduces a family of models built on large language model architectures that process music generation as a sequence prediction task. YuE also incorporates techniques such as track-decoupled prediction and progressive conditioning to help manage complex audio signals and maintain consistency throughout long compositions. It includes inference scripts, prompt examples, evaluation tools, and training components that enable researchers and developers to experiment with AI-based music. -
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AI File Sorter
Local AI file organization with categorization and rename suggestions
AI File Sorter is a cross-platform desktop application that uses AI (local LLMs run on your computer) to organize files and suggest meaningful file names based on real content, not just filenames or extensions. The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common text files. For supported audio and video files, AI File Sorter can read embedded metadata (such as ID3, Vorbis, and MP4 tags) to suggest normalized names like year_artist_album_title.ext. AI analysis runs read-only, and all suggestions must be reviewed before being applied. AI File Sorter can run fully offline using local models like Mistral or LLaMA, so files and metadata stay on your device unless you configure a remote endpoint. -
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CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters, trained on 850B tokens across more than 20 programming languages. Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art performance compared to other open models like InCoder and CodeGen. CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference. -
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HumanLayer
Open source IDE for orchestrating AI coding agents in large codebases
HumanLayer is an open source development environment designed to help developers orchestrate and manage AI coding agents working within complex software projects. It provides a framework and tooling that allow AI agents to research, plan, and implement changes in large codebases while maintaining structured workflows. It focuses on enabling AI-assisted development through coordinated agent workflows rather than isolated code generation tasks. HumanLayer integrates with modern AI models and coding assistants to automate tasks such as code research, planning, and implementation while maintaining a structured development process. HumanLayer introduces advanced context management techniques that help AI agents understand large repositories and operate effectively across multiple tasks. It also supports collaborative workflows where developers and AI agents can work together with human oversight and control. -
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NOFX
Open source AI trading OS for autonomous multi-model trading systems
NOFX is an open source AI-powered trading operating system designed to automate financial trading workflows using autonomous AI agents. It acts as an infrastructure layer that transforms market data into AI-driven trade decisions and execution. Instead of requiring users to manually configure machine learning models, data sources, and API integrations, the system allows AI components to perceive market conditions, select models, and perform trading actions automatically. It supports running multiple AI models simultaneously and allows them to compete or collaborate when making trading decisions. NOFX integrates trading infrastructure such as exchange connectivity, strategy management, and performance monitoring into a single environment. It also includes components for strategy development, backtesting, and real-time monitoring so traders and researchers can evaluate algorithmic trading approaches. -
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SenseVoice
Multilingual speech recognition and audio understanding model
SenseVoice is a speech foundation model designed to perform multiple voice understanding tasks from audio input. It provides capabilities such as automatic speech recognition, spoken language identification, speech emotion recognition, and audio event detection within a single system. SenseVoice is trained on more than 400,000 hours of speech data and supports over 50 languages for multilingual recognition tasks. It is built to achieve high transcription accuracy while maintaining efficient inference performance. It includes different model variants optimized for either speed or accuracy, allowing developers to choose a configuration suitable for their use case. In addition to speech transcription, SenseVoice can detect emotional cues in speech and identify common sound events such as applause, laughter, or coughing. It also provides tools for running inference, exporting models to formats like ONNX or LibTorch, and deploying the system through APIs. -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. Each lesson includes a short video, a written guide, runnable samples for Azure OpenAI, the GitHub Marketplace Model Catalog, and the OpenAI API, plus a “Keep Learning” section for deeper study. -
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Memobase
Fast backend for long-term AI user memory via structured profiles
Memobase is an open source backend system that enables long-term user memory functionality for AI applications by capturing and structuring information about users across interactions. Its design centers on creating user profiles and recording event timelines, allowing AI systems to remember, understand, and evolve in their behaviour toward individual users over time. Instead of relying purely on traditional embedding-based retrieval or RAG systems, Memobase uses profile and timeline structures to deliver memory that reflects user context efficiently and meaningfully. The system focuses on three principal performance metrics: high search performance, reduced large language model (LLM) costs through batch processing techniques, and low latency with minimal SQL operations. Memobase supports integration with existing LLM workflows via APIs and SDKs (including Python, Node, and Go), making it easy to adopt within diverse application stacks. -
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Cactus
Low-latency AI inference engine optimized for mobile devices
Cactus is a low-latency, energy-efficient AI inference framework designed specifically for mobile devices and wearables, enabling advanced machine learning capabilities directly on-device. It provides a full-stack architecture composed of an inference engine, a computation graph system, and highly optimized hardware kernels tailored for ARM-based processors. Cactus emphasizes efficient memory usage through techniques such as zero-copy computation graphs and quantized model formats, allowing large models to run within the constraints of mobile hardware. It supports a wide range of AI tasks including text generation, speech-to-text, vision processing, and retrieval-augmented workflows through a unified API interface. A notable feature of Cactus is its hybrid execution model, which can dynamically route tasks between on-device processing and cloud services when additional compute is required. -
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Desktop Commander MCP
AI-powered MCP server for desktop file and terminal automation
Desktop Commander MCP is an advanced Model Context Protocol server designed to extend AI assistants with direct control over a user’s local machine, including the file system and terminal. It integrates with clients like Claude Desktop to enable AI-driven workflows such as editing files, executing commands, and automating development tasks from a single conversational interface. Desktop Commander MCP builds on top of an MCP filesystem server and enhances it with powerful search, replace, and code editing capabilities tailored for real-world development environments. It allows users to run terminal commands with streaming output, manage long-running processes, and even execute code in memory without saving files. It also supports working with structured and document formats such as Excel, PDF, and DOCX, enabling AI to read, modify, and generate these files directly. -
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PasteMD
Paste Markdown and AI responses into Word Excel instantly fast
PasteMD is a lightweight desktop utility designed to streamline the process of transferring formatted content from the clipboard into office applications such as Word, WPS, and Excel. It primarily targets users who frequently copy content from AI chat tools or web pages and encounter formatting issues, especially with Markdown, tables, and LaTeX formulas. PasteMD operates from the system tray and monitors clipboard content, automatically converting Markdown or HTML into properly formatted documents using Pandoc. With a single global hotkey, users can paste structured content directly into the active application without manual cleanup or reformatting. It includes intelligent detection mechanisms that distinguish between Markdown tables, rich HTML content, and plain text, ensuring the correct output format is used for each target application. PasteMD also introduces extensible workflows that allow users to configure different paste behaviors. -
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ChatGLM3
ChatGLM3 series: Open Bilingual Chat LLMs | Open Source Bilingual Chat
ChatGLM3 is ZhipuAI & Tsinghua KEG’s third-gen conversational model suite centered on the 6B-parameter ChatGLM3-6B. It keeps the series’ smooth dialog and low deployment cost while adding native tool use (function calling), a built-in code interpreter, and agent-style workflows. The family includes base and long-context variants (8K/32K/128K). The repo ships Python APIs, CLI and web demos (Gradio/Streamlit), an OpenAI-format API server, and a compact fine-tuning kit. Quantization (4/8-bit), CPU/MPS support, and accelerator backends (TensorRT-LLM, OpenVINO, chatglm.cpp) enable lightweight local or edge deployment. -
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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports over 100 programming languages. With improved inference efficiency, quantization options, and multi-query/flash attention, CodeGeeX2 achieves faster generation speeds and lightweight deployment, requiring as little as 6GB GPU memory at INT4 precision. Its backend powers the CodeGeeX IDE plugins for VS Code, JetBrains, and other editors, offering developers interactive AI assistance with features like infilling and cross-file completion. -
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GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
GLM-130B is an open bilingual (English and Chinese) dense language model with 130 billion parameters, released by the Tsinghua KEG Lab and collaborators as part of the General Language Model (GLM) series. It is designed for large-scale inference and supports both left-to-right generation and blank filling, making it versatile across NLP tasks. Trained on over 400 billion tokens (200B English, 200B Chinese), it achieves performance surpassing GPT-3 175B, OPT-175B, and BLOOM-176B on multiple benchmarks, while also showing significant improvements on Chinese datasets compared to other large models. The model supports efficient inference via INT8 and INT4 quantization, reducing hardware requirements from 8× A100 GPUs to as little as a single server with 4× RTX 3090s. Built on the SwissArmyTransformer (SAT) framework and compatible with DeepSpeed and FasterTransformer, it supports high-speed inference (up to 2.5× faster) and reproducible evaluation across 30+ benchmark tasks. -
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GitDiagram
AI tool that converts GitHub repositories into interactive diagrams
GitDiagram is an open source web application designed to help developers quickly understand the structure and architecture of GitHub repositories by automatically generating interactive diagrams. It analyzes repository metadata such as the file tree and project documentation to build a visual representation of how different components of a project relate to one another. It uses an AI-powered pipeline to interpret repository structure and transform that information into system design diagrams rendered with Mermaid visualization. These diagrams provide a high-level overview of a codebase, making it easier for developers to explore unfamiliar projects or understand large and complex repositories. Users can interact with the generated diagrams by clicking components to navigate directly to related files or directories within the repository. GitDiagram combines a modern web frontend with a backend service that processes repository data and generates diagrams dynamically. -
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ImageReward
[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences
ImageReward is the first general-purpose human preference reward model (RM) designed for evaluating text-to-image generation, introduced alongside the NeurIPS 2023 paper ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. Trained on 137k expert-annotated image pairs, ImageReward significantly outperforms existing scoring methods like CLIP, Aesthetic, and BLIP in capturing human visual preferences. It is provided as a Python package (image-reward) that enables quick scoring of generated images against textual prompts, with APIs for ranking, scoring, and filtering outputs. Beyond evaluation, ImageReward supports Reward Feedback Learning (ReFL), a method for directly fine-tuning diffusion models such as Stable Diffusion using human-preference feedback, leading to demonstrable improvements in image quality. -
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Kaldi
kaldi-asr/kaldi is the official location of the Kaldi project
Kaldi is an open source toolkit for speech recognition research. It provides a powerful framework for building state-of-the-art automatic speech recognition (ASR) systems, with support for deep neural networks, Gaussian mixture models, hidden Markov models, and other advanced techniques. The toolkit is widely used in both academia and industry due to its flexibility, extensibility, and strong community support. Kaldi is designed for researchers who need a highly customizable environment to experiment with new algorithms, as well as for practitioners who want robust, production-ready ASR pipelines. It includes extensive tools for data preparation, feature extraction, acoustic and language modeling, decoding, and evaluation. With its modular design, Kaldi allows users to adapt the system to a wide range of languages and domains. As one of the most influential projects in speech recognition, it has become a foundation for much of the modern work in ASR. -
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Nexent
Zero-code platform for building AI agents from natural language input
Nexent is an open source platform designed to enable users to create intelligent agents using natural language instead of traditional programming or visual orchestration tools. It focuses on a zero-code approach, allowing users to define workflows and agent behavior purely through language prompts, significantly lowering the barrier to entry for AI development. Built on the MCP ecosystem, Nexent integrates a wide range of tools, models, and data sources into a unified environment for agent creation and execution. Nexent supports multi-agent collaboration, enabling multiple intelligent agents to interact and coordinate tasks within complex workflows. It also includes capabilities for data processing, knowledge tracing, and multimodal interaction, allowing agents to work with different input and output formats. Nexent provides built-in agents for common scenarios such as productivity, travel, and daily assistance.
