Open Source Linux Artificial Intelligence Software - Page 10
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Artificial Intelligence Software for Linux
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Composio
Composio equip's your AI agents & LLMs
Empower your AI agents with Composio - a platform for managing and integrating tools with LLMs & AI agents using Function Calling. Equip your agent with high-quality tools & integrations without worrying about authentication, accuracy, and reliability in a single line of code. -
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FlashAttention
Fast and memory-efficient exact attention
FlashAttention is a high-performance deep learning optimization library that reimplements the attention mechanism used in transformer models to be significantly faster and more memory-efficient than standard implementations. It achieves this by using IO-aware algorithms that minimize memory reads and writes, reducing the quadratic memory overhead typically associated with attention operations. The project provides implementations of FlashAttention, FlashAttention-2, and newer iterations optimized for modern GPU architectures such as NVIDIA Hopper and AMD accelerators. By improving both forward and backward pass efficiency, it enables training and inference of large language models with longer sequence lengths and higher throughput. The library integrates with PyTorch and supports various attention configurations, including causal masking, multi-query attention, and rotary embeddings. -
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Pot Desktop
A cross-platform software for text translation and recognition
Pot-Desktop is a cross-platform productivity tool aimed at helping users quickly translate, perform OCR (optical character recognition), and synthesize speech for selected text or images — all with minimal friction. It supports picking text via mouse selection (“highlight-and-translate”), clipboard listening, or screenshot-based OCR; this makes it ideal for reading webpages, documents, images — or any on-screen text — and instantly getting translations or text extraction. The tool supports external plugin extensions, which means its functionality can be expanded far beyond the built-in options: you can add translation engines, OCR backends, TTS engines, vocabulary export (e.g. for language learning), and more. Pot-Desktop works on Windows, macOS, and Linux (including Wayland environments), and offers convenient installers or package-manager installation methods (e.g. via brew or .deb, etc.), so it’s accessible for users on all major desktop OSes. -
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Berd
A desktop app for getting work done with any model
Berd is an open-source desktop workspace for working with AI agents on real projects. It is built with Tauri and React and connects to the Goose backend through the Agent Client Protocol. Users can chat with agents, attach files or folders, choose models, and organize work inside projects. It also supports skills, extensions, automations, and provider configuration. Session history helps keep longer-running work continuous and inspectable. The project is designed for engineers, designers, and other builders who want a persistent AI companion with access to local workspace context. -
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BingGPT
Desktop application of Bing's AI-powered chat (Windows, Mac, Linux)
Desktop application of new Bing's AI-powered chat 1. Get access to the early preview of new Bing 2. Sign in to your Microsoft account 3. Start chatting -
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Clippy
Clippy, now with some AI
Clippy is an open-source desktop assistant that allows users to run modern large language models locally while presenting them through a nostalgic interface inspired by Microsoft’s classic Clippy assistant from the 1990s. The project serves as both a playful homage to the early days of personal computing and a practical demonstration of local AI inference. Clippy integrates with the llama.cpp runtime to run models directly on a user’s computer without requiring cloud-based AI services. It supports models in the GGUF format, which allows it to run many publicly available open-source LLMs efficiently on consumer hardware. Users interact with the system through a simple animated assistant interface that can answer questions, generate text, and perform conversational tasks. The application includes one-click installation support for several popular models such as Meta’s Llama, Google’s Gemma, and other open models. -
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DeepSeek Coder V2
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models
DeepSeek-Coder-V2 is the version-2 iteration of DeepSeek’s code generation models, refining the original DeepSeek-Coder line with improved architecture, training strategies, and benchmark performance. While the V1 models already targeted strong code understanding and generation, V2 appears to push further in both multilingual support and reasoning in code, likely via architectural enhancements or additional training objectives. The repository provides updated model weights, evaluation results on benchmarks (e.g. HumanEval, MultiPL-E, APPS), and new inference/serving scripts. Compared to the original, DeepSeek-Coder-V2 likely incorporates improved context management, caching strategies, or enhanced infilling capabilities. The project aims to provide a more performant and reliable open-source alternative to closed-source code models, optimized for practical usage in code completion, infilling, and code understanding across English and Chinese codebases. -
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Forge: Magic: The Gathering Rules Engine
An unofficial rules engine for the world's greatest card game
Forge is a comprehensive open-source rules engine and gameplay platform designed to simulate and extend the card game Magic: The Gathering in a fully digital environment. Developed collaboratively by a community of contributors, it implements the vast majority of the game’s rules and card interactions, allowing players to experience both casual and competitive gameplay without official clients. The software supports multiple play modes, including single-player against AI opponents, multiplayer sessions, and even adventure-style gameplay modes that expand beyond traditional card duels. Its architecture is highly extensible, enabling developers to script new cards, create custom sets, and modify gameplay mechanics through its API and scripting system. Forge includes tools for deck building, card searching, and automated gameplay assistance, making it accessible to both new players and experienced enthusiasts. -
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LightZero
[NeurIPS 2023 Spotlight] LightZero
LightZero is an efficient, scalable, and open-source framework implementing MuZero, a powerful model-based reinforcement learning algorithm that learns to predict rewards and transitions without explicit environment models. Developed by OpenDILab, LightZero focuses on providing a highly optimized and user-friendly platform for both academic research and industrial applications of MuZero and similar algorithms. -
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RAPTOR
The official implementation of RAPTOR
RAPTOR is a retrieval architecture designed to improve retrieval-augmented generation systems by organizing documents into hierarchical structures that enable more effective context retrieval. Traditional RAG systems typically retrieve small text chunks independently, which can limit a model’s ability to understand broader document context. RAPTOR addresses this limitation by recursively embedding, clustering, and summarizing documents to create a tree-structured hierarchy of information. Each level of the tree represents summaries at different levels of abstraction, allowing retrieval to operate at both detailed and high-level conceptual layers. During inference, the system can navigate this hierarchical representation to retrieve information that best matches the user’s query while preserving broader contextual understanding. This approach improves question-answering performance on complex tasks that require reasoning across long documents or multiple sources. -
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Reor Project
Private & local AI personal knowledge management app
Reor is an AI-powered desktop note-taking app: it automatically links related notes, answers questions on your notes, provides semantic search and can generate AI flashcards. Everything is stored locally and you can edit your notes with an Obsidian-like markdown editor. The hypothesis of the project is that AI tools for thought should run models locally by default. Reor stands on the shoulders of the giants Ollama, Transformers.js & LanceDB to enable both LLMs and embedding models to run locally. -
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scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
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KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes
Image restoration toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSR/GAN, SwinIR. -
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Kiro
Kiro is an agentic IDE that works alongside you from prototype
Kiro is an agentic AI-powered integrated development environment and command-line tool designed to help developers move from early-stage prototypes to production-ready software through structured, spec-driven development. Unlike traditional AI coding assistants that rely heavily on ad hoc prompts, Kiro transforms natural language input into formalized requirements, architectural plans, and step-by-step implementation tasks, ensuring clarity and maintainability throughout the development lifecycle. It introduces the concept of “specs,” which act as executable documentation that defines system behavior, constraints, and acceptance criteria, allowing developers to guide AI agents more precisely. Kiro also incorporates event-driven automation through “hooks,” enabling AI agents to perform tasks such as generating tests, updating documentation, or optimizing code whenever specific development events occur. -
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OpenClaw
Your own personal AI assistant. Any OS. Any Platform.
OpenClaw (formerly Clawdbot/Moltbot) is an open-source, self-hosted autonomous AI assistant designed to run on user-controlled hardware and bridge conversational natural language with real-world task execution, effectively acting as a proactive digital assistant rather than a reactive chatbot. It lets you send instructions through familiar messaging platforms like WhatsApp, Telegram, Discord, Slack, Signal, iMessage, and more, and then interprets those instructions to carry out actions such as managing calendars, sending emails or messages, browsing the web, executing system commands, and coordinating workflows across services — all while maintaining long-term memory and context across sessions. Because it runs locally or on infrastructure you choose (like a personal computer, VPS, or Raspberry Pi), OpenClaw emphasizes data ownership, privacy, and full transparency into how your instructions are handled and what actions are taken, giving users autonomy over their AI workflows. -
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ROOT
Analyzing, storing and visualizing big data, scientifically
ROOT is a unified software package for the storage, processing, and analysis of scientific data: from its acquisition to the final visualization in the form of highly customizable, publication-ready plots. It is reliable, performant and well supported, easy to use and obtain, and strives to maximize the quantity and impact of scientific results obtained per unit cost, both of human effort and computing resources. ROOT provides a very efficient storage system for data models, that demonstrated to scale at the Large Hadron Collider experiments: Exabytes of scientific data are written in columnar ROOT format. ROOT comes with histogramming capabilities in an arbitrary number of dimensions, curve fitting, statistical modeling, and minimization, to allow the easy setup of a data analysis system that can query and process the data interactively or in batch mode, as well as a general parallel processing framework, RDataFrame, that can considerably speed up an analysis. -
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Voice-Pro
Comprehensive Gradio WebUI for audio processing
Voice-Pro is the best gradio WebUI for transcription, translation and text-to-speech. It can be easily installed with one click. Create a virtual environment using Miniconda, running completely separate from the Windows system (fully portable). Supports real-time transcription and translation, as well as batch mode. -
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Z-Image
Image generation model with single-stream diffusion transformer
Z-Image is an efficient, open-source image generation foundation model built to make high-quality image synthesis more accessible. With just 6 billion parameters — far fewer than many large-scale models — it uses a novel “single-stream diffusion Transformer” architecture to deliver photorealistic image generation, demonstrating that excellence does not always require extremely large model sizes. The project includes several variants: Z-Image-Turbo, a distilled version optimized for speed and low resource consumption; Z-Image-Base, the full-capacity foundation model; and Z-Image-Edit, fine-tuned for image editing tasks. Despite its compact size, Z-Image produces outputs that closely rival those from much larger models — including strong rendering of bilingual (English and Chinese) text inside images, accurate prompt adherence, and good layout and composition. -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any cluster. Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more. -
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ChatGPT DAN
ChatGPT DAN, Jailbreaks prompt
ChatGPT_DAN is a repository that explores prompt engineering techniques designed to alter or expand the behavior of AI language models by crafting specific instructions that influence how responses are generated. The project focuses on creating and refining prompts that encourage models to operate outside of their default constraints, often simulating alternative personas or unrestricted modes of interaction. It serves as a collection of experimental prompt patterns that demonstrate how language models can be guided through carefully structured input. The repository is often used as a learning resource for understanding the mechanics of prompt design and model behavior manipulation. It highlights both the capabilities and limitations of large language models when exposed to unconventional instructions. The project reflects ongoing experimentation within the AI community around control, alignment, and creative use of generative systems. -
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GitHub MCP Server
GitHub's official MCP Server
The GitHub MCP Server exposes GitHub as a Model Context Protocol server so AI assistants can safely act on repos, issues, pull requests, gists, and more through a consistent tool interface. It’s designed to run locally or remotely and then be attached to MCP-capable clients (for example, Copilot Chat) so an LLM can search code, open files, create branches, draft PRs, label or triage issues, and query metadata without hard-coding GitHub APIs. The server defines tools and resources with fine-grained scopes, leaning on GitHub’s auth to enforce least privilege and auditable access. It supports both stdio and HTTP transports, enabling IDE and headless integrations, and adopts common MCP behaviors like prompts, schemas, and tool definitions to keep agent calls predictable. Documentation covers setup, tokens, and client configuration, highlighting native editor integrations. Its design goal is to give AI agents first-class, governed access to GitHub workflows. -
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Hunyuan3D-2.1
From Images to High-Fidelity 3D Assets
Hunyuan3D-2.1 is Tencent Hunyuan’s advanced 3D asset generation system that produces high-fidelity 3D models with Physically Based Rendering (PBR) textures. It is fully open-source with released model weights, training, and inference code. It improves on prior versions by using a PBR texture pipeline (enabling realistic material effects like reflections and subsurface scattering) and allowing community fine-tuning and extension. It supports both shape generation (mesh geometry) and texture generation modules. Physically Based Rendering texture synthesis to model realistic material effects, including reflections, subsurface scattering, etc. Cross-platform support (MacOS, Windows, Linux) via Python / PyTorch, including diffusers-style APIs. -
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Meetily
Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper
This project is a privacy-first AI meeting assistant that captures meeting audio, produces real-time transcripts, and generates summaries while keeping processing entirely on your own machine or infrastructure. It’s built for organizations that want meeting intelligence without sending recordings or transcripts to third-party cloud services, which helps address compliance and data sovereignty requirements. The app supports live transcription with local model options (including Whisper- and Parakeet-based workflows) and presents the transcript as the meeting happens, making it useful both for note-taking and accessibility. After or during the session, it can produce structured, AI-generated summaries, and it’s designed to be flexible about where that summarization comes from, supporting local providers as well as external endpoints when allowed by policy. -
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Mistral Vibe CLI
Minimal CLI coding agent by Mistral
Mistral Vibe is an AI-powered “vibe-coding” command-line interface (CLI) and coding-assistant framework built by Mistral AI to let developers write, refactor, search, and manage code through natural language and context-aware automation, rather than manual typing only. It aims to take developers out of repetitive boilerplate and let them stay “in the flow”: you can ask the tool to generate functions, refactor code, search across the codebase, manipulate files, commit changes via Git, or run commands — all from a unified CLI interface. Behind the scenes, it leverages Mistral’s coding-optimized LLM stack (including models tuned for code understanding and generation), with project-wide context awareness: it scans your file structure, Git status, and recent history to inform suggestions so that generated code aligns with existing context.
