Search Results for "gnu/linux" - Page 30
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Showing 7390 open source projects for "gnu/linux"
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Dynamiq
An orchestration framework for agentic AI and LLM applications
Dynamiq is an open-source orchestration framework designed to streamline the development of generative AI applications that rely on large language models and autonomous agents. The framework focuses on simplifying the creation of complex AI workflows that involve multiple agents, retrieval systems, and reasoning steps. Instead of building each component manually, developers can use Dynamiq’s structured APIs and modular architecture to connect language models, vector databases, and external... -
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AI as Workspace
An elegant AI chat client. Full-featured, lightweight
AI as Workspace, short for AI as Workspace, is an open-source AI client application that provides a unified interface for interacting with multiple large language models and AI tools within a single workspace environment. The platform is designed as a lightweight yet powerful desktop or web application that organizes AI interactions through structured workspaces. Instead of managing individual chat sessions separately, users can group conversations, artifacts, and tasks within customizable... -
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oterm
the terminal client for Ollama
Oterm is an open-source terminal client designed to provide a lightweight command-line interface for interacting with large language models through the Ollama ecosystem. The tool allows users to chat with local AI models directly from the terminal without needing a graphical interface or web application. Its interface is designed to be simple and intuitive, enabling developers to launch conversations quickly using a single command. Oterm supports persistent chat sessions that store... -
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LangGraph.js
Framework to build resilient language agents as graphs
LangGraphJS is a JavaScript framework designed to build stateful AI applications and autonomous agents using graph-based execution models. Developed as part of the LangChain ecosystem, the framework allows developers to represent complex AI workflows as graphs where nodes represent tasks and edges define the flow of execution. This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. LangGraphJS supports... -
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Superset LLM
Run an army of Claude Code, Codex, etc. on your machine
Superset is a development environment and terminal-based platform designed to orchestrate multiple AI coding agents simultaneously within a single workspace. The tool enables developers to run many autonomous coding agents in parallel without the typical overhead of manually managing multiple terminals, repositories, or branches. Each agent task is isolated in its own Git worktree, ensuring that code changes from different agents do not interfere with each other while allowing developers to... -
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BAML
The AI framework that adds the engineering to prompt engineering
BAML is an open-source framework and domain-specific language designed to bring structured engineering practices to prompt development for large language model applications. Instead of treating prompts as unstructured text, BAML introduces a schema-driven approach where prompts are defined as typed functions with explicit inputs and outputs. This design allows developers to treat language model interactions as predictable software components rather than ad-hoc prompt strings. The framework... -
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NVIDIA Earth2Studio
Open-source deep-learning framework
NVIDIA Earth2Studio is an open-source Python package and framework designed to accelerate the development and deployment of AI-driven weather and climate science workflows. It provides a unified API that lets researchers, data scientists, and engineers build complex forecasting and analysis pipelines by combining modular prognostic and diagnostic AI models with a diverse range of real-world data sources such as global forecast systems, reanalysis datasets, and satellite feeds. The toolkit... -
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Step 3.5 Flash
Fast, Sharp & Reliable Agentic Intelligence
Step 3.5 Flash is a cutting-edge, open-source large language model developed by StepFun-AI that pushes the frontier of efficient reasoning and “agentic” intelligence in a way that makes powerful AI accessible beyond proprietary black boxes. Unlike dense models that activate all their parameters for every token, Step 3.5 Flash uses a sparse Mixture-of-Experts (MoE) architecture that selectively engages only about 11 billion of its roughly 196 billion total parameters per token, delivering... -
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Get Shit Done
A light-weight and powerful meta-prompting, context engineering
Get Shit Done is a high-impact, open-source meta-prompting and spec-driven development system designed to streamline building software with AI assistants like Claude Code, OpenCode, and Gemini CLI. It solves “context rot” — the degradation of AI quality as a chat session grows — by structuring your idea into precise, context-engineered steps that are researched, scoped, planned, executed, and verified with clear commands and outputs instead of ad-hoc prompts. The project emphasizes... -
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LuxTTS
A high-quality rapid TTS voice cloning model
LuxTTS is an open-source text-to-speech (TTS) system focused on delivering high-quality, rapid voice synthesis and voice cloning that runs extremely fast and efficiently on consumer hardware. It implements a lightweight architecture based on ZipVoice and optimized sampling techniques so that it can generate speech at speeds up to roughly 150 times real-time on a single GPU and faster than real-time on CPU, all while producing audio at high fidelity with 48 kHz quality. The project supports... -
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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),... -
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IDA Pro MCP
MCP Server for IDA Pro
The IDA Pro MCP Server is a Model Context Protocol (MCP) server designed to integrate with IDA Pro, a popular disassembler and debugger. It enables AI assistants to interact with IDA Pro, facilitating tasks such as code analysis and reverse engineering. -
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E2B Infra
Infrastructure for AI code interpreting that's powering E2B
E2B Infra is an infrastructure management tool that simplifies the deployment and scaling of applications across cloud environments, focusing on automation and efficiency. -
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OpenFold
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction
OpenFold carefully reproduces (almost) all of the features of the original open source inference code (v2.0.1). The sole exception is model ensembling, which fared poorly in DeepMind's own ablation testing and is being phased out in future DeepMind experiments. It is omitted here for the sake of reducing clutter. In cases where the Nature paper differs from the source, we always defer to the latter. OpenFold is trainable in full precision, half precision, or bfloat16 with or without... -
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CARLA Simulator
Open-source simulator for autonomous driving research.
CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites, environmental conditions, full control of all static and dynamic actors, maps generation and much more. Multiple clients... -
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Interpretable machine learning
Book about interpretable machine learning
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the... -
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MCP Bridge
A middleware to provide an openAI compatible endpoint
MCP-Bridge serves as a middleware that connects the OpenAI API with MCP tools, allowing developers to utilize MCP functionalities through the OpenAI API interface. It provides endpoints compatible with OpenAI, facilitating seamless integration and enabling the use of MCP tools without requiring explicit MCP support in clients. -
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Kheish
Kheish: A multi-role LLM agent for tasks like code auditing
Kheish is a framework designed for cybersecurity professionals to automate penetration testing tasks, providing tools to streamline security assessments. -
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Moltis
A Rust-native claw you can trust
Moltis is an open-source personal AI assistant platform written in Rust that is designed to run as a fully self-hosted, local-first agent environment. It compiles the entire assistant stack, including the web interface, model routing, memory, and tools, into a single self-contained binary with no external runtime dependencies. The system supports multiple large language model providers alongside local models, enabling users to maintain privacy while still accessing cloud capabilities when... -
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Microsoft Agent Skills
Skills, MCP servers, Custom Agents, Agents.md for SDKs
Microsoft Agent Skills is an actively maintained repository of skills, custom agents, templates, and MCP configuration files designed to extend AI coding assistants with deep knowledge about Azure SDKs and Microsoft AI Foundry services. The project bundles over a hundred domain-specific skills that teach AI agents how to perform tasks like Azure resource provisioning, SDK usage patterns, infrastructure setup, and common DevOps workflows, bridging the gap between agent reasoning and... -
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Matcha-TTS
A fast TTS architecture with conditional flow matching
Matcha-TTS is a non-autoregressive neural text-to-speech architecture that uses conditional flow matching to generate speech quickly while maintaining natural quality. It models speech as an ODE-based generative process, and conditional flow matching lets it reach high-quality audio in only a few synthesis steps, which greatly reduces latency compared to score-matching diffusion approaches. The model is fully probabilistic, so it can generate diverse realizations of the same text while still... -
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Depth Pro
Sharp Monocular Metric Depth in Less Than a Second
Depth Pro is a foundation model for zero-shot metric monocular depth estimation, producing sharp, high-frequency depth maps with absolute scale from a single image. Unlike many prior approaches, it does not require camera intrinsics or extra metadata, yet still outputs metric depth suitable for downstream 3D tasks. Apple highlights both accuracy and speed: the model can synthesize a ~2.25-megapixel depth map in around 0.3 seconds on a standard GPU, enabling near real-time applications. The... -
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Gymnasium
An API standard for single-agent reinforcement learning environments
Gymnasium is a fork of OpenAI Gym, maintained by the Farama Foundation, that provides a standardized API for reinforcement learning environments. It improves upon Gym with better support, maintenance, and additional features while maintaining backward compatibility. -
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Browser Use
Make websites accessible for AI agents
Browser Use is an AI-powered browser automation framework designed to let agents interact with websites just like humans do. It enables developers and AI systems to perform complex online tasks such as form filling, data extraction, and navigation through natural language instructions. Built with Python and compatible with modern LLMs, it integrates seamlessly with tools like ChatBrowserUse, Google Gemini, and Anthropic models. The platform supports both open-source deployment and a fully... -
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Manifest
🦞 Take control of your OpenClaw costs
Manifest is an open-source OpenClaw plugin designed to help users take control of their LLM costs through intelligent routing and real-time observability. Instead of sending every request to the same large model, Manifest intercepts each query and evaluates it using a 23-dimension scoring algorithm in under 2 milliseconds. It then routes the request to the most cost-effective and suitable model, potentially reducing costs by up to 90%. The platform includes a real-time dashboard that...
