Search Results for "operating" - Page 5
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Showing 433 open source projects for "operating"
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AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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MusicGPT
Generate music based on natural language prompts using LLMs
...The software allows users to run advanced music generation systems directly on their own devices without requiring heavy dependencies such as Python or full machine learning frameworks. Instead, it provides a lightweight environment capable of executing music generation models locally on CPUs or GPUs while maintaining strong performance across operating systems including Windows, macOS, and Linux. Users can describe a musical style, mood, or instrumentation using text prompts, and the system produces original audio samples based on those instructions. The application currently integrates with models such as MusicGen and is designed to support additional models transparently in the future. ... -
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xiaogpt
Play ChatGPT and other LLM with Xiaomi AI Speaker
...The project works by listening for user interactions, forwarding prompts to supported model providers, and sending generated responses back through the speaker. It supports multiple operating modes, wake-word styles, and model backends depending on the user’s setup. The tool is aimed at hobbyists and technical users who want to extend smart speakers with more flexible AI behavior. It is especially useful for experimenting with voice-controlled assistants, home automation ideas, and custom LLM interactions through existing Xiaomi hardware. -
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DeepSeek V4
Description of the special control instructions for DeepSeek-V4
...Rather than providing a conventional application, it acts as a practical prompt-control reference for users who want to influence the model’s thinking-mode style during roleplay sessions. It describes three operating styles: default behavior, role immersion, and pure analysis. Role immersion encourages character-like internal framing, while pure analysis pushes the model toward structured scene planning without dramatic inner monologue. The guide explains how to apply the instructions in the web client and how developers can append the markers programmatically when calling supported DeepSeek V4 API models. ... -
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Machine Learning Engineering Open Book
Machine Learning Engineering Open Book
...Rather than focusing purely on theory, the project emphasizes engineering tradeoffs and production realities that often determine success at scale. It is continuously updated as a knowledge dump, making it especially valuable for engineers operating complex AI systems in the wild. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Wondel.ai Skills
Agent skills for Claude Code and other agentskills-compatible agents
Wondel.ai Skills is a repository that provides modular capability packages for AI agents, particularly those operating within Claude Code and similar ecosystems. Each skill represents a structured set of instructions, workflows, and tools that enable agents to perform specific tasks with expertise. These skills can cover a wide range of domains, including development, automation, and problem-solving, allowing agents to extend their capabilities dynamically. -
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macai
All-in-one native macOS AI chat application
macai is a native macOS AI chat application that consolidates access to multiple AI providers into a single, polished desktop interface. It is built specifically for macOS using Swift and SwiftUI, delivering a lightweight and responsive experience that integrates seamlessly with the operating system. The app supports a wide range of providers, including OpenAI, Anthropic, Google Gemini, xAI, Perplexity, and Ollama, allowing users to switch between local and cloud-based models without changing tools. It includes advanced features such as multimodal capabilities, image generation, search integration, and reasoning workflows, making it more than just a simple chat client. ... -
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. It includes tools for collecting and processing market data from multiple sources, performing statistical and machine learning analysis, and generating trading signals based on quantitative models. The system supports real-time data streaming, allowing strategies to respond to market conditions as they evolve. ... -
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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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OM1
Modular AI runtime for robots
...The project focuses on creating a modular architecture where language models can coordinate with external tools, APIs, and knowledge sources to accomplish multi-step objectives. Instead of operating as simple conversational systems, OM1 agents can plan actions, retrieve information, and execute tasks across different services. The framework integrates reasoning modules, planning strategies, and tool interfaces that allow agents to operate in dynamic environments. Developers can extend the system by connecting new tools, services, or data sources to the agent architecture. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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LMOps
General technology for enabling AI capabilities w/ LLMs and MLLMs
...The initiative also investigates techniques for improving the reliability, scalability, and maintainability of applications powered by large models. By addressing challenges such as prompt engineering, evaluation strategies, and deployment infrastructure, LMOps aims to establish best practices for operating large language model systems in real-world environments. -
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nono
Secure, kernel-enforced sandbox CLI and SDKs for AI agents
nono is an open-source, kernel-enforced capability shell designed to safely run AI agents and other untrusted processes under strict operating system controls. The project addresses a growing security concern: modern coding agents typically execute with full user permissions, which means they can potentially read sensitive files, modify system configurations, or exfiltrate credentials if compromised. nono solves this by applying default-deny sandboxing at the kernel level using technologies such as Landlock on Linux and Seatbelt on macOS, making unauthorized actions structurally impossible rather than merely discouraged. ... -
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SlowFast
Video understanding codebase from FAIR for reproducing video models
SlowFast is a video understanding framework that captures both spatial semantics and temporal dynamics efficiently by processing video frames at two different temporal resolutions. The slow pathway encodes semantic context by sampling frames sparsely, while the fast pathway captures motion and fine temporal cues by operating on densely sampled frames with fewer channels. Together, these two pathways complement each other, allowing the network to model both appearance and motion without excessive computational cost. The architecture is modular and supports tasks like action recognition, temporal localization, and video segmentation, performing strongly on benchmarks like Kinetics and AVA. ... -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
...The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach allows long-running agent sessions to continue operating efficiently without losing critical context. It is especially useful in autonomous workflows where agents accumulate large volumes of interaction history over time. The project aligns with broader strategies in AI systems that balance memory retention with computational constraints. Overall, claw-compactor functions as an infrastructure component that enhances scalability and stability in persistent AI agent environments. -
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OpenShell
OpenShell is the safe, private runtime for autonomous AI agents.
...OpenShell also features a privacy-aware routing system that prevents sensitive information from leaving the sandbox environment. By combining container isolation, policy enforcement, and agent orchestration, OpenShell offers a secure infrastructure for developing and operating AI agents. -
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Interactive Machine Learning Experiments
Interactive Machine Learning experiments
Interactive Machine Learning Experiments is a collection of interactive demonstrations that showcase how various machine learning models can be trained and used in real applications. The project combines Jupyter or Colab notebooks with browser-based visual demos that allow users to see trained models operating in real time. Many experiments involve tasks such as image classification, object detection, gesture recognition, and simple generative models. The models are typically trained in Python using TensorFlow and then exported for interactive demonstrations in a web environment using JavaScript and TensorFlow.js. Because the project focuses on experimentation rather than production systems, it acts as a sandbox where developers can explore machine learning concepts and observe model behavior. ... -
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Memori
SQL-native memory layer enabling persistent context for AI agents
...It provides a memory layer that automatically captures conversations and interactions between users and AI models, allowing systems to retain knowledge across sessions instead of operating statelessly. It extracts structured information such as facts, preferences, rules, and summaries from interactions and stores them in standard SQL databases for later retrieval. By recalling relevant context during future model calls, Memori helps AI agents produce more consistent and context-aware responses while reducing the need to repeatedly provide background information. ... -
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Agent Behavior Monitoring
The open source post-building layer for agents
Agent Behavior Monitoring is an open-source framework designed to monitor, evaluate, and improve the behavior of AI agents operating in real or simulated environments. The system focuses on agent behavior monitoring by collecting interaction data and analyzing how agents perform across different scenarios and tasks. Developers can use the framework to observe agent actions in both online production environments and offline evaluation settings, making it useful for debugging and performance analysis. ... -
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Shell-AI
LangChain powered shell command generator and runner CLI
...This approach improves productivity for developers and system administrators who frequently use terminal environments but may not recall every command variation. The application is designed to work across major operating systems including Linux, macOS, and Windows, making it broadly accessible to different development environments. -
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GPUStack
Performance-optimized AI inference on your GPUs
...Instead of requiring complex orchestration systems such as Kubernetes, GPUStack provides a lightweight environment that automatically selects appropriate inference engines, configures deployment parameters, and schedules workloads across available GPUs. The platform supports GPUs from a wide range of vendors and can run on laptops, workstations, and servers across operating systems such as macOS, Windows, and Linux. It also enables developers to deploy models from common repositories like Hugging Face and access them through APIs similar to cloud-based AI services. -
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Eko
Build Production-ready Agentic Workflow with Natural Language
Eko (Eko Keeps Operating) is a JavaScript framework designed for building production-ready agent-based workflows using natural language commands. It allows developers to create automated agents that can handle complex workflows in both computer and browser environments. With a focus on high development efficiency, Eko simplifies the creation of multi-step workflows, enabling users to integrate and automate tasks across platforms. -
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zhangxuefeng-skill
Zhang Xuefeng's cognitive operating system
zhangxuefeng-skill is an AI agent skill package that distills the decision-making frameworks and practical reasoning style of Chinese educator Zhang Xuefeng into a reusable, executable cognitive system. Rather than functioning as a simple quote collection, it encodes structured heuristics, mental models, and decision logic derived from books, interviews, and real-life case analysis. The skill is designed to be used within AI coding agents such as Claude Code, where it can be invoked to... -
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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. -
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Anyquery
Query anything (GitHub, Notion, +40 more) with SQL and let LLMs
...The platform also supports querying multiple data sources simultaneously and joining them together within a single SQL query, enabling powerful cross-system analysis. In addition to operating as a local query engine, the system can run as a MySQL-compatible server so that traditional database tools can connect to it. -
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handy-ollama
Implement CPU from scratch and play with large model deployments
handy-ollama is an open-source educational project designed to help developers and AI enthusiasts learn how to deploy and run large language models locally using the Ollama platform. The repository serves as a structured tutorial that explains how to install, configure, and use Ollama to run modern language models on personal hardware without requiring advanced infrastructure. A key focus of the project is enabling users to run large models even without GPUs by leveraging optimized CPU-based... -
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MimiClaw
Run OpenClaw on a $5 chip
MimiClaw (from the mimiclaw project) is an edge-AI personal assistant that runs directly on extremely low-cost hardware like an ESP32-S3 microcontroller without a full operating system, Node.js, or cloud backend. By running pure C on a bare-metal chip, MimiClaw brings AI interactions and persistent memory to a tiny USB-powered device you can carry in your pocket. You connect the device to Wi-Fi and chat with it using Telegram, making it a convenient always-on assistant for tasks like reminders, quick lookups, or custom AI interactions. ...
