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Showing 7374 open source projects for "linux-debian"
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Bootstrap Your Own Latent (BYOL)
Usable Implementation of "Bootstrap Your Own Latent" self-supervised
Practical implementation of an astoundingly simple method for self-supervised learning that achieves a new state-of-the-art (surpassing SimCLR) without contrastive learning and having to designate negative pairs. This repository offers a module that one can easily wrap any image-based neural network (residual network, discriminator, policy network) to immediately start benefitting from unlabelled image data. There is now new evidence that batch normalization is key to making this technique... -
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Determined
Determined, deep learning training platform
The fastest and easiest way to build deep learning models. Distributed training without changing your model code. Determined takes care of provisioning machines, networking, data loading, and fault tolerance. Build more accurate models faster with scalable hyperparameter search, seamlessly orchestrated by Determined. Use state-of-the-art algorithms and explore results with our hyperparameter search visualizations. Interpret your experiment results using the Determined UI and TensorBoard, and... -
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RecBole
A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library. We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. RecBole is developed based on Python and PyTorch for... -
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TensorRT Backend For ONNX
ONNX-TensorRT: TensorRT backend for ONNX
Parses ONNX models for execution with TensorRT. Development on the main branch is for the latest version of TensorRT 8.4.1.5 with full dimensions and dynamic shape support. For previous versions of TensorRT, refer to their respective branches. Building INetwork objects in full dimensions mode with dynamic shape support requires calling the C++ and Python API. Current supported ONNX operators are found in the operator support matrix. For building within docker, we recommend using and setting... -
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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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with... -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference... -
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning... -
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zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt.... -
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Vowpal Wabbit
Machine learning system which pushes the frontier of machine learning
Vowpal Wabbit is a machine learning system that pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state-of-the-art algorithms with performance in mind. The input format for... -
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Venom
Venom is the most complete javascript library for Whatsapp
Venom is a high-performance system developed with JavaScript to create a bot for WhatsApp, support for creating any interaction, such as customer service, media sending, sentence recognition based on artificial intelligence and all types of design architecture for WhatsApp. It's a high-performance alternative API to whatzapp, you can send, text messages, files, images, videos and more. Remember, the API was developed on a platform called RESTful Web services, providing interoperability... -
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AI Chatbot Framework
Python chatbot framework with Natural Language Understanding
Building a chatbot can sound daunting, but it’s totally doable. AI Chatbot Framework is an AI powered conversational dialog interface built in Python. With this tool, it’s easy to create Natural Language conversational scenarios with no coding efforts whatsoever. The smooth UI makes it effortless to create and train conversations to the bot and it continuously gets smarter as it learns from conversations it has with people. AI Chatbot Framework can live on any channel of your choice (such as... -
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agentmemory
#1 Persistent memory for AI coding agents
agentmemory is a persistent memory server for AI coding agents that captures project context, past decisions, bugs, preferences, and implementation details so users do not have to re-explain the same information in every session. It works across tools such as Claude Code, Cursor, Gemini CLI, Codex CLI, OpenCode, Cline, and any client that supports MCP or REST APIs. The system automatically records what agents do, compresses that activity into searchable memory, and injects relevant context... -
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birdclaw
Stores all your tweets nicely claw-able for agents
birdclaw is a local-first Twitter workspace designed for archiving, organizing, searching, and interacting with Twitter or X content in a structured environment. The project combines a web application and command-line interface to help users import personal archives, cache live content locally, and manage replies or engagement workflows from a centralized system. Its architecture prioritizes data ownership and offline accessibility, allowing users to retain searchable records of posts,... -
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Agentic Inbox
A self-hosted email client with an AI agent, running entirely on Cloud
Agentic Inbox is a self-hosted email client that integrates an AI agent directly into the inbox experience, enabling automated reading, organization, and drafting of emails. It runs entirely on Cloudflare Workers, using serverless infrastructure to manage incoming and outgoing messages without relying on external email services. Each mailbox is isolated with its own storage, ensuring data separation and security while maintaining performance. The system supports full email functionality,... -
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Pro Workflow
Claude Code learns from your corrections: self-correcting memory
Pro Workflow is a productivity framework for Claude Code that introduces self-improving workflows through memory, context engineering, and structured agent orchestration. The system learns from user corrections over time, storing feedback and refining its behavior across sessions to improve accuracy and efficiency. It supports advanced development setups such as parallel worktrees, enabling multiple tasks to be handled simultaneously without interference. The framework includes a collection... -
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A2UI
A Protocol for Agent-Driven Interfaces
A2UI (Agent-to-User Interface) is an open-source protocol and set of libraries developed by Google to enable AI agents to generate rich, interactive user interfaces instead of relying solely on text-based responses. The project introduces a declarative JSON format that allows agents to describe the structure, components, and behavior of a user interface, which is then rendered by the client using its own native components. This approach separates UI intent from UI implementation, making it... -
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Evo 2
Genome modeling and design across all domains of life
Evo 2 is a DNA language model system designed for long-context genome modeling and biological sequence design across all domains of life. The project models DNA at single-nucleotide resolution and supports context windows of up to one million base pairs, which places it in a class of models built for very large genomic reasoning tasks. According to the repository, it uses the StripedHyena 2 architecture, was pretrained with Savanna, and was trained autoregressively on the OpenGenome2 dataset... -
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Evolver
The GEP-Powered Self-Evolution Engine for AI Agents
Evolver is the core engine behind EvoMap and is positioned as a self-evolution system for AI agents rather than a conventional application framework. Its purpose is to turn isolated prompt adjustments into reusable, auditable evolution assets, giving agent teams a more structured way to improve behavior over time. The project uses a protocol-constrained approach centered on concepts such as genes, capsules, and events, which are stored as structured assets and selected through signal... -
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Autoskills
One command. Your entire AI skill stack. Installed
The Autoskills project is a developer tool that automates the installation of AI agent skills based on a project’s technology stack. It operates through a simple command-line interface that scans configuration files such as package.json and build scripts to detect the frameworks, languages, and tools used in a project. Once the stack is identified, it automatically installs a curated set of AI skills tailored to those technologies, significantly reducing setup time for AI-assisted... -
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MiniCPM4.1
Achieving 3+ generation speedup on reasoning tasks
MiniCPM4.1 is an enhanced iteration of the MiniCPM4 architecture, introducing improvements in reasoning capabilities, inference speed, and hybrid operation modes that allow dynamic switching between deep reasoning and standard generation. It builds upon the same efficiency-focused philosophy but further optimizes decoding performance, achieving substantial speed gains in reasoning-intensive tasks while maintaining high-quality outputs. One of its key innovations is the hybrid reasoning mode,... -
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MiniCPM4
Ultra-Efficient LLMs on End Device
MiniCPM4 is part of the MiniCPM family of ultra-efficient large language models designed specifically for high performance on edge devices and resource-constrained environments. Unlike traditional large-scale models that require extensive computational resources, MiniCPM4 focuses on delivering competitive reasoning and language capabilities while maintaining significantly lower latency and higher efficiency. It achieves this through optimized architectures, scalable training strategies, and... -
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49 Agents IDE
Open-source 2D IDE for managing AI agents in native CLIs
49Agents is an open-source “agentic IDE” that reimagines how developers interact with multiple AI agents, terminals, and development environments by placing everything onto a single infinite, zoomable canvas. Instead of relying on traditional tab-based workflows, it provides a spatial interface where terminals, editors, Git views, and monitoring tools coexist as movable panes, enabling users to manage complex multi-agent systems visually and intuitively. The platform is designed to work... -
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Pika Skills
A collection of open-source skills for AI coding agents
Pika Skills is an open-source framework designed to extend the capabilities of AI coding agents by introducing modular, reusable “skills” that can be dynamically invoked during development workflows. Each skill acts as a self-contained unit composed of structured instructions, executable scripts, and dependency definitions, enabling agents to autonomously perform complex tasks without requiring manual configuration or orchestration. The system is tightly integrated with the Pika Developer... -
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BMad Method
Breakthrough Method for Agile Ai Driven Development
BMad Method is a comprehensive AI-driven software development framework that structures the entire lifecycle of building applications through coordinated agent workflows and agile methodologies. It transforms AI from a reactive assistant into a structured team of specialized roles such as product manager, architect, developer, and QA, each operating within predefined workflows. The system guides users through phases including analysis, planning, solution design, and implementation, ensuring... -
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AgentField
Build and run AI agents like microservices
AgentField is an open-source control plane designed to run AI agents as production-grade backend services, applying cloud-native principles similar to Kubernetes to the world of autonomous software. Instead of treating agents as isolated scripts or prototypes, the system elevates them to first-class infrastructure components that can be deployed, orchestrated, and managed at scale across distributed environments. Developers define agents as typed functions, and the platform automatically...
