Open Source Linux Artificial Intelligence Software - Page 34
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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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Claude Code Best V3
The original Claude Code is runnable and buildable
Claude Code Best V3 is an agentic coding system that enables developers to interact with their codebases through natural language while automating repetitive and complex development tasks. It operates primarily in terminal or IDE environments, where it can analyze code, explain logic, and perform actions such as refactoring, debugging, and managing version control. The system is designed to understand the structure and intent of a codebase, allowing it to provide context-aware assistance rather than generic suggestions. It supports plugin-based extensions that enhance its capabilities with specialized commands and workflows. The architecture emphasizes productivity, enabling developers to offload routine tasks and focus on higher-level problem-solving. It also integrates with existing development tools, making it adaptable to different workflows and environments. -
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Claude Codex Settings
My personal Claude Code and OpenAI Codex setup
Claude Codex Settings is a configuration-focused repository that provides curated settings, prompts, and workflow optimizations for improving AI-assisted coding environments. It is designed to help developers fine-tune how Claude and similar models behave within coding workflows, ensuring more consistent and high-quality outputs. The project emphasizes practical usability, offering ready-to-use configurations that can be directly integrated into development environments. It also includes guidelines for structuring prompts, managing context, and optimizing interactions with AI systems. The repository serves as both a toolkit and a reference for improving developer productivity when working with AI assistants. It is particularly useful for users who want to standardize their workflows and reduce variability in results. Overall, Claude Codex Settings acts as a configuration layer that enhances the effectiveness of AI coding tools. -
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Cody
Type less, code more: Cody is an AI code assistant
Cody is an AI coding assistant that uses advanced search and codebase context to help you understand, write, and fix code faster. Generate code on demand using AI. Cody also unblocks you when you’re jumping into new projects or trying to understand legacy code. Run Cody's one-click prompts or create your own custom prompts to execute AI workflows. Cody generates single lines, or whole functions, in any programming language, configuration file, or documentation. Cody uses your code graph plus Code Search to autocomplete, explain, and edit your code with additional context. Cody supports the latest LLMs including Claude 3.5, GPT-4o, Gemini 1.5, and Mixtral-8x7B. You can also bring your own LLM key with Amazon Bedrock and Azure OpenAI. -
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CubeSandbox
Instant, Concurrent, Secure & Lightweight Sandbox for AI Agents
CubeSandbox is a lightweight, high-performance sandboxing environment designed to securely run AI agents and untrusted code in isolated containers. Built in Rust, it focuses on enabling concurrent execution while maintaining strong isolation and low overhead. The system is optimized for scenarios where multiple AI agents need to execute tasks in parallel without compromising system integrity. It provides fast startup times and efficient resource management, making it suitable for large-scale agent orchestration. CubeSandbox integrates well with cloud-native workflows and modern infrastructure pipelines. Its design prioritizes security, concurrency, and performance in AI-driven environments. It is particularly useful for agent frameworks requiring safe execution of generated code. -
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CyberStrikeAI
CyberStrikeAI is an AI-native security testing platform built in Go
CyberStrikeAI is an AI-native security testing platform built in Go that brings autonomous penetration testing, vulnerability discovery, and attack chain analysis into a unified interface. The platform integrates over 100 security tools out of the box and pairs them with an intelligent orchestration engine that can be directed via natural language or policy definitions, allowing users to automate reconnaissance, scanning, exploitation, and reporting without manual sequencing of tools. It supports role-based testing, letting teams define security roles with tailored tool access and prompts, and includes a skills system that encapsulates specialized testing strategies that the AI can incorporate into its planning. Through comprehensive lifecycle management, results are tracked, aggregated, and visualized, with support for versioned persistence, search, and risk severity scoring. -
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DBpedia Spotlight
DBpedia Spotlight is a tool for automatically annotating
It is a tool for automatically annotating mentions of DBpedia resources in text, providing a solution for linking unstructured information sources to the Linked Open Data cloud through DBpedia. With a four step approach, DBpedia Spotlight performs named entity extraction, including entity detection and name resolution. It can also be used for named entity recognition, amongst other information extraction tasks. Empower the user experience reusing, interlinking and making semantic queries among high-quality open datasets, extracting meaning from unstructured data. -
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DS2API
A lightweight, high-performance full-stack middleware
DS2API is a developer tool that provides an interface for interacting programmatically with Dark Souls II game data and systems. It exposes game functionality through APIs, enabling developers to build tools, mods, or integrations on top of the game. The project focuses on reverse engineering and structuring game data into usable endpoints. It allows access to in-game entities, player states, and events for analysis or modification. The API can be used for automation, experimentation, or extending gameplay experiences. It is particularly useful for modders and developers interested in game internals. The design emphasizes flexibility and extensibility. -
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DeepSeek Coder
DeepSeek Coder: Let the Code Write Itself
DeepSeek-Coder is a series of code-specialized language models designed to generate, complete, and infill code (and mixed code + natural language) with high fluency in both English and Chinese. The models are trained from scratch on a massive corpus (~2 trillion tokens), of which about 87% is code and 13% is natural language. This dataset covers project-level code structure (not just line-by-line snippets), using a large context window (e.g. 16K) and a secondary fill-in-the-blank objective to encourage better contextual completions and infilling. Multiple sizes of the model are offered (e.g. 1B, 5.7B, 6.7B, 33B) so users can trade off inference cost vs capability. The repo provides model weights, documentation on training setup, evaluation results on common benchmarks (HumanEval, MultiPL-E, APPS, etc.), and inference tools. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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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 reproduce experiments with artifact tracking. Deploy your model using Determined's built-in model registry. Easily share on-premise or cloud GPUs with your team. Determined’s cluster scheduling offers first-class support for deep learning and seamless spot instance support. Check out examples of how you can use Determined to train popular deep learning models at scale. -
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Dickinson
Text generation language
Dickinson is a text-generation language. You can try out the language on the web without installing anything. Binaries for some platforms are available on the releases page. There is an install script that will try to download the right release for your computer. -
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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 tools into cohesive pipelines. The framework supports the creation of multi-agent systems where different AI agents collaborate to solve tasks such as information retrieval, document analysis, or automated decision making. Dynamiq also includes built-in support for retrieval-augmented generation pipelines that allow models to access external documents and knowledge bases during inference. -
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EFAK-AI
A AI-Driven, Distributed and high-performance monitoring system
EFAK (Eagle For Apache Kafka) is an open-source monitoring and management platform designed to provide comprehensive visibility and operational control over Apache Kafka clusters through a unified web interface. The project focuses on simplifying Kafka administration by offering real-time insights into cluster health, performance metrics, and consumer activity, allowing engineers to quickly diagnose issues and optimize system behavior. It integrates advanced features such as intelligent monitoring, alerting, and automated analysis, enabling proactive management rather than reactive troubleshooting. The platform supports multiple Kafka clusters simultaneously, making it suitable for enterprise environments with distributed data pipelines. It also incorporates AI-driven capabilities that assist with diagnostics, performance recommendations, and conversational querying of system data. -
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Easy DataSet
A powerful tool for creating datasets for LLM fine-tuning
Easy DataSet is a comprehensive open-source tool designed to make creating high-quality datasets for large language model fine-tuning, retrieval-augmented generation (RAG), and evaluation as easy and automated as possible by providing intuitive interfaces and powerful parsing, segmentation, and labeling tools. It supports ingesting domain-specific documents in a wide range of formats — including PDF, Markdown, DOCX, EPUB, and plain text — and can intelligently segment, clean, and structure content into rich datasets tailored for downstream LLM training needs. The system includes automated question-generation capabilities, hierarchical label trees, and answer generation pipelines that use LLM APIs to produce coherent paired data with customizable templates. Beyond dataset creation, Easy-dataset also provides a built-in evaluation system with model testing and blind-test features, helping teams validate model performance using curated test sets. -
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Exposure
Learning infinite-resolution image processing with GAN and RL
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. Moreover, jpg and most pngs assume an sRGB color space, which contains a roughly 1/2.2 Gamma correction, making the data distribution different from training images (which are linear). Exposure is just a prototype (proof-of-concept) of our latest research, and there are definitely a lot of engineering efforts required to make it suitable for a real product. -
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FastChat
Open platform for training, serving, and evaluating language models
FastChat is an open platform for training, serving, and evaluating large language model-based chatbots. If you do not have enough memory, you can enable 8-bit compression by adding --load-8bit to the commands above. This can reduce memory usage by around half with slightly degraded model quality. It is compatible with the CPU, GPU, and Metal backend. Vicuna-13B with 8-bit compression can run on a single NVIDIA 3090/4080/T4/V100(16GB) GPU. In addition to that, you can add --cpu-offloading to commands above to offload weights that don't fit on your GPU onto the CPU memory. This requires 8-bit compression to be enabled and the bitsandbytes package to be installed, which is only available on linux operating systems. -
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Forge Code
AI enabled pair programmer for Claude, GPT, O Series, Grok, Deepseek
Forge is a modern, open-source tool that brings AI-powered code assistance directly into your terminal workflow, effectively turning your shell into a “pair programmer”, without ever leaving your development environment. Written in Rust (with a command-line interface), Forge integrates with your existing shell (bash, zsh, fish, etc.) or IDE-agnostic workflows, allowing you to interact with your codebase, command-line tools, and version control as usual, but with the added support of large language models (LLMs) to help with code generation, refactoring, bug fixing, code review, and even design advice. Rather than requiring a separate UI or web-based IDE, Forge respects the developer’s existing habits and setups, and keeps all operations local, ensuring your code doesn’t get sent to unknown external services — a strong point for privacy and security. It supports many model providers (e.g. GPT, Claude, Grok, and others) via API keys. -
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Freqtrade
Free, open source crypto trading bot
Freqtrade is a free and open-source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or WebUI. It contains backtesting, plotting, and money management tools as well as strategy optimization by machine learning. Always start by running a trading bot in Dry-run and do not engage money before you understand how it works and what profit/loss you should expect. We strongly recommend you have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms, and techniques implemented in it. Write your strategy in python, using pandas. Example strategies to inspire you are available in the strategy repository. Download historical data of the exchange and the markets you may want to trade with. Find the best parameters for your strategy using hyper optimization which employs machining learning methods. -
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Genv
GPU environment management and cluster orchestration
Genv is an open-source environment and cluster management system for GPUs. Genv lets you easily control, configure, monitor and enforce the GPU resources that you are using in a GPU machine or cluster. It is intended to ease up the process of GPU allocation for data scientists without code changes. -
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Godot RL Agents
An Open Source package that allows video game creators
godot_rl_agents is a reinforcement learning integration for the Godot game engine. It allows AI agents to learn how to interact with and play Godot-based games using RL algorithms. The toolkit bridges Godot with Python-based RL libraries like Stable-Baselines3, making it possible to create complex and visually rich RL environments natively in Godot. -
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Gp.nvim
Gp.nvim (GPT prompt) Neovim AI plugin
Gp.nvim (GPT prompt) Neovim AI plugin: ChatGPT sessions & Instructable text/code operations & Speech to text [OpenAI, Ollama, Anthropic, ..] The goal is to extend Neovim with the power of GPT models in a simple unobtrusive extensible way. Trying to keep things as native as possible - reusing and integrating well with the natural features of (Neo)vim. -
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Grok Build
SpaceXAI's coding agent harness and TUI. Fullscreen, mouse interactive
Grok Build is a terminal-based AI coding agent that operates through a full-screen text interface. It can inspect repositories, edit files, run shell commands, search the web, and coordinate longer development tasks. Users can work interactively, invoke it headlessly in scripts or CI, or connect it to editors through the Agent Client Protocol. The Rust codebase includes separate components for the interface, agent runtime, tools, workspace control, configuration, and sandboxing. It also supports MCP servers, skills, plugins, hooks, themes, checkpoints, and slash commands. Prebuilt binaries are available for macOS, Linux, and Windows, while the source can be compiled with the pinned Rust toolchain. -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used across DeepMind. It preserves Sonnet’s module-based programming model for state management while retaining access to JAX’s function transformations. Haiku can be expected to compose with other libraries and work well with the rest of JAX. Similar to Sonnet modules, Haiku modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. -
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Hermes Web UI
The best way to use Hermes Agent from the web or from your phone
Hermes WebUI is a browser-based interface for interacting with the Hermes autonomous agent, providing full feature parity with its command-line experience. It offers a clean, multi-panel layout that includes chat interaction, session management, and workspace file browsing. The interface allows users to manage agent sessions, configure models, and interact with persistent memory systems directly from a web environment. It is built using simple technologies like Python and vanilla JavaScript, avoiding complex frontend frameworks. The UI supports real-time interaction, context tracking, and visualization of token usage. It connects to a self-hosted agent that continuously learns and evolves over time. The project emphasizes usability, accessibility, and seamless integration with existing workflows. -
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ISLR-python
An Introduction to Statistical Learning
ISLR-python is an educational repository that provides Python implementations and notebooks corresponding to examples and exercises from the book An Introduction to Statistical Learning. The project recreates tables, figures, and laboratory exercises originally presented in the book so that readers can explore the concepts using Python rather than the original R environment. The repository includes Jupyter notebooks demonstrating statistical learning methods such as linear regression, classification algorithms, resampling methods, and model evaluation techniques. These notebooks combine theoretical explanations with practical coding exercises that allow users to reproduce the analyses described in the book. The datasets used in the book are also included so that users can run experiments directly within the provided notebooks. By translating the statistical learning material into Python code, the repository makes the book’s concepts accessible to a wider community of Python users.
