Open Source Lua Artificial Intelligence Software
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Lua Artificial Intelligence Software
View 17851 business solutionsBrowse free open source Lua Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Lua Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Databend
Cloud-native open source data warehouse for analytics and AI queries
Databend is an open source cloud-native data warehouse designed for large-scale analytics and modern data workloads. Built in Rust, the system focuses on high performance, scalability, and efficient data processing for analytical queries. It is designed with a separation of compute and storage, allowing compute nodes to scale independently while storing data in object storage systems. This architecture enables cost-efficient storage and elastic scaling for workloads that involve large datasets and complex queries. Databend provides a unified engine capable of handling analytics, vector search, and full-text search within a single platform. Databend supports SQL-based workflows and enables real-time data ingestion, transformation, and analysis through streaming and task orchestration features. With its cloud-native design and distributed architecture, Databend can run both as a self-hosted system or within managed environments to power data analytics, AI workloads, and large-scale data. -
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MultiPathNet
A Torch implementation of the object detection network
MultiPathNet is a Torch-7 implementation of the “A MultiPath Network for Object Detection” paper (BMVC 2016), developed by Facebook AI Research. It extends the Fast R-CNN framework by introducing multiple network “paths” to enhance feature extraction and object recognition robustness. The MultiPath architecture incorporates skip connections and multi-scale processing to capture both fine-grained details and high-level context within a single detection pipeline. This results in improved detection accuracy across various object sizes and categories compared to standard single-path architectures. The repository supports training, evaluation, and visualization for object detection tasks on popular datasets such as PASCAL VOC and MS COCO. It provides pre-trained models for VGG, AlexNet, and ResNet backbones, along with integration for SharpMask and DeepMask proposal generators. -
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opencode.nvim
Integrate the opencode AI assistant with Neovim
opencode.nvim is a Neovim plugin that integrates the opencode AI coding assistant directly into the editor, enabling developers to interact with AI agents in a deeply context-aware and workflow-native way. It allows users to send prompts that automatically include relevant editor context such as the current buffer, selected text, diagnostics, and visible content, making AI interactions far more precise and useful during development. The plugin supports a prompt library system, allowing developers to reuse predefined prompts or create custom ones tailored to their workflows. It also enables direct execution of AI-driven actions, such as code modifications or command execution, while giving users full control to review, accept, or reject changes through diff-based interfaces. The system integrates with an in-process Language Server Protocol implementation, enabling familiar features like hover explanations and code actions powered by AI. -
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Kong
The Cloud-Native API Gateway
Kong is a next generation cloud-native API platform for multi-cloud and hybrid organizations. When building for the web, mobile, or Internet of Things, you’ll need a common functionality to run your software, and Kong is that solution. Kong acts as a gateway, connecting microservices requests and APIs natively while also providing load balancing, logging, monitoring, authentication, rate-limiting, and so much more through plugins. Kong is highly extensible as well as platform agnostic, connecting APIs across different environments, platforms and patterns. Achieve architectural freedom with Kong today. -
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KoboldAI
Your gateway to GPT writing
This is a browser-based front-end for AI-assisted writing with multiple local & remote AI models. It offers the standard array of tools, including Memory, Author's Note, World Info, Save & Load, adjustable AI settings, formatting options, and the ability to import existing AI Dungeon adventures. You can also turn on Adventure mode and play the game like AI Dungeon Unleashed. Stories can be played like a Novel, a text adventure game or used as a chatbot with an easy toggles to change between the multiple gameplay styles. This makes KoboldAI both a writing assistant, a game and a platform for so much more. The way you play and how good the AI will be depends on the model or service you decide to use. No matter if you want to use the free, fast power of Google Colab, your own high end graphics card, an online service you have an API key for (Like OpenAI or Inferkit) or if you rather just run it slower on your CPU you will be able to find a way to use KoboldAI that works for you. -
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CodeCompanion
AI-powered coding, seamlessly in Neovim. Supports Anthropic, etc.
Currently supports Anthropic, Copilot, Gemini, Ollama and OpenAI adapters. -
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Minuet
Dance with Intelligence in Your Code
Minuet-AI.nvim is an open-source Neovim plugin that provides AI-powered code completion by connecting the editor to modern large language models. The project is designed to bring real-time AI assistance directly into the developer’s editing environment while maintaining the speed and flexibility expected from the Neovim ecosystem. Instead of relying on a single provider, the plugin supports a variety of LLM backends, allowing developers to choose among services such as OpenAI, Claude, Gemini, Codestral, Ollama, or local inference engines. The system provides both traditional chat-based prompt completion and fill-in-the-middle code generation for models that support that capability. This design allows developers to receive context-aware suggestions as they type, helping accelerate coding tasks such as writing boilerplate code, completing functions, or generating small code blocks. -
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OpenFace Face Recognition
Face recognition with deep neural networks
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources. Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art. See our accuracy comparisons on the famous LFW benchmark. -
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Tick5 is a board game known as gomoku, omok, piskvorky or wuziqi. Tick5 has a built-in AI and also allows users to build their own AI with script language Lua. Tick5 runs cross-platform and has both GUI and textual version.
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BoomBot is a developer's framework for writing a bot connecting to the AIM network. It manages the network communication for the bots so that they can be easily written without worrying about AIM itself at all. Bots can be written in Objective-C or Lua.
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Brainiac, Is C/C++ Libraries, Programs, And Python, And Lua Scripts For Neural Networking And Genetic Programming, In An Attempt To Create A "Glue-It-All-Together" Project, Striving Towards General Artificial Intelligence
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Chippy is a free and open source adventure game in development where the player controls an AI agent named "Chippy", a small robotic chip that takes control of enemy robots and uses their abilities to fight other robots and solve puzzles.
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CoPP is an object-oriented framework for developing algorithms for robot path planning. One of of the design goals is to make it easy to make comparisons between various path planning algorithms.
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CycleGAN
Software that can generate photos from paintings
CycleGAN — in its original form — is a landmark in deep learning for image-to-image translation without paired data. Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image. This innovation lets the model learn domain-to-domain translations like turning horses into zebras, changing seasons, or transforming photos into paintings, using only collections of images from each domain. The original implementation (in Torch) has since been complemented by other re-implementations (including in PyTorch), but the core idea remains: unpaired image-to-image translation. Because of its flexibility, CycleGAN has become one of the most widely adopted generative models for domain translation tasks. -
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DCGAN.torch
Train your own image generator
DCGAN.torch is an open-source implementation of Deep Convolutional Generative Adversarial Networks using the Torch7 machine learning framework. It allows developers to train image generators that create synthetic pictures from random latent vectors. The project follows the architecture introduced in the DCGAN research paper and includes configurable generator and discriminator networks. Users can train models with CelebA, LSUN, ImageNet, or custom image datasets. Pretrained generators support synthetic image creation and exploration of learned visual representations. Additional scripts enable latent space interpolation and vector arithmetic between generated images. The software supports NVIDIA GPU acceleration through CUDA and cuDNN, with optional browser-based training visualization. It serves as a historical research implementation of convolutional generative modeling. -
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DeepMask
Torch implementation of DeepMask and SharpMask
DeepMask is an early, influential approach to class-agnostic object segmentation that learns to propose pixel-accurate masks directly from images. Instead of first generating boxes and then refining them, the network predicts a foreground mask and an “objectness” score for a given image patch, yielding high-quality segment proposals suitable for downstream detection or instance segmentation. The model is trained end-to-end to align mask shape with object extent, which markedly improves recall at a manageable number of proposals. In practice, DeepMask is run on an image pyramid with a sliding window, followed by non-maximum suppression to produce a compact set of candidates. A companion refinement model (SharpMask) sharpens the coarse predictions, recovering fine boundaries like thin limbs or object edges. The repository (in the original Torch/Lua stack) includes pretrained weights, training scripts, and evaluation utilities. -
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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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IRC bot based on the concept of (re-)loadable user-coded Lua modules, editable by anyone, which handle all the bot's functions. The project's main goal is to create a full written-language interface with no special predefined commands.
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LabLOVE (Life On a Virtual Environment) is an evolutionary multi-agent simulation environment. It is fast, modular and extensible. Contains the reference implementation for the gridbrain algorithm.
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MCP Hub
An MCP client for Neovim that seamlessly integrates MCP servers
mcphub.nvim is an MCP (Model Context Protocol) client plugin for Neovim that seamlessly integrates MCP servers into your editing workflow with an intuitive interface for managing, testing, and using MCP servers with your favorite chat plugins. Create your first MCP capable agent you need only 6 lines of code. Works with any langchain-supported LLM that supports tool calling (OpenAI, Anthropic, Groq, LLama etc.) Explore MCP capabilities and generate starter code with the interactive code builder. An MCP client for Neovim that seamlessly integrates MCP servers into your editing workflow with an intuitive interface for managing, testing, and using MCP servers with your favorite chat plugins. -
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Neovim 99
Neovim AI agent done right
Neovim 99 is an experimental GitHub repository created by well-known developer and educator ThePrimeagen that explores what he describes as the “ideal AI workflow” for developers who want a streamlined, high-quality integration of AI tooling into real coding environments — particularly focused on tools like Neovim and agent-centric workflows. Rather than a polished end-product, this repo serves as a playground for testing, iterating, and documenting workflows that integrate AI agents directly into everyday coding tools, emphasizing rapid feedback loops, automation, and minimal friction. The project often includes configuration files, scripts, and examples that show how to coerce modern AI assistants into productive roles within editors, plugins, and terminal workflows, with a focus on “no excuses” productivity. It blends examples from Neovim, agent automation, and developer ergonomics to illustrate how AI can be baked into existing environments. -
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Robot Warfare is a real-time strategy game that gives the user an opportunity to manage and program a team of battling robots that autonomously face opposing teams in a network-enabled environment.
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Torch5 provides a Matlab-like environment for state-of-the-art machine learning algorithms. This project aims at gathering all contributed packages for Torch5, which are not part of the Torch5 core.
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Torch5 provides a matlab-like environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to a easy and fast scripting language (Lua) and a underlying C++ implementation.
