Dataset Viewer is a modern, high-performance tool built with Tauri, React, and TypeScript, designed to handle massive datasets from multiple sources with efficient streaming for large files (100GB+) and lightning-fast search capabilities. It supports instant large file opening, real-time search, direct archive preview, multi-protocol and multi-format support, and features a modern interface with dark/light themes and responsive design. The tool is perfect for data scientists, log analysis, archive management, remote access, and performance-critical tasks.

DB2Rest is a modern low-code REST DATA API platform that simplifies the development of intelligent applications. It seamlessly integrates existing and new databases with language models (LMs/LLMs) and vector stores, enabling the rapid delivery of context-aware, reasoning applications without vendor lock-in.

NeuroPilot is an open-source AI-powered education platform that transforms study materials into interactive learning resources. It provides tools like contextual chat, smart notes, flashcards, quizzes, and AI podcasts. Supported by various AI models and embedding providers, it offers features like WebSocket streaming, JSON or vector database support, file-based storage, and configurable multi-provider setup for LLMs and TTS engines. The technology stack includes Node.js, TypeScript, Vite, React, TailwindCSS, JSON database, multiple LLM providers, and Docker for deployment. Users can contribute to the project by integrating AI models, adding mobile app support, improving performance, enhancing accessibility features, and creating documentation and tutorials.

Notely Voice is a free, modern, cross-platform AI voice transcription and note-taking application. It offers powerful Whisper AI Voice to Text capabilities, making it ideal for students, professionals, doctors, researchers, and anyone in need of hands-free note-taking. The app features rich text editing, simple search, smart filtering, organization with folders and tags, advanced speech-to-text, offline capability, seamless integration, audio recording, theming, cross-platform support, and sharing functionality. It includes memory-efficient audio processing, chunking configuration, and utilizes OpenAI Whisper for speech recognition technology. Built with Kotlin, Compose Multiplatform, Coroutines, Android Architecture, ViewModel, Koin, Material 3, Whisper AI, and Native Compose Navigation, Notely follows Android Architecture principles with distinct layers for UI, presentation, domain, and data.

PennyWise AI Tracker is a free and open-source expense tracker that uses on-device AI to turn bank SMS into a clean and searchable money timeline. It offers smart SMS parsing, clear insights, subscription tracking, on-device AI assistant, auto-categorization, data export, and supports major Indian banks. All processing happens on the user's device for privacy. The tool is designed for Android users in India who want automatic expense tracking from bank SMS, with clean categories, subscription detection, and clear insights.

Rivet is a desktop application for creating complex AI agents and prompt chaining, and embedding it in your application. Rivet currently has LLM support for OpenAI GPT-3.5 and GPT-4, Anthropic Claude Instant and Claude 2, [Anthropic Claude 3 Haiku, Sonnet, and Opus](https://www.anthropic.com/news/claude-3-family), and AssemblyAI LeMUR framework for voice data. Rivet has embedding/vector database support for OpenAI Embeddings and Pinecone. Rivet also supports these additional integrations: Audio Transcription from AssemblyAI. Rivet core is a TypeScript library for running graphs created in Rivet. It is used by the Rivet application, but can also be used in your own applications, so that Rivet can call into your own application's code, and your application can call into Rivet graphs.

RobustMQ is a next-generation, high-performance, multi-protocol message queue built in Rust. It aims to create a unified messaging infrastructure tailored for modern cloud-native and AI systems. With features like high performance, distributed architecture, multi-protocol support, pluggable storage, cloud-native readiness, multi-tenancy, security features, observability, and user-friendliness, RobustMQ is designed to be production-ready and become a top-level Apache project in the message queue ecosystem by the second half of 2025.

ABI (Agentic Brain Infrastructure) is a Python-based AI Operating System designed to serve as the core infrastructure for building an Agentic AI Ontology Engine. It empowers organizations to integrate, manage, and scale AI-driven operations with multiple AI models, focusing on ontology, agent-driven workflows, and analytics. ABI emphasizes modularity and customization, providing a customizable framework aligned with international standards and regulatory frameworks. It offers features such as configurable AI agents, ontology management, integrations with external data sources, data processing pipelines, workflow automation, analytics, and data handling capabilities.

Sokuji is a desktop application that provides live speech translation using advanced AI models from OpenAI, Google Gemini, CometAPI, Palabra.ai, and Kizuna AI. It aims to bridge language barriers in live conversations by capturing audio input, processing it through AI models, and delivering real-time translated output. The tool goes beyond basic translation by offering audio routing solutions with virtual device management (Linux only) for seamless integration with other applications. It features a modern interface with real-time audio visualization, comprehensive logging, and support for multiple AI providers and models.

RisuAI, or Risu for short, is a cross-platform AI chatting software/web application with powerful features such as multiple API support, assets in the chat, regex functions, and much more.

astrsk is a tool that pushes the boundaries of AI storytelling by offering advanced AI agents, customizable response formatting, and flexible prompt editing for immersive roleplaying experiences. It provides complete AI agent control, a visual flow editor for conversation flows, and ensures 100% local-first data storage. The tool is true cross-platform with support for various AI providers and modern technologies like React, TypeScript, and Tailwind CSS. Coming soon features include cross-device sync, enhanced session customization, and community features.

LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings. The key features of LanceDB include: Production-scale vector search with no servers to manage. Store, query, and filter vectors, metadata, and multi-modal data (text, images, videos, point clouds, and more). Support for vector similarity search, full-text search, and SQL. Native Python and Javascript/Typescript support. Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index(*). Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB, and more on the way. LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.

The HDU Computer Science Lecture Notes is a comprehensive guide designed to help students navigate through various challenges in the field of computer science. It covers topics such as programming languages, artificial intelligence, software development, and more. The notes provide insights on how to effectively utilize university time, balance grades with project experience, and make informed decisions regarding career paths. Created by a collaborative effort involving students, teachers, and industry experts, the lecture notes aim to serve as a guiding tool for individuals seeking guidance in the computer science domain.

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs). It allows AI applications to connect with various data sources and tools in a consistent manner, enhancing their capabilities and flexibility. This repository contains core libraries, test frameworks, engineering systems, pipelines, and tooling for Microsoft MCP Server contributors to unify engineering investments and reduce duplication and divergence. For more details, visit the official MCP website.

Clash Royale Build-A-Bot is a project that allows users to build their own bot to play Clash Royale. It provides an advanced state generator that accurately returns detailed information using cutting-edge technologies. The project includes tutorials for setting up the environment, building a basic bot, and understanding state generation. It also offers updates such as replacing YOLOv5 with YOLOv8 unit model and enhancing performance features like placement and elixir management. The future roadmap includes plans to label more images of diverse cards, add a tracking layer for unit predictions, publish tutorials on Q-learning and imitation learning, release the YOLOv5 training notebook, implement chest opening and card upgrading features, and create a leaderboard for the best bots developed with this repository.

The llms.txt hub is a centralized repository for llms.txt implementations and resources, facilitating interactions between LLM-powered tools and services with documentation and codebases. It standardizes documentation access, enhances AI model interpretation, improves AI response accuracy, and sets boundaries for AI content interaction across various projects and platforms.

Dataset Viewer is a modern, high-performance tool built with Tauri, React, and TypeScript, designed to handle massive datasets from multiple sources with efficient streaming for large files (100GB+) and lightning-fast search capabilities. It supports instant large file opening, real-time search, direct archive preview, multi-protocol and multi-format support, and features a modern interface with dark/light themes and responsive design. The tool is perfect for data scientists, log analysis, archive management, remote access, and performance-critical tasks.

This repository serves as a comprehensive guide for technical teams interested in developing conversational AI solutions using Retrieval-Augmented Generation (RAG) techniques. It covers theoretical knowledge and practical code implementations, making it suitable for individuals with a basic technical background. The content includes information on large language models (LLMs), transformers, prompt engineering, embeddings, vector stores, and various other key concepts related to conversational AI. The repository also provides hands-on examples for two different use cases, along with implementation details and performance analysis.

LLM-Viewer is a tool for visualizing Language and Learning Models (LLMs) and analyzing performance on different hardware platforms. It enables network-wise analysis, considering factors such as peak memory consumption and total inference time cost. With LLM-Viewer, users can gain valuable insights into LLM inference and performance optimization. The tool can be used in a web browser or as a command line interface (CLI) for easy configuration and visualization. The ongoing project aims to enhance features like showing tensor shapes, expanding hardware platform compatibility, and supporting more LLMs with manual model graph configuration.

llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.

The Eureka ML Insights Framework is a repository containing code designed to help researchers and practitioners run reproducible evaluations of generative models efficiently. Users can define custom pipelines for data processing, inference, and evaluation, as well as utilize pre-defined evaluation pipelines for key benchmarks. The framework provides a structured approach to conducting experiments and analyzing model performance across various tasks and modalities.

Pixelle-MCP is a multi-channel publishing tool designed to streamline the process of publishing content across various social media platforms. It allows users to create, schedule, and publish posts simultaneously on platforms such as Facebook, Twitter, and Instagram. With a user-friendly interface and advanced scheduling features, Pixelle-MCP helps users save time and effort in managing their social media presence. The tool also provides analytics and insights to track the performance of posts and optimize content strategy. Whether you are a social media manager, content creator, or digital marketer, Pixelle-MCP is a valuable tool to enhance your online presence and engage with your audience effectively.

Trae-agent is a Python library for building and training reinforcement learning agents. It provides a simple and flexible framework for implementing various reinforcement learning algorithms and experimenting with different environments. With Trae-agent, users can easily create custom agents, define reward functions, and train them on a variety of tasks. The library also includes utilities for visualizing agent performance and analyzing training results, making it a valuable tool for both beginners and experienced researchers in the field of reinforcement learning.

The ASReview project implements active learning for systematic reviews, utilizing AI-aided pipelines to assist in finding relevant texts for search tasks. It accelerates the screening of textual data with minimal human input, saving time and increasing output quality. The software offers three modes: Oracle for interactive screening, Exploration for teaching purposes, and Simulation for evaluating active learning models. ASReview LAB is designed to support decision-making in any discipline or industry by improving efficiency and transparency in screening large amounts of textual data.

Dataset Viewer is a modern, high-performance tool built with Tauri, React, and TypeScript, designed to handle massive datasets from multiple sources with efficient streaming for large files (100GB+) and lightning-fast search capabilities. It supports instant large file opening, real-time search, direct archive preview, multi-protocol and multi-format support, and features a modern interface with dark/light themes and responsive design. The tool is perfect for data scientists, log analysis, archive management, remote access, and performance-critical tasks.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.