Best AI tools for< Climb Leaderboards >

v3RPG is an AI-powered RPG adventure platform that allows users to create stories, characters, and embark on thrilling adventures. With decentralized AI judging powered by the Ora Protocol, players can climb leaderboards, earn rewards, and participate in community battles. The platform offers unique gameplay mechanics where players interact with an AI dungeon master, choose characters, make decisions, and roll dice to determine outcomes. Players can communicate with the AI dungeon master to ask questions and make adjustments to the adventure. v3RPG provides a dynamic and engaging RPG experience for players seeking immersive storytelling and strategic gameplay.

DINGR is an AI-powered solution designed to help gamers analyze their performance in League of Legends. The tool uses AI algorithms to provide accurate insights into gameplay, comparing performance metrics with friends and offering suggestions for improvement. DINGR is currently in development, with a focus on enhancing the gaming experience through data-driven analysis and personalized feedback.

This website offers a game where users can play Wordle against an AI. The goal of the game is to guess a 5-letter word in six tries or less. The AI uses Chat GPT to try to guess the word in as few tries as possible. Users can also view a scoreboard to see how they compare to other players.

ClimbContent is an AI content generator plugin designed specifically for WordPress websites. It utilizes advanced AI technology to analyze top-ranking pages for keywords, guide content creation, and optimize SEO strategies. By blending industry expertise with AI insights, ClimbContent helps users craft SEO-optimized content that aligns with Google preferences and brand voice. The tool enhances readability, engagement, and on-page SEO through features like intelligent keyword analysis, AI-driven content creation, and automated enhancements. ClimbContent empowers users to create top-ranking content that attracts organic traffic and boosts search engine rankings.

AI Art Master is a thrilling game tailor-made for AI Art enthusiasts who crave excitement and competition. Engage in heart-pounding AI Art Contests and showcase your incredible AI-generated art in a vast array of virtual exhibition spaces to climb the ranks and become the next AI Art Master. Unleash your inner artist today!

The Korean SAT LLM Leaderboard is a benchmarking project that allows users to test their fine-tuned Korean language models on a 10-year dataset of the Korean College Scholastic Ability Test (CSAT). The project provides a platform to compare human academic ability with the performance of large language models (LLMs) on various question types to assess reading comprehension, critical thinking, and sentence interpretation skills. It aims to share benchmark data, utilize a reliable evaluation dataset curated by the Korea Institute for Curriculum and Evaluation, provide annual updates to prevent data leakage, and promote open-source LLM advancement for achieving top-tier performance on the Korean CSAT.

The Chinese LLM Benchmark is a continuous evaluation list of large models in CLiB, covering a wide range of commercial and open-source models from various companies and research institutions. It supports multidimensional evaluation of capabilities including classification, information extraction, reading comprehension, data analysis, Chinese encoding efficiency, and Chinese instruction compliance. The benchmark not only provides capability score rankings but also offers the original output results of all models for interested individuals to score and rank themselves.

llm-resource is a comprehensive collection of high-quality resources for Large Language Models (LLM). It covers various aspects of LLM including algorithms, training, fine-tuning, alignment, inference, data engineering, compression, evaluation, prompt engineering, AI frameworks, AI basics, AI infrastructure, AI compilers, LLM application development, LLM operations, AI systems, and practical implementations. The repository aims to gather and share valuable resources related to LLM for the community to benefit from.

DeepBattler is a tool designed for Hearthstone Battlegrounds players, providing real-time strategic advice and insights to improve gameplay experience. It integrates with the Hearthstone Deck Tracker plugin and offers voice-assisted guidance. The tool is powered by a large language model (LLM) and can match the strength of top players on EU servers. Users can set up the tool by adding dependencies, configuring the plugin path, and launching the LLM agent. DeepBattler is licensed for personal, educational, and non-commercial use, with guidelines on non-commercial distribution and acknowledgment of external contributions.

IW5 Bot Warfare is a GSC mod for the PlutoniumIW5 project that adds playable AI to the multiplayer games of Modern Warfare 3. It features a Waypoint Editor for creating and modifying bot's waypoints, a customizable menu for editing bot DVARs, compatibility with other mods, AI clients that simulate real players, and various bot behaviors such as capturing objectives, using killstreaks, targeting equipment, and more. The mod aims to provide a comprehensive Combat Training experience for MW3 multiplayer games.

IW4 Bot Warfare is a GSC mod for IW4x that adds playable AI to the multiplayer games of Modern Warfare 2. It features a Waypoint Editor for creating and modifying bot's waypoints, a customizable menu for editing bot DVARs, compatibility with other mods, loading waypoints from CSV files, and AI clients that simulate real players in various game modes with realistic behaviors and abilities. The mod aims to enhance the multiplayer experience by providing a challenging and dynamic gameplay environment with intelligent bots that interact with the game world effectively.

x-crawl is a flexible Node.js AI-assisted crawler library that offers powerful AI assistance functions to make crawler work more efficient, intelligent, and convenient. It consists of a crawler API and various functions that can work normally even without relying on AI. The AI component is currently based on a large AI model provided by OpenAI, simplifying many tedious operations. The library supports crawling dynamic pages, static pages, interface data, and file data, with features like control page operations, device fingerprinting, asynchronous sync, interval crawling, failed retry handling, rotation proxy, priority queue, crawl information control, and TypeScript support.

The 'gritlm' repository provides all materials for the paper Generative Representational Instruction Tuning. It includes code for inference, training, evaluation, and known issues related to the GritLM model. The repository also offers models for embedding and generation tasks, along with instructions on how to train and evaluate the models. Additionally, it contains visualizations, acknowledgements, and a citation for referencing the work.

This repository provides a demo that tracks aircraft using Redis and Node.js by receiving aircraft transponder broadcasts through a software-defined radio (SDR) and storing them in Redis. The demo includes instructions for setting up the hardware and software components required for tracking aircraft. It consists of four main components: Radio Ingestor, Flight Server, Flight UI, and Redis. The Radio Ingestor captures transponder broadcasts and writes them to a Redis event stream, while the Flight Server consumes the event stream, enriches the data, and provides APIs to query aircraft status. The Flight UI presents flight data to users in map and detail views. Users can run the demo by setting up the hardware, installing SDR software, and running the components using Docker or Node.js.

This repo contains a comprehensive paper list of **Model Quantization** for efficient deep learning on AI conferences/journals/arXiv. As a highlight, we categorize the papers in terms of model structures and application scenarios, and label the quantization methods with keywords.

VideoTuna is a codebase for text-to-video applications that integrates multiple AI video generation models for text-to-video, image-to-video, and text-to-image generation. It provides comprehensive pipelines in video generation, including pre-training, continuous training, post-training, and fine-tuning. The models in VideoTuna include U-Net and DiT architectures for visual generation tasks, with upcoming releases of a new 3D video VAE and a controllable facial video generation model.

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