Best AI tools for< Webxr Developer >

Wonda is an AI-powered platform that enables users to create immersive learning experiences and simulations. It offers a range of features such as AI companions, quiz and assessments, virtual tours, role-playing games, and virtual workshops. Users can easily build interactive learning journeys without the need for coding. Wonda aims to enhance engagement and collaboration in training and onboarding processes, making learning experiences unforgettable and impactful. The platform supports integration with various learning management systems and provides advanced role management options for secure sharing. With Wonda, users can unleash their creativity and build virtual exhibits to showcase their ideas and projects in a fun and engaging way.

Holovolo is a platform that allows users to create and share immersive volumetric VR180 videos and photos, as well as 3D stable diffusion models. The platform is designed to be easy to use, even for beginners, and it offers a variety of features that make it a powerful tool for creating and sharing immersive content.

Vossle is an AI-powered cloud-based SaaS platform for businesses and agencies to create web-based augmented reality experiences. Reach millions of users instantly with App-less Augmented Reality (WebAR) Experience that works on every modern smartphone browser the moment you publish! No app installs are required! No need to write a line of code or to develop costly apps. Build immersive AR experiences without installing any apps.

Experiments with Google is a website that showcases a collection of experiments created by coders using Chrome, Android, AI, AR, and more. The experiments are designed to inspire others to create new experiments and explore the possibilities of these technologies. The website also provides helpful tools and resources for creating experiments.

Free AI Video Upscaler is a free, open-source tool that allows users to upscale videos with AI right in their browser. It is quick, easy to use, and does not require any signups or installation. The tool is particularly well-suited for upscaling animated content.

Airi is a VTuber project heavily inspired by Neuro-sama. It is capable of various functions such as playing Minecraft, chatting in Telegram and Discord, audio input from browser and Discord, client side speech recognition, VRM and Live2D model support with animations, and more. The project also includes sub-projects like unspeech, hfup, Drizzle ORM driver for DuckDB WASM, and various other tools. Airi uses models like whisper-large-v3-turbo from Hugging Face and is similar to projects like z-waif, amica, eliza, AI-Waifu-Vtuber, and AIVTuber. The project acknowledges contributions from various sources and implements packages to interact with LLMs and models.

XR Animator is a video/webcam-based AI motion capture application designed for VTubing and the metaverse era. It uses machine learning solutions to detect 3D poses from a live webcam video, driving a 3D avatar as if controlled by the user's body. It supports full-body AI motion tracking, face tracking, and various XR/3D purposes. The tool can be used for VTubing, recording mocap motion, exporting motions to different formats, customizing backgrounds and scenes, and animating 3D models in other applications. It also supports AR on Android Chrome browser, AR selfie feature, and has relatively low system requirements for wide device compatibility.

LLM Agents Papers is a repository containing must-read papers on Large Language Model Agents. It covers a wide range of topics related to language model agents, including interactive natural language processing, large language model-based autonomous agents, personality traits in large language models, memory enhancements, planning capabilities, tool use, multi-agent communication, and more. The repository also provides resources such as benchmarks, types of tools, and a tool list for building and evaluating language model agents. Contributors are encouraged to add important works to the repository.

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

Prompt4ReasoningPapers is a repository dedicated to reasoning with language model prompting. It provides a comprehensive survey of cutting-edge research on reasoning abilities with language models. The repository includes papers, methods, analysis, resources, and tools related to reasoning tasks. It aims to support various real-world applications such as medical diagnosis, negotiation, etc.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

The 'awesome-ai-llm4education' repository is a curated list of papers related to artificial intelligence (AI) and large language models (LLM) for education. It collects papers from top conferences, journals, and specialized domain-specific conferences, categorizing them based on specific tasks for better organization. The repository covers a wide range of topics including tutoring, personalized learning, assessment, material preparation, specific scenarios like computer science, language, math, and medicine, aided teaching, as well as datasets and benchmarks for educational research.

Awesome deep logic is a curated list of papers and resources focusing on integrating symbolic logic into deep neural networks. It includes surveys, tutorials, and research papers that explore the intersection of logic and deep learning. The repository aims to provide valuable insights and knowledge on how logic can be used to enhance reasoning, knowledge regularization, weak supervision, and explainability in neural networks.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

Professional yet friendly WebXR coding assistant, utilizing primarily A-frame and Three.js frameworks.