AI tools for beam eye tracker cracked
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Beam Eye Tracker
Beam Eye Tracker is an AI-powered webcam eye tracking software designed for PC gamers to enhance gaming immersion. It allows users to turn their webcam into an eye tracker, unlocking 6DoF head and eye tracking capabilities in over 200 PC games. The application offers features such as Eye Tracking Overlay for gameplay insights, AI-powered performance comparable to high-end hardware devices, and compatibility with various webcams and mobile devices. Beam Eye Tracker aims to provide a seamless and immersive gaming experience without the need for bulky hardware trackers.
Beam AI
Beam AI is a leading platform for Agentic Automation and AI Agents, offering solutions for automating manual workflows with AI agents to boost productivity. The platform is used by Fortune 500 companies and scale-ups, providing a seamless experience from start to finish. With features like pre-trained agents, integrations, and easy-to-navigate UI, Beam AI empowers users to create and deploy AI tools tailored to their specific needs. The platform is designed to scale any business, offering industry-specific solutions and world-class support for building AI-native organizations.
Beam AI
Beam AI is the #1 end-to-end automated takeoff software designed for General Contractors, Subcontractors, and Suppliers in the construction industry. It leverages cutting-edge Artificial Intelligence technology to provide accurate and fast quantity takeoffs for various trades, saving up to 90% of the time typically spent on manual takeoffs. With Beam AI, users can streamline their bidding process, send out more estimates, and focus on value engineering to build competitive estimates. The software offers features such as cloud-based collaboration, 100% done-for-you quantity takeoffs, auto-detection of spec details, and the ability to process multiple takeoffs in parallel.
Qlerify
Qlerify is an AI-powered software modeling tool that helps digital transformation teams accelerate the digitalization of enterprise business processes. It allows users to quickly create workflows with AI, generate source code in minutes, and reuse actionable models in various formats. Qlerify supports powerful frameworks like Event Storming, Domain Driven Design, and Business Process Modeling, providing a user-friendly interface for collaborative modeling.
MacWhisper
MacWhisper is a native macOS application that utilizes OpenAI's Whisper technology for transcribing audio files into text. It offers a user-friendly interface for recording, transcribing, and editing audio, making it suitable for various use cases such as transcribing meetings, lectures, interviews, and podcasts. The application is designed to protect user privacy by performing all transcriptions locally on the device, ensuring that no data leaves the user's machine.
Beam Eye Tracker Extension Copilot
Build extensions using the Eyeware Beam eye tracking SDK
Beam-Dyer Operator Assistant
Hello I'm Beam-Dyer Operator Assistant! What would you like help with today?
Pelles GPT for MEP engineers
Specialized assistant for MEP engineers, aiding in calculations and system design.
Mechanical Engineering Tutor
An mechanical engineering tutor, aiding with concepts and problem-solving.
VedAstro
VedAstro is an open-source Vedic astrology tool that provides accurate astrological predictions and data. It offers a user-friendly website, a chat API, an open API, a JavaScript SDK, a Swiss Ephemeris API, and a machine learning table generator. VedAstro is free to use and is constantly being updated with new features and improvements.
beta9
Beta9 is an open-source platform for running scalable serverless GPU workloads across cloud providers. It allows users to scale out workloads to thousands of GPU or CPU containers, achieve ultrafast cold-start for custom ML models, automatically scale to zero to pay for only what is used, utilize flexible distributed storage, distribute workloads across multiple cloud providers, and easily deploy task queues and functions using simple Python abstractions. The platform is designed for launching remote serverless containers quickly, featuring a custom, lazy loading image format backed by S3/FUSE, a fast redis-based container scheduling engine, content-addressed storage for caching images and files, and a custom runc container runtime.
big-AGI
big-AGI is an AI suite designed for professionals seeking function, form, simplicity, and speed. It offers best-in-class Chats, Beams, and Calls with AI personas, visualizations, coding, drawing, side-by-side chatting, and more, all wrapped in a polished UX. The tool is powered by the latest models from 12 vendors and open-source servers, providing users with advanced AI capabilities and a seamless user experience. With continuous updates and enhancements, big-AGI aims to stay ahead of the curve in the AI landscape, catering to the needs of both developers and AI enthusiasts.
LlamaV-o1
LlamaV-o1 is a Large Multimodal Model designed for spontaneous reasoning tasks. It outperforms various existing models on multimodal reasoning benchmarks. The project includes a Step-by-Step Visual Reasoning Benchmark, a novel evaluation metric, and a combined Multi-Step Curriculum Learning and Beam Search Approach. The model achieves superior performance in complex multi-step visual reasoning tasks in terms of accuracy and efficiency.
vllm
vLLM is a fast and easy-to-use library for LLM inference and serving. It is designed to be efficient, flexible, and easy to use. vLLM can be used to serve a variety of LLM models, including Hugging Face models. It supports a variety of decoding algorithms, including parallel sampling, beam search, and more. vLLM also supports tensor parallelism for distributed inference and streaming outputs. It is open-source and available on GitHub.
Synthalingua
Synthalingua is an advanced, self-hosted tool that leverages artificial intelligence to translate audio from various languages into English in near real time. It offers multilingual outputs and utilizes GPU and CPU resources for optimized performance. Although currently in beta, it is actively developed with regular updates to enhance capabilities. The tool is not intended for professional use but for fun, language learning, and enjoying content at a reasonable pace. Users must ensure speakers speak clearly for accurate translations. It is not a replacement for human translators and users assume their own risk and liability when using the tool.
sqlcoder
Defog's SQLCoder is a family of state-of-the-art large language models (LLMs) designed for converting natural language questions into SQL queries. It outperforms popular open-source models like gpt-4 and gpt-4-turbo on SQL generation tasks. SQLCoder has been trained on more than 20,000 human-curated questions based on 10 different schemas, and the model weights are licensed under CC BY-SA 4.0. Users can interact with SQLCoder through the 'transformers' library and run queries using the 'sqlcoder launch' command in the terminal. The tool has been tested on NVIDIA GPUs with more than 16GB VRAM and Apple Silicon devices with some limitations. SQLCoder offers a demo on their website and supports quantized versions of the model for consumer GPUs with sufficient memory.
open-llms
Open LLMs is a repository containing various Large Language Models licensed for commercial use. It includes models like T5, GPT-NeoX, UL2, Bloom, Cerebras-GPT, Pythia, Dolly, and more. These models are designed for tasks such as transfer learning, language understanding, chatbot development, code generation, and more. The repository provides information on release dates, checkpoints, papers/blogs, parameters, context length, and licenses for each model. Contributions to the repository are welcome, and it serves as a resource for exploring the capabilities of different language models.
KsanaLLM
KsanaLLM is a high-performance engine for LLM inference and serving. It utilizes optimized CUDA kernels for high performance, efficient memory management, and detailed optimization for dynamic batching. The tool offers flexibility with seamless integration with popular Hugging Face models, support for multiple weight formats, and high-throughput serving with various decoding algorithms. It enables multi-GPU tensor parallelism, streaming outputs, and an OpenAI-compatible API server. KsanaLLM supports NVIDIA GPUs and Huawei Ascend NPU, and seamlessly integrates with verified Hugging Face models like LLaMA, Baichuan, and Qwen. Users can create a docker container, clone the source code, compile for Nvidia or Huawei Ascend NPU, run the tool, and distribute it as a wheel package. Optional features include a model weight map JSON file for models with different weight names.
chatllm.cpp
ChatLLM.cpp is a pure C++ implementation tool for real-time chatting with RAG on your computer. It supports inference of various models ranging from less than 1B to more than 300B. The tool provides accelerated memory-efficient CPU inference with quantization, optimized KV cache, and parallel computing. It allows streaming generation with a typewriter effect and continuous chatting with virtually unlimited content length. ChatLLM.cpp also offers features like Retrieval Augmented Generation (RAG), LoRA, Python/JavaScript/C bindings, web demo, and more possibilities. Users can clone the repository, quantize models, build the project using make or CMake, and run quantized models for interactive chatting.
Data-and-AI-Concepts
This repository is a curated collection of data science and AI concepts and IQs, covering topics from foundational mathematics to cutting-edge generative AI concepts. It aims to support learners and professionals preparing for various data science roles by providing detailed explanations and notebooks for each concept.
djl-demo
The Deep Java Library (DJL) is a framework-agnostic Java API for deep learning. It provides a unified interface to popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. DJL makes it easy to develop deep learning applications in Java, and it can be used for a variety of tasks, including image classification, object detection, natural language processing, and speech recognition.
AutoGPTQ
AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on GPTQ algorithm (weight-only quantization). It provides a simple and efficient way to quantize large language models (LLMs) to reduce their size and computational cost while maintaining their performance. AutoGPTQ supports a wide range of LLM models, including GPT-2, GPT-J, OPT, and BLOOM. It also supports various evaluation tasks, such as language modeling, sequence classification, and text summarization. With AutoGPTQ, users can easily quantize their LLM models and deploy them on resource-constrained devices, such as mobile phones and embedded systems.
aiotieba
Aiotieba is an asynchronous Python library for interacting with the Tieba API. It provides a comprehensive set of features for working with Tieba, including support for authentication, thread and post management, and image and file uploading. Aiotieba is well-documented and easy to use, making it a great choice for developers who want to build applications that interact with Tieba.
llm-reasoners
LLM Reasoners is a library that enables LLMs to conduct complex reasoning, with advanced reasoning algorithms. It approaches multi-step reasoning as planning and searches for the optimal reasoning chain, which achieves the best balance of exploration vs exploitation with the idea of "World Model" and "Reward". Given any reasoning problem, simply define the reward function and an optional world model (explained below), and let LLM reasoners take care of the rest, including Reasoning Algorithms, Visualization, LLM calling, and more!
MING
MING is an open-sourced Chinese medical consultation model fine-tuned based on medical instructions. The main functions of the model are as follows: Medical Q&A: answering medical questions and analyzing cases. Intelligent consultation: giving diagnosis results and suggestions after multiple rounds of consultation.
lmql
LMQL is a programming language designed for large language models (LLMs) that offers a unique way of integrating traditional programming with LLM interaction. It allows users to write programs that combine algorithmic logic with LLM calls, enabling model reasoning capabilities within the context of the program. LMQL provides features such as Python syntax integration, rich control-flow options, advanced decoding techniques, powerful constraints via logit masking, runtime optimization, sync and async API support, multi-model compatibility, and extensive applications like JSON decoding and interactive chat interfaces. The tool also offers library integration, flexible tooling, and output streaming options for easy model output handling.