LynxHub is a platform that allows users to seamlessly install, configure, launch, and manage all their AI interfaces from a single, intuitive dashboard. It offers features like AI interface management, arguments manager, custom run commands, pre-launch actions, extension management, in-app tools like terminal and web browser, AI information dashboard, Discord integration, and additional features like theme options and favorite interface pinning. The platform supports modular design for custom AI modules and upcoming extensions system for complete customization. LynxHub aims to streamline AI workflow and enhance user experience with a user-friendly interface and comprehensive functionalities.

Cua is a tool for creating and running high-performance macOS and Linux VMs on Apple Silicon, with built-in support for AI agents. It provides libraries like Lume for running VMs with near-native performance, Computer for interacting with sandboxes, and Agent for running agentic workflows. Users can refer to the documentation for onboarding and explore demos showcasing the tool's capabilities. Additionally, accessory libraries like Core, PyLume, Computer Server, and SOM offer additional functionality. Contributions to Cua are welcome, and the tool is open-sourced under the MIT License.

ComfyUI for Photoshop is a plugin that integrates with an AI-powered image generation system to enhance the Photoshop experience with features like unlimited generative fill, customizable back-end, AI-powered artistry, and one-click transformation. The plugin requires a minimum of 6GB graphics memory and 12GB RAM. Users can install the plugin and set up the ComfyUI workflow using provided links and files. Additionally, specific files like Check points, Loras, and Detailer Lora are required for different functionalities. Support and contributions are encouraged through GitHub.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

Omi is an open-source AI wearable that provides automatic, high-quality transcriptions of meetings, chats, and voice memos. It revolutionizes how conversations are captured and managed by connecting to mobile devices. The tool offers features for seamless documentation and integration with third-party services.

X-AnyLabeling is a robust annotation tool that seamlessly incorporates an AI inference engine alongside an array of sophisticated features. Tailored for practical applications, it is committed to delivering comprehensive, industrial-grade solutions for image data engineers. This tool excels in swiftly and automatically executing annotations across diverse and intricate tasks.

Astra.ai is a multimodal agent powered by TEN, showcasing its capabilities in speech, vision, and reasoning through RAG from local documentation. It provides a platform for developing AI agents with features like RTC transportation, extension store, workflow builder, and local deployment. Users can build and test agents locally using Docker and Node.js, with prerequisites including Agora App ID, Azure's speech-to-text and text-to-speech API keys, and OpenAI API key. The platform offers advanced customization options through config files and API keys setup, enabling users to create and deploy their AI agents for various tasks.

The NewEraAI-Papers repository provides links to collections of influential and interesting research papers from top AI conferences, along with open-source code to promote reproducibility and provide detailed implementation insights beyond the scope of the article. Users can stay up to date with the latest advances in AI research by exploring this repository. Contributions to improve the completeness of the list are welcomed, and users can create pull requests, open issues, or contact the repository owner via email to enhance the repository further.

Helicone is an open-source observability platform designed for Language Learning Models (LLMs). It logs requests to OpenAI in a user-friendly UI, offers caching, rate limits, and retries, tracks costs and latencies, provides a playground for iterating on prompts and chat conversations, supports collaboration, and will soon have APIs for feedback and evaluation. The platform is deployed on Cloudflare and consists of services like Web (NextJs), Worker (Cloudflare Workers), Jawn (Express), Supabase, and ClickHouse. Users can interact with Helicone locally by setting up the required services and environment variables. The platform encourages contributions and provides resources for learning, documentation, and integrations.

The second edition of "PyTorch Practical Tutorial" was completed after 5 years, 4 years, and 2 years. On the basis of the essence of the first edition, rich and detailed deep learning application cases and reasoning deployment frameworks have been added, so that this book can more systematically cover the knowledge involved in deep learning engineers. As the development of artificial intelligence technology continues to emerge, the second edition of "PyTorch Practical Tutorial" is not the end, but the beginning, opening up new technologies, new fields, and new chapters. I hope to continue learning and making progress in artificial intelligence technology with you in the future.

YaneuraOu is the World's Strongest Shogi engine (AI player), winner of WCSC29 and other prestigious competitions. It is an educational and USI compliant engine that supports various features such as Ponder, MultiPV, and ultra-parallel search. The engine is known for its compatibility with different platforms like Windows, Ubuntu, macOS, and ARM. Additionally, YaneuraOu offers a standard opening book format, on-the-fly opening book support, and various maintenance commands for opening books. With a massive transposition table size of up to 33TB, YaneuraOu is a powerful and versatile tool for Shogi enthusiasts and developers.

ASTRA is an open-source platform designed for developing applications utilizing large language models. It merges the ideas of Backend-as-a-Service and LLM operations, allowing developers to swiftly create production-ready generative AI applications. Additionally, it empowers non-technical users to engage in defining and managing data operations for AI applications. With ASTRA, you can easily create real-time, multi-modal AI applications with low latency, even without any coding knowledge.

This repository provides an in-depth review of industry trends in AI, Large Language Models (LLMs), Machine Learning, Computer Science, and Quantitative Finance. It covers various topics such as NVIDIA GTC conferences, DeepSeek theory and applications, LangGraph & Cursor AI, LLM essentials, system design, computer systems, big data and AI in finance, C++ design patterns, high-frequency finance, machine learning for algorithmic trading, stochastic volatility modeling, and quant job interview questions.

AI-Notes is a repository dedicated to practical applications of artificial intelligence and deep learning. It covers concepts such as data mining, machine learning, natural language processing, and AI. The repository contains Jupyter Notebook examples for hands-on learning and experimentation. It explores the development stages of AI, from narrow artificial intelligence to general artificial intelligence and superintelligence. The content delves into machine learning algorithms, deep learning techniques, and the impact of AI on various industries like autonomous driving and healthcare. The repository aims to provide a comprehensive understanding of AI technologies and their real-world applications.

LynxHub is a platform that allows users to seamlessly install, configure, launch, and manage all their AI interfaces from a single, intuitive dashboard. It offers features like AI interface management, arguments manager, custom run commands, pre-launch actions, extension management, in-app tools like terminal and web browser, AI information dashboard, Discord integration, and additional features like theme options and favorite interface pinning. The platform supports modular design for custom AI modules and upcoming extensions system for complete customization. LynxHub aims to streamline AI workflow and enhance user experience with a user-friendly interface and comprehensive functionalities.

Ragpi is an open-source AI assistant that answers questions using your documentation, GitHub issues, and READMEs. It combines LLMs with intelligent search to provide relevant, documentation-backed answers through a simple API. It supports multiple providers like OpenAI, Ollama, and Deepseek, and has built-in integrations with Discord and Slack. Ragpi builds knowledge bases from docs, GitHub issues, and READMEs, with an agentic RAG system for dynamic document retrieval. It has an API-first design with Docker deployment.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.