The openai-realtime-api is a TypeScript client for interacting with OpenAI's real-time voice API. It offers strong typing features and is a perfect alternative to the official JavaScript version provided by OpenAI. This client has resolved several minor bugs and inconsistencies and is fully compatible with both official and unofficial events. It supports multiple environments including Node.js, browsers, Deno, Bun, and Cloudflare Workers, and is published on NPM. The significance of this technology lies in its ability to provide developers with a safer and more convenient way to integrate and utilize OpenAI's real-time voice capabilities, particularly when handling large volumes of data and requests.

RTVI-AI is an open standard designed to simplify the development of AI voice-to-voice and real-time video applications. It provides open-source SDK code along with documentation for standard endpoint shapes, event messages, and data structures, supporting developers to use any inference service, while enabling those services to leverage open-source tools for complex client solutions in real-time multimedia development.

This GitHub repository contains training and inference code to replicate our winning solution in the AI Mathematics Olympic (AIMO) Progress Prize 1. Our solution consists of four main parts: a recipe for fine-tuning DeepSeekMath-Base 7B for use in solving math problems using Tool Integrated Reasoning (TIR); two high-quality datasets of about 10 million math questions and solutions; an algorithm for generating solution candidates with coding execution feedback (SC-TIR); and four carefully selected validation sets from AMC, AIME, and MATH to guide model selection and avoid overfitting the public leaderboard.

RAG Search API, developed by thinkany.ai, is an intelligent search API that utilizes RAG (Retrieval-Augmented Generation) technology. It combines retrieval and generation capabilities to deliver efficient and accurate information retrieval for users. The API supports customizable configurations, including search quantity, re-ranking options, and filtering, to meet diverse user needs.

simple-one-api is a program that adapts to various large model interfaces, supporting OpenAI interfaces, allowing users to call different large model services through a unified API format, simplifying the complexity brought by differences in different platform interfaces. It supports multiple platforms, including Qianfan Large Model Platform, Xfyun Xinghuo Large Model, and Tencent HunYuan Large Model, and provides the convenience of one-click deployment and out-of-the-box usability.

HippoRAG is a novel Retriever-Augmented Generation (RAG) framework inspired by human long-term memory, enabling Large Language Models (LLMs) to continuously integrate knowledge across external documents. Experiments demonstrate that HippoRAG can provide the capabilities of RAG systems, typically requiring expensive and high-latency iterative LLM pipelines, at a lower computational cost.

Supports converting the calls to model services from platforms like openai, claude, azure openai, gemini, kimi, Zhipu AI, Tongyi Qianwen, and Xufeng Xinghuo to an openai-style call. It masks the differences between various large model APIs and uses a unified openai api standard format for accessing large models. Provides multiple large model support, including load balancing, routing, and configuration management features.

Gemini API Cookbook is a collection of guides and examples for using the Gemini API, designed to help developers get started quickly and effectively. Most of these examples are Python-based Colab Notebooks, which can be opened directly in Google Colab or downloaded to a local environment for execution.

openapi-ui is a more concise and beautiful OpenAPI documentation website than Swagger UI. It can quickly generate simulated parameters and call API requests, and it is also a simplified version of Postman.

The openai-realtime-api is a TypeScript client for interacting with OpenAI's real-time voice API. It offers strong typing features and is a perfect alternative to the official JavaScript version provided by OpenAI. This client has resolved several minor bugs and inconsistencies and is fully compatible with both official and unofficial events. It supports multiple environments including Node.js, browsers, Deno, Bun, and Cloudflare Workers, and is published on NPM. The significance of this technology lies in its ability to provide developers with a safer and more convenient way to integrate and utilize OpenAI's real-time voice capabilities, particularly when handling large volumes of data and requests.

Pioneer Computing Cloud is a one-stop AIGC cloud service platform offering cost-effective GPU infrastructure and seamlessly integrated cutting-edge AI inference technology. It comprehensively covers AI needs at different business stages, accelerating business growth. Key advantages of Pioneer Computing Cloud include: high-performance cloud service products, no management or maintenance of GPU infrastructure required, automatic elastic scaling, global coverage of GPU container instances, and 24/7 customer service. The product background indicates that Pioneer Computing Cloud aims to provide powerful computing support for AI application developers, reducing development costs and increasing efficiency.

The Piao Computing Cloud Large Model API offers easily integrable multi-modal API services, including large language models, images, audio, and video, aimed at helping users effortlessly build their unique AIGC applications. The platform boasts a rich repository of model resources, supports personalized model training and hosting, while ensuring the confidentiality of users' private models. It is characterized by cost-effectiveness, high throughput, and a high-performance inference engine, making it suitable for various AI application scenarios such as chatbots, summarization, and novel generators.

The Intel? Core? Ultra 200 series desktop processors are the first AI PC processors designed for the desktop platform, delivering exceptional gaming experiences and industry-leading computing performance while significantly reducing power consumption. These processors feature up to 8 next-generation performance cores (P-cores) and up to 16 next-generation efficiency cores (E-cores), resulting in up to a 14% performance improvement in multi-threaded workloads compared to the previous generation. They are also the first desktop processors equipped with a neural processing unit (NPU) for enthusiasts, and include built-in Xe GPU technology, supporting advanced media features.

The Message Batches API by Anthropic allows developers to process a high volume of queries asynchronously, with each batch containing up to 10,000 queries. This API is especially suited for non-time-sensitive tasks that do not require real-time responses, such as customer feedback analysis and language translation. It offers high throughput while costing only half of a standard API call, making large-scale data processing more economical.

The Intel? Gaudi? 3 AI Accelerator is a high-performance artificial intelligence accelerator launched by Intel, built on the efficient Intel? Gaudi? platform. It boasts outstanding MLPerf benchmark performance and is designed to handle demanding training and inference tasks. The accelerator supports AI applications such as large language models, multimodal models, and enterprise RAG in data centers or the cloud, operating on your existing Ethernet infrastructure. Whether you need a single accelerator or thousands, Intel Gaudi 3 can play a crucial role in your AI success.

RTVI-AI is an open standard designed to simplify the development of AI voice-to-voice and real-time video applications. It provides open-source SDK code along with documentation for standard endpoint shapes, event messages, and data structures, supporting developers to use any inference service, while enabling those services to leverage open-source tools for complex client solutions in real-time multimedia development.

This GitHub repository contains training and inference code to replicate our winning solution in the AI Mathematics Olympic (AIMO) Progress Prize 1. Our solution consists of four main parts: a recipe for fine-tuning DeepSeekMath-Base 7B for use in solving math problems using Tool Integrated Reasoning (TIR); two high-quality datasets of about 10 million math questions and solutions; an algorithm for generating solution candidates with coding execution feedback (SC-TIR); and four carefully selected validation sets from AMC, AIME, and MATH to guide model selection and avoid overfitting the public leaderboard.