Nes2Net is a lightweight nested architecture designed for foundation model-driven speech anti-fraud tasks, featuring a low error rate and suitability for audio deepfake detection. This model performs excellently on multiple datasets, and the pre-trained model and code have been released on GitHub for easy use by researchers and developers. Suitable for audio processing and security fields, it primarily aims to improve the efficiency and accuracy of speech recognition and anti-fraud.

The MCP Security Checklist is compiled and maintained by the SlowMist team, aiming to help developers identify and mitigate security risks during MCP implementation. With the rapid development of AI tools based on the MCP standard, security issues are becoming increasingly important. This checklist provides detailed security guidance, covering the security requirements of MCP servers, clients, and various scenarios to protect user privacy and enhance the overall stability and controllability of the system.

MCP-Scan is a security scanning tool specifically designed for MCP servers. It can detect common security vulnerabilities such as prompt injection and tool poisoning. By checking configuration files and tool descriptions, it helps users ensure system security. Suitable for various developers and system administrators, it is an important tool for maintaining system security.

Skywork-OR1 is a high-performance mathematical code reasoning model developed by Kunlun Wanwei's Tiangong team. This model series achieves industry-leading inference performance with comparable parameter scales, breaking through the bottleneck of large models in logical understanding and complex task solving. The Skywork-OR1 series includes three models: Skywork-OR1-Math-7B, Skywork-OR1-7B-Preview, and Skywork-OR1-32B-Preview, focusing on mathematical reasoning, general reasoning, and high-performance reasoning tasks, respectively. This open-source release not only includes model weights but also fully opens the training dataset and complete training code. All resources have been uploaded to GitHub and Hugging Face, providing the AI community with a fully reproducible practical reference. This comprehensive open-source strategy helps to promote the common progress of the entire AI community in reasoning ability research.

Kimi-VL is an advanced expert-mixed visual language model designed for multi-modal reasoning, long-context understanding, and powerful agent capabilities. This model excels in several complex domains, boasting efficient 2.8B parameters while exhibiting outstanding mathematical reasoning and image understanding capabilities. Kimi-VL sets a new standard for multi-modal models with its optimized computational performance and ability to handle long inputs.

Dream 7B is the latest diffusion large language model jointly launched by the NLP group of the University of Hong Kong and Huawei Noah's Ark Lab. It demonstrates excellent performance in text generation, especially in complex reasoning, long-term planning, and contextual coherence. The model adopts advanced training methods, possesses strong planning capabilities and flexible reasoning capabilities, and provides stronger support for various AI applications.

Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model based on Llama-3.1-405B-Instruct, which has undergone multi-stage post-training to enhance reasoning and chat capabilities. This model supports context lengths up to 128K, offering a good balance between accuracy and efficiency. Suitable for commercial use, it aims to provide developers with powerful AI assistant functionality.

HiDream-I1 is a new open-source image generation base model with 17 billion parameters, capable of generating high-quality images within seconds. This model is suitable for research and development and has performed excellently in multiple evaluations, demonstrating high efficiency and flexibility, making it suitable for various creative design and generation tasks.

EasyControl is a framework that provides efficient and flexible control for Diffusion Transformer (DiT), aiming to solve the efficiency bottlenecks and lack of model adaptability in the current DiT ecosystem. Its main advantages include: supporting multiple conditional combinations, improving generation flexibility and inference efficiency. This product is developed based on the latest research results and is suitable for use in image generation, style transfer, and other fields.

viAct.ai is an automated construction management software/app that uses computer vision and video analytics technology to provide workplace safety monitoring. Its main advantages include reducing accident rates, saving labor costs, error-free progress tracking, and saving high compensation fees.

EmaFusion? is an innovative AI model that integrates over 100 foundation and specialized models to deliver the highest accuracy at the lowest cost and latency. Tailored for enterprises, it ensures secure, efficient, and scalable AI applications with built-in fault tolerance and customized controls. EmaFusion? is designed to boost the efficiency of AI applications and is suitable for a wide range of business needs.

Nes2Net is a lightweight nested architecture designed for foundation model-driven speech anti-fraud tasks, featuring a low error rate and suitability for audio deepfake detection. This model performs excellently on multiple datasets, and the pre-trained model and code have been released on GitHub for easy use by researchers and developers. Suitable for audio processing and security fields, it primarily aims to improve the efficiency and accuracy of speech recognition and anti-fraud.

mcpscan.ai is a security scanning tool focused on Model Context Protocol (MCP) servers. It detects various security vulnerabilities in MCP servers, ensuring the secure interaction of large language models (LLMs) with external tools. This product aims to help developers identify and fix potential security risks, thereby protecting sensitive data and systems from attacks. The core value of mcpscan.ai lies in its specialized security scans for MCP implementations, providing real-time monitoring and detailed vulnerability analysis to support secure deployments.

The MCP Security Checklist is compiled and maintained by the SlowMist team, aiming to help developers identify and mitigate security risks during MCP implementation. With the rapid development of AI tools based on the MCP standard, security issues are becoming increasingly important. This checklist provides detailed security guidance, covering the security requirements of MCP servers, clients, and various scenarios to protect user privacy and enhance the overall stability and controllability of the system.

MCP-Scan is a security scanning tool specifically designed for MCP servers. It can detect common security vulnerabilities such as prompt injection and tool poisoning. By checking configuration files and tool descriptions, it helps users ensure system security. Suitable for various developers and system administrators, it is an important tool for maintaining system security.

GPT-4.1 is a series of new models that offer significant performance improvements, particularly in coding, instruction following, and handling long text contexts. Its context window has been expanded to 1 million tokens, and it excels in real-world applications, making it suitable for developers to create more efficient applications. This model is relatively low-cost and offers fast response times, making it more efficient for developing and executing complex tasks.

GLM-4-32B is a high-performance generative language model designed to handle various natural language tasks. Trained using deep learning techniques, it can generate coherent text and answer complex questions. This model is suitable for academic research, commercial applications, and developers. It is reasonably priced, precisely positioned, and a leading product in the field of natural language processing.

InternVL3 is a multimodal large language model (MLLM) open-sourced by OpenGVLab, possessing superior multimodal perception and reasoning capabilities. This model series includes 7 sizes ranging from 1B to 78B parameters, capable of simultaneously processing various information types such as text, images, and videos, demonstrating excellent overall performance. InternVL3 excels in industrial image analysis and 3D visual perception, with its overall text performance even surpassing the Qwen2.5 series. The open-sourcing of this model provides strong support for multimodal application development and helps promote the application of multimodal technology in more fields.