CameraBench is a model for analyzing camera motion in videos, aimed at understanding the motion patterns of cameras through video interpretation. Its main advantage lies in using generative visual language models for principle classification of camera motions and video-text retrieval. Compared with traditional Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) methods, this model shows significant advantages in capturing scene semantics. The model is open-source and suitable for use by researchers and developers, with more improved versions to be released later.

Fin-R1 is a large language model designed specifically for the financial field, aimed at enhancing financial reasoning capabilities. Jointly developed by Shanghai University of Finance and Economics and Caiyue Xingchen, it is fine-tuned and reinforced learning based on Qwen2.5-7B-Instruct, possessing efficient financial reasoning capabilities and is suitable for core financial scenarios such as banking and securities. This model is free and open-source, facilitating user adoption and improvement.

This product is a full-stack application that leverages LLM (Large Language Models) and LangChain technology, combined with LangGraph, to retrieve and analyze stock data and news. It uses ChromaDB as a vector database to support semantic search and data visualization, providing users with deep insights into the stock market. It is primarily aimed at investors, financial analysts, and data scientists, helping them quickly acquire and analyze stock-related information to support decision-making. The product is currently open-source and free, making it suitable for users who need to efficiently process financial data and news.

MarS is a financial market simulation engine driven by a generative foundational model (LMM), capable of dynamically generating sequences of orders based on historical financial market data in response to various conditions, including user-injected interactive orders, fuzzy target scenario descriptions, and current/recent market data. MarS matches generated order sequences and user interactive orders in real-time within a simulation clearinghouse, producing fine-grained simulated market trajectories. The flexibility of MarS allows it to support various downstream applications such as forecasting, detection systems, analysis platforms, and agent training environments.

Claude Financial Data Analyst is a Next.js application that combines Claude AI capabilities with interactive data visualization, specifically designed for analyzing financial data. This product offers intelligent data analysis through chat, supports multi-format file uploads, and generates various charts based on the analysis. It is applicable not only in finance but also adaptable to areas like environmental data, sports performance, and social media analysis.

EasyFin is an AI-driven stock analysis platform that uses advanced artificial intelligence technology and big data analysis to provide users with accurate and comprehensive stock analysis and investment advice. The main advantage of EasyFin lies in quickly analyzing large amounts of data, intelligently recommending investment strategies, providing real-time market dynamics, and helping users make wiser investment decisions. EasyFin is positioned to provide high-quality investment analysis tools for investors, traders, and financial professionals.

Amsflow offers an all-in-one solution for modern investment analysis, leveraging smart tools to accelerate analysis and provide users with unique advantages. The product has rich background and is positioned to offer comprehensive market analysis services.

Generated Assets is an AI product that helps users turn their ideas into investable indices. Users can use AI to search for stocks and ETFs, create portfolios, and compare key metrics with the benchmark S&P 500 index. The site is only for educational purposes and does not allow actual investments.

CameraBench is a model for analyzing camera motion in videos, aimed at understanding the motion patterns of cameras through video interpretation. Its main advantage lies in using generative visual language models for principle classification of camera motions and video-text retrieval. Compared with traditional Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) methods, this model shows significant advantages in capturing scene semantics. The model is open-source and suitable for use by researchers and developers, with more improved versions to be released later.

Reimbursable is a website designed to help users easily track medical expenses. It provides a detailed directory of HSA-eligible projects, helping users better manage and track medical costs. Users can quickly find specific project HSA eligibility through the search function.

Thunder.vc is a technology-driven investment bank that helps founders raise funds, execute mergers and acquisitions, and plan exit strategies. It has raised over $1.2 billion and connected over 6800 investors.

Fin-R1 is a large language model designed specifically for the financial field, aimed at enhancing financial reasoning capabilities. Jointly developed by Shanghai University of Finance and Economics and Caiyue Xingchen, it is fine-tuned and reinforced learning based on Qwen2.5-7B-Instruct, possessing efficient financial reasoning capabilities and is suitable for core financial scenarios such as banking and securities. This model is free and open-source, facilitating user adoption and improvement.

Mantle is an equity structure management platform designed for startups and growth-stage companies, helping businesses save on 409A valuations and cap table management costs. Offering a user-friendly interface, seamless migration, and ongoing customer support, Mantle suits companies of all sizes, particularly registered Series C companies. Its affordable pricing includes a one-month free trial, allowing businesses to fully understand the platform's capabilities and benefits before committing.

Cardamon is an AI-driven platform for the financial compliance field. It uses automation technology to translate complex regulatory texts into specific compliance obligations for businesses, significantly improving compliance efficiency. The product leverages advanced AI algorithms to quickly process regulatory texts, generate obligation mappings and risk assessments, helping businesses save time and costs. Its background is the increasingly complex regulatory environment of the financial industry, where businesses need efficient tools to address compliance challenges. Cardamon is positioned for the mid-to-high-end market, primarily targeting financial institutions and compliance teams. Pricing strategy is yet to be defined, but it is expected to be a paid model.