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.

SmolDocling-256M-preview is a 256M parameter language model launched by ds4sd, focusing on the medical field. Its importance lies in providing effective tools for medical text processing and medical knowledge extraction tasks. In medical research and clinical practice, a large amount of text data needs to be analyzed and processed, and this model can understand and process medical professional language. The main advantages include good performance in the medical field, the ability to handle a variety of medical-related text tasks, such as assisting in disease diagnosis and summarizing medical literature. The background of this model is that with the growth of medical data, the technical demand for processing medical text is increasing. Its positioning is to provide language processing capabilities for researchers, doctors, developers, and others in the medical field. Pricing information is not currently available.

This product is a project based on the Lattice Boltzmann Method (LBM), a numerical technique for computational fluid dynamics that describes macroscopic fluid behavior by simulating the motion of microscopic particles. Its significance lies in its ability to efficiently simulate complex fluid systems, such as multiphase flow and flow in porous media. Key advantages include high computational efficiency, relatively simple boundary condition handling, and ease of parallelization. The project, hosted on GitHub, is open-source and suitable for researchers and students for fluid dynamics simulation research and learning. It is intended for research and academic use and is currently free.

Goedel-Prover is an open-source large language model specializing in automated theorem proving. It significantly enhances the efficiency of automated mathematical problem solving by translating natural language mathematical questions into formal languages (such as Lean 4) and generating formal proofs. The model achieved a success rate of 57.6% on the miniF2F benchmark, surpassing other open-source models. Its key advantages include high performance, open-source extensibility, and a deep understanding of mathematical problems. Goedel-Prover aims to advance automated theorem proving technologies and provide powerful tool support for mathematical research and education.

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.

OpenDeepResearcher is an AI-driven research tool that utilizes services such as SERPAPI, Jina, and OpenRouter to perform auto-iterative searches based on user-defined query topics, collecting sufficient information and generating a final report. Its core advantages include efficient asynchronous processing, deduplication functionality, and robust LLM decision support, significantly enhancing research efficiency. It is primarily aimed at researchers, students, and professionals in related fields who need to conduct extensive literature searches and organize information, assisting them in quickly obtaining high-quality research materials. The tool is currently available in an open-source format, allowing users to deploy and use it as needed.

Agent Laboratory is a project developed by Samuel Schmidgall and others, aiming to support researchers throughout the entire research process—from literature review to experimental execution to report writing—through dedicated agents driven by large language models. It is not intended to replace human creativity, but rather to complement it, allowing researchers to concentrate on conceptualization and critical thinking while automating repetitive and time-consuming tasks such as coding and documentation. The tool's source code is licensed under the MIT License, permitting the use, modification, and distribution of the code provided that the terms of the MIT License are followed.

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.

LLaMA-O1 is a large inference model framework that integrates Monte Carlo Tree Search (MCTS), self-reinforcement learning, Proximal Policy Optimization (PPO), and draws from the dual strategy paradigm of AlphaGo Zero alongside large language models. This model primarily targets Olympic-level mathematical reasoning problems, providing an open platform for training, inference, and evaluation. According to product background information, this is an individual experimental project and is not affiliated with any third-party organizations or institutions.

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.

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.

SmolDocling-256M-preview is a 256M parameter language model launched by ds4sd, focusing on the medical field. Its importance lies in providing effective tools for medical text processing and medical knowledge extraction tasks. In medical research and clinical practice, a large amount of text data needs to be analyzed and processed, and this model can understand and process medical professional language. The main advantages include good performance in the medical field, the ability to handle a variety of medical-related text tasks, such as assisting in disease diagnosis and summarizing medical literature. The background of this model is that with the growth of medical data, the technical demand for processing medical text is increasing. Its positioning is to provide language processing capabilities for researchers, doctors, developers, and others in the medical field. Pricing information is not currently available.

This product is a project based on the Lattice Boltzmann Method (LBM), a numerical technique for computational fluid dynamics that describes macroscopic fluid behavior by simulating the motion of microscopic particles. Its significance lies in its ability to efficiently simulate complex fluid systems, such as multiphase flow and flow in porous media. Key advantages include high computational efficiency, relatively simple boundary condition handling, and ease of parallelization. The project, hosted on GitHub, is open-source and suitable for researchers and students for fluid dynamics simulation research and learning. It is intended for research and academic use and is currently free.

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.