探索AI前沿,掌握行业发展趋势
每日精选AI热点,追踪最新行业动态
精准筛选产品,多维度产品调研
热门AI产品实力、热度、年/月/日排行
提交AI产品信息,助力产品推广和用户转化
一站式AI工具指南,快速找到你需要的工具
企业级监测平台,全域追踪品牌在 12+ AI 平台的表现
输入品牌生成综合健康度得分,快速定位整体位置与短板
单次提问,立刻看到品牌在多个 AI 平台回答中的排名
批量问题 × 定频GEO排名查询 长期追踪排名变化曲线
挖出用户会问 AI 的高热度问题,决定做哪些内容
追踪投放的推广链接,评估哪些渠道真正被 AI 引用
拥有属于自己的GEO系统,助您成为专业GEO优化服务商
通过AI搜索优化服务,让品牌在AI中实现霸屏
聚集热门MCP服务,快速找到适合你的服务
轻松接入MCP客户端,调用强大的AI能力
学习MCP使用技巧,从入门到精通
热门MCP服务性能排行,帮你找到最佳选择
发布你的MCP服务,推广你的MCP服务
自由测试MCP服务,线上快速体验
快速测试MCP服务,快速上线
国内外主流大模型的统一API接入与调用服务
涵盖各类AI模型,满足你的开发与研究需求
寻找优质模型提供商,获取可靠模型支持
热门AI大模型性能、热度、年/月/日排行
多维度对比大模型,找到最适合你的模型
精准计算大模型使用成本,合理规划预算
多模型实时评测,模型输出结果快速比对
一键检测电脑配置,研判运行模型的兼容性
根据算力需求,推荐匹配的服务器配置
发现与 Recommend 相关的最受欢迎的开源项目和工具,了解最新的开发趋势和创新。
Conjurr is an AI recommendation tool that uses Tautulli watch data to recommend what users should watch next.
基于Apache Spark的ALS算法的Java推荐引擎。 能够在N秒后重新训练模型。
根据词向量神经网络推荐你感兴趣的Subreddit!
A powerful CLI tool that analyzes your GitHub profile to recommend open-source projects tailored to your interests and skills. DevProjectConnector connects you with potential collaborators, helping you grow your skills and expand your network within the open-source community.
Legal Up 利用先进算法,根据客户简要的案件描述,推荐合适的律师,确保客户找到合适的法律专业人士。
Book-Up | A social platform for bibliophiles to track, review, and recommend books. Built with Django + Leptos - Group Project @ WUT
Smart ATS Sytem that can search for the best candidates according to the job description similarity from a vector database of resume documents using RAG and recommend the best candidate using LLM
A model to recommend movies based on collaborative filtering (using ALS algorithm) and perform various analysis on the data.
Recommend similar apparel searched by user on Amazon.
An Autonomous Skill Builder Agent built with Next.js, TypeScript, Firebase, and Genkit AI. The system uses AI-driven personalization to recommend learning paths, predict learner needs, and adapt difficulty dynamically.
Build a recommendation system that uses BERT-based sentiment analysis of product reviews to enhance collaborative filtering. Fine-tune a BERT model for sentiment classification, then integrate it with a matrix factorization model to recommend products based on user ratings and review sentiment.
Full-stack AI web app combining ML, NLP, CV, and GenAI to recommend products, generate creative descriptions, and visualize analytics insights. Built with FastAPI, React, and Pinecone for semantic vector search, and LangChain for GenAI workflows.
Read-only MCP server for querying TMDB API. For AI assistants to search, retrieve, recommend, and explore movie and TV show data via MCP tools.