关键词 "Expert Advisor" 的搜索结果, 共 18 条, 只显示前 480 条
Wolfram|Alpha is an advanced tool that provides expert-level answers on various topics. Wolfram|Alpha 是一种先进的工具,可提供各种主题的专家级答案。Wolfram 的突破性技术和知识库计算答案,受到数百万学生和专业人士的信赖,涵盖数学、科学、营养学、历史、地理、工程、数学、语言学、体育、金融、音
Study resources, notes, test prep, homework help, and expert tutors at your fingertips.
Website Generator enables AI to create complete, editable websites from simple prompts, returning links to view and customize the site without requiring technical expertise.
Use Trusted Advisor as a model context protocol.
use mcp server to add a team of experts into the agent flow
MCP server for the Apache Camel based Weik.io Integration Platform
A MCP server that can create queries and fetch information from APi documentation.
Mirror of
council of models for decision
MCP Server to assist LLMs and humans on Model Context Protocol spec compliance and understanding
Comprehensive notes for Graph Databases and MCP Servers in order to have enough context from a LM to quickly get up to speed on Graph DB projects.
Sensei MCP is a Model Context Protocol (MCP) server that provides expert guidance for Dojo and Cairo development on Starknet.
A Model Context Protocol (MCP) server for financial advisory and bookkeeping, providing structured financial data and context for AI agents.
微软发布了名为 “NLWeb” 的开源项目,旨在简化创建网站的自然语言对话界面,使任何网站都能轻松转变为 AI 驱动的应用程序。 NLWeb 定位为 “智能体 Web 时代的 HTML”,支持自定义模型与数据交互,旨在帮助开发者轻松将网站转化为支持自然语言交互的 AI 应用,可提供 “类似 ChatGPT 的聊天界面”。 NLWeb 开源地址:https://github.com/micr
BAGEL是字节跳动开源的多模态基础模型,拥有140亿参数,其中70亿为活跃参数。采用混合变换器专家架构(MoT),通过两个独立编码器分别捕捉图像的像素级和语义级特征。BAGEL遵循“下一个标记组预测”范式进行训练,使用海量多模态标记数据进行预训练,包括语言、图像、视频和网络数据。在性能方面,BAGEL在多模态理解基准测试中超越了Qwen2.5-VL和InternVL-2.5等顶级开源视觉语言模型
Path is a team of more than 300+ image-editing experts and graphic designers who provide professional Photoshop services to e-commerce businesses, product photographers, and small and medium-sized bus
小红书hi lab(Humane Intelligence Lab,人文智能实验室)团队首次开源文本大模型 dots.llm1。 dots.llm1是一个中等规模的Mixture of Experts (MoE)文本大模型,在较小激活量下取得了不错的效果。该模型充分融合了团队在数据处理和模型训练效率方面的技术积累,并借鉴了社区关于 MoE 的最新开源成果。hi lab团队开源了所有模型和必要的训练
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