如何在自己的笔记本电脑上运行大型语言模型（LLM）：隐私与自由的终极选择
发布日期：2025-07-18
随着技术的进步，曾经需要昂贵GPU才能运行的大型语言模型（LLM）现在可以在普通笔记本电脑甚至智能手机上运行。本文详细解析了如何在本地运行LLM，探讨了隐私保护、技术门槛降低以及开源模型的优势，为技术爱好者和隐私敏感用户提供了实用指南。
软件新闻
如何在本地运行开源大语言模型（LLM）：隐私、控制与乐趣
发布日期：2025-07-17
随着大语言模型（LLM）的普及，越来越多的人开始关注隐私、数据控制以及模型的可定制性。本文探讨了为什么你应该考虑在本地运行开源LLM，从隐私保护到摆脱大公司的控制，再到技术探索的乐趣。我们将介绍如何通过简单工具如Ollama和LM Studio快速上手，并分享实际运行中的经验与技巧。
GitHub MCP Server – GitHub推出基于 MCP 的服务器工具
发布日期：2025-04-25
GitHub MCP Server是什么 GitHub MCP Server 是GitHub 官方推出的基于 Model Co...
使用指南
What is Spydr Memory MCP?
Spydr Memory MCP is introduced as the first multimodal, interoperable context engine designed for any AI client. It aims to solve the problem of scattered information and context residing in siloed applications, enabling users to carry their context over seamlessly.
How to use Spydr Memory MCP?
Users can 'Intake. Create. Connect. Share.' their context, allowing AI to curate it and transform it into starting points for discovery. Access is available by signing up, either by continuing with Google or creating a new account. The service is currently in BETA.
Spydr Memory MCP's Core Features
Multimodal, interoperable context engine
AI-powered context curation
Transforms context into starting points for discovery
Enables context to be 'On the Go'
Breaks down information silos
Spydr Memory MCP's Use Cases
#1
Organizing and centralizing scattered information for AI interactions.
#2
Carrying personal or professional context across different applications and AI clients.
#3
Generating new insights and discovery points from curated information.
#4
Enhancing AI client performance with comprehensive, accessible context.
FAQ from Spydr Memory MCP
What is Spydr Memory MCP?
How does Spydr Memory MCP help with information management?
How can I access Spydr Memory MCP?
