aigc-weekly — Agent Tool by miantiao-me

by miantiao-me · Agent Tool · ★ 542

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About aigc-weekly

Agili 的 AIGC 周刊 一个由 Agentic AI Agent 驱动的 AIGC(人工智能生成内容)精选周刊。本项目利用最新的 AI 和 Serverless 技术,为您提供最新的资讯、工具和资源。 在线阅读: RSS订阅: 🚀 特性 AI 智能策展:利用 Agentic AI Agent 自动发现和筛选内容。 现代技术栈:基于 Next.js 15、Payload CMS 3.0 和 Cloudflare 边缘基础设施构建。 Serverless 架构:完全部署在 Cloudflare (Workers, D1, R2, Containers) 上,实现高性能和低延迟。 Agent MCP 集成:使用模型上下文协议 (MCP) 允许 AI Agent 直接与 CMS 交互。 🛠 技术栈 框架:Next.js (App Router) & OpenNext CMS:Payload CMS (Headless) 数据库:Cloudflare D1 (SQLite) 存储:Cloudflare R2 (对象存储) Agent 框架:OpenCode Agent 运行时:Cloudflare Containers (需要付费套餐,或者在本地运行) 边缘运行时:Cloudflare Workers 🏗 架构 本项目包含三个主要组件: Next.js 应用 ():负责面向读者的前端页面以及 Payload CMS 管理界面。 OpenCode Agent ():一个独立的 Agent 服务,运行在 Cloudflare Containers 上,负责收集信息并通过 MCP 更新 CMS。 Cloudflare Worker ():转发请求到 Container 并控制其生命周期。 🏁 快速开始 前置要求 Node.js:v22 或更高版本 pnpm:v10 或更高版本 Cloudflare 账号:用于 D1、R2 和 Workers

agentaiclaude-agentclaude-codecloudflare-containerspayloadcms

Quick Facts

Stars542
Forks60
LanguageTypeScript
CategoryAgent Tool
LicenseAGPL-3.0
Quality Score49.54/100
Open Issues2
Last Updated2026-06-17
Created2025-11-29
Platformsclaude-code, node
Est. Tokens~14k

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Frequently Asked Questions

What is aigc-weekly?

aigc-weekly is Agili 的 AIGC 周刊 - 一个由 Agentic AI Agent 驱动的 AIGC(人工智能生成内容)精选周刊。. It is categorized as a Agent Tool with 542 GitHub stars.

What programming language is aigc-weekly written in?

aigc-weekly is primarily written in TypeScript. It covers topics such as agent, ai, claude-agent.

How do I install or use aigc-weekly?

You can find installation instructions and usage details in the aigc-weekly GitHub repository at github.com/miantiao-me/aigc-weekly. The project has 542 stars and 60 forks, indicating an active community.

What license does aigc-weekly use?

aigc-weekly is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to aigc-weekly?

The top alternatives to aigc-weekly on Agent Skills Hub include code-graph-rag, mcp-shrimp-task-manager, chunkhound. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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