docs: add WeChat group QR code link to README

README.md

@@ -10,6 +10,14 @@
 [![Latest](https://img.shields.io/badge/Latest-v0.2.1-f28500)](./fluxon_release)
 [![Interfaces](https://img.shields.io/badge/Interfaces-KV%2FRPC%20%7C%20MQ%20%7C%20FS-1f6feb)](#interface-capabilities)
 
+<details>
+  <summary>
+    <img src="https://img.shields.io/badge/WeChat-Join%20Group-07C160?logo=wechat&logoColor=white" alt="WeChat group" />
+  </summary>
+  <br/>
+  <img src="./pics/wechat_group.png" width="260" alt="WeChat group QR code" />
+</details>
+
 [English](./README.md) | [中文](./README_CN.md) | [Docs](https://tele-ai.github.io/Fluxon/) | [中文文档](https://tele-ai.github.io/Fluxon/cn/) | <a href="https://github.com/Tele-AI/Fluxon" title="GitHub Repository"><img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" width="18" height="18" alt="GitHub repository" /></a>
 
 </div>

README_CN.md

@@ -2,6 +2,25 @@
 
 ![](./pics/post_en.png)
 
+<div align="center">
+
+[![Linux Only](https://img.shields.io/badge/Linux-Only-2ea44f)](#运行要求)
+[![Python](https://img.shields.io/badge/Python-%3E%3D3.10-3776AB)](#运行要求)
+[![Rust](https://img.shields.io/badge/Rust-1.93.0-000000)](./fluxon_rs/rust-toolchain.toml)
+[![Latest](https://img.shields.io/badge/Latest-v0.2.1-f28500)](./fluxon_release)
+[![Interfaces](https://img.shields.io/badge/Interfaces-KV%2FRPC%20%7C%20MQ%20%7C%20FS-1f6feb)](#接口能力)
+
+<details>
+  <summary>
+    <img src="https://img.shields.io/badge/WeChat-加入微信群-07C160?logo=wechat&logoColor=white" alt="WeChat group" />
+  </summary>
+  <br/>
+  <img src="./pics/wechat_group.png" width="260" alt="微信群二维码" />
+</details>
+
+[中文](./README_CN.md) | [English](./README.md) | [用户文档](https://tele-ai.github.io/Fluxon/cn/) | [English Docs](https://tele-ai.github.io/Fluxon/) | <a href="https://github.com/Tele-AI/Fluxon" title="GitHub 仓库"><img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" width="18" height="18" alt="GitHub repository" /></a>
+
+</div>
 
 当 GPU 算力持续提升，AI 系统的瓶颈正在从单点算子扩展到数据面。推理服务需要跨节点复用 `KV Cache`；训练流水线需要在异构资源池之间传递中间态；模型文件与 `Checkpoint` 需要在远端访问与本地缓存间稳定流动。
 
@@ -24,17 +43,7 @@ Fluxon 的设计正是围绕这些问题展开。它将数据面资源、对象
 ![](./pics/fluxon架构图20260423.png)
 
 
-<div align="center">
 
-[![Linux Only](https://img.shields.io/badge/Linux-Only-2ea44f)](#运行要求)
-[![Python](https://img.shields.io/badge/Python-%3E%3D3.10-3776AB)](#运行要求)
-[![Rust](https://img.shields.io/badge/Rust-1.93.0-000000)](./fluxon_rs/rust-toolchain.toml)
-[![Latest](https://img.shields.io/badge/Latest-v0.2.1-f28500)](./fluxon_release)
-[![Interfaces](https://img.shields.io/badge/Interfaces-KV%2FRPC%20%7C%20MQ%20%7C%20FS-1f6feb)](#接口能力)
-
-[中文](./README_CN.md) | [English](./README.md) | [用户文档](https://tele-ai.github.io/Fluxon/cn/) | [English Docs](https://tele-ai.github.io/Fluxon/) | <a href="https://github.com/Tele-AI/Fluxon" title="GitHub 仓库"><img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" width="18" height="18" alt="GitHub repository" /></a>
-
-</div>
 
 <a id="当前目录"></a>