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Monday, 7 September 2026

如何将静态网站根目录的内容推送到 GitLab Pages



与普通 Git 推送有所不同:GitLab Pages 必须依赖 .gitlab-ci.yml 自动化构建文件,且网页文件必须存放在构建产物文件夹(通常命名为 public)中,GitLab 才会自动将其部署发布。

以下是将本地的静态网站根目录推送到 GitLab Pages 的最简流程:

第一步:在项目根目录里,创建 CI/CD 配置文件.

在包含 index.html 的本地网站根目录下,新建一个名为 .gitlab-ci.yml 的文件,并写入以下内容

pages:
  stage: deploy
  script:
    - mkdir -p public
    # 将根目录下除 public 以外的所有静态文件复制到 public 文件夹中
    - cp -r $(ls -A | grep -v '^public$') public/
  artifacts:
    paths:
      - public
  rules:
    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH


原理说明:GitLab Pages 要求最终对外展示的文件存放在名为 public 的产物(artifacts)目录中。上面的脚本会在 GitLab 服务器端自动创建 public 目录并将根目录的文件复制进去,不需要改动你本地的文件结构。

第二步:提交并推送根目录内容在终端中依次运行以下命令,将代码推送到 GitLab:

1.初始化与添加文件:确认已在项目根目录下,将所有文件(包括新建的 .gitlab-ci.yml)加入 Git:
git init .
git add .
git commit -m "xx"


2.关联 GitLab 远程仓库:先在 GitLab.com/your-username/上新建一个空仓库,我新建的空仓库是qdb, 仓库地址是
https://gitlab.com/briteming/qdb
复制 SSH 或 HTTPS 仓库链接并关联:
git remote set-url origin https://gitlab.com/你的用户名/你的仓库名
(我的情况为git remote set-url origin https://gitlab.com/briteming/qdb)

3.推送至 GitLab:将代码推送到主分支:
git branch -m main

git push -f origin main

演示静态网站的网址:https://briteming.gitlab.io/qdb,会跳转到

https://qdb-350ec8.gitlab.io/ (要登录gitlab.com后,才能打开该网站。有点奇怪

验证部署:

    推送完成后,打开 GitLab仓库页面,进入侧边栏的 Build > Pipelines(构建 > 流水线)。

    等待名为 pages 的任务运行完毕(显示绿色勾标志)。

    进入侧边栏的 Deploy > Pages。

--------------------------------------------

 多亏了google gemini的指点,才搞定。真不容易,看下面的内容,看半天,没看明白:

 https://about.gitlab.com/blog/gitlab-pages-setup/
https://docs.gitlab.com/ci/environments/deployments/
https://docs.gitlab.com/user/project/pages/
https://docs.gitlab.com/user/project/pages/introduction/

( https://docs.gitlab.com/user/ssh/)

Chatbox


Powerful AI Client.

Your Ultimate AI Copilot on the Desktop.
Chatbox is a desktop client for ChatGPT, Claude and other LLMs, available on Windows, Mac, Linux

macOS Windows Linux Downloads Privacy

Chatbox - Better UI & Desktop App for ChatGPT, Claude and other LLMs. | Product Hunt chatboxai%2Fchatbox | Trendshift

This is the repository for the Chatbox Community Edition, open-sourced under the GPLv3 license.

Chatbox is going open-source Again!

We regularly sync code from the pro repo to this repo, and vice versa.

Download

Desktop

Windows MacOS Linux

Setup.exe

Intel

Apple Silicon

AppImage

iOS/Android

.APK

For more information: chatboxai.app

Quick Start

For End Users

  1. Download the appropriate installer for your platform from the releases page
  2. Install and launch Chatbox
  3. Configure your AI provider (OpenAI, Claude, etc.) in settings
  4. Start chatting!

System Requirements

Platform Minimum Version Architecture
Windows Windows 10 x64
macOS macOS 11 (Big Sur) Intel/Apple Silicon
Linux Ubuntu 20.04+ / AppImage supported distros x64

Features

🤖 AI Model Support

  • Support for Multiple LLM Providers
    ⚙️ Seamlessly integrate with a variety of cutting-edge language models:

    • OpenAI (ChatGPT)
    • Azure OpenAI
    • Claude
    • Google Gemini Pro
    • Ollama (enable access to local models like llama2, Mistral, Mixtral, codellama, vicuna, yi, and solar)
    • ChatGLM-6B
  • Image Generation with Dall-E-3
    🎨 Create the images of your imagination with Dall-E-3.

  • Enhanced Prompting
    💬 Advanced prompting features to refine and focus your queries for better responses.

🖥️ User Experience

  • Local Data Storage
    💾 Your data remains on your device, ensuring it never gets lost and maintains your privacy.

  • No-Deployment Installation Packages
    📦 Get started quickly with downloadable installation packages. No complex setup necessary!

  • Ergonomic UI & Dark Theme
    🌑 A user-friendly interface with a night mode option for reduced eye strain during extended use.

  • Keyboard Shortcuts
    ⌨️ Stay productive with shortcuts that speed up your workflow.

  • Streaming Reply
    ▶️ Provide rapid responses to your interactions with immediate, progressive replies.

📄 Content & Formatting

  • Markdown, Latex & Code Highlighting
    📜 Generate messages with the full power of Markdown and Latex formatting, coupled with syntax highlighting for various programming languages, enhancing readability and presentation.

  • Prompt Library & Message Quoting
    📚 Save and organize prompts for reuse, and quote messages for context in discussions.

👥 Collaboration & Sharing

  • Team Collaboration
    👥 Collaborate with ease and share OpenAI API resources among your team. Learn More

🌐 Platform Availability

  • Cross-Platform Desktop
    💻 Chatbox is ready for Windows, Mac, and Linux users.

  • Web Version
    🌐 Use the web application on any device with a browser, anywhere.

  • Mobile Apps
    ☎️ Native iOS and Android applications for on-the-go access.

🌍 Localization

  • Multilingual Support
    🌎 Catering to a global audience by offering support in multiple languages:
    • English
    • 简体中文 (Simplified Chinese)
    • 繁體中文 (Traditional Chinese)
    • 日本語 (Japanese)
    • 한국어 (Korean)
    • Français (French)
    • Deutsch (German)
    • Русский (Russian)
    • Español (Spanish)

✨ More Features

  • And More...
    ✨ Constantly enhancing the experience with new features!

FAQ

from  https://github.com/chatboxai/chatbox

chineseocr_lite

 超轻量级中文ocr,支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M

CLI · Web Demo · Models · Multi-platform Demo

轻量级中文 OCR 项目,当前分支以 ONNX Runtime 推理为主,包含 Web 服务、Python 推理代码,以及 C++、JVM、Android、.NET 等多端 Demo。

功能

  • 中文 OCR 文字检测与识别
  • ONNX Runtime CPU 推理
  • Tornado Web 服务与前端展示页面
  • Android、C++、JVM、.NET 多端参考 Demo
  • 附带轻量模型文件,便于本地快速试跑

环境

推荐环境:

  • Python 3.6
  • Windows、Linux 或 macOS
  • CPU 推理,无需 CUDA

安装依赖:

pip install -r requirements.txt

启动 Web 服务

cd chineseocr_lite
python backend/main.py

服务默认监听 8089 端口。启动后终端会输出类似:

server is running: 192.168.x.x:8089

在浏览器打开该地址即可使用 Web OCR 页面。

CLI 使用

当前项目也可以作为命令行 OCR 工具使用,适合被 agent、脚本或批处理任务调用。

本地开发安装:

pip install -e .

识别单张图片并输出 JSON:

chineseocr test_imgs/res.jpg

写入 JSON 文件:

chineseocr test_imgs/res.jpg --output result.json

同时输出带检测框的图片:

chineseocr test_imgs/res.jpg --output result.json --draw result.jpg

调整检测前的短边尺寸:

chineseocr test_imgs/res.jpg --compress 960

CLI 输出为稳定 JSON,便于 agent 解析:

{
  "text": "识别出的全文",
  "blocks": [
    {
      "text": "单个文本块",
      "score": 0.93,
      "box": [[12, 30], [210, 31], [209, 60], [11, 59]]
    }
  ],
  "elapsed": 1.24
}

也可以用 Python 模块方式运行:

python -m chineseocr_lite test_imgs/res.jpg

模型文件

当前仓库包含基础推理模型:

目录 文件 用途
models/ dbnet.onnx 文本检测
models/ crnn_lite_lstm.onnx 文本识别
models/ angle_net.onnx 文字方向分类
models_ncnn/ *.bin, *.param NCNN Demo 使用的模型

作为 Python CLI 包安装时,models/*.onnx 会随包一起安装,CLI 开箱即可使用。models_ncnn/ 和各端 Demo 不会作为 CLI 必需资源。

不要把训练权重、Python wheel、大型第三方库或构建产物直接提交到 Git。推荐放到 GitHub Releases、对象存储或 Git LFS。

项目结构

.
├── backend/             Web 服务与接口
├── models/              ONNX 模型
├── models_ncnn/         NCNN 模型
├── dbnet/               文本检测相关代码
├── crnn/                文本识别相关代码
├── angnet/              方向分类相关代码
├── cpp_projects/        C++ Demo
├── jvm_projects/        JVM Demo
├── android_projects/    Android Demo
├── dotnet_projects/     .NET Demo
├── test_imgs/           示例图片与效果图
├── config.py            模型与推理配置
├── model.py             OCR 推理入口
└── utils.py             通用工具函数

多端 Demo

以下 Demo 均为参考 Python 版本翻译或封装而来,彼此相互独立。

C++ Demo

目录:cpp_projects

  • ONNX Runtime C++ Demo,支持 Windows、Linux、macOS,目前仅支持 CPU
  • NCNN C++ Demo,支持 Windows、Linux、macOS,包含 CPU 版与 Vulkan GPU 版
  • MNN C++ Demo,支持 Windows、Linux、macOS,目前仅支持 CPU

JVM Demo

目录:jvm_projects

  • ONNX Runtime JVM Demo,基于 ONNX Runtime C++ 编译 JNI 供 Java/Kotlin 调用
  • NCNN JVM Demo,基于 NCNN C++ 编译 JNI 供 Java/Kotlin 调用,包含 CPU 版与 GPU 版

Android Demo

目录:android_projects

  • ONNX Runtime Android Demo
  • NCNN Android Demo,包含 CPU 版与 GPU 版
  • MNN Android Demo,支持 CPU 版

如果不想自行整合依赖库,完整源码工程可到 QQ 群共享中下载。

.NET Demo

目录:dotnet_projects

  • ONNX Runtime C# Demo
  • ONNX Runtime VB.NET Demo

字符检测 OCR Demo

第三方项目:fanqie03/char-detection

根据本项目和 CRNN 原理推导每个字符的位置,并支持将字符组合成单词。

TNN 第三方 Demo

第三方项目:Tencent/TNN OCR 示例

基于本项目实现的轻量级中文 OCR Demo,支持 iOS 和 Android,使用 TNN 的 ARM CPU、OpenCL、Metal 后端加速模型计算。

效果展示

Web 识别

Web OCR result 1

Web OCR result 2

Android 识别

Android IMEI OCR

Android ID card OCR

Android plate OCR

.NET 识别

.NET OCR

字符检测 OCR

Character detection OCR

TNN 第三方 Demo

TNN camera OCR

TNN poster OCR 1

from https://github.com/DayBreak-u/chineseocr_lite

MeterSphere 是新一代的开源持续测试工具

 

内置 AI 助手,让软件测试工作更简单、更高效,不再成为持续交付的瓶颈。


新一代的开源持续测试工具

Codacy GitHub release Stars Download Gitee Stars GitCode Stars

metersphere%2Fmetersphere | Trendshift


MeterSphere 是新一代的开源持续测试工具,内置 AI 助手,让软件测试工作更简单、更高效,不再成为持续交付的瓶颈。

  • AI 赋能:内置基于大模型的 AI 助手,支持 AI 生成功能用例、接口用例等,提升测试效率;
  • 测试管理:从测试用例管理,到测试计划执行、缺陷管理、测试报告生成,使用体验远超传统测试管理工具;
  • 接口测试:集 Postman 的易用与 JMeter 的灵活于一体,接口调试、接口定义、接口 Mock、场景自动化、接口报告,端到端支持;
  • 团队协作:采用“系统-组织-项目”分层设计理念,帮助用户摆脱单机测试工具的束缚,方便快捷地开展团队协作;
  • 插件体系:提供各种类别的插件,快速实现 MeterSphere 测试能力的扩展以及与 DevOps 流水线的集成。

快速开始

你可以通过 1Panel 应用商店 快速部署 MeterSphere。

如用于生产环境,推荐使用 离线安装包方式 进行安装部署。

如果你需要安装插件,请访问 MeterSphere 插件市场









版本说明

MeterSphere 当前最新版本为 V3,MeterSphere V1 和 V2 版本已停止维护。

MeterSphere V3 分为社区版和企业版,详情请参见:MeterSphere 产品版本对比

技术栈

from  https://github.com/metersphere/metersphere/

MaxKB, 强大易用的开源企业级智能体平台

 

MaxKB is an open-source platform for building enterprise-grade agents. 强大易用的开源企业级智能体平台。

maxkb.cn

[中文(简体)] | [English]


MaxKB = Max Knowledge Brain, it is an open-source platform for building enterprise-grade agents. MaxKB integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities. MaxKB is widely applied in scenarios such as intelligent customer service, corporate internal knowledge bases, academic research, and education.

  • RAG Pipeline: Supports direct uploading of documents / automatic crawling of online documents, with features for automatic text splitting, vectorization. This effectively reduces hallucinations in large models, providing a superior smart Q&A interaction experience.
  • Agentic Workflow: Equipped with a powerful workflow engine, function library and MCP tool-use, enabling the orchestration of AI processes to meet the needs of complex business scenarios.
  • Seamless Integration: Facilitates zero-coding rapid integration into third-party business systems, quickly equipping existing systems with intelligent Q&A capabilities to enhance user satisfaction.
  • Model-Agnostic: Supports various large models, including private models (such as DeepSeek, Llama, Qwen, etc.) and public models (like OpenAI, Claude, Gemini, MiniMax, etc.).
  • Multi Modal: Native support for input and output text, image, audio and video.

Quick start

Execute the script below to start a MaxKB container using Docker:

docker run -d --name=maxkb --restart=always -p 8080:8080 -v ~/.maxkb:/opt/maxkb 1panel/maxkb

Access MaxKB web interface at http://your_server_ip:8080 with default admin credentials:

  • username: admin
  • password: MaxKB@123..

中国用户如遇到 Docker 镜像 Pull 失败问题,请参照该 离线安装文档 进行安装。





Technical stack

from  https://github.com/1Panel-dev/MaxKB

DataEase, 数据可视化神器



什么是 DataEase?

DataEase 是开源的 BI 工具,帮助用户快速分析数据并洞察业务趋势,从而实现业务的改进与优化。DataEase 支持丰富的数据源连接,能够通过拖拉拽方式快速制作图表,并可以方便的与他人分享。

DataEase 的优势:

  • 开源开放:零门槛,线上快速获取和安装,按月迭代;
  • 简单易用:极易上手,通过鼠标点击和拖拽即可完成分析;
  • 全场景支持:多平台安装和多样化嵌入支持;
  • 安全分享:支持多种数据分享方式,确保数据安全;
  • AI 加持:无缝集成 SQLBot 实现智能问数。

DataEase 支持的数据源:

  • OLTP 数据库: MySQL、Oracle、SQL Server、PostgreSQL、MariaDB、Db2、TiDB、MongoDB-BI 等;
  • OLAP 数据库: ClickHouse、Apache Doris、Apache Impala、StarRocks 等;
  • 数据仓库/数据湖: Amazon RedShift 等;
  • 数据文件: Excel、CSV 等;
  • API 数据源。

快速开始

# 准备一台 2 核 4G 以上的 Linux 服务器,并以 root 用户运行以下一键安装脚本:

curl -sSL https://dataease.oss-cn-hangzhou.aliyuncs.com/quick_start_v2.sh | bash

# 用户名: admin
# 密码: DataEase@123456

如果是用于生产环境,推荐使用 离线安装包方式 进行安装部署。

如你有更多问题,可以查看在线文档,或者通过论坛和交流群与我们交流。

image

UI 展示

DataEase 工作台 DataEase 仪表板
DataEase 数据源 DataEase 模板中心

技术栈

from  https://github.com/dataease/dataease/

chrome的ai插件Nanobrowser

 

Open-Source Chrome extension for AI-powered web automation. Run multi-agent workflows using your own LLM API key. Alternative to OpenAI Operator.


GitHub Twitter Discord Ask DeepWiki Sponsor

Nanobrowser is an open-source AI web automation tool that runs in your browser. A free alternative to OpenAI Operator with flexible LLM options and multi-agent system.

⬇️ Get Nanobrowser from Chrome Web Store for free

👏 Join the community in Discord | X

🌟 Loving Nanobrowser? Give us a star and help spread the word!

❤️ Support the project by sponsoring us - every contribution helps keep Nanobrowser free and open source!

Nanobrowser Demo GIF

Nanobrowser's multi-agent system analyzing HuggingFace in real-time, with the Planner intelligently self-correcting when encountering obstacles and dynamically instructing the Navigator to adjust its approach—all running locally in your browser.

🔥Why Nanobrowser?

Looking for a powerful AI browser agent without the $200/month price tag of OpenAI Operator? Nanobrowser , as a chrome extension, delivers premium web automation capabilities while keeping you in complete control:

  • 100% Free - No subscription fees or hidden costs. Just install and use your own API keys, and you only pay what you use with your own API keys.
  • Privacy-Focused - Everything runs in your local browser. Your credentials stay with you, never shared with any cloud service.
  • Flexible LLM Options - Connect to your preferred LLM providers with the freedom to choose different models for different agents.
  • Fully Open Source - Complete transparency in how your browser is automated. No black boxes or hidden processes.

Note: We currently support OpenAI, Anthropic, Gemini, Ollama, Groq, Cerebras, Llama and custom OpenAI-Compatible providers, more providers will be supported.

📊 Key Features

  • Multi-agent System: Specialized AI agents collaborate to accomplish complex web workflows
  • Interactive Side Panel: Intuitive chat interface with real-time status updates
  • Task Automation: Seamlessly automate repetitive web automation tasks across websites
  • Follow-up Questions: Ask contextual follow-up questions about completed tasks
  • Conversation History: Easily access and manage your AI agent interaction history
  • Multiple LLM Support: Connect your preferred LLM providers and assign different models to different agents

🌐 Browser Support

Officially Supported:

  • Chrome - Full support with all features
  • Edge - Full support with all features

Not Supported:

  • Firefox, Safari, and other Chromium variants (Opera, Arc, etc.)

Note: While Nanobrowser may function on other Chromium-based browsers, we recommend using Chrome or Edge for the best experience and guaranteed compatibility.

🚀 Quick Start

  1. Install from Chrome Web Store (Stable Version):

Important Note: For latest features, install from "Manually Install Latest Version" below, as Chrome Web Store version may be delayed due to review process.

  1. Configure Agent Models:
    • Click the Nanobrowser icon in your toolbar to open the sidebar
    • Click the Settings icon (top right)
    • Add your LLM API keys
    • Choose which model to use for different agents (Navigator, Planner)

🔧 Manually Install Latest Version

To get the most recent version with all the latest features:

  1. Download

    • Download the latest nanobrowser.zip file from the official Github release page.
  2. Install:

    • Unzip nanobrowser.zip.
    • Open chrome://extensions/ in Chrome
    • Enable Developer mode (top right)
    • Click Load unpacked (top left)
    • Select the unzipped nanobrowser folder.
  3. Configure Agent Models

    • Click the Nanobrowser icon in your toolbar to open the sidebar
    • Click the Settings icon (top right).
    • Add your LLM API keys.
    • Choose which model to use for different agents (Navigator, Planner)
  4. Upgrading:

    • Download the latest nanobrowser.zip file from the release page.
    • Unzip and replace your existing Nanobrowser files with the new ones.
    • Go to chrome://extensions/ in Chrome and click the refresh icon on the Nanobrowser card.

🛠️ Build from Source

If you prefer to build Nanobrowser yourself, follow these steps:

  1. Prerequisites:

  2. Clone the Repository:

    git clone https://github.com/nanobrowser/nanobrowser.git
    cd nanobrowser
  3. Install Dependencies:

    pnpm install
  4. Build the Extension:

    pnpm build
  5. Load the Extension:

    • The built extension will be in the dist directory
    • Follow the installation steps from the Manually Install section to load the extension into your browser
  6. Development Mode (optional):

    pnpm dev

🤖 Choosing Your Models

Nanobrowser allows you to configure different LLM models for each agent to balance performance and cost. Here are recommended configurations:

Better Performance

  • Planner: Claude Sonnet 4
    • Better reasoning and planning capabilities
  • Navigator: Claude Haiku 3.5
    • Efficient for web navigation tasks
    • Good balance of performance and cost

Cost-Effective Configuration

  • Planner: Claude Haiku or GPT-4o
    • Reasonable performance at lower cost
    • May require more iterations for complex tasks
  • Navigator: Gemini 2.5 Flash or GPT-4o-mini
    • Lightweight and cost-efficient
    • Suitable for basic navigation tasks

Local Models

  • Setup Options:

    • Use Ollama or other custom OpenAI-compatible providers to run models locally
    • Zero API costs and complete privacy with no data leaving your machine
  • Recommended Models:

  • Prompt Engineering:

    • Local models require more specific and cleaner prompts
    • Avoid high-level, ambiguous commands
    • Break complex tasks into clear, detailed steps
    • Provide explicit context and constraints

Note: The cost-effective configuration may produce less stable outputs and require more iterations for complex tasks.

Tip: Feel free to experiment with your own model configurations! Found a great combination? Share it with the community in our Discord to help others optimize their setup.

💡 See It In Action

Here are some powerful tasks you can accomplish with just a sentence:

  1. News Summary:

    "Go to TechCrunch and extract top 10 headlines from the last 24 hours"

  2. GitHub Research:

    "Look for the trending Python repositories on GitHub with most stars"

  3. Shopping Research:

    "Find a portable Bluetooth speaker on Amazon with a water-resistant design, under $50. It should have a minimum battery life of 10 hours"

    from  https://github.com/nanobrowser/nanobrowser