Open-IM-Server

Open-IM-Server: Open source Instant Messaging Server

Instant messaging server. Backend in pure Golang, wire transport protocol is JSON over websocket.

Everything is a message in Open-IM-Server, so you can extend custom messages easily, there is no need to modify the server code.

Using microservice architectures, Open-IM-Server can be deployed using clusters.

By deployment of the Open-IM-Server on the customer's server, developers can integrate instant messaging and real-time network capabilities into their own applications free of charge and quickly, and ensure the security and privacy of business data.

Features

• Everything in Free
• Scalable architecture
• Easy integration
• Good scalability
• High performance
• Lightweight
• Supports multiple protocols

Community

• Join the Telegram-OpenIM group: https://t.me/joinchat/zSJLPaHBNLZmODI1
• 中文官网访问这里：Open-IM中文开发文档

Quick start

Installing Open-IM-Server

Open-IM relies on five open source high-performance components: ETCD, MySQL, MongoDB, Redis, and Kafka. Privatization deployment Before Open-IM-Server, please make sure that the above five components have been installed. If your server does not…

GitHub Copilot

Welcome to the GitHub Copilot user community In this repository, you can find documentation, walkthroughs, examples, and the latest resources you need to use GitHub Copilot.

Getting Started

To install GitHub Copilot, check out the Getting Started guides:

• Visual Studio Code
• JetBrains
• Neovim

For a tour of GitHub Copilot, visit the homepage at copilot.github.com.

How to get help

Have a question, or want to provide feedback? Visit the Feedback forum to ask questions, share bugs or feedback, or chat with other users in the Preview The GitHub Copilot team will respond as often as possible, but we also welcome you to share your experiences and help others in the community.

Safety

We take safety seriously and are constantly working to improve GitHub Copilot. If you discover dangerous, biased or offensive output from GitHub Copilot, please report it privately to copilot-safety@github.com.

Useful links

• GitHub Copilot homepage
• Gallery
• …

Copilot.vim

GitHub Copilot is an AI pair programmer which suggests line completions and entire function bodies as you type. GitHub Copilot is powered by the OpenAI Codex AI system, trained on public Internet text and billions of lines of code.

Copilot.vim is a Vim plugin for GitHub Copilot. For now, it requires a Neovim 0.6 prerelease (for virtual lines support) and a Node.js installation.

To learn more about GitHub Copilot, visit https://copilot.github.com.

Technical Preview

Access to GitHub Copilot is limited to a small group of testers during the technical preview of GitHub Copilot. If you don’t have access to the technical preview, you will see an error when you try to use this extension.

Don’t have access yet? Sign up for the waitlist for your chance to try it out. GitHub will notify you once you have access.

This technical preview is a Beta Preview under the GitHub Terms…

Features

Comprehensive

With more than 60 crafted components that you can use out of the box.

Customizable theme

Extensive design tokens can be customized to build your own theme. Two ways of customization are supported:

• With less-loader
• Design Lab - Recommended!

Reusable custom materials

Material market provides a one-stop solution for materials management. Reuse customized modules to make a breakthrough in efficiency.

TypeScript friendly

All components are written in TypeScript so it's type friendly.

Installation

Available as an npm package

// with npm
npm install @arco-design/web-react
// with yarn
yarn add @arco-design/web-react

Examples

import React from 'react';
import ReactDOM from 'react-dom';
import { Button } from '@arco-design/web-react';
import '@arco-design/web-react/dist/css/arco.css';
function App() {
  return (
    <Button type='secondary'>
      Hello World
    </Button>
  );

禁止Star/Fork!!

请勿使用Action运行脚本!

有条（科学）件（上网）的可以 点此加入组织

本仓库部分脚本已加入JDHelloWorld大佬助力池，默认加入助力池互助

由于限制TG群内成员提交助力码，请需要互助的 加入组织，回复助力池，获取使用教程

不需要助力池请添加环境变量，变量名：JD_JOIN_ZLC，变量值：false

请勿直接fork！！云函数用户先按照下方教程建立私库！！！

已经创建公开仓库的请点击仓库右上角Setting，拉到页面最下方，点击Change visibility，选择Make private，填入黑体仓库名称进行确认!

方式一(如果有一定github基础，十分建议直接方式二)

建议阅读@hshx123大佬的教程

方式二

• 有能力用户可以进行尝试,通过任何方法都可以,在空仓库内(保证分支名称为main),按需运行一次 https://github.com/Ca11back/doge-template 的action即可
• 第二种:直接clone一个scf2分支(名字需要为main,clone哪个取决于你要用的部署方式)

自动同步本仓库脚本教程：点此查看

使用教程

• 【青龙】拉取仓库命令：ql repo https://github.com/zero205/JD_tencent_scf.git "jd_|jx_|getJDCookie" "backUp|icon" "^jd[^_]|USER|sendNotify|sign_graphics_validate|JDJR|JDSign" "main"

  • 上面命令拉取错误的使用这个：ql repo https://ghproxy.com/https://github.com/zero205/JD_tencent_scf.git "jd_|jx_|getJDCookie" "backUp|icon" "^jd[^_]|USER|sendNotify|sign_graphics_validate|JDJR|JDSign" "main"

• 腾讯云函数部署建议阅读@hshx123大佬的教程【推荐】.旧教程【备用】

• elecV2P 部署【随缘维护，建议转战➟Docker 青龙】

  • 安装教程：点此查看
  • 订阅任务：https://ghproxy.com/https://raw.githubusercontent.com/zero205/JD_tencent_scf/main/jd_task.json
  • elecV2P京东CK、通知填写格式等相关补充说明：点此查看

• 获取京东cookie教程可参考：

  • 浏览器获取京东cookie教程

  • 插件获取京东cookie教程

  • 浏览器扩展程序获取京东cookie教程

特别声明:

• 本仓库发布的Script项目中涉及的任何解锁和解密分析脚本，仅用于测试和学习研究，禁止用于商业用途，不能保证其合法性，准确性，完整性和有效性，请根据情况自行判断.

• 本项目内所有资源文件，禁止任何公众号、自媒体进行任何形式的转载、发布。

• lxk0301对任何脚本问题概不负责，包括但不限于由任何脚本错误导致的任何损失或损害.

• 间接使用脚本的任何用户，包括但不限于建立VPS或在某些行为违反国家/地区法律或相关法规的情况下进行传播, lxk0301 对于由此引起的任何隐私泄漏或其他后果概不负责.

• 请勿将Script项目的任何内容用于商业或非法目的，否则后果自负.

• 如果任何单位或个人认为该项目的脚本可能涉嫌侵犯其权利，则应及时通知并提供身份证明，所有权证明，我们将在收到认证文件后删除相关脚本.

• 任何以任何方式查看此项目的人或直接或间接使用该Script项目的任何脚本的使用者都应仔细阅读此声明。lxk0301 保留随时更改或补充此免责声明的权利。一旦使用并复制了任何相关脚本或Script项目的规则，则视为您已接受此免责声明.

您必须在下载后的24小时内从计算机或手机中完全删除以上内容.

您使用或者复制了本仓库且本人制作的任何脚本，则视为已接受此声明，请仔细阅读

环境变量

• 环境变量集合

特别感谢(排名不分先后)：

• @NobyDa

• @chavyleung

• @liuxiaoyucc

• @Zero-S1

• @uniqueque

• @nzw9314

• @JDHelloWorld

• @smiek2221

• @star261

• @Wenmoux

• @Tsukasa007

• @Aaron

Data Science for Beginners - A Curriculum

Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

Hearty thanks to our authors: Jasmine Greenaway, Dmitry Soshnikov, Nitya Narasimhan, Jalen McGee, Jen Looper, Maud Levy, Tiffany Souterre, Christopher Harrison.

🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers and content contributors, notably Aaryan Arora, Aditya Garg, Alondra Sanchez, Ankita Singh, Anupam Mishra, Arpita Das, ChhailBihari Dubey, Dibri Nsofor, Dishita Bhasin, Majd Safi, Max Blum, Miguel Correa, Mohamma Iftekher (Iftu) Ebne Jalal, Nawrin Tabassum, Raymond Wangsa Putra…

This project is supported by:

croc is a tool that allows any two computers to simply and securely transfer files and folders. AFAIK, croc is the only CLI file-transfer tool that does all of the following:

• allows any two computers to transfer data (using a relay)
• provides end-to-end encryption (using PAKE)
• enables easy cross-platform transfers (Windows, Linux, Mac)
• allows multiple file transfers
• allows resuming transfers that are interrupted
• local server or port-forwarding not needed
• ipv6-first with ipv4 fallback
• can use proxy, like tor

For more information about croc, see my blog post.

Install

Download the latest release for your system, or install a release from the command-line:

curl https://getcroc.schollz.com | bash

On macOS you can install the latest release with Homebrew:

brew install croc

On macOS you can also install the latest release with MacPorts:

sudo port selfupdate
sudo port install croc

On Windows…

English | 简体中文 | العربية

QuestDB

QuestDB is a high-performance, open-source SQL database for applications in financial services, IoT, machine learning, DevOps and observability. It includes endpoints for PostgreSQL wire protocol, high-throughput schema-agnostic ingestion using InfluxDB Line Protocol, and a REST API for queries, bulk imports, and exports.

QuestDB implements ANSI SQL with native extensions for time-oriented language features. These extensions make it simple to correlate data from multiple sources using relational and time series joins. QuestDB achieves high performance from a column-oriented storage model, massively-parallelized vector execution, SIMD instructions, and various low-latency techniques. The entire codebase was built from the ground up in Java and C++, with no dependencies, and is 100% free from garbage collection.

Try QuestDB

We provide a live demo provisioned with the latest QuestDB release and a 1.6 billion row dataset with 10 years of NYC taxi trips to query.

To run QuestDB, Docker can…

🎉 Features

• 💪 Up to 58 high-quality Components.
• 💅 Thousands Design Tokens. Powerful Themes Customizing.
• 🌍 Internationalization Support for Dozens of Languages.
• 👏 Written in Typescript, Friendly Static Type Support.
• 🥳 SSR (Server Side Rendering) Compatible.

🔥 Install

# with npm
npm install @douyinfe/semi-ui

# with yarn
yarn add @douyinfe/semi-ui

👍 Usage

Here is a quick example to get you started, it's all you need:

import React from 'react';
import ReactDOM from 'react-dom';
import { Button, Switch } from '@douyinfe/semi-ui';
const App = () => (
  <>
    <Button type='primary'>primary button</Button>
    <Switch size='large' />
  </>
);

ReactDOM.render(<App />, document.querySelector('#app')

Tutorial | Examples | Forum

Documentation | 简体中文文档 | Contributor Guidelines

Overview

Taichi (太极) is a parallel programming language for high-performance numerical computations. It is embedded in Python, and its just-in-time compiler offloads compute-intensive tasks to multi-core CPUs and massively parallel GPUs.

Advanced features of Taichi include spatially sparse computing, differentiable programming [examples], and quantized computation.

Please check out our SIGGRAPH 2020 course on Taichi basics: YouTube, Bilibili, slides (pdf).

中文视频教程: [哔哩哔哩], [幻灯片]

Examples (More...)

Installation

python3 -m pip install taichi

Supported OS: Windows, Linux, Mac OS X; Python: 3.6-3.9 (64-bit only); Backends: x64 CPUs, CUDA, Apple Metal, Vulkan, OpenGL Compute Shaders.

Please build from source for other configurations (e.g., your CPU is ARM, or you want to try out our experimental C backend).

Note:

• The PyPI package supports x64 CPU, CUDA 10/11, Metal, Vulkan and OpenGL…