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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…

ethereum-boilerplate

React components and hooks for fast building dApps without running own backend

This boilerplate is built on react-moralis and Moralis. Also has its own context provider for quick access to chainId or ethAddress

There are many components in this boilerplate that do not require an active web3 provider, they use Moralis Web3 API. Moralis supports the most popular blockchains and their test networks. You can find a list of all available networks in Moralis Supported Chains

Please check the official documentation of Moralis for all the functionalities of Moralis.

⭐️ Star us

If this boilerplate helps you build Ethereum dapps faster - please star this project, every star makes us very happy!

🚀 Quick Start

📄 Clone or fork ethereum-boilerplate:

git clone https://github.com/ethereum-boilerplate/ethereum-boilerplate.git

💿 Install all dependencies:

cd ethereum-boilerplate
yarn install 

✏ Rename .env.example to .env in the main folder and provide your appId and serverUrl from…

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…

Dogecoin Core [DOGE, Ð]

Select language: EN | CN

Dogecoin is a community-driven cryptocurrency that was inspired by a Shiba Inu meme. The Dogecoin Core software allows anyone to operate a node in the Dogecoin blockchain networks and uses the Scrypt hashing method for Proof of Work. It is adapted from Bitcoin Core and other cryptocurrencies.

For information about the default fees used on the Dogecoin network, please refer to the fee recommendation.

Website: dogecoin.com

Usage 💻

To start your journey with Dogecoin Core, see the installation guide and the getting started tutorial.

The JSON-RPC API provided by Dogecoin Core is self-documenting and can be browsed with dogecoin-cli help, while detailed information for each command can be viewed with dogecoin-cli help <command>. Alternatively, see the Bitcoin Core documentation - which implement a similar protocol - to get a browsable version.

Such ports

Dogecoin Core by default uses…

👨‍💻CodeArt🎨

📸 Screenshots 🚧 Installation ⬆️📅 How to update CodeArt ✨ Important features ✅ TODOS

🚧 Install CodeArt easily

To install CodeArt you must have python3 and pip3 installed.

🐧 On linux:

sudo pip3 install distro
python3 install.py

⚠️ IMPORTANT NOTE: The installer for MacOS is not tested since I don't have a device with macOS (and the installer does not work on debian)

🍎 On macOS:

python3 install.py

🪟 on Windows:

please backup any existing configuration files

⚡🐚 open a powershell session as administrator and run these commands one by one:

Set-ExecutionPolicy AllSigned
Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","

Machine Learning for Beginners - A Curriculum

🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well!

Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

✍️ Hearty thanks to our…

NVIDIA Deep Learning Examples for Tensor Cores

Introduction

This repository provides State-of-the-Art Deep Learning examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X software stack running on NVIDIA Volta, Turing and Ampere GPUs.

NVIDIA GPU Cloud (NGC) Container Registry

These examples, along with our NVIDIA deep learning software stack, are provided in a monthly updated Docker container on the NGC container registry (https://ngc.nvidia.com). These containers include:

• The latest NVIDIA examples from this repository
• The latest NVIDIA contributions shared upstream to the respective framework
• The latest NVIDIA Deep Learning software libraries, such as cuDNN, NCCL, cuBLAS, etc. which have all been through a rigorous monthly quality assurance process to ensure that they provide the best possible performance
• Monthly release notes for each of the NVIDIA optimized containers

Computer Vision

@solana/spl-token-registry

Solana Token Registry is a package that allows application to query for list of tokens The JSON schema for the tokens includes: chainId, address, name, decimals, symbol, logoURI (optional), tags (optional), and custom extensions metadata.

Installation

npm install @solana/spl-token-registry

yarn add @solana/spl-token-registry

Examples

Query available tokens

new TokenListProvider().resolve().then((tokens) => {
  const tokenList = tokens.filterByClusterSlug('mainnet-beta').getList();
  console.log(tokenList);
});

Render icon for token in React

import React, { useEffect, useState } from 'react';
import { TokenListProvider, TokenInfo } from '@solana/spl-token-registry';
export const Icon = (props: { mint: string }) => {
  const [tokenMap, setTokenMap] = useState<Map<string, TokenInfo>>(new Map())

ruDALL-E

Generate images from texts

pip install rudalle==0.0.1rc8

🤗 HF Models:

ruDALL-E Malevich (XL)

Minimal Example:

Example usage ruDALL-E Malevich (XL) with 3.5GB vRAM!

Finetuning example

generation by ruDALLE:

from rudalle.pipelines import generate_images, show, super_resolution, cherry_pick_by_clip
from rudalle import get_rudalle_model, get_tokenizer, get_vae, get_realesrgan, get_ruclip
from rudalle.utils import seed_everything
# prepare models:
device = 'cuda'
dalle = get_rudalle_model('Malevich', pretrained=True, fp16=True, device=device)
tokenizer = get_tokenizer()
vae = get_vae(dwt=True).to(device)

# pipeline utils:
realesrgan = get_realesrgan('x2', device=device)
ruclip, ruclip_processor = get_ruclip('ruclip-vit-base-patch32-v5')
ruclip = ruclip.to(device)

text = 'изображение радуги на фоне ночного города'

seed_everything(42)
pil_images = []
scores = []
for top_k, top_p

Python-Mini-Projects

A collection of simple python mini projects to enhance your Python skills.

If you want to learn about python, visit here.

If you are new to Github and open source then, visit here.

Steps To Follow

• Select an issue and ask to be assigned to it.

• Check existing scripts in the projects directory.

• Star this repository.

• On the python-mini-projects repo page, click the Fork button
  

• Clone your forked repository to your local machine. This button will show you the URL to run
  

  For example, run this command inside your terminal:

  git clone https://github.com/<your-github-username>/python-mini-projects.git

  Replace <your-github-username>!

  Learn more about forking and cloning a repo.

• Before you make any changes, keep your fork in sync to avoid merge conflicts:

  git remote add upstream https://github.com/Python-World/python-mini-projects.git
  git fetch upstream
  git pull upstream master
  git push

  Alternatively, GitHub also provides syncing now - click "Fetch upstream" at the top…