Flutter Plugin-Push Kit - Developer Platform


A new Flutter plugin.


在工程 pubspec.yaml 中加入 dependencies

        flutter_huaji_push: 1.0.0

、## 使用



  • iOS端需要在注册方法startXg之前调用以下域名配置函数
    • domainStr 对应集群域名
       void configureClusterDomainName(String domainStr);
  • Android端需要在Manifest 文件 application 标签内添加以下元数据:
     // 其他安卓组件
         android:value="其他地区域名" />


  • 在 xcode8 之后需要点开推送选项: TARGETS -> Capabilities -> Push Notification 设为 on 状态
import 'package:flutter_huaji_push/flutter_huaji_push.dart';


1. 环境配置

       android: {
          defaultConfig {
            applicationId "替换成自己应用 ID"
            ndk {
         /// 选择要添加的对应.so 库。
         abiFilters 'armeabi', 'armeabi-v7a', 'x86', 'x86_64', 'mips', 'mips64', 'arm64-v8a',
            manifestPlaceholders = [
                XG_ACCESS_ID : "替换自己的ACCESS_ID",  // 信鸽官网注册所得ACCESS_ID
                XG_ACCESS_KEY : "替换自己的ACCESS_KEY",  // 信鸽官网注册所得ACCESS_KEY


2. 代码混淆

       -keep public class * extends android.app.Service
       -keep public class * extends android.content.BroadcastReceiver
       -keep class com.tencent.android.tpush.** {*;}
       -keep class com.tencent.tpns.baseapi.** {*;}
       -keep class com.tencent.tpns.mqttchannel.** {*;}
       -keep class com.tencent.tpns.dataacquisition.** {*;}

       -keep class com.tencent.bigdata.baseapi.** {*;}   // TPNS-Android-SDK 及以上版本不需要此条配置
       -keep class com.tencent.bigdata.mqttchannel.** {*;}  // TPNS-Android-SDK 及以上版本不需要此条配置

3. 厂商通道接入说明

说明 : 提供安卓各厂商通道接入方法。



说明 : 提供TPNS的所有业务接口。


TPNS-Flutter 使用常见问题参考


Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add flutter_huaji_push

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

  flutter_huaji_push: ^1.0.12

Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:flutter_huaji_push/flutter_huaji_push.dart'; 


import 'package:flutter/material.dart';
import 'dart:async';

import 'package:flutter/services.dart';
import 'package:flutter_huaji_push/flutter_huaji_push.dart';

void main() {

class MyApp extends StatefulWidget {
  _MyAppState createState() => _MyAppState();

class _MyAppState extends State<MyApp> {
  String _platformVersion = 'Unknown';

  void initState() {

  // Platform messages are asynchronous, so we initialize in an async method.
  Future<void> initPlatformState() async {
    String platformVersion;
    // Platform messages may fail, so we use a try/catch PlatformException.
    try {
      platformVersion = await FlutterHuajiPush.xgSdkVersion;
    } on PlatformException {
      platformVersion = 'Failed to get platform version.';

    // If the widget was removed from the tree while the asynchronous platform
    // message was in flight, we want to discard the reply rather than calling
    // setState to update our non-existent appearance.
    if (!mounted) return;

    setState(() {
      _platformVersion = platformVersion;

  Widget build(BuildContext context) {
    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(
          title: const Text('Plugin example app'),
        body: Center(
          child: Text('Running on: $_platformVersion\n'),

Download Details:

Author: wskkhn-hezhong

Source Code: https://github.com/wskkhn-hezhong/flutter_huaji_push

#flutter #platform #push 

Flutter Plugin-Push Kit - Developer Platform
Royce  Reinger

Royce Reinger


EliteQuant: A List Of online Resources for Quantitative Modeling


A list of online resources for quantitative modeling, trading, portfolio management

There are lots of other valuable online resources. We are not trying to be exhaustive. Please feel free to send a pull request if you believe something is worth recommending. A general rule of thumb for open source projects is having already received 100 stars on github.

Quantitative Trading Platform

awesome-quant - Awesome quant is another curated list of quant resources

Quantopian - First Python-based online quantitative trading platform; its core library zipline and its performance evaluation library pyfolio; and alphalens

QuantConnect - C# based online quantitative trading platform; its core library Lean

Quantiacs - The Marketplace For Algorithmic Trading Strategies; its Matlab and Python toolbox

Numerai - crowd-sourced trading strategies; its Python API

Collective2 - The platform that allows investors subscribe to top-traders; its algotrades system

ZuluTrade - The platform that allows investors subscribe to top-traders

Tradingview - It provides free widgets used for example Huobi

Investing.com - Real time multi-assets and markets

KloudTrader Narwhal - Trading algorithm deployment platform with flat-rate commission-free brokerage

Trading System

MetaTrader 5 - Multi-Asset trading system

TradeStation - Trading system

SmartQuant(OpenQuant) - C# Trading system

RightEdge - Trading system

AmiBroker - Trading system

Algo Terminal - C# Trading system

NinjaTrader - Trading system

QuantTools - Enhanced Quantitative Trading Modelling in R

vnpy - A popular and powerful trading platform

pyalgotrade - Python Algorithmic Trading Library

finmarketpy - Python library for backtesting trading strategies

IBridgePy - A Python system derived from zipline

Backtrader - Blog, trading community, and github

IbPy - Interactive Brokers Python API

PyLimitBook - Python implementation of fast limit-order book

qtpylib - Pythonic Algorithmic Trading via IbPy API and its Website

Quantdom - Python-based framework for backtesting trading strategies & analyzing financial markets [GUI]

ib_insync - Python sync/async framework for Interactive Brokers API

rqalpha - A popular trading platform

bt - flexible backtesting for Python

TradingGym - Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

btgym - Gym-compatible backtesting

prophet - Python backtesting and trading platform

OpenHFT - Java components for high-frequency trading

libtrading - C API, low latency, fix support

thOth - open-source high frequency trading library in C++ 11

qt_tradingclient - multithreaded Qt C++ trading application, QuantLib-1.2.1, CUDA 5.0

SubMicroTrading - Java Ultra Low Latency Trading Framework

WPF/MVVM Real-Time Trading Application - Architechture

TradeLink - TradeLink, one of the earliest open source trading system

Reactive Trader - using reactive Rx framework, includes Reactive Trader and Reactive Trader Cloud. The demo is here.

QuantTrading - Pure C# trading system

StockTrading - C# system utilising WPF, WCF, PRISM, MVVM, Threading

Quanter - StockTrader

StockSharp - C# trading system

SharpQuant - C# trading system

QuantSys - C# trading system

StockTicker - C# trading system

gotrade - Electronic trading and order management system written in Golang

gofinance - Financial information retrieval and munging in golang

goib - Pure Go interface to Interactive Brokers IB API

Matlab Trading Toolbox - Official toolbox from Matlab; acommpanying Introduction to Matlab Trading Toolbox, and webinar Automated Trading System Development with MATLAB, and webinar Automated Trading with MATLAB, as well as webinar A Real-Time Trading System in MATLAB, Automated Trading with Matlab, Commodities Trading with Matlab, Cointegration and Pairs Trading with Econometrics Toolbox

Matlab risk management Toolbox - Official toolbox from Matlab

Matlab Walk Forward Analysis Toolbox - toolbox for walk-forward analysis

IB4m - matlab interface to interactive broker

IB-Matlab - introduction to another matlab interface to interactive broker and demo video

openAlgo Matlab - openAlgo's Matlab library

MatTest - Matlab backtest system

Quantitative Library

Quantlib - famous C++ library for quantitative finance; tranlated into other langugages via Swig

TA-Lib - Python wrapper for TA-Lib

DX Analytics - Python-based financial analytics library

FinMath - Java analytics library

OpenGamma - Java analytics library named STRATA

Quantiacs - Matlab toolbox

pyflux - Open source time series library for Python

arch - ARCH models in Python

flint - A Time Series Library for Apache Spark

Statsmodels - Statsmodels’s Documentation

Quantitative Model

awesome-deep-trading - A list of machine learning resources for trading

Awesome-Quant-Machine-Learning-Trading - Another list of machine learning resources for trading

awesome-ai-in-finance - A collection of AI resources in finance

deepstock - Technical experimentations to beat the stock market using deep learning

qtrader - Reinforcement Learning for Portfolio Management

stockPredictor - Predict stock movement with Machine Learning and Deep Learning algorithms

stock_market_reinforcement_learning - Stock market environment using OpenGym with Deep Q-learning and Policy Gradient

deep-algotrading - deep learning techniques from regression to LSTM using financial data

deep_trader - Use reinforcement learning on stock market and agent tries to learn trading.

Deep-Trading - Algorithmic trading with deep learning experiments

Deep-Trading - Algorithmic Trading using RNN

100 Day Machine Learning - Machine Learning tutorial with code

Multidimensional-LSTM-BitCoin-Time-Series - Using multidimensional LSTM neural networks to create a forecast for Bitcoin price

QLearning_Trading - Learning to trade under the reinforcement learning framework

bulbea - Deep Learning based Python Library for Stock Market Prediction and Modelling

PGPortfolio - source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"

gym-trading - Environment for reinforcement-learning algorithmic trading models

Thesis - Reinforcement Learning for Automated Trading

DQN - Reinforcement Learning for finance

Deep-Trading-Agent - Deep Reinforcement Learning based Trading Agent for Bitcoin

deep_portfolio - Use Reinforcement Learning and Supervised learning to Optimize portfolio allocation.

Deep-Reinforcement-Learning-in-Stock-Trading - Using deep actor-critic model to learn best strategies in pair trading

Stock-Price-Prediction-LSTM - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

DeepDow - Portfolio optimization with deep learning

Personae - Quantitative trading with deep learning

tensortrade - Reinforcement learning and trading

stockpredictionai - AI models such as GAN and PPO applied to stock markets

machine-learning-for-trading - Machine learning for algorithmic trading book

algorithmic-trading-with-python - Algorithmic Trading with Python book (2020)

machine-learning-asset-management - Machine Learning in Asset Management by firmai.org

Trading API

Interactive Brokers - popular among retail trader

Bloomberg API - from Bloomberg

Data Source

Quandl - free and premium data sources

iex - free market data

one tick - historical tick data

iqfeed - real time data feed

quantquote - tick and live data

algoseek - historical intraday

EOD data - historical data

EOD historical data - historical data

intrinio - financial data

arctic - High performance datastore from Man AHL for time series and tick data

SEC EDGAR API -- Query company filings on SEC EDGAR


Blockchain-stuff - Blockchain and Crytocurrency Resources

cryptrader - Node.js Bitcoin bot for MtGox/Bitstamp/BTC-E/CEX.IO; cryptrade

BitcoinExchangeFH - Cryptocurrency exchange market data feed handler

hummingbot - free open source crypto trading bot that supports both DEXes and CEXes

blackbird - C++ trading system that does automatic long/short arbitrage between Bitcoin exchanges

Peatio - An open-source crypto currency exchange on github

Qt Bitcoin Trader - Qt C++ Bitcoin trading

ccxt - A JavaScript / Python / PHP cryptocurrency trading library with support for more than 130 bitcoin/altcoin exchanges

r2 - Qan automatic arbitrage trading system powered by Node.js + TypeScript

bcoin - Javascript bitcoin library for node.js and browsers

XChange - Java library providing a streamlined API for interacting with 60+ Bitcoin and Altcoin exchanges

Krypto-trading-bot - Self-hosted crypto trading bot (automated high frequency market making) in node.js, angular, typescript and c++

freqtrade - Simple High Frequency Trading Bot for crypto currencies

Gekko - A bitcoin trading bot written in node

viabtc_exchange_server - A trading engine with high-speed performance and real-time notification

catalyst - An Algorithmic Trading Library for Crypto-Assets in Python Enigma

buttercoin - Opensource Bitcoin Exchange Software

zenbot - A command-line cryptocurrency trading bot using Node.js and MongoDB.

tribeca - A high frequency, market making cryptocurrency trading platform in node.js

rbtc_arbitrage - A gem for automating arbitrage between Bitcoin exchanges.

automated-trading - Automated Trading: Trading View Strategies => Bitfinex, itBit, DriveWealth

gocryptotrader - A cryptocurrency trading bot and framework supporting multiple exchanges written in Golang

btcrobot - Golang bitcoin trading bot

bitex - Open Source Bitcoin Exchange; and its front-end

cryptoworks - A cryptocurrency arbitrage opportunity calculator. Over 800 currencies and 50 markets; cryptocurrency-arbitrage

crypto-exchange - list of crypto exchanges to interact with their API's in a uniform fashion

bitcoin-abe - block browser for Bitcoin and similar currencies

MultiPoolMiner - Monitors crypto mining pools in real-time in order to find the most profitable for your machine. Controls any miner that is available via command line

tai - An open source, composable, real time, market data and trade execution toolkit. Written in Elixir

crypto-signal - Technical signals for multiple exchanges


Not trying to be exhaustive

Sell Side

FIA PTG and FIA Europe

Allston Trading



D.E. Shaw


Flow Traders





Jane Street

Jump Trading



Quantlab Financial


Bridgewater Associates

Man Group, AHL


Tower Research

Tradebot Systems

Two Sigma

Virtu Financial

XR Trading

XTX Markets

Commodity Focused












Websites Forums Blogs

Top Geeky Quant Blogs - A quant blogs check out list

Quantocracy - Aggregation of news on quants

seekingalpha - Seeking Alpha community

Quantivity - quantitative and algorithmic trading

Wilmott - quantitative finance community forum

Elitetrader - trading forum

nuclearphynance - quantitative finance forum

Investopedia - The Encyclopedia of investments

Quantpedia - The Encyclopedia of Quantitative Trading Strategies

EpChan - Dr. Ernie Chan's blog

Quantinsti - Quant Institute

QuantStart - Michael Halls-Moore's quantstart, quant trading 101; its Python backtest platform qstrader and qsforex

Algotrading 101 - Algo trading 101

Systematic Investor/old version - Michael Kapler's blog, one of the best R quantitative blog; Systematic Investor Toolkit

R-Finance - R-Finance repository. It has backtest quantstrat, trade blotter, famous performance analytics package, and package portfolio analytics, portfolio attribution.

quantmod - R modelling and trading framework

r programming - Guy Yollin's R backtesting

Seer Trading - R Backtest and live trading

Trading with Python

python programming finance - python finance tutorial and quantopian toturial

python for finance - python finance

Quant Econ - open source python and julia codes for economic modeling; and lectures

JuliaQuant - Quantitative Finance in Julia

Portfolio Effect - real time portfolio and risk management

quant365 - Henry Moo's blog and trading system; including Sentosa, pysentosa binding, rsentosa binding and qblog.

hpc quantlib - HPC + QuantLib

Quant Corner

quantstrat trader - Backtesting trading ideas with R QuantStrat package

Backtesting Strategies - Backtesting in R; codes at Github

The Quant MBA - good quant blog

Foss Trading - Algorithmic trading with free open source software

Gekko Quant - Quantitative Trading

Investment Idiocy - Systematic Trading, Quantitative Finance, Investing, Financial Activism, Economic decision making by Robert Carver; his book and his Python library

Quantifiable Edges/old version - Assessing market action with indicators and history

My Simple Quant - Market analysis utilizing historical, back-tessted data

Vix and more - discussions on Vix

Timely Portfolio - Strategies and tests in R

Quantitative Research and Trading

Qusma - Quantitative Systematic Market Analysis

return and risk - Quantitative finance, analysis, and applications

Physics of Finance - Inspiration from physics for thinking about economics, finance and social systems

Quantum Financier - algorithmic trading

Trading the Odds -- market timing & quantitative analysis

CSSA - new concepts in quantitative research

The Practical Quant

Tr8dr - strategies, statistics, computer science, numerical techniques

Deniz's Note - blog of a quant Deniz Turan

Quant at risk - quantitative analysis and risk management

Quant Blog - Quantitative trading, portfolio management, and machine learning, with source codes on Github

The R Trader - Using R in quant finance

rbresearch - Using R for trading strategy ideas in FX and equity markets

NaN Quantivity - quant trading, statistical learning, coding and brainstorming

Factor Investing - blog on wordpress

Meb Faber Research

Big Mike Trading - Youtube chanel in quant trading

Mechanical Markets

Humble Student of the Markets

Predict Stock Prices Using RNN

BlackArbs - blog and machine learning notebooks on Github

Download Details:

Author: EliteQuant
Source Code: https://github.com/EliteQuant/EliteQuant 
License: Apache-2.0 license

#machinelearning #trading #platform #quantitative #finance #algorithmic 

EliteQuant: A List Of online Resources for Quantitative Modeling
Monty  Boehm

Monty Boehm


Using a Cloud Data Platform

The global cloud computing market is a rapidly growing one, valued at over 405.65 billion USD in 2021. According to predictions from Fortune Business Insights, professionals in the cloud computing industry are expected to enjoy its impressive compound annual growth rate (CAGR) of 19.9% and increasing demand across many regions.

To beginner developers and those who are looking into digitally transforming their businesses, the concept of cloud data platforms might be a bit difficult to grasp. But worry not – in this article, we’ll be tackling all you need to know about the basics of cloud data platforms.

1. What is a cloud data platform?

A post on ‘What is a Data Platform?’ by MongoDB defines it as a set of technologies that can completely meet an organization’s end-to-end data needs. ‘End-to-end’ often refers to everything from the collection, storage, preparation, delivery, and safekeeping of data.

Cloud data platforms were essentially created as a solution to the problems that big data posted over 20 years ago.

Now, cloud data platforms and tools are the new norm, and have been developed to handle large data volumes at incredible speeds.

2. Why run your database on the cloud?

One of the major considerations for those who are looking into cloud platforms is how it differs from running databases on-premises.

An article by TechTarget explains that any IaaS or DBaaS is similar to running an on-premise database. Traditionally, organizations build data centers and servers with everything needed to manage data. Instead, major cloud platform providers can provide these services and tools to minimize the work needed for developers. Time and resources can then be spent on the development process itself.

3. How do I build a cloud database?

First, make sure to look into database management systems that are compatible with your OS. Most of the top providers can be installed on Windows and MacOS, but some are more suited for specific operating systems. There are also three different types of cloud databases to choose from: self-managed, autonomous, and automated cloud databases. We recommend doing your research to choose the best database category for the type of program or application you are creating.

Next, you will need to learn about the process of data normalization, which refers to the structured approach to organizing a database. This reduces the chances of data redundancy and ensures that the database is easy to navigate.

  1. Adding the primary key to a database table: Each database row is represented by a key that builds relationships within the database. An example of a key can be any arrangement of unique characters or any number chain.
  2. Creating smaller tables: Split your database into smaller tables along with primary keys.
  3. Configure relationships: Now that you have separate tables consisting of different information, you can start building relationships. For instance, a customer-focused table can be used as a parent table, and a pending orders table can be considered a child table.
  4. Relationship types: Relationships can be one-to-one, one-to-many, and many-to-many. Although this is quite self-explanatory, it might take a bit of trial and error to figure out what works best for the database you’re building.

4. More advanced information to keep in mind

Managed cloud database services that can handle aspects like the necessary hardware, automated backups, and storage capacity management.

Most providers are also written in popular languages such as JavaScript, Python, and Ruby, which makes them accessible even to beginners. Regardless of your experience as a developer, cloud database building is essentially the same at the core. Most of the work lies in understanding the structure of your cloud database, and keeping your records and attributes organized.

6. Conclusion

In the next decade, we will continue to see a rise in the usage of cloud data platforms and their impact on industries like retail, finance, manufacturing, healthcare, and even for government use. It’s a great time to learn about this technology and its many uses.

Original article source at: https://makitweb.com/

#cloud #data #platform 

Using a Cloud Data Platform
Lawrence  Lesch

Lawrence Lesch


Open-source Web IDE, Scalable Runtime & Platform for Serverless


Open-source developer infrastructure for internal tools. Self-hostable alternative to Airplane, Pipedream, Superblocks and a simplified Temporal with autogenerated UIs to trigger workflows and scripts as internal apps. Scripts are turned into UIs and no-code modules, no-code modules can be composed into very rich flows, and script and flows can be triggered from internal UIs made with a low-code builder. The script languages supported are: Python, Typescript, Go, Bash.


Disclaimer: Windmill is in BETA. It is secure to run in production but we are still improving the product fast.

Windmill Screenshot Windmill Screenshot

Main Concepts

Define a minimal and generic script in Python, Typescript, Go or Bash that solves a specific task. Here sending an email with SMTP. The code can be defined in the provided Web IDE or synchronized with your own github repo: Step 1

Your scripts parameters are automatically parsed and generate a frontend. Step 2 Step 3

Make it flow! You can chain your scripts or scripts made by the community shared on WindmillHub. Step 4

Build complex UI on top of your scripts and flows. Step 5

Scripts and flows can also be triggered by a cron schedule '*/5 * * * *' or through webhooks.

You can build your entire infra on top of Windmill!


We have a powerful CLI to interact with the windmill platform and sync your scripts from your own github repo. See more details

CLI Screencast


  • backend/: Rust backend
  • frontend: Svelte frontend
  • lsp/: Lsp asssistant for the monaco editor
  • <lang>-client/: Windmill client for the given <lang>


  • Postgres as the database
  • backend in Rust with the following highly-available and horizontally scalable architecture:
    • stateless API backend
    • workers that pull jobs from a queue in Postgres (and later, Kafka or Redis. Upvote #173 if interested )
  • frontend in Svelte
  • scripts executions are sandboxed using google's nsjail
  • javascript runtime is the deno_core rust library (which itself uses the rusty_v8 and hence V8 underneath)
  • typescript runtime is deno
  • python runtime is python3
  • golang runtime is 1.19.1


Sandboxing and workload isolation

Windmill uses nsjail on top of the deno sandboxing. It is production multi-tenant grade secure. Do not take our word for it, take fly.io's one

Secrets, credentials and sensitive values

There is one encryption key per workspace to encrypt the credentials and secrets stored in Windmill's K/V store.

In addition, we strongly recommend that you encrypt the whole Postgres database. That is what we do at https://app.windmill.dev.


Once a job started, there is no overhead compared to running the same script on the node with its corresponding runner (Deno/Go/Python/Bash). The added latency from a job being pulled from the queue, started, and then having its result sent back to the database is ~50ms. A typical lightweight deno job will take around 100ms total.



Big-picture Architecture

Technical Architecture


How to self-host

We only provide docker-compose setup here. For more advanced setups, like compiling from source or using without a postgres super user, see documentation

Docker compose

docker compose up with the following docker-compose is sufficient: https://github.com/windmill-labs/windmill/blob/main/docker-compose.yml

Go to https://localhost et voilà :)

For older kernels < 4.18, set DISABLE_NUSER=true as env variable, otherwise nsjail will not be able to launch the isolated scripts.

To disable nsjail altogether, set DISABLE_NSJAIL=true.

The default super-admin user is: admin@windmill.dev / changeme

From there, you can create other users (do not forget to change the password!)

Kubernetes (k8s) and Helm charts

We publish helm charts at: https://github.com/windmill-labs/windmill-helm-charts

Commercial license

To self-host Windmill, you must respect the terms of the AGPLv3 license which you do not need to worry about for personal uses. For business uses, you should be fine if you do not re-expose it in any way Windmill to your users and are comfortable with AGPLv3.

To re-expose any Windmill parts to your users as a feature of your product, or to build a feature on top of Windmill, to comply with AGPLv3 your product must be AGPLv3 or you must get a commercial license. Contact us at license@windmill.dev if you have any doubts.

In addition, a commercial license grants you a dedicated engineer to transition your current infrastructure to Windmill, support with tight SLA, audit logs export features, SSO, unlimited users creation, advanced permission managing features such as groups and the ability to create more than one workspace.

OAuth for self-hosting (very optional)

To get the same oauth integrations as Windmill Cloud, mount oauth.json with the following format:

  "<client>": {
    "id": "<CLIENT_ID>",
    "secret": "<CLIENT_SECRET>",
    "allowed_domains": ["windmill.dev"] //restrict a client OAuth login to some domains

and mount it at /usr/src/app/oauth.json.

The redirect url for the oauth clients is: <instance_url>/user/login_callback/<client>

The list of all possible "connect an app" oauth clients

To add more "connect an app" OAuth clients to the Windmill project, read the Contributor's guide. We welcome contributions!

You may also add your own custom OAuth2 IdP and OAuth2 Resource provider:

  "<client>": {
    "id": "<CLIENT_ID>",
    "secret": "<CLIENT_SECRET>",
    // To add a new OAuth2 IdP
    "login_config": {
      "auth_url": "<auth_endpoint>",
      "token_url": "<token_endpoint>",
      "userinfo_url": "<userinfo endpoint>",
      "scopes": ["scope1", "scope2"],
      "extra_params": "<if_needed>"
    // To add a new OAuth2 Resource
    "connect_config": {
      "auth_url": "<auth_endpoint>",
      "token_url": "<token_endpoint>",
      "scopes": ["scope1", "scope2"],
      "extra_params": "<if_needed>"

Resource types

You will also want to import all the approved resource types from WindmillHub. There is no automatic way to do this automatically currently, but it will be possible using a command with the upcoming CLI tool.

Environment Variables

Environment Variable nameDefaultDescriptionApi Server/Worker/All
DATABASE_URL The Postgres database url.All
DISABLE_NSJAILtrueDisable Nsjail Sandboxing 
NUM_WORKERS3The number of worker per Worker instance (set to 1 on Eks to have 1 pod = 1 worker)Worker
METRICS_ADDRNoneThe socket addr at which to expose Prometheus metrics at the /metrics path. Set to "true" to expose it on port 8001All
JSON_FMTfalseOutput the logs in json format instead of logfmtAll
BASE_URLhttp://localhost:8000The base url that is exposed publicly to access your instanceServer
BASE_INTERNAL_URLhttp://localhost:8000The base url that is reachable by your workers to talk to the Servers. This help avoiding going through the external load balancer for VPC-internal requests.Worker
TIMEOUT300The timeout in seconds for the execution of a scriptWorker
SLEEP_QUEUE50The number of ms to sleep in between the last check for new jobs in the DB. It is multiplied by NUM_WORKERS such that in average, for one worker instance, there is one pull every SLEEP_QUEUE ms.Worker
MAX_LOG_SIZE500000The maximum number of characters a job can emit (log + result)Worker
DISABLE_NUSERfalseIf Nsjail is enabled, disable the nsjail's clone_newuser settingWorker
KEEP_JOB_DIRfalseKeep the job directory after the job is done. Useful for debugging.Worker
LICENSE_KEY (EE only)NoneLicense key checked at startup for the Enterprise Edition of WindmillWorker
S3_CACHE_BUCKET (EE only)NoneThe S3 bucket to sync the cache of the workers toWorker
TAR_CACHE_RATE (EE only)100The rate at which to tar the cache of the workers. 100 means every 100th job in average (uniformly randomly distributed).Worker
SLACK_SIGNING_SECRETNoneThe signing secret of your Slack app. See Slack documentationServer
COOKIE_DOMAINNoneThe domain of the cookie. If not set, the cookie will be set by the browser based on the full originServer
SERVE_CSPNoneThe CSP directives to use when serving the frontend static assetsServer
DENO_PATH/usr/bin/denoThe path to the deno binary.Worker
PYTHON_PATH/usr/local/bin/python3The path to the python binary.Worker
GO_PATH/usr/bin/goThe path to the go binary.Worker
PIP_INDEX_URLNoneThe index url to pass for pip.Worker
PIP_EXTRA_INDEX_URLNoneThe extra index url to pass to pip.Worker
PIP_TRUSTED_HOSTNoneThe trusted host to pass to pip.Worker
PATHNoneThe path environment variable, usually inheritedWorker
HOMENoneThe home directory to use for Go and Bash , usually inheritedWorker
DATABASE_CONNECTIONS50 (Server)/3 (Worker)The max number of connections in the database connection poolAll
SUPERADMIN_SECRETNoneA token that would let the caller act as a virtual superadmin superadmin@windmill.devServer
TIMEOUT_WAIT_RESULT20The number of seconds to wait before timeout on the 'run_wait_result' endpointWorker
QUEUE_LIMIT_WAIT_RESULTNoneThe number of max jobs in the queue before rejecting immediately the request in 'run_wait_result' endpoint. Takes precedence on the query arg. If none is specified, there are no limit.Worker
DENO_AUTH_TOKENSNoneCustom DENO_AUTH_TOKENS to pass to worker to allow the use of private modulesWorker
DENO_FLAGSNoneOverride the flags passed to deno (default --allow-all) to tighten permissions. Minimum permissions needed are "--allow-read=args.json --allow-write=result.json"Worker
PIP_LOCAL_DEPENDENCIESNoneSpecify dependencies that are installed locally and do not need to be solved nor installed againWorker

Run a local dev setup

only Frontend

This will use the backend of https://app.windmill.dev but your own frontend with hot-code reloading.

  1. Install caddy
  2. Go to frontend/:
    1. npm install, npm run generate-backend-client then npm run dev
    2. In another shell sudo caddy run --config CaddyfileRemote
  3. Et voilà, windmill should be available at http://localhost/

Backend + Frontend

See the ./frontend/README_DEV.md file for all running options.

  1. Create a Postgres Database for Windmill and create an admin role inside your Postgres setup. The easiest way to get a working postgres is running cargo install sqlx-cli && sqlx migrate run. This will also avoid compile time issue with sqlx's query! macro
  2. Install nsjail and have it accessible in your PATH
  3. Install deno and python3, have the bins at /usr/bin/deno and /usr/local/bin/python3
  4. Install caddy
  5. Install the lld linker
  6. Go to frontend/:
    1. npm install, npm run generate-backend-client then npm run dev
    2. In another shell npm run build otherwise the backend will not find the frontend/build folder and will crash
    3. In another shell sudo caddy run --config Caddyfile
  7. Go to backend/: DATABASE_URL=<DATABASE_URL_TO_YOUR_WINDMILL_DB> RUST_LOG=info cargo run
  8. Et voilà, windmill should be available at http://localhost/

Try it (personal workspaces are free forever): https://app.windmill.dev

Documentation: https://docs.windmill.dev

Discord: https://discord.gg/V7PM2YHsPB

Hub: https://hub.windmill.dev

Contributor's guide: https://docs.windmill.dev/docs/contributors_guide

Roadmap: https://github.com/orgs/windmill-labs/projects/2

You can show your support for the project by starring this repo.

Windmill Labs offers commercial licenses, an enterprise edition, local hub mirrors, and support: contact ruben@windmill.dev.

Download Details:

Author: Windmill-labs
Source Code: https://github.com/windmill-labs/windmill 
License: Unknown and 2 other licenses found

#typescript #python #opensource #platform 

Open-source Web IDE, Scalable Runtime & Platform for Serverless
Oral  Brekke

Oral Brekke


The Top 9 Online Learning Platforms

The best 9 online learning platforms in 2021 intro

If you want to learn something, doing so online is a good idea. This is because if you think about it, everything is on the internet nowadays. Besides, you don’t get old textbooks or anything of that kind. In this age we count with very nice interactive and multimedia material!

But because everything is on the internet, there are many learning platforms available. Knowing which one is best could be a big challenge.

For that reason, in today’s article we will talk about 9 of the best online learning platforms out there.

Let’s start.


Let’s begin with one of the best programming learning platforms out there. It offers a broad range of subjects to learn about.

The offer courses:

HTML CSS, JavaScript, React JS, React Native, Python, Blockchain.

The courses are very interactive, easy to digest, and insightful. They count with a nice constant learning feedback loop. Which shows you a multiple-choice question after most definitions/explanations.

The organization of courses is always coherent and nice. It organizes them in career paths and in a comprehensive chart.

The platform promises to give you enough tools to be able to build projects on your own after a few courses. It also offers certificates, and there is a very neat dark mode available.

The platform offers several free courses and a pro upgrade that starts at $25/mo. or 240$ per year or once in a limited offer with unlimited access to the library and certificates.


Udemy is one of the biggest, well-known learning platforms to this day. Their library is over 130k courses, encompassing career and personal skills. Offered by top industry instructors.

In their main categories we got:

Design, (Web) Development, Marketing, IT and Software, Personal Development, Business, Photography, and Music.

You count with a search bar and featured sections where you will see courses as a product in a marketplace.

Each of them counts with a detailed description, a rating system, and the language offer.

There is also a course content section where you’ll see the main areas of the courses and estimated time. Plus, there are also requirements, reviews, and info about the instructor. And they got a mobile app.

About prices since this is a marketplace rather than a focused teaching platform. You must pay for every individual course, prices range and start at 15-20 Euro.

LinkedIn Learning

If you’ve been online for enough time, you’ve heard about LinkedIn already. Well, they also got a learning platform.

The platform offers courses from industry experts. It has over 16k course and learning paths.

Among the popular topics are:

Personal Effectiveness, Spreadsheets, Illustration, Personal Branding, Data Analysis, Drawing, Business Intelligence, etc.

Courses are video based, count with a preview video and a table of content. Each section has estimated times and transcripts.

Also, view count, general overview and some have exercise files/quizzes. Plus, they have a mobile app as well.

There are no free plans/courses available. They have a fixed plan of 39$/mo. It gives you full access to the library, certificates, and access to LinkedIn Premium.


Skillshare is one the most famous learning platforms out there. Offer by experienced industry people. Its main categories are: Creative, Business, Technology, and Lifestyle.

The platform has a lot of focus on creative work so let me tell you about some of its categories:

Animation, Design, Illustration, Lifestyle, Photography and Film, Business (creative), Writing, etc.

But they count with everything else like:

Data science, Mobile/web development, Language, Culinary, etc.

The courses count with a welcome video, a table of content, and level of difficulty.

Each section with estimated times. You will also find the number of students, and projects available. Plus, comments and detailed descriptions. There is a nice rating system that includes an expectation met rubric on offer as well. And there are transcripts for each section, a mobile app, and offline watching.


Coursera is one the most corporate/university-based learning platform there is. It collaborates with over 200 of them, like Google, IBM, and Stanford. It provides industry-recognized credentials and a mobile app at your disposal.

The library consists of +5100 courses/specification, +40 certificates and +25 degrees.

Among the popular subjects are:

Business, Computer Science, Data Science, Language, etc.

Among the Online master/university degrees you got those based on:

IT, Business, Health, and other sectors. The offer is big, there are free courses and paid ones. One generalization is that those free courses charge a fee for the certificate.


Udacity is a learning platform focused on digital skills of all sorts.

Its categories named “schools”, and these are currently available:

Data Science, Programming, Business, AI, Autonomous Systems, Cloud Computing, Cybersecurity.

Courses have a syllabus, estimated times, prerequisites, detailed descriptions, full list of offerings.

Also, there is a list of instructors with their info, reviews, and ratings, and 24⁄7 questions to mentors. There are free trials available, but prices will depend per course. It seems that most of the “top ones” start from 399$/mo. with discounts, if paid once per semester, say nearing $200/mo.


Edx is a not-for-profit organization only partnered with universities. It includes over 150 of them, such as MIT and Harvard.

It uses cognitive science learning techniques and has 24 distinct learning features. Which includes videos, graphs, interactive segments, quizzes, etc.

Its library consists of over 2800 courses, and those in the top courses have to do with:

Computer Sciences, Business, Data Science, Engineering, Design, Humanities, Language.

Each course will have an overview, detailed info, avg effort/time spent. There is also, info about the instructor, and endorsements. There is no rating system available.

There are some caveats though. Some programs are not full if not behind a paywall, and others are full, but the certificate is not.


Thinkific, like Teachable, is famous for being a course creation platform. But they both also offer courses.

But this platform is different since you won’t find courses like in a marketplace on it. Creators will create them and post them by their own.

Stating which are the most popular courses/subjects is difficult for one main reason. Because there is no platform to compare them. But what we can tell you is what to expect.

Based on best practices, you will a few things. Which includes at least an overview, a detailed description, requisites, estimated time.

Talking about prices won’t be possible either for the same reasons. And, because each course will have its own.


Teachable is a well know online learning platform. Between the courses you can take, you will find those related to:

Marketing, Productivity, Course Creation, Writing, Business. Also, Art, Language, Tech and Programing, Health and Fitness.

In each course, you will find a description, downloadable materials (videos included).

There is also a list of content with estimated time, structure info, and exercise sheets.

Plus, you also got a section of FAQ per course and unfortunately, a rating system is missing. On prices, there are free and paid plans, that could range from $100-200 or reach +$1000.

Conclusion of The Best 9 Online Learning Platforms in 2021.

Online learning has never been this accessible and varied. Regardless of your interests if you are committed to gain knowledge all doors are open. Whether you decide to take free short courses or pay for an online university degree, there is a ton to make the most of.

With some research and reflection, we’re certain you’ll find what suits you best.

Thanks for reading this article, we expect to have been educating and useful. To our fellow learners out there, we wish you all the best of lucks.

Original article source at: https://www.blog.duomly.com/

#online #learning #platform 

The Top 9 Online Learning Platforms
Gordon  Matlala

Gordon Matlala


The Best Platform for Machine Learning - What is Kaggle?

Artificial intelligence has taken off with a speed that few could have predicted five years ago. With companies like Google and Facebook investing billions of dollars every year into AI research, we’re able to see self-driving cars and virtual assistants that can recognize our voices while responding almost instantaneously to our commands after only a couple of iterations. 

In this post, I’ll go into more depth about how Kaggle works, what types of competitions are available, and then give details about how one would solve the challenge at hand using machine learning.

If you’d like to learn more about what Kaggle is, how it works, and why 600 000 people use their platform, read on below!

1. What is Kaggle?

Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detecting cancer cells. Kaggle has a massive community of data scientists who are always willing to help others with their data science problems. In addition to the competitions, Kaggle also has many tutorials and resources that can help you get started in machine learning.

If you are an aspiring data scientist, Kaggle is the best way to get started. Many companies will give offers to those who rank highly in their competitions. In fact, Kaggle may become your full-time job if you can hit one of their high rankings.

2. What are typical use cases for Kaggle?

Kaggle is best for businesses that have data that they feel needs to be analyzed. The most significant benefit of Kaggle is that these companies can easily find someone who knows how to work with their data, which makes solving the problem much easier than if they were trying to figure out what was wrong with their system themselves.

3. What are some popular competitions on Kaggle?

There are many different types of competitions available on Kaggle. You can enter a contest in everything from predicting cancer cells in microscope images to analyzing satellite images for changes overtime on any given day. 

Examples include:

  • Predicting car prices based on features such as horsepower and distance traveled
  • Predicting voting patterns by state
  • Analyzing satellite images to see which countries have the most deforestation

4. How does Kaggle work?

Every competition on Kaggle has a dataset associated with it and a goal you must reach (i.e., predict housing prices or detect cancer cells). You can access the data as often as possible and build your prediction model. Still, once you submit your solution, you cannot use it to make future submissions. 

This ensures that everyone is starting from the same point when competing against one another, so there are no advantages given to those with more computational power than others trying to solve the problem. 

Competitions are separated into different categories depending on their complexity level, how long they take, whether or not prize money is involved, etc., so users with varying experience levels can compete against each other in the same arena.

5. What type of skills do you need to compete on Kaggle?

You should be comfortable with data analysis and machine learning if you’re looking to get involved in competitions.

Data science is a very broad term that can be interpreted in many ways depending on who you talk to. But suppose we’re talking specifically about competitive data science like what you see on Kaggle. In that case, it’s about solving problems or gaining insights from data.

It doesn’t necessarily involve machine learning, but you will need to understand the basics of machine learning to get started. There are no coding prerequisites either, though I would recommend having some programming experience in Python or R beforehand.

That being said, if competitive data science sounds interesting to you and you want to get started right away, we have a course for that on Duomly!

Machine Learning Basics

The best way to improve is just practice, so feel free to give any of their challenges a shot!

6. How does one enter a competition on Kaggle?

The sign-up process for entering a competition is very straightforward: Most competitions ask competitors to submit code that meets specific criteria at the end of each challenge. However, there may be times when they want competitors to explain what algorithms they used or provide input about how things work.

7. What are some Kaggle competitions I could consider solving?

Suppose you want to solve one of their business-related challenges. In that case, you’ll need to have a good understanding of machine learning and what models work well with certain types of data. Suppose you want to do one of their custom competition. You’ll need to have a background in computer science to code in the language associated with the problem.

8. How do Kaggle competitions make money?

Many companies on Kaggle are looking for solutions, so there is always a prize attached to each competition. If your solution is strong enough, you can win a lot of money! 

Some of these competitions are just for fun or learning purposes but still award winners with cash or merchandise prizes.

9. What tools should I use to compete on Kaggle?

The most important tool that competitors rely on every day is the Python programming language. It’s used by over 60% of all data scientists, so it has an extremely large community behind it. It’s also extremely robust and has many different packages available for data manipulation, preprocessing, exploration to get you started.

TensorFlow is another popular tool that machine learning enthusiasts use to solve Kaggle competitions. It allows quick prototyping of models to get the best possible results. Several other tools are used in addition to Python and Tensorflow, such as R (a statistical programming language), Git (version control), and Bash (command-line interface). Still, I’ll let you research those on your own! 

10. What is the main benefit of using Kaggle to solve problems?

Kaggle aims to give you the tools necessary to become a world-class data scientist. They provide you with access to real data in real-time so you can practice solving problems similar to what companies face around the world. 

They’re constantly updating their site for you to have the most up-to-date learning.

11. How would a beginner benefit from using Kaggle?

Kaggle gives beginners a way to learn more about machine learning and will allow them to utilize their skills no matter where they’re at. 

Using Kaggle allows beginners to see what’s going on in the industry, keep up with trends, and become an expert with their tools as things change. 

It also offers free training material for those just starting out or those who want a refresher course on specific concepts or who need help getting started. 

12. Who would be interested in using Kaggle?

With many tutorials and datasets readily available, Machine Learning enthusiasts would be very interested in Kaggle. 

It is an excellent place to learn more about machine learning, practice what they’ve learned, and compete with other data scientists. This will help them become better at their craft. 

Data analysts that want to use machine learning in their work can refer to Kaggle when choosing tools to improve the performance of business-related tasks such as forecasting sales numbers or predicting customer behavior. 

In addition, businesses who are looking for third-party solutions can benefit from Kaggle’s extensive list of companies offering the service they need. 

If you need machine learning services, don’t hesitate to contact us. We have a team of experts who can help you with your needs.

Original article source at: https://www.blog.duomly.com/

#machinelearning #platform 

The Best Platform for Machine Learning - What is Kaggle?
Gordon  Murray

Gordon Murray


An Amazing New Feature in Power Platform

What is Power Apps Card?

Power Apps cards are simple lightweight micro-apps which holds and display enterprise data along with workflow and logic functions.

Does creating Power Apps Cards require coding knowledge?

No need, using simple drag and drop function cards can be quickly built and shared with other applications as an actionable app without any coding expertise.

What is the difference between Power Apps data card and Power Apps Card?

Data card are inserted inside the Power Apps itself as an integrated card. But Power Apps cards are micro-apps which are standalone in the Power Platform ecosystem where this card UI elements can be used by other applications to display the content.

Where can I find the Cards feature in the Power Platform?

To find the Cards go to https://make.powerapps.com/ on the left side navigation you can find a new feature available.

An awesome new feature in Power Platform : Power Apps Cards

What are the key components of a card?

  1. Card designer
  2. Power Apps data and resource management

What is the use of Card Designer?

Microsoft has provided an interface to create cards. The UI controls can be dragged and dropped in the card and attach to a function logic and connect to all the Power Platform connectors to display data.

What are the UI controls available in the Card Designer?

You can add

  1. Buttons
  2. Tables
  3. Labels
  4. Images
  5. Checkboxes
  6. Textboxes
  7. Date Picker

And so on.

What are Power Apps data and resource management?

It is a service where you can manage the cards which is used to send and receive data and to embed the data and the workflow.

What is the advantage of adding data and services with connectors?

Power Platform connectors can be connected within no time with all the safety and security enabled to the environment.

Can I add any business logic to this card or static data will be displayed?

 Not just static data but you can definitely build business logic using Power Fx which can be added to create

  • Inline calculations
  • Dynamic calculations
  • Data operations

And those responses can be targeted and inserted into the UI Elements available in the cards.

What are other applications that can use these cards?

You can send cards from the card designer to a Microsoft Teams chat or channel to share your cards with others.

Is this available for all?

No, this is a preview feature and previews are not suitable for the production environment. You can play around and wait for the general availability.

Can I read more about the Cards

Yes definitely, please visit the Microsoft Learn docs to read more about it.

Original article source at:: https://www.c-sharpcorner.com/

#powerapp #power #platform #card 

An Amazing New Feature in Power Platform
Gordon  Taylor

Gordon Taylor


What is Google Cloud Platform (GCP)?

What is Google Cloud Platform (GCP)? – Introduction to GCP Services & GCP Account

In recent years the market for Cloud Computing has grown unexpectedly. There are many Cloud providers in the market today such as VM Ware, Amazon Web Services, Google Cloud Platform, Microsoft Azure, IBM Cloud and many more. According to Gartner’s prediction, the worldwide public cloud service market will be $178 Billion in 2018, from $146 Billion in 2017 and will continue to grow at 22% CAGR (Compound Annual Growth Rate). So let’s begin with our What is Google Cloud Blog.

Below are the topics it will cover. What is Google Cloud definition in this article:

  • What is Google Cloud (GCP)?
  • What is Cloud Computing?
  • Why Google Cloud Platform?
  • GCP Regions and Zones
  • Google Cloud Platform Services
  • Creating a Free Tier GCP Account


Cloud Growth - What is Google Cloud Platform - Edureka

What is Google Cloud Platform used for (GCP)?

Google Cloud Platform is a set of Computing, Networking, Storage, Big Data, Machine Learning and Management services provided by Google that runs on the same Cloud infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, Google Photos and YouTube.

You can go through this Google Cloud Provider video lecture where our GCP Training expert is discussing each & every nitty-gritty for what is Google Cloud technology.

So before looking into the details of Google Cloud Platform, Let’s understand Cloud Computing First.

What is Google Cloud used for??

Cloud computing is the on-demand delivery of compute power, database storage, applications, and other IT resources through a cloud services platform via the internet with pay-as-you-go pricing. It is the use of remote servers on the internet to store, manage and process data rather than a local server or your personal computer.

Cloud computing allows companies to avoid or minimize up-front IT infrastructure costs to keep their applications up and running faster, with improved manageability and less maintenance, and that it enables IT teams, to adjust resources rapidly to meet fluctuating and unpredictable demand.

Google Cloud Platform - What is Google Cloud Platform - Edureka

Cloud-computing providers offer their services according to different models, of which the three standard models per NIST (National Institute of Standards and Technology ) are :

  • Infrastructure as a Service (IaaS)
  • Platform as a Service (PaaS), and
  • Software as a Service (SaaS)


Iaas,Paas,Saas - What is Google Cloud Platform - Edureka

Why Google Cloud Platform?

Now that you have a brief idea of What is Google Cloud Platform and Cloud Computing, let’s understand why one must go for it. Google Cloud Platform, is a suite of cloud computing services that run on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, Google Photos and YouTube. We all know how big is the database of Gmail, Youtube and Google Search.

And I don’t think in the recent years, Google’s server has gone down. It’s one of the biggest in the world, so it seems an obvious choice, to trust them, Right?

Find out our Google Cloud Training in Top Cities/Countries

IndiaUSAOther Cities/Countries
BangaloreNew YorkUK

So now look at some of the features of GCP what really gives it an upper hand over other vendors.

Why GCP - What is Google Cloud Platform - Edureka


what is google cloud and how does it work in Regions and Zones

Google Cloud Platform services are available in various locations across North America, South America, Europe, Asia, and Australia. These locations are divided into regions and zones. You can choose where to locate your applications to meet your latency, availability and durability requirements.

Google Cloud Platform Zones - What is Google Cloud Platform - Edureka

Here you can see that there is a total of 15 regions with at least 3 zones in every region.

What is Google Cloud Services?

Google offers a seven wide range of Services. What is google cloud for:

  • Compute
  • Networking
  • Storage and Databases
  • Big Data
  • Machine Learning
  • Identity & Security
  • Management and Developer Tools

GCP Services - What is Google Cloud Platform - Edureka

Compute: GCP provides a scalable range of computing options you can tailor to match your needs. It provides highly customizable virtual machines. and the option to deploy your code directly or via containers.

  • Google Compute Engine
  • Google App Engine
  • Google Kubernetes Engine
  • Google Cloud Container Registry
  • Cloud Functions

Networking: The Storage domain includes services related to networking, it includes the following services

  • Google Virtual Private Cloud (VPC)
  • Google Cloud Load Balancing
  • Content Delivery Network
  • What is Google Cloud Connect
  • Google Cloud DNS
  • What is Google Cloud Web Hosting

Storage and Databases: The Storage domain includes services related to data storage, it includes the following services

  • Google Cloud Storage
  • Cloud SQL
  • Cloud Bigtable
  • Google Cloud Datastore
  • Persistent Disk

Big Data: The Storage domain includes services related to big data, it includes the following services

  • Google BigQuery
  • Google Cloud Dataproc
  • Google Cloud Datalab
  • Google Cloud Pub/Sub

Cloud AI: The Storage domain includes services related to machine learning, it includes the following services

  • Cloud Machine Learning
  • Vision API
  • Speech API
  • Natural Language API
  • Translation API
  • Jobs API

Identity & Security: The Storage domain includes services related to security, it includes the following services

  • Cloud Resource Manager
  • Cloud IAM
  • Cloud Security Scanner
  • Cloud Platform Security

Management Tools: The Storage domain includes services related to monitoring and management, it includes the following services

  • Stackdriver
  • Monitoring
  • Logging
  • Error Reporting
  • Trace
  • Cloud Console

Developer Tools: The Storage domain includes services related to development, it includes the following services

    • Cloud SDK
    • Deployment Manager
    • Cloud Source Repositories
    • Cloud Test Lab

What is Google Cloud Account? How to create free

Now that we have learned What is Google Cloud Computing, To gain access to these Services, you need to just create a free account on GCP. You get $300 worth credit to spend it over a period of 12 Months. You need to provide your card details, but you won’t be charged extra after your trial period ends or you have exhausted the $300 credit.

GCP Free Tier - What is Google Cloud Platform - Edureka

After you create an account. Go to Console.

Google Cloud Platform Console - What is Google Cloud Platform - Edureka

Here you will have a Dashboard which gives a summary of the what is Google Cloud Platform services which GCP Services you are using, along with the Stats and Billing Report.

GCP Dashboard - What is Google Cloud Platform - Edureka

In this section of Google Cloud Platform, you can find the summarized view of the following:

  • Project Info
  • Resources being used
  • Various API’s running
  • Compute Engine (CPU Usage %)
  • Google Cloud Platform Status
  • Billing of Services per Project
  • Error Reporting
  • Data Trace
  • Tutorials
  • News and Updates for What is Google Cloud all about
  • Documentation

So this is it, guys!

I hope you enjoyed this What is Google Cloud Platform used for blog. If you are reading this, Congratulations! You are no longer a newbie to GCP.

Now that you have understood hat is Google Cloud Server and what is Google Cloud Engine, check out the GCP Certification Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. The Edureka Google Cloud Computing course is designed to help you pass the Professional Cloud Architect – Google Cloud Certification.

Got a question for us? Please mention it in the comments section and we will get back to you or join our GCP Training in Singapore today.

What is Google Cloud Platform | Edureka

Original article source at: https://www.edureka.co/

#google #cloud #platform 

What is Google Cloud Platform (GCP)?

A Flutter Plugin Recognize User Activity on android and IOS Platforms

This plugin is used to recognize user activity on Android and iOS platforms. To implement this plugin, Android used ActivityRecognitionClient and iOS used CMMotionActivityManager.


  • Can check or request activity recognition permission.
  • Subscribe to an activity stream to detect user activity in real time.

Getting started

To use this plugin, add flutter_activity_recognition as a dependency in your pubspec.yaml file. For example:

  flutter_activity_recognition: ^1.3.0

After adding the flutter_activity_recognition plugin to the flutter project, we need to specify the platform-specific permissions and services to use for this plugin to work properly.

🐤 Android

Open the AndroidManifest.xml file and add the following permissions between the <manifest> and <application> tags.

<uses-permission android:name="android.permission.WAKE_LOCK" />
<uses-permission android:name="android.permission.ACTIVITY_RECOGNITION" />
<uses-permission android:name="com.google.android.gms.permission.ACTIVITY_RECOGNITION" />

🐤 iOS

Open the ios/Runner/Info.plist file and add the following permission inside the <dict> tag.

<string>Used to recognize user activity information.</string>

How to use

  1. Create a FlutterActivityRecognition instance.
final activityRecognition = FlutterActivityRecognition.instance;
  1. Checks whether activity recognition permission is granted.
Future<bool> isPermissionGrants() async {
  // Check if the user has granted permission. If not, request permission.
  PermissionRequestResult reqResult;
  reqResult = await activityRecognition.checkPermission();
  if (reqResult == PermissionRequestResult.PERMANENTLY_DENIED) {
    dev.log('Permission is permanently denied.');
    return false;
  } else if (reqResult == PermissionRequestResult.DENIED) {
    reqResult = await activityRecognition.requestPermission();
    if (reqResult != PermissionRequestResult.GRANTED) {
      dev.log('Permission is denied.');
      return false;

  return true;
  1. Subscribe to an activity stream to receive activity data in real time.
// Subscribe to the activity stream.
final _activityStreamSubscription = activityRecognition.activityStream
  1. When the widget is dispose or the plugin is finished using, cancel the subscription.
void dispose() {


🐔 PermissionRequestResult

Defines the type of permission request result.

GRANTEDOccurs when the user grants permission.
DENIEDOccurs when the user denies permission.
PERMANENTLY_DENIEDOccurs when the user denies the permission once and chooses not to ask again.

🐔 Activity

A model representing the user's activity.

typeThe type of activity recognized.
confidenceThe confidence of activity recognized.

🐔 ActivityType

Defines the type of activity.

IN_VEHICLEThe device is in a vehicle, such as a car.
ON_BICYCLEThe device is on a bicycle.
RUNNINGThe device is on a user who is running. This is a sub-activity of ON_FOOT.
STILLThe device is still (not moving).
WALKINGThe device is on a user who is walking. This is a sub-activity of ON_FOOT.
UNKNOWNUnable to detect the current activity.

🐔 ActivityConfidence

Defines the confidence of activity.

HIGHHigh accuracy: 80~100
MEDIUMMedium accuracy: 50~80
LOWLow accuracy: 0~50


If you find any bugs or issues while using the plugin, please register an issues on GitHub. You can also contact us at hwj930513@naver.com.

Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add flutter_activity_recognition

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

  flutter_activity_recognition: ^1.3.0

Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:flutter_activity_recognition/flutter_activity_recognition.dart'; 


import 'dart:async';
import 'dart:developer' as dev;

import 'package:flutter/material.dart';
import 'package:flutter_activity_recognition/flutter_activity_recognition.dart';

void main() => runApp(ExampleApp());

class ExampleApp extends StatefulWidget {
  _ExampleAppState createState() => _ExampleAppState();

class _ExampleAppState extends State<ExampleApp> {
  final _activityStreamController = StreamController<Activity>();
  StreamSubscription<Activity>? _activityStreamSubscription;

  void _onActivityReceive(Activity activity) {
    dev.log('Activity Detected >> ${activity.toJson()}');

  void _handleError(dynamic error) {
    dev.log('Catch Error >> $error');

  void initState() {
    WidgetsBinding.instance?.addPostFrameCallback((_) async {
      final activityRecognition = FlutterActivityRecognition.instance;

      // Check if the user has granted permission. If not, request permission.
      PermissionRequestResult reqResult;
      reqResult = await activityRecognition.checkPermission();
      if (reqResult == PermissionRequestResult.PERMANENTLY_DENIED) {
        dev.log('Permission is permanently denied.');
      } else if (reqResult == PermissionRequestResult.DENIED) {
        reqResult = await activityRecognition.requestPermission();
        if (reqResult != PermissionRequestResult.GRANTED) {
          dev.log('Permission is denied.');

      // Subscribe to the activity stream.
      _activityStreamSubscription = activityRecognition.activityStream

  Widget build(BuildContext context) {
    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(
          title: const Text('Flutter Activity Recognition'),
          centerTitle: true
        body: _buildContentView()

  void dispose() {

  Widget _buildContentView() {
    return StreamBuilder<Activity>(
      stream: _activityStreamController.stream,
      builder: (context, snapshot) {
        final updatedDateTime = DateTime.now();
        final content = snapshot.data?.toJson().toString() ?? '';

        return ListView(
          physics: const BouncingScrollPhysics(),
          padding: const EdgeInsets.all(8.0),
          children: [
            Text('•\t\tActivity (updated: $updatedDateTime)'),
            SizedBox(height: 10.0),

Download Details:

Author: Dev-hwang

Source Code: https://github.com/Dev-hwang/flutter_activity_recognition

#flutter #platform 

A Flutter Plugin  Recognize User Activity on android and IOS Platforms

Core: OwnCloud Web Server Core (Files, DAV, Etc.)

ownCloud Core   

ownCloud gives you freedom and control over your own data. A personal cloud which runs on your own server.

Why Is This so Awesome?

  • 📁 Access your Data You can store your files, contacts, calendars and more on a server of your choosing.
  • 📦 Sync your Data You keep your files, contacts, calendars and more synchronized amongst your devices.
  • 🔄 Share your Data You share your data with others, and give them access to your latest photo galleries, your calendar or anything else you want them to see.
  • 🚀 Expandable with dozens of Apps ...like Calendar, Contacts, Mail or News.
  • ☁️ All Benefits of the Cloud ...on your own Server.
  • 🔒 Encryption You can encrypt data in transit with secure https connections. You can enable the encryption app to encrypt data on storage for improved security and privacy.
  • ...

Installation Instructions

For installing ownCloud, see the official ownCloud 10 installation manual.

Development Build Prerequisites

Note that when doing a local development build, you need to have Composer v2 installed. If your OS provides a lower version than v2, you can install Composer v2 manually. As an example, which may be valid for other releases/distros too, see How to install Composer on Ubuntu 22.04 | 20.04 LTS.

You also must have installed yarn and node (v14 or higher).

Contribution Guidelines



Learn about the different ways you can get support for ownCloud: https://owncloud.com/support/

Get in Touch

Important Notice on Translations

Please submit translations via Transifex: https://explore.transifex.com/owncloud-org/

See the detailed information about translations here.

Download Details:

Author: Owncloud
Source Code: https://github.com/owncloud/core 
License: AGPL-3.0, Unknown licenses found

#php #javascript #platform 

Core: OwnCloud Web Server Core (Files, DAV, Etc.)

A internet File Getter That Works In All Platforms


The library is made to allow direct access to Internet files on all platforms. It also has the middleware to store files locally if needed. Aimed primarily at use with plugins, without the ability to work with the Internet

Getting Started

Simple usage anywhere:

import 'package:internet_file/internet_file.dart';

final Uint8List bytes = await InternetFile.get(
    progress: (receivedLength, contentLength) {
      final percentage = receivedLength / contentLength * 100;
          'download progress: $receivedLength of $contentLength ($percentage%)');

For local store files you can usage InternetFileStorageIO (not works on web):

import 'package:internet_file/storage_io.dart';

final storageIO = InternetFileStorageIO();

await InternetFile.get(
    storage: storageIO,
    storageAdditional: storageIO.additional( 
      filename: 'ui_icons.ttf',
      location: '',

Or you can write you own storage not requires io (web support etc.):

class MyOwnInternetFileStorage extends InternetFileStorage {
  Future<Uint8List?> findExist(
    String url,
    InternetFileStorageAdditional additional,
  ) {
    # find local here

    # access you own string property:
    print(additional['my_string_property'] as String);

    # access you own any type property:
    print((additional['my_date_property'] as DateTime).toString())

  Future<void> save(
    String url,
    InternetFileStorageAdditional additional,
    Uint8List bytes,
  ) async {
    # save file here

final myOwnStorage = MyOwnInternetFileStorage();
await InternetFile.get(
    storage: myOwnStorage,
    storageAdditional: {
        'my_string_property': 'string',
        'my_int_property': 99,
        'my_date_property': DateTime.now(),


InternetFile.get params

urlLink to network filerequired-
headersHeaders passed for wile loadoptional-
progressCallback with received & all bytes length progress value called when file loadsoptional-
storageImplements of InternetFileStorage with save & find local methods for saving filesoptional-
storageAdditionalAdditional args for pass to InternetFileStorage implementation passed in storageoptional{}

Full api reference available here


Inspired by flutter_cache_manager, but make for support all platforms


  • http - for file loading from internet
  • path - for filename & location joint in InternetFileStorageIO
  • universal_file - for work File in web

Created for usage in:

Use this package as a library

Depend on it

Run this command:

With Dart:

 $ dart pub add internet_file

With Flutter:

 $ flutter pub add internet_file

This will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):

  internet_file: ^1.2.0

Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:internet_file/internet_file.dart'; 


import 'package:internet_file/internet_file.dart';
import 'package:internet_file/storage_io.dart';

void main() async {
  final storageIO = InternetFileStorageIO();

  await InternetFile.get(
    storage: storageIO,
    storageAdditional: storageIO.additional(
      filename: 'ui_icons.ttf',
      location: '',
    force: true,
    progress: (receivedLength, contentLength) {
      final percentage = receivedLength / contentLength * 100;
          'download progress: $receivedLength of $contentLength ($percentage%)');

Download Details:

Author: ScerIO

Source Code: https://github.com/ScerIO/packages.dart/tree/master/packages/internet_file

#flutter #platform 

A internet File Getter That Works In All Platforms

A Platform interface for Mobile and Web Sdk

What is Rudder?

Short answer: Rudder is an open-source Segment alternative written in Go, built for the enterprise.

Long answer: Rudder is a platform for collecting, storing and routing customer event data to dozens of tools. Rudder is open-source, can run in your cloud environment (AWS, GCP, Azure or even your data-centre) and provides a powerful transformation framework to process your event data on the fly.

Released under MIT License

Getting Started with Flutter SDK

  • Add the SDK as a dependency by performing the following steps:
  1. Open pubspec.yaml and add rudder_sdk_flutter under dependencies section:
  2. Navigate to your Application's root folder and install all the required dependencies with:

Import RudderClient

  1. Add the below line to import the RudderClient.

Initialize RudderClient

Somewhere in your Application, add the following code

    RudderConfigBuilder builder = RudderConfigBuilder();
    final client = RudderClient.instance;
    client.initialize(WRITE_KEY,config: builder.build());

Send Events

An example track call is as below

    RudderProperty property = RudderProperty();
    property.put("test_key_1", "test_key_1");
    client.track("test_track_event", properties: property);

Device Tokens

You can pass your device-token for Push Notifications to be passed to the destinations which support Push Notification. We set the token under context.device.token. An example of setting the device-token is as below


Anonymous ID

We use the deviceId as anonymousId by default. You can use the following method to override and use your own anonymousId with the SDK. You need to call setAnonymousId method before calling getInstance. An example of setting the anonymousId is as below


Advertising ID

You can use the setAdvertisingId method to pass your Android and iOS AAID and IDFA respectively. The setAdvertisingId method accepts a string argument :

  • advertisingId : Your Android advertisingId (AAID) (or) Your iOS advertisingId (IDFA) On Android device you need to call setAdvertisingId method before calling getInstance Example Usage:The advertisingId parameter you pass to the above method is assigned as AAID if you are on android device and as IDFA if you are on a iOS device. For more detailed documentation check the documentation page.

Contact Us

If you come across any issues while configuring or using RudderStack, please feel free to contact us or start a conversation on our Slack channel. We will be happy to help you.

import 'package:rudder_sdk_flutter/RudderClient.dart';
flutter pub get
  rudder_sdk_flutter: ^1.2.0

Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add rudder_sdk_flutter_platform_interface

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

  rudder_sdk_flutter_platform_interface: ^2.1.0

Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:rudder_sdk_flutter_platform_interface/platform.dart';
import 'package:rudder_sdk_flutter_platform_interface/rudder_sdk_platform.dart'; 

Download Details:

Author: rudderlabs

Source Code: https://github.com/rudderlabs/rudder-sdk-flutter

#flutter #platform 

A Platform interface for Mobile and Web Sdk

An Easy Way to Get The Current Renderer on Web Platform

Get Web Renderer

This package help you to detect current web renderer.

How to use

This package provide very basic apis to recognize current web renderer.

You just need to add get_web_renderer: ^any to your pubspec.yaml and this is all apis for you to use:

/// Return true if current renderer is HTML
bool _isHtmlRenderer = WebRenderer.isHtmlRenderer;

/// Return true if current renderer is CanvasKit
bool _isCanvasKitRenderer = WebRenderer.isCanvasKitRenderer;

/// Return true if current renderer is not the web platform
bool _isOtherRenderer = WebRenderer.isOtherRenderer;

// return CurrentRenderer.html, CurrentRenderer.canvasKit, CurrentRenderer.other
CurrentRenderer _currentRenderer = WebRenderer.getCurrentRenderer; 

Use this package as a library

Depend on it

Run this command:

With Dart:

 $ dart pub add get_web_renderer

With Flutter:

 $ flutter pub add get_web_renderer

This will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):

  get_web_renderer: ^1.1.0

Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:get_web_renderer/get_web_renderer.dart'; 


import 'dart:async';

import 'package:flutter/material.dart';
import 'package:get_web_renderer/get_web_renderer.dart';

void main() {
  runApp(const MyApp());

class MyApp extends StatefulWidget {
  const MyApp({Key? key}) : super(key: key);

  State<MyApp> createState() => _MyAppState();

class _MyAppState extends State<MyApp> {
  CurrentRenderer? _currentRenderer;

  void initState() {

  // Platform messages are asynchronous, so we initialize in an async method.
  Future<void> initPlatformState() async {
    final currentRenderer = WebRenderer.getCurrentRenderer;

    // If the widget was removed from the tree while the asynchronous platform
    // message was in flight, we want to discard the reply rather than calling
    // setState to update our non-existent appearance.
    if (!mounted) return;

    setState(() {
      _currentRenderer = currentRenderer;

  Widget build(BuildContext context) {
    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(
          title: const Text('Current Web Renderer'),
        body: _currentRenderer == null
            ? const CircularProgressIndicator()
            : Center(
                child: Text('This device using $_currentRenderer renderer'),

Download Details:

Author: vursin

Source Code: https://github.com/vursin/get_web_renderer

#flutter #android #platform 

An Easy Way to Get The Current Renderer on Web Platform

A Cross-platform Flutter Plugin for `C/C++/ObjC` Crash Report


A cross-platform flutter plugin for C/C++/ObjC crash report via Google Breakpad

Use breakpad for quick_breakpad_example

$CLI_BREAKPAD is local clone of https://github.com/Sunbreak/cli-breakpad.trial


  • run on macOS/Linux
# Device/emulator connected
$ android_abi=`adb shell getprop ro.product.cpu.abi`
$ pushd example
$ flutter run
✓ Built build/app/outputs/flutter-apk/app-debug.apk.
I/quick_breakpad(28255): JNI_OnLoad
I quick_breakpad_example(28255): JNI_OnLoad
D quick_breakpad(28255): Dump path: /data/data/com.example.quick_breakpad_example/cache/54ecbb9d-cef5-4fa9-5b6869b2-198bc87e.dmp
$ popd
$ adb shell "run-as com.example.quick_breakpad_example sh -c 'cat /data/data/com.example.quick_breakpad_example/cache/54ecbb9d-cef5-4fa9-5b6869b2-198bc87e.dmp'" >| 54ecbb9d-cef5-4fa9-5b6869b2-198bc87e.dmp

Only C/C++ crash for now

$ $CLI_BREAKPAD/breakpad/linux/$(arch)/dump_syms example/build/app/intermediates/cmake/debug/obj/${android_abi}/libquick-breakpad-example.so > libquick-breakpad-example.so.sym
$ uuid=`awk 'FNR==1{print \$4}' libquick-breakpad-example.so.sym`
$ mkdir -p symbols/libquick-breakpad-example.so/$uuid/
$ mv ./libquick-breakpad-example.so.sym symbols/libquick-breakpad-example.so/$uuid/
$ $CLI_BREAKPAD/breakpad/linux/$(arch)/minidump_stackwalk 54ecbb9d-cef5-4fa9-5b6869b2-198bc87e.dmp symbols/ > libquick-breakpad-example.so.log
  • Show parsed Android log: head -n 20 libquick-breakpad-example.so.log

So the crash is at line 30 of quick_breakpad_example.cpp



  • run on macOS
  1. Get simulator UUID and run on it
$ flutter devices
1 connected device:
iPhone SE (2nd generation) (mobile) • C7E50B0A-D9AE-4073-9C3C-14DAF9D93329 • ios        • com.apple.CoreSimulator.SimRuntime.iOS-14-5 (simulator)
$ device=C7E50B0A-D9AE-4073-9C3C-14DAF9D93329
$ pushd example
$ flutter run -d $device
Running Xcode build...                                                  
 └─Compiling, linking and signing...                      2,162ms
Xcode build done.                                            6.2s
Lost connection to device.
$ popd
  1. Find application data and get dump file
$ data=`xcrun simctl get_app_container booted com.example.quickBreakpadExample data`
$ ls $data/Library/Caches/Breakpad
A1D2CF75-848E-42C4-8F5C-0406E8520647.dmp        Config-FsNxCZ
$ cp $data/Library/Caches/Breakpad/A1D2CF75-848E-42C4-8F5C-0406E8520647.dmp .
  1. Parse the dump file via symbols of Runner

Only C/C++/Objective-C crash for now

$ dsymutil example/build/ios/Debug-iphonesimulator/Runner.app/Runner -o Runner.dSYM
$ $CLI_BREAKPAD/breakpad/mac/dump_syms Runner.dSYM > Runner.sym
$ uuid=`awk 'FNR==1{print \$4}' Runner.sym`
$ mkdir -p symbols/Runner/$uuid/
$ mv ./Runner.sym symbols/Runner/$uuid/
$ $CLI_BREAKPAD/breakpad/mac/$(arch)/minidump_stackwalk A1D2CF75-848E-42C4-8F5C-0406E8520647.dmp symbols > Runner.log
  • Show parsed iOS log: head -n 20 Runner.log

So the crash is at line 11 of AppDelegate.m



  1. Run the example
  • run on Windows
rem Command Prompt
> pushd example
> flutter run -d windows
Building Windows application...                                         
dump_path: .
minidump_id: 34cd2b95-aef1-4003-ae75-1c848b18aad2
> popd
> copy example\34cd2b95-aef1-4003-ae75-1c848b18aad2.dmp .
  1. Parse the dump file
  • run on Windows
rem Command Prompt
> %CLI_BREAKPAD%\windows\%PROCESSOR_ARCHITECTURE%\dump_syms example\build\windows\runner\Debug\quick_breakpad_example.pdb > quick_breakpad_example.sym
  • run on Linux
# bash or zsh
$ uuid=`awk 'FNR==1{print \$4}' quick_breakpad_example.sym`
$ mkdir -p symbols/quick_breakpad_example.pdb/$uuid/
$ mv ./quick_breakpad_example.sym symbols/quick_breakpad_example.pdb/$uuid/
$ ./breakpad/linux/$(arch)/minidump_stackwalk 34cd2b95-aef1-4003-ae75-1c848b18aad2.dmp symbols > quick_breakpad_example.log
  1. Show parsed Linux log
  • run on Linux
# bash or zsh
$ head -n 20 quick_breakpad_example.log

So the crash is at line 23 of flutter_windows.cpp





  • run on Linux
$ pushd example
$ flutter run -d linux
Building Linux application...                                           
Dump path: /tmp/d4a1c6ac-2ad7-4301-c22e3c9b-0a4c5588.dmp
$ popd
$ cp /tmp/d4a1c6ac-2ad7-4301-c22e3c9b-0a4c5588.dmp .
  • parse the dump file
# flutterArch=x64 or arm64
$ $CLI_BREAKPAD/breakpad/linux/$(arch)/dump_syms build/linux/${flutterArch}/debug/bundle/quick_breakpad_example > quick_breakpad_example.sym
$ uuid=`awk 'FNR==1{print \$4}' quick_breakpad_example.sym`
$ mkdir -p symbols/quick_breakpad_example/$uuid/
$ mv ./quick_breakpad_example.sym symbols/quick_breakpad_example/$uuid/
$ $CLI_BREAKPAD/breakpad/linux/$(arch)/minidump_stackwalk d4a1c6ac-2ad7-4301-c22e3c9b-0a4c5588.dmp symbols/ > quick_breakpad_example.log
  • Show parsed Linux log: head -n 20 quick_breakpad_example.log

So the crash is at line 19 of my_application.cc


Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add quick_breakpad

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

  quick_breakpad: ^0.3.0

Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:quick_breakpad/quick_breakpad.dart'; 


import 'package:flutter/material.dart';
import 'dart:async';

import 'package:flutter/services.dart';
import 'package:quick_breakpad/quick_breakpad.dart';

void main() {

class MyApp extends StatefulWidget {
  _MyAppState createState() => _MyAppState();

class _MyAppState extends State<MyApp> {
  String _platformVersion = 'Unknown';

  void initState() {

  // Platform messages are asynchronous, so we initialize in an async method.
  Future<void> initPlatformState() async {
    String platformVersion;
    // Platform messages may fail, so we use a try/catch PlatformException.
    try {
      platformVersion = await QuickBreakpad.platformVersion;
    } on PlatformException {
      platformVersion = 'Failed to get platform version.';

    // If the widget was removed from the tree while the asynchronous platform
    // message was in flight, we want to discard the reply rather than calling
    // setState to update our non-existent appearance.
    if (!mounted) return;

    setState(() {
      _platformVersion = platformVersion;

  Widget build(BuildContext context) {
    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(
          title: const Text('Plugin example app'),
        body: Center(
          child: Text('Running on: $_platformVersion\n'),

Download Details:

Author: woodemi

Source Code: https://github.com/woodemi/quick_breakpad

#flutter #platform 

A Cross-platform Flutter Plugin for `C/C++/ObjC` Crash Report
Nat  Grady

Nat Grady


DistributedR: A Scalable High-performance Platform for The R Language


Distributed R is a scalable high-performance platform for the R language. It enables and accelerates large scale machine learning, statistical analysis, and graph processing.

The Distributed R platform exposes data structures, such as distributed arrays, to store data across a cluster. Arrays act as a single abstraction to efficiently express both machine learning algorithms, which primarily use matrix operations, and graph algorithms, which manipulate the graph’s adjacency matrix. In addition to distributed arrays, the platform also provides distributed data frames, lists and loops.

Using Distributed R constructs, data can be loaded in parallel from any data source. Distributed R also provides a parallel data loader from the Vertica database. Please see vRODBC repository.

Installing from binaries

Distributed R is delivered in a single, easy-to-install tar file. The installation tool "distributedR_install" installs the platform and all parallel algorithm R packages. You can register and get the tar file here.

You can also get a Virtual Machine with everything installed here.

Installing from source

  1. Install dependencies:

On Ubuntu:

  $ sudo apt-get install -y make gcc g++ libxml2-dev rsync bison byacc flex

On CentOS:

  $ sudo yum install -y make gcc gcc-c++ libxml2-devel rsync bison byacc flex
  1. Install R:

On Ubuntu:

  $ echo "deb http://cran.r-project.org//bin/linux/ubuntu trusty/" | sudo tee /etc/apt/sources.list.d/r.list
  $ sudo apt-get update
  $ sudo apt-get install -y --force-yes r-base-core

On CentOS:

  $ curl -O http://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
  $ sudo rpm -i epel-release-latest-7.noarch.rpm
  $ sudo yum update
  $ sudo yum install R R-devel

Install R dependencies:

 $ sudo R  # to install globally
 R> install.packages(c('Rcpp','RInside','XML','randomForest','data.table'))

Compile and install Distributed R:

 $ R CMD INSTALL platform/executor
 $ R CMD INSTALL platform/master

Or directly from the R console:

 R> devtools::install_github('vertica/DistributedR',subdir='platform/executor')
 R> devtools::install_github('vertica/DistributedR',subdir='platform/master')

Open R and run an example:

 distributedR_start()  # start DR

 B <- darray(dim=c(9,9), blocks=c(3,3), sparse=FALSE) # create a darray
 foreach(i, 1:npartitions(B),
   init<-function(b = splits(B,i), index=i) {
   b <- matrix(index, nrow=nrow(b), ncol=ncol(b))
 })  # initialize it

 getpartition(B) # collect darray data

 distributedR_shutdown() # stop DR

How to Contribute

You can help us in different ways:

  1. Reporting issues.
  2. Contributing code and sending a Pull Request.

In order to contribute the code base of this project, you must agree to the Developer Certificate of Origin (DCO) 1.1 for this project under GPLv2+:

By making a contribution to this project, I certify that:

(a) The contribution was created in whole or in part by me and I have the 
    right to submit it under the open source license indicated in the file; or
(b) The contribution is based upon previous work that, to the best of my 
    knowledge, is covered under an appropriate open source license and I 
    have the right under that license to submit that work with modifications, 
    whether created in whole or in part by me, under the same open source 
    license (unless I am permitted to submit under a different license), 
    as indicated in the file; or
(c) The contribution was provided directly to me by some other person who 
    certified (a), (b) or (c) and I have not modified it.
(d) I understand and agree that this project and the contribution are public and
    that a record of the contribution (including all personal information I submit 
    with it, including my sign-off) is maintained indefinitely and may be 
    redistributed consistent with this project or the open source license(s) involved.

To indicate acceptance of the DCO you need to add a Signed-off-by line to every commit. E.g.:

Signed-off-by: John Doe <john.doe@hisdomain.com>

To automatically add that line use the -s switch when running git commit:

$ git commit -s

Download Details:

Author: Vertica
Source Code: https://github.com/vertica/DistributedR 
License: GPL-2.0 license

#r #platform 

DistributedR: A Scalable High-performance Platform for The R Language