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Pytorch vs TensorFlow

Deep learning is a subset of artificial intelligence (AI). There has been intense interest in this field for the last few years, with several companies doing pathbreaking work in deep learning and artificial intelligence. Several deep learning tools are on the market today, but the two most famous are PyTorch and TensorFlow. Both are open-source libraries, and it can be confusing to distinguish between them. In this blog, we examine the key differences between Pytorch vs TensorFlow.

What are deep learning frameworks?

Deep learning frameworks are software packages that data scientists and academics use to create and train deep learning models. Frameworks are designed to allow users to train their models without learning machine learning, deep learning, or neural network techniques.

Through a high-level programming interface, frameworks provide the building blocks for constructing, training, and validating models. High-performance multi-GPU accelerated training provided by deep learning frameworks like Pytorch, TensorFlow, and MXNet, and with GPU-accelerated libraries like cuDNN and NCCL.

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What is Pytorch?

In this section, we examine what Pytorch is and its various characteristics. Pytorch is an open-source library used for machine learning. Facebook developed the library and released it in 2016. Pytorch is primarily used for natural language processing applications.

The program is imperative or runs in real-time. Programmers use this feature to check if their program is working before writing the entire program. Pytorch has become popular because of its user-friendly interface, flexibility, efficient memory usage, and dynamic computational graphs. If we compare Pytorch vs TensorFlow – Pytorch is more user-friendly but has fewer features.

How does Pytorch Mechanism work?

Pytorch is Pythonic in nature and adds to the coding features of Python making the code easy to read. Python also offers dynamic computation graphs. Pytorch mechanism allows developers, researchers, and neural network debuggers to run and test a section of code in real-time. Some of the features of Pytorch are –

• Tensor Computation – A tensor is a generic n-dimensional array used for computation and is made faster by graphics processing units. You can manipulate and operate these multidimensional structures using an application program interface (API).
• TorchScript is the production environment of PyTorch and helps users to seamlessly transition between modes. TorchScript optimizes functionality, speed, ease of use, and flexibility.
• Dynamic graph computation allows users to modify network behavior immediately without waiting for all the code to run.
• Automated differentiation is used to train and create neural networks.
• Because Pytorch is based on Python it can use libraries like NumPy, SciPy, Numba, and Cynthon.
• Variables represent nodes in the graph.
• Modules represent a neural network and are the foundation of stateful computation.
• The function is the relationship that exists between two variables.

How to do Pytorch Visualization of Neural Networks

If you want to learn deep learning visualization is a great idea. These networks have several layers and you will not be able to learn everything from the summary. Building a neural network from Pytorch is not difficult. You do not require extensive knowledge in the library and you can follow deep learning packages easily.

1. You can try building your model around Iris dataset for two reasons

• No data preparation is required.
• You do not need a large network to get accurate results.

1. Use Torchwiz to create a visualization of the Pytorch execution of graphs and traces.
2. Neutron is a great Desktop app for the visualization of ONNX models.
3. Use TensorBoard which is a visualization and tooling framework required for machine learning experimentations.

Also read: R vs Python | A complete analysis

How to  Pytorch Deployment of model in minutes?

Pytorch deployment of a model can be done in minutes. 

• Start with developing a trained model. 
• After you have the basic script with the model you can deploy it in the cloud. 
• Prepare the model During the model preparation process, you need to ensure the following files are in the git repository: requirements.txt and syndicai.py
• Connect to the repository – After it is time for deployment you need to log in to the syndicate platform to create your new model.

What is the Pytorch model Availability?

Pre-trained models are neural network models trained on large benchmark datasets like ImageNet. The deep learning community has used Pytorch models extensively. Pretrained models have contributed to the rapid advances in computer vision research. The Pytorch model’s availability depends on the module class and parameter class. The module class encapsulates models and model components such as neural network layers, and the parameter class represents learning weights. When a parameter is assigned, it becomes an attribute of the module. Common layer types available are –

• Linear layer
• Convolutional layer
• Recurrent layer

What are the three most used Pytorch libraries?

Pytorch libraries are based on the Python programming language and Torch libraries. Torch is an open-source machine-learning library based on the Luna scripting language. Pytorch libraries support over 200 mathematical operations. The three most used libraries in Pytorch are –

• Pytorch lighting is a high-level interface for the Pytorch libraries. It has emerged as the standard for submitting Pytorch-based code.  
• Fast AI is a machine learning library built on top of Pytorch with prototyping and adaptability capabilities.
• Argumentation is a Python library for performing data augmentation.

What is Pytorch GPU?

Using a GPU can speed up computation in a parallel format, completing your work faster. Users can monitor both synchronous and asynchronous processes when data is copied simultaneously between the CPU and GPU or between two GPUs since actions are completed in queue form. Queuing guarantees that parallel activities will finish and that the operations synchronously. In PyTorch, cross-GPU operations are not possible.

Pytorch GPU ideal for deep learning is –

• NVIDIA GeForce RTX 2060
• NVIDIA GeForce GTX 1080
• ZOTAC GeForce GTX 1070
• NVIDIA Tesla K80

Also read: Sushiswap Vs Uniswap: What Are The Differences?

What are the different Pytorch data types?

Torch has 10 tensor types with CPU and  GPU variants 

Data type	Dtype
32-bit floating point	torch.float32 or torch.float
64-bit floating point	torch.float64 or torch.double
16-bit floating point	torch.float16 or torch.half
32-bit complex	torch.complex32 or torch.chalf
64-bit complex	torch.complex64 or torch.cfloat
128-bit complex	torch.complex128 or torch.cdouble
8-bit integer (unsigned)	torch.uint8
8-bit integer (signed)	torch.int8
16-bit integer (signed)	torch.int16 or torch.short
32-bit integer (signed)	torch.int32 or torch.int
64-bit integer (signed)	torch.int64 or torch.int
Boolean	torch.bool
quantized 8-bit integer (unsigned)	torch.quint8
quantized 8-bit integer (signed)	torch.qint8
quantized 32-bit integer (signed)	torch.qint32
quantized 4-bit integer (unsigned)	torch.quint4x2

Pytorch example for learning data types

# importing torch module

import torch 

# create one dimensional tensor with

# integer type elements

a = torch.tensor([10, 20, 30, 40, 50])

print(a)

# create one dimensional tensor with 

# float type elements

b = torch.tensor([10.12, 20.56, 30.00, 40.3, 50.4])

print(b)

What is TensorFlow?

Let us begin this section by understanding what is TensorFlow. TensorFlow is an open-source framework for creating machine learning applications and supports deep machine learning. TensorFlow was created by Google Brain Team and is an open-source library for mathematical operations and large-scale machine learning.

It is an extensive mathematical library that carries out several operations targeted at deep neural network training and inference using dataflow and differentiable programming. It offers a simple front-end API to create apps using JavaScript or Python and high-performance languages like C++ to run the programs. Programmers use TensorFlow to build machine learning applications utilizing tools, libraries, and community resources.

Also read: How to add Avalanche to MetaMask? – A quick guide

Understanding the TensorFlow Mechanism

Developers use the TensorFlow mechanism to create dataflow graphs. These graphs are structures that depict how data flows across a graph or a collection of processing nodes. Each node on the graph depicts mathematical operations and each collection or edge between the nodes depicts a Tensor.

You can run most TensorFlow applications on CPU, GPU, cloud cluster, or android or IOS smartphones. If you use Google’s cloud and need further acceleration you can use Google’s specialized TensorFlow Processing Unit (TPU). You have the option of storing the results on almost any device to make predictions.

How does the TensorFlow visualization model help?

The TensorFlow visualization tool is called TensorBoard. It is used to understand machine learning and Data Flow Graphs. An important characteristic of TensorBoard includes a view of different types of data about the characteristics and details of each graph in vertical alignment.

You can find up to 36000 nodes in a deep neural network. TensorBoard helps in emphasizing comparable structures and condensing nodes into high-level blocks. This enables you to analyze graphs better and concentrate on the main sections of the computational graph portions. Tensor board visualization is very interactive and you can pan, zoom, and expand nodes to view the details.

What are the steps for TensorFlow Deployment?

To start TensorFlow deployment you need to first create a model-

• Import the fashion MNST dataset – in this example, we use the Fashion MNIST dataset that has 70,000 greyscale images in 10 categories. Fashion MNIST is used as a drop-replacement for the classic MNIST dataset (considered the “Hello World” in machine learning programs). Simply import and load the data to use TensorFlow to access the Fashion MNIST.
• Train and evaluate your model
• Save your model to load the trained model into TensorFlow serving.
• Examine your save model.

What is some TensorFlow model availability?

There are several trained open-source TensorFlow lite models you can use to accomplish your machine-learning tasks. You can install these models to add machine learning functionality to your mobile application. Finding TensorFlow model availability can be tricky for your application with TensorFlow lite example. You can discover a model with TensorFlow Lite to find use cases or can consider the data input type. Some TensorFlow models are –

• Image classification models
• Object detection models
• Text classification models
• Text embedding models
• Audio speech synthesis models
• Audio embedding models

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What are the top TensorFlow libraries used by researchers?

Tensorflow is an open-source library used for fast numerical computing. It was created for use in both research and development and production systems, including but not limited to RankBrain in Google search and the entertaining DeepDream project. The top TensorFlow Libraries used are –

• Lattice is a TensorFlow library that allows users to include their domain knowledge in the learning process using constrained and limited lattice-based models.
• TensorFlow Probability helps to merge probabilistic models and deep learning on TPUs and GPUs.
• TensorFlow Hub is a collection of trained machine-learning models that can be adjusted and deployed anywhere.

Comparing TensorFlow vs Pytorch- TensorFlow was developed on Theno which is a python library and Pytorch uses Torch library.

What is TensorFlow GPU required for programming?

TensorFlow GPU is supported in the following GPI-enabled devices.

• NVIDIA® GPU card with CUDA® architectures 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, and higher.
• For GPUs with unsupported CUDA® architectures, avoid JIT compilation from PTX, or to use different versions of the NVIDIA® libraries.

• Packages do not contain PTX code except for the latest supported CUDA® architecture; therefore, TensorFlow fails to load on older GPUs when CUDA_FORCE_PTX_JIT=1 is set. 

System Requirement 

• Ubuntu 16.04 or higher (64-bit)
• macOS 10.12.6 (Sierra) or higher (64-bit) (no GPU support)
• Windows Native – Windows 7 or higher (64-bit) (no GPU support after TF 2.10)
• Windows WSL2 – Windows 10 19044 or higher (64-bit)

Software requirement 

• Python 3.7–3.10
• pip version 19.0 or higher for Linux (requires manylinux2010 support) and Windows. pip version 20.3 or higher for macOS.
• Windows Native Requires Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019

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What are TensorFlow data types?

TensorFlow has its data types. You can use Python data types like Booleans, Strings, and Integers it is preferable to use TensorFlow data types. This is because TensorFlow has to infer Python data types. TensorFlow has several data types including 32-bit and 64-bit data numbers. You need to initialize all variables. Some of the commonly used data types are

floating point	tf.float32, tf.float64
Integers	tf.int8, tf.int16, tf.int32, tf.int64
unsigned integers	tf.uint8, tf.unit16
strings	tf.string
booleans	tf.bool
complex numbers:	tf.complex64, tf.complex128
integer with quantuized ops:	tf.qint8, tf.qint32, tf.quint8

Tensors are an integral part of TensorFlow. Tensors are multidimensional arrays that have three main attributes – rank, shape, and data types. Some types of Tensor are  0-d tensor (scalar), 1-d tensor (vector) or 2-d tensor (matrix).

TensorFlow example

## Import the library

import tensorflow as tf

import numpy as np

X_train = (np.random.sample((10000,5)))

y_train =  (np.random.sample((10000,1)))

X_train.shape

Output (1000,1)

FAQ: Pytorch vs TensorFlow

What is TensorFlow used for?

Tensorflow is a priceless tool for developers working on machine learning as it allows for abstraction. Users do not have to deal with the minute details of implementing the algorithm but focus on overall application logic. TensorFlow takes care of details behind the scene. TensorFlow is used for text-based applications, image recognition, and voice search. Google uses TensorFlow in all its apps to improve user experience. Facebook’s image recognition system – Deepface uses the TensorFlow image recognition system. TensorFlow is also used by Apple’s Siri for voice recognition.

How do you use PYT in machine learning?

Do you want to use Pyt or Python in machine learning but don’t know where to start? Most machine learning projects have the following steps

1. Define Problem.
2. Prepare Data.
3. Evaluate Algorithms.
4. Improve and present Results.

To use Python for machine learning start by

1. Installing the Python and SciPy platform.
2. Loading the dataset.
3. Summarizing the dataset.
4. Visualizing the dataset.
5. Evaluating some algorithms.
6. Making some predictions.

How do convert numpy to tensor Pytorch array?

Pytorch tensor is like a numpy.ndarray. The difference between these two is that a tensor utilizes the GPUs to accelerate numeric computation. We need to convert numpy,ndarray to a PyTorch tensor using torch.from_numy() function and a tensor is converted to numpy.ndarray using .numpy() method. Steps to covert convert numpy to tensor Pytorch are –

• Import the required libraries. Here, the required libraries are torch and numpy.
• Create a numpy.ndarray or a PyTorch tensor.
• Convert the numpy.ndarray to a PyTorch tensor using torch.from_numpy() function or convert the PyTorch tensor to numpy.ndarray using the .numpy() method.
• Finally, print the converted tensor or numpy.ndarray.

Is Ethereum Still Safe And Easy Choice For Investors? Let’s find out!

Ethereum is an open-source, decentralized platform that underpins most cryptocurrency industries. Is Ethereum a wise investment? This is the issue we address today. We’ll discuss this cryptocurrency’s performance and provide information on highs and lows.

Ethereum’s performance since launch and until 2022

You should research the market performance of cryptocurrencies before thinking about  buying any. Investors frequently use the finest crypto analysis tools to assess the general health and volatility of the markets. 

Brief ETH price history 

In July 2015, Ethereum was introduced at $0.75 per token. Within three months after the launch, the price of Ethereum reached $0.42, its all-time low. In January 2018, ETH temporarily surpassed $1,360 before declining and staying below $1,000 until January 2021. Before falling by over 60% to nearly $1,700 in July of the same year, the cryptocurrency had just set a new high of around $4,170 in May 2021. When tokens surpassed $4,890 in value in November 2021, this cryptocurrency had its most significant value.

This shows a growth of about 652,000% from Ethereum’s first introduction. This peak didn’t endure since cryptocurrencies are volatile by nature. The following month, Ethereum had decreased by around 8% to trim over $3,700. If you had invested in Ethereum at its launch, you would still have made a healthy 411,000% return. By the beginning of 2022, a $1,000 investment would result in a massive portfolio of ETH tokens valued at $4.1 million.

The current price

Investing in Ethereum between its 2015 introduction and the beginning of 2018 would have cost you somewhere between $0.75 and $1,360, as we’ve already mentioned. Since early 2021, Ethereum has not gone below $1,000, and it is still one of the most widely used cryptocurrencies among all types of cryptocurrency investors. It’s interesting to note that Ethereum’s infamously high transaction fee dropped by almost 50% around its all-time high of $4,890 in November 2021. In 2022, Ethereum’s volatility has increased more recently. 

Beginning in 2022, the price of Ethereum was around $3,770. The cost of the cryptocurrency dropped steadily until about $2,400 at the end of January. Since July 2021, Ethereum has fallen below $1,000 to its lowest price. Ethereum’s volatility was stoked by indications that the Federal Reserve may continue raising rates to handle inflation. Government officials are interested in producing government-issued digital currency and more crypto laws. Due to these events, bitcoin prices have been similarly downward. As of the beginning of November 2022, the Ethereum price is trading around $1,200.

Also read: What is the Relationship Between Bitcoin and Ethereum?

Highs and Lows

Since the cryptocurrency’s launch, we have tracked Ethereum’s performance and have started to determine if it is a wise investment.

See Ethereum’s highs and lows here for a recap:

• Launch of Ethereum in July 2015 – $0.75
• Ethereum reached a record low in October 2015 of $0.42.
• ETH increased to over $1,360 in January 2018, marking a more than 181,000% growth since its debut.
• Tokens climbed to about $4,170 in May 2021, up 990,000% from their all-time low.
• July 2021: Crashes to $1,700, a 60% drop in only two months.
• The asset reached an all-time high of $4,890 in November 2021, a rise of over 158% in just four months after updating the local minimum.
• Ethereum fell to $2,400 in January 2022, a 50% decline from its all-time high two months earlier.

Due to these historical peaks and troughs, you should now better grasp Ethereum’s volatility and its suitability as an investment. Naturally, most cryptocurrencies are speculative and unstable. Also, always use verified crypto exchanges when you decide to make money from crypto trading.

Also read: Solana vs Ethereum | A Comparative study of top 2 blockchains

Is Ethereum’s utility still good?

Ethereum has a first-mover advantage in the smart contract field, which is considered to be so significant since many users will continue to use Ethereum as the standard option to deploy decentralized apps. It is simpler to spend ETH since its utility is substantially more significant than Bitcoin’s. Everything from NFTs and enterprise blockchain solutions to DeFi applications uses Ethereum’s technology. ETH, the native token of Ethereum, is the second most valued cryptocurrency in the market.

Below are some applications for Ethereum:

• By running a contract and asking the public for donations, developers may utilize Ethereum to fundraise for their projects.
• The Ethereum blockchain is the foundation for several of the top NFT games and collectibles.
• Ethereum switched to the less energy-intensive Proof of Stake (PoS) mechanism from Proof of Work (PoW).
• The network switch to PoS, also known as Serenity or Eth2, is to support more transactions and eliminate bottlenecks.

Conclusion

We have discussed cryptocurrencies today and attempted to provide an answer to the question, “Is Ethereum a Good Investment?” There are several advantages to including Ethereum in your portfolio, such as further upgrades that will boost the network’s scalability and bandwidth, making it well-positioned to withstand the test of time.

Sushiswap Vs Uniswap: Introduction to decentralized exchanges

Sushiswap (SUSHI) and Uniswap (UNI) are two of the top decentralized Exchanges (DEXs) running on the Ethereum (ETH) network. They are the two dominant DEXs running in the decentralized finance (Defi) sector. 

To avoid the security issues associated with centralized exchanges, DEXs allow users to exchange their assets without outsourcing the retention of those assets to other parties. Additionally, DEXs provide improved privacy because Know Your Customer (KYC) validation is less common. In this blog, let us compare Sushiswap vs Uniswap and understand what makes them so special.

What is Sushiswap?

Let us first examine what is Sushiswap. Sushiswap is based on the Ethereum blockchain employing the AMM protocol and is a decentralized exchange. Sushiswap was forked from Uniswap in 2020.  It offers new financial services in addition to maintaining some fundamental aspects. For instance, it does not use an order book but smart contracts, simplifying the process of buying and selling cryptocurrency. 

How does Sushiswap work?

Before investing, it is important to understand how Sushiswap works. Users can become liquidity providers so that others can trade tokens inside token pairs. Decentralized exchanges like Sushiswap use the Automated Market Marker (AMM) by matching traders with liquidity providers. 

Liquidity providers or LPs are motivated to offer tokens for staking benefits. A fee of 0.25%, equivalent to LP’s share of the liquidity pool, will be charged on all token swaps.

To enable token switching SushiSwap offers two primary features: Trade (to exchange tokens) and Liquidity (to add money to liquidity pools).

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How does Sushiswap liquidity mining work?

Before you learn how Sushiswap liquidity mining works. You will need to invest in some expensive hardware ASICs. You can mine Sushiswap with mining pools. You need to download the unMinable miner app to join the pool and start mining. unMinable is a mining app that allows you to mine several digital assets. 

The program starts and runs mining operations using a CPU or graphic processing unit. The capabilities of the PC will impact the profitability of your mining operation. You will need to invest in GPU for its superior processing and solving mathematical problems in mining. 

After downloading the Unminable app, you need to set up your wallet. Set up and customize the mining app to start mining.  

Understanding Sushiswap tokenomics

SUSHI is the native governance token of Sushiswap. You can stake SUSHI can be staked in exchange for xSUSHI, which also receives fees (0.05% on swaps) and gives voting rights. SUSHI acquired through staking in xSUSHI pools is time-locked for six months. Users who withdraw their SUSHI from the SushiBar receive their SUSHI back along with any costs they accrued while keeping their xSUSHI.

There was an unlimited supply of SUSHI at first, but it was later suggested to limit the supply to 250M. The suggestion was approved, putting a cap on the total quantity of SUSHI produced. It is anticipated the hard cap will be reached in November 2023.

The top Sushiswap wallet in the market

Users use their Sushiwap wallet to hold, buy, exchange, or stake SUSHI tokens. There are several types of Sushiswap wallets each with a distinct style and benefits. You can opt for a desktop SUSHI wallet, Mobile SUSHI wallet, or hardware SUSHI wallet. The top Sushiswap wallets are – 

• YouHodler

• Keystone wallet

• MetaMask

• Ledger Nano S

• Im Tokens

Also read: 10 Best web3 wallet | Price, Safety, Features

What are Sushiswap gas fees?

You can divide Sushiswap gas fees into –

• Trading Fees – Users incur 0.3% fees when they trade on the platform. Sushiswap charges the fees directly. 
• Gas Fee – Ethereum charges a gas fee for every transaction on the Ethereum blockchain. The gas fees are the highest component of Sushiswap gas fees. 

If we compare Sushiswap vs Uniswap gas fees spent by users between 2021-12-01 and 2022-01-26 62.61 million USD and 408.89 million respectively.  Uniswap costs $115 per transaction, and Sushiswap costs $113 per transaction in the same period. The average transaction fee was  $66 for Sushiswap and $68 for Uniswap. The average fee is lower of Sushiswap is lower because the platform uses smart contracts.

How does Sushi staking work?

With your Sushi tokens, SushiSwap not only gives you the chance to make $SUSHI and split platform fees, but you can also make xSUSHI tokens from your $SUSHI tokens! No matter whether you own any other platform tokens or have any liquidity pairings or not, holders of xSUSHI get 4.5% of all platform transaction fees from Sushi and, soon, 2.5% of fees from Shyu. To get started, all you need are some $SUSHI tokens and some Ethereum (ETH), as usual. 

• Visit the website and download the app and connect your wallet to the app.
• Ensure you have Sushi tokens in your wallet that you can stake. Click the explorer tab to select the SushiBar option. 
• Confirm staking and enter the tokens you want to stake. 
• Approve the transaction in your wallet.

Also read: How to write a smart contract? | Step-by-Step Guide

How to use Sushiswap?

If you want to learn to use Sushiswap, you need to have knowledge of Ethereum and a central exchange. Change the fiat money that a centralized exchange accepts into ETH. Then, after conducting your study, select a liquidity pool in the SushiSwap. This will aid in avoiding investment rug pulls. After that, connect an ERC-20 compatible wallet to the exchange platform.

Click on the swap option on the SushiSwap application’s left-hand side of the screen. You will see the SushiSwap token displayed on your screen following the completion of additional transactions on the Ethereum network. On SushiSwap, choose the token from the drop-down menu. In the box, type the amount you want to exchange. You can deposit cryptocurrency investments on the platform.

A transaction fee is charged when the platform executes swaps. The liquidity providers receive the next portion, with 0.05% going to Sushi token owners. The owners of the around 0.3%–0.25% transaction fees are liquidity providers. Additionally, users can buy a SushiSwap token and stake it to generate passive revenue.

How to buy Sushiswap?

Here is how to buy Sushiswap –

• Sign up for the service by first making an account on an app like Coinbase and confirming your ID on the app.
• The next step is to select a payment option from the method box. The procedure can then be started by using a debit card or bank account.
• Select “Buy” to initiate. Choose SushiSwap from the list of assets at this point. Additionally, open the SushiSwap browser and press to access the checkout screen.
• Enter the amount of cryptocurrency you wish to buy in your home currency. The amount will instantly change to a SushiSwap value.
• The “Preview buy” button should be tapped to complete your purchase. It will display the specifics of your purchase. Remember to confirm your purchase at a later time.

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What is Uniswap?

Let us understand, what is Uniswap. Users can use the Uniswap platform to trade in Ethereum tokens. It is a decentralized platform powered by the Ethereum blockchain. Uniswap was introduced in November 2018 and is presently the top DEX (Decentralized Exchange) on the Ethereum platform. A decentralized exchange is not dependent on a third party to handle the user’s cash. Instead, the users are in charge of their finances. Users utilize smart contracts stored on the blockchains to accomplish this. Some of Uniswap’s competitors are-

• Injective

• Vertex Protocol

• Kine

What is Uniswap liquidity mining?

Uniswap functions as an AMM by using cryptocurrency funds from its users. Each liquidity pool that houses these funds has a pair of cryptocurrencies in it. Anyone can use cryptocurrency to join these pools and serve as a liquidity provider. This is known as Uniswap liquidity mining. All transactions on Uniswap are subject to modest fees divided among the pool liquidity providers as Uniswap miner fees. If we compare Sushiswap vs Uniswap – Uniswap has no liquid mining program other than the initial token distribution, while Sushiswap has an active liquid mining program that rewards SUSHI governance tokens for contributing to liquid.

How do you mine Uniswap?

Here is how you mine Uniswap. You first need to know Uniswap is mined through liquid mining, and not traditional mining seen in Bitcoins. Decentralized exchanges or DEX like Uniswap need liquidity. Users with tokens provide the required liquidity (LP) or funds to Uniswap and are rewarded by the DEX. Users earn a commission reward and can generate a passive income on their contribution.  Here is how it works,

• Users get a special liquidity token sent to the address provided by them.
• The tokens are distributed in proportion to the user’s Uniswap contribution.
• When transactions are completed,  0.3% is charged to the sender of the transaction. The fee is divided proportionally to all the LP in the group. 
• The liquid providers must burn their liquidity tokens, effectively trading them for their share of the liquidity pool, to recoup the undying liquidity and collect fees.

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Understanding Uniswap tokenomics

Uniswap released its native token UNI in September 2020. The token is currently ranked 16 on the coin market cap. Uniswap initially intended to distribute the 1 billion UNI that was created at Genesis for four years. 2 The community will receive about 60% of the money, with the remaining 40% going to the staff, investors, and advisors. A constant inflation rate of 2% will exist after the first four years. UNI is currently ranked 16 on Coinmarketcap. 

If you hold UNI tokens, you can vote on platform updates, new development, and pricing structure adjustments.

The UNI token was developed to stop users from leaving Uniswap for the competing DEX Sushiswap fork.

What is the top Uniswap wallet?

Users have the option of connecting their wallets directly with the Uniswap exchange. They can connect to the network by clicking “connect to a wallet” in the upper right corner if they want to start supplying liquidity to the Uniswap network.  Some of the wallets supported are – 

• MetaMask
• WalletConnect
• Coinbase Wallet
• Fortmatic
• Portis

What are the Uniswap gas fees?

Uniswap liquidity fees range from 0.01 to 1% of the transaction. The DEX has a four-tier fee structure corresponding to the volatility of the cryptocurrencies in the cryptopool. 0.01% (v3 governance); 0.05% (stable/stable pairs like USDC/USDT); 0.3% (stable/blue chip pairs like USDC/ETH); 1% (wild pairs like ETH/DOGE). The gas fees for ETH-related transactions have become extremely high- more so when the network is congested. If you compare Uniswap v2 Vs v3 fees for retail users –

• With the v2 pool, the fee/liquidity ratio is 184,461 / 218,728,477 = 0.000843. 

• The same ratio on v3 is 703,170 / 332,460,000 = 0.00211. However since you don’t provide liquidity in a tight range / actively manage your position, your actual ratio will be divided by 10 or 0.00021.

Also read: Why are NFT Gas fees so high? | Deep dive into gas fees

How does Uniswap staking work?

Uniswap staking is a unique process and uses an automated liquidity protocol. You need an ETH and ETH20 secure wallet to participate in Uniswap liquidity staking. Uniswap helps to eliminate the traditional order book method and helps create a liquidity pool for each ERC-20 token thus contributing to creating high liquidity in trading.

Each pool is a smart contract with a combination of ERC-20 tokens. There is no restriction in investing in the liquidity pool. Uniswap is completely decentralized, so you have total control over your money.

Uniswap staking is distinct from all other types of bitcoin staking. However, after you’ve worked out how to do it, you may use your ERC-20 tokens to earn a passive income by adding them to liquidity pools with Uniswap.

How to use Uniswap?

Use a compatible crypt wallet to use Uniswap. You can do the following things on the exchange. 

• Trade crypto – select the “Swap” option, then the coins you wish to send and receive.
• Liquidity mining: Select “Pool” from the menu. You can create a new position and fund it with any two cryptocurrencies that already have a Uniswap pool. To find out which couples are popular, you may also look at the top pools.

How to buy Uniswap?

Uniswap is available in several crypto exchanges. You can trade US dollars or other flat currencies for UNI at Coinbase, Gemini, or Kraken. You can buy Uniswaps in three simple steps. 

Choose the cryptocurrency exchange- you can acquire Uni with other currencies through the Uniswap platform. You’ll need to pick a cryptocurrency exchange if you’ve never invested in cryptocurrencies before or if you wish to use fiat money. Exchanges may differ might differ in terms of charges and features for each transaction. 

• Buy: You can insert a payment method after selecting the exchanges. Several payment options are available to buy cryptocurrencies like a bank account or debit card, or credit card. Buying with your credit card is not recommended because they charge a hefty transaction fee. 
• Visit the trading platform and type the ticker symbol UNI to buy Uniswap. Specify the number of tokens you wish to buy or the sum of money you want to invest. 
• Market orders and limit orders are typical options. Limit orders are executed if the cryptocurrency price reaches a price you specify, whereas market orders are executed immediately at the best price available. You can store your UNI in digital or hardware wallets.

Also read: Flutter vs react native: A Detailed Comparison with examples

FAQ: Uniswap VS Sushiswap

Traditional bitcoin exchanges are centralized. Decentralized exchanges are viable alternatives to centralized exchanges and have different design frameworks. In this section, we focus on some common FAQs Uniswap vs Sushiswap.

1. Why are Uniswap fees so high?

There are a few reasons why Uniswap fees are so high. Uniswap fees can be categorized into – 

• Liquid pool fee or platform fee – the fee for swapping two cryptocurrencies on the platform. This is usually 0.3% of the transaction.
• Gas fees are transaction fees to interact on the Ethereum platform. This is the biggest part of your fees, and Uniswap has no control over the gas fees. With limited transaction bandwidth, the gas fees for Ethereum have become very high.
• Slippage fee – It is the difference between the anticipated price and the executed price.

2. How high can Uniswap go? 

Are you wondering, how high can Uniswap go? Presently, Uniswap has the 11th highest market cap demonstrating its tremendous popularity and confidence. It is considered to be a secure investment because of its large-cap designation (which has decreased to roughly $4 billion after the de-pegging of Terra). Uniswap is a large-cap project, yet it is also a new project in a rapidly changing sector. By selecting Uniswap, you get the best of both worlds: a developing, early-stage product with less volatility than altcoins with modest market caps.

3. Is Sushiswap a good investment?

After Sushiswap had a rocky beginning in 2020, several investors wondered if Sushiswap was a good investment. Here are some reasons why it is a good investment- 

• Sushiswap is a safe platform to invest.

• Several big financial firms plan to enter the Defi space

• The app is making high revenue

• You can influence Sushiswap’s future. 

Data scientists have long been debating between R vs Python. Both languages are now quite well-liked among data scientists. Data scientists can extract valuable insights from the sea of data by using Python and R. Languages for data science are extremely popular because of their extra capabilities and seamless functionality.

What is R?

The website of the R Foundation is the best resource for finding a comprehensive definition of What is R?  R is the programming language for statistical computing and visual design. John Chambers and colleagues created at it is GNU project that is comparable to the S language.  Data miners, bioinformatics, and statisticians use R for data analysis and developing statistical software.

Advantages of R

There are several advantages of R are –

• Since R is open-source and free and is the ideal platform if you’re creating a new program because it’s a low-risk endeavor. You do not need to make payments or acquire licenses. Developers contribute to the development of R by adding and optimizing packages and solving bugs if any.
• It is platform-independent, and it runs all operating systems. R is cost-effective because of its independence.  Coders have the flexibility of writing programs once for several platforms. 
• R is the most recognized statistical language. R is the preferred programming language for the creation of statistical tools.
• R offers a wide variety of packages. Over 10,000 packages, including R statistical packages, are found in the CRAN repository, and that number keeps rising. It is perfect for data science and machine learning tasks.

Also read: Flutter vs react native: A Detailed Comparison with examples

Disadvantages of R

Some of the disadvantages of R are-

• Objects are kept in physical memory in R. Comparing programming languages R vs Python, R uses more memory. All information is in a centralized location in the memory. This is not the ideal solution when dealing with Big Data. 
• R is not adequately secure. Security is a crucial component of any programming language.  R has a lot of limitations since we cannot integrate it into a web application.
• R does not have the support of dynamics or 3D graphics because it shares its origin with older languages like S.
• R is much slower than programming languages than Mathlab and Python.

Difference between  R vs RStudio

The difference between R vs RStudio is as follows 

• R is a programming language while R Studio is an integrated development environment. 
• R is installed on most computers but you need to install RStudio on your computer. RStudio is more elaborate than R. 
• RStudio is more elaborate when compared to R. R is a programming language and works independently.
• R is used for statistical computing, while users utilize R Studio to develop statistical programs.

What is Python?

Python is an interpreted, object-oriented, high-level, dynamically semantic programming language. Guido van Rossum developed the language and released it in 1991. Python is popularly used for Rapid Application Development and as a scripting or glue language to tie existing components together because of its high-level, built-in data structures, dynamic typing, and dynamic binding.

The language is used for server-side web development, software development, mathematics, and system scripting. It has an easy-to-learn syntax and emphasis on readability resulting in lower program maintenance costs. Additionally, Python’s support for modules and packages makes it easier to reuse code and create modular programs. As an open-source community language, multiple independent programmers are constantly developing Python.

Also read: Detailed Comparison between Cosmos vs Polkadot

What are the Python Advantages?

There are several advantages of Python.

• Python is user-friendly and easy to understand. It is an extremely advanced programming language with a syntax that resembles English. These features make the language easy to learn and adapt. Python’s foundations make it easier to implement than other computer languages due to their simplicity.
• Python is open-source software. Users have the flexibility of working and distributing code. They have the option of downloading and modifying the code.
• Python offers greater flexibility than other programming languages. 
• Python is easy to use because it is an interpreted language, and the code is executed line by line. If there is a mistake, it stops the operation and reports the issue. Debugging the program becomes efficient.
• Python has a large library that contains every conceivable function. Coders do not need to use libraries from other programming languages to write programs.
• Python supports portability, and programmers need to write the code only once and can run anywhere.

What are the disadvantages of Python?

Some of the disadvantages of Python are –

• Python requires a lot of memory, and this can be problematic if you want to create memory-efficient programs.
• The slow speed is a major drawback. Python executes one line of code at a time, and you cannot use it where speed is crucial. 
• Python does not have an advantageous database access layer compared to other programming languages like Java.
• Python is generally not used for front-end programming and mobile apps because of high memory utilization and sluggish pace.
• Python is prone to run-time errors.

Also read: How to become a blockchain developer

What are the Differences between R and Python

There has been a battle about the best programming language for data science, and there are two front runners R and Python. Data scientists choose between the two languages based on costs, requirements, and the tools required for building the program they need. In this section, we will discuss some of the key differences between R and Python.

Comparing Data Speed: R vs Python

If we compare data speed: R vs Python, we find that R is a low-level language resulting in longer codes and slower processing speeds. Python is a high-level language, and it processes data faster. Python is faster with a simpler syntax. 

Who are the Primary users: Python vs R?

If we compare the primary users: Python vs R, programmers, and developers use Python, while academics and R&D experts use R. Python has a more encompassing approach to data science and developers use it for deployment and production. R is focused on statistical analysis and is used for data analysis and developers. As a result, Python is more popular and has a vast user base.

Comparing Libraries: Python vs R

If we compare libraries: Python vs R, we find Python has a more extensive library. Most programmers do not need to know about all the libraries in Python but are familiar with the ones they need. R has fewer libraries than Python, and programmers find it easier to understand the libraries in R. R allows programmers to use available libraries, and Python allows programs to be created from scratch.

Comparing Integration: Python vs R

If we compare integration: Python vs R we find R runs locally while Python runs on a well-integrated app. In general, Python integrates better than R in any technical context. It generally provides a better solution by combining distinct components using a Python wrapper, regardless of whether the designers seek to leverage a lower-level language like C, C++, or Java. 

Comparing IDE: R vs Python

IDE or Integrated Development Environment is a tool where programmers can write, test, and debug their code easily. The IDE offers code insight by highlighting, resource management, compiling, and debugging. If we compare IDE: Python vs. R. 

Python offers five development environments specifically for data science. The top 5 IDE are –

• Spyder
• PyCharm
• Thonny
• Atom
• Jupyter Notebook

The best IDE for R are –

• RStudio
• Visual Studio Core
• Atom
• Sublime Text
• Rider

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What are the Applications: Python vs R?

If we compare applications: Python vs R, R might be a good fit for you if you’re passionate about the statistical computation and data visualization aspects of data analysis. Python might be a better choice if, on the other hand, you’re interested in working as a data scientist and utilizing big data, artificial intelligence, and deep learning methods. Python for data analysis is a suitable option. The same holds if your interests in programming, development or other areas of computer science go beyond data and into your personal or professional life. Compared to R, Python is a general-purpose language, and you can use it for various activities.

Comparing Popularity: R vs Python

If we compare popularity: R vs Python, we see Python steals a march. According to Stack Overflow, Python is the third most popular language trailing behind JavaScript and HTML, while R is 24 on the list. Though this difference looks stark and startling at first, it soon becomes clear that R has little competition above it in terms of a programming language for analyzing data and generating graphs and reports. R has an edge over other programming languages, especially when we rank languages based on the PYPL index (which ranks languages based on Google searches for tutorials for those languages), Python is ranked at the top spot, and R is ranked at number seven.

What is the Average salary: Python vs R

Python and R are popular choices for organizations, start-ups, and companies looking to improve efficiency at lower costs.  Skill in Python is quoted more frequently in various job descriptions in job portals.  According to the 2019 Stack Overflow Developer Survey, R is a more in-demand skill worldwide. R developers are earning an average of $64k annually, $1k more than the $63k reported by Python developers. The scenario in the US is reversed, with R programmers earning $108k and Python programmers earning $116k annually.

Also read: 25 Highest paying remote jobs in 2023 | Best work from home jobs

Which language has a steeper learning curve: Python vs R?

Comparing the learning curve: Python and R will depend on your skills as a programmer. If you are a beginner, Python is simple and easy to understand. Although R is not regarded as a good first language to learn, it shouldn’t be too difficult to understand if you are already familiar with programming languages.

R could seem simple to start, but the learning curve jumps exponentially when you dive into more sophisticated concepts. Python, on the other hand, is easy to begin and has a learning curve that is linear and smooth.

Python vs R for data science

Python and R are both suited for data science and data mining. Data Scientists find deciding which platform to use challenging. Python distinguishes out because of its general-purpose qualities and the fact that it has a very regular syntax, whereas R was exclusively created for statisticians, making it represent analysis as a specific advantage for visualizing data. Here are some key differences between Python vs R that will help you decide.

Why you should choose Python for big data?

Developers like using Python for big data because it is open-source and easy to learn. Python is scalable and can process a large amount of data with ease. If you compare it with other programming languages, such as R and Java, Python is more scalable and flexible. Developers and coders can use a large number of directories and framework Python offers. Libraries like Panda, NumPy, and Scikit-learn, specifically cater to data analytics and machine learning. Python’s fast-processing speed makes it ideal for processing big data. The language is portable and extendable.

Why choose R for data science solutions?

Several programming languages are compatible with handling data science processes but R evaluates and uniquely collects data. We choose R for data science solutions because R produces error-free plots and graphics. This makes R the perfect tool for visualization, and it can perform complex statistical computations and calculations easily. It offers a variety of advanced data analysis techniques like machine learning and is the perfect tool for analyzing large amounts of data quickly.  R can perform various data science operations like data wrangling, data visualization, statistical computation without vectors, and web application.

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What are some popular Python data mining tools?

Python is ideally suited for data mining. Data mining requires statistics, code, and machine learning algorithms instead of explosives and smelting.  Python provides several data mining tools and it has an ecosystem of third-party modules. Some popular Python data mining tools are –

• Pandas 
• Matplotlib
• Seaborn
• Jupyter
• Satsmodels

What are some popular R data mining tools?

The largest library of data mining modules is undoubtedly in R, which is also more advanced than Python’s Scikit-learn. The only issue with them is that because the data is processed in memory, working with greater volumes of data is difficult and time-consuming. Some of the best R data mining libraries are 

• Dpylr
• Caret
• Ggplot2
• Shiny

Python packages for data science

Python never fails to astound its users when it comes to handling jobs and problems related to data science. The majority of data scientists already regularly use Python’s power. Python is a popular, object-oriented, open-source, high-performance language that is simple to learn and easy to debug, among many other advantages. Python has outstanding data science packages that programmers utilize daily to solve challenges. The top 5 Python libraries for data science are listed below:

• TensorFlow
• NumPy
• SciPy 
• Pandas
• Matplotlib

Also read: How much does it cost to create an NFT | Tips to reduct the cost of creating an NFT

Top R packages for data science

R is one of the most popular programming languages for statistics and has a firm foothold in data science. You will find over 10000 packages in the CRAN repository. Data scientists use some of these packages for data science –

• Dplyr
• Ggplot2
• Tidyr
• Shiny 
• Caret

R vs Python for machine learning – Which is better?

If you are working on a machine learning project, you will have to choose the right programming language. The choice often comes down to selecting R vs Python for machine learning. 

The most common tools used by data scientists are R and Python, which both have aspects in common. Both are open-source and therefore free, however, R was designed for statistical analysis, whereas Python is set up as a broadly useful programming language. However, Python seems to perform better when dealing with repeated jobs and manipulating data. If you intend to develop a machine learning-based digital product, Python is the best option. Additionally, choose R if you need to create a tool for ad-hoc analysis at a crucial point of your project.

The usage of apps for various purposes is now the trending lifestyle of people around the world. And to satisfy the interests, app development is nowadays on a rise. But then the question arises of which is the best for app development. In this article, we will discuss the differences between Flutter vs React native, created by Google and Meta subsequently.

Also read: Polkadot vs Cardano | Compare Ecosystems, Features, Prices

What is flutter?

Before focusing on the differences between flutter vs react native, let us first understand what is flutter. Flutter release date is on May 2017 and was done by Google. It is a portable UI open-source software development kit. It is used to create mobile, web, and desktop applications from a single codebase for various platforms like Android, iOS, Linux, Windows, macOS, and Google Fuchsia. 

The flutter framework

The flutter framework is UI based and comprises two components:

1. The software development kit or SDK and 
2. The UI library is widget-based, consisting of numerous reusable UI elements like buttons, sliders, and the text inputs

The libraries in the flutter framework can be further subdivided into more layers:

• The basic foundational class layer
• The widget layer
• The rendering layer

Animation, painting, and gestures are also an inherent part of the framework

New updates in flutter 3

Do you want to know what’s new in Flutter 3? Here you go:

1. Involves advanced device testing features, authentication, cloud services, and data storage
2. App development optimization through critical alerting metrics and real-time crash reporting
3. Integration menus for specific platforms for faster compilation 
4. Ease of interfacing design by enabling compatibility with Material 3

The flutter language

The flutter language used for programming is Dart. It is an object-type programming language for front-end development, used by mobile application developers. It uses syntax like Javascript.

Flutter framework leverages many of the advanced features of the language. When you write or create an application or debug an application, Flutter runs on the Dart virtual machine. The VM consists of a just-in-time execution engine. The users can publish and use Flutter plugins and custom packages since flutter inherits the attributes of the package manager and software repository of Dart.

Three-layered flutter architecture

The flutter architecture consists of three layers:

1. Framework – We have already discussed the flutter framework which is the key layer of the flutter architecture. It is based on Dart and is divided into 3 sub-layers.
2. Engine – The engine layer is written in C or C++ programming language. It provides low-level implementation or support using the Impeller graphics layer or the Skia graphics layer of Google.
3. Embedder – This particular layer provisions Flutter to run on any operating system and is specific for each platform

Some flutter apps examples

Here are some flutter apps examples, well-known in the market:

1. Google Ads – helps to manage Google ad campaigns from smartphones. Very user-friendly and intuitive
2. Reflectly – AI-powered personal journaling application. Provides detailed insights into your daily life.
3. KlasterMe – Content sharing website that can be used by developers on social media platforms. 
4. Postmuse – The photo editing app of Postmuse
5. Lunching – an app used for ordering and delivering food
6. Watermaniac – an app used to monitor water intake and usage

Flutter vs flutter js

If we think of the differences between flutter vs flutter JS, then the most common among them is that Javascript is easy to use being a mature and stable language. Further JS is in the market for quite a long time and hence more popular and user-friendly. JS also has the advantage of being faster and lighter compared to other languages.

Also read: Detailed Comparison between Cosmos vs Polkadot

What is React Native?

Now comes the next topic for Flutter vs React native. What is React native? Similar to Flutter, it is an open-source UI software framework, developed and created by Meta Platforms, Inc. It is used extensively to write and build applications for react native Android, iOS, macOS, Windows, UWP, tvOS, Web, and AndroidTV.

The React Native framework

React Native is a mobile app framework based on Javascript. This enables building mobile apps which are natively rendered for the popular iOS and Android platforms. The framework helps to create and develop applications using the same code base, even on different platforms. Thus significant savings for time, effort, and resources whilst creating a robust application for the mobile platforms. It was released by Facebook in 2015.

The React native language

The React native language used is Javascript which is a prominent language among web developers. It is used to build cross-platform mobile applications with ease. Learning Javascript is easy and requires minimal training to adopt and use it. It is far advanced in terms of simplicity and speed and provides numerous interfaces to web developers for creating attractive web pages. Besides, it improves the performance of the applications significantly.

The React native architecture

Let us now understand the React native architecture. It has three threads in its execution environment – the Native main thread, the Javascript thread, and the background thread. “Bridge” in the library helps in communication between these threads.

React native has an inherent UI abstraction layer. It does not have any native code but is built on native libraries like Gesture Handler, Reanimated, and Screens that are used to implement the navigation patterns of the applications being created.

Some react native apps examples

Some react native apps examples are:

1. Facebook – one of the most popular react native apps
2. Skype – started using react native for the apps from 2017
3. Instagram – integrated react native into its current native app
4. Walmart – American grocery retail chain for its Android and iOS apps
5. Shine – a medication and motivation app
6. Ubereats – an app for food delivery at doorstep
7. Pinterest – powered by Gestalt based on react native
8. SoundCloudPulse – app for music creation

Difference between react js vs react native

Coming to the difference between react js vs react native:

1. React js uses the Javascript library comprising of reusable components. It is to create the skeleton of the applications. React native, on the other hand, is to build and create the native mobile applications with the reusable components
2. React js is used to build the User interfaces for the web applications. React native is used to build the rich mobile user interface using Javascript and from declarative components

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Differences between Flutter and React native

Now comes the most significant topic for the article Flutter vs React native and that is understand the differences between Flutter and React Native. React native is mainly focused on native components whereas Flutter focuses on interactive widgets, and visual and structural platform.

In the subsequent sub-sections, we will deep dive into the details of the differences Flutter vs React native.

Flutter UI vs React UI : Which one is better?

In the comparison of Flutter UI vs React UI: Which one is better? The answer is Flutter UI and it scores higher points. The main reason is the development experience offered by Flutter, focused on its own customized development language and also uses its own widgets. The further advantage is the widgets are very much compatible and stable with the Cupertino for Apple and Material Design for Google.

Documentation : Reactnative vs Flutter

When it comes to documentation : Reactnative vs Flutter differences are significant. The points earned is again Flutter more than Reactnative. The documentation guides of Flutter are superior with graphics and videos available within the framework itself. This helps developers to easily adopt or learn the framework and even bring in optimization. Whereas reactnative has poor documentation, forcing developers often to look for help from third party sources.

App development: Flutter vs Reactnative

In comparison for app development: Flutter vs Reactnative, Reactnative earns higher points. The programming language Dart used in Flutter is not the popular one and is not very easily understood by the developers. Its cons are that it doesn’t support many IDEs and text editors. Whereas Javascript used in Reactative is widely used, easing the development work with its pure and simple concepts. Any IDE or text editors can be easily used freely in this framework.

Background service: Flutter and Reactnative

When it comes to background service: Flutter and Reactnative differences are noticeable and the clear winner is Reactnative. The reason is firstly it is very popular in the market and among the developers. Secondly, it has innumerable tutorials for guidance and support, turning out its developer base to over 10,000. Thirdly, various developer conferences are held around the world to bring in interactions and collaborations among the users.

Performance of ReactNative VS Flutter

While comparing the performance of Reactnative vs Flutter, Flutter experience is better. The reason being the framework does not leverage the Javascript bridges and hence the development is faster. The other factors that boost the performance of Flutter are its animation standard set at 60 FPS and its direct compilation into the native ARM code. Reactnative uses Javascript and the JS bridges to its back down and along with it the plug-and-play components affect react native performance.

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Frequently asked questions on react vs flutter

We have now reached the conclusion of Flutter vs React Native. In this section, we will discuss on the frequently asked questions on react vs flutter. These will help the developers to decide on whether to use flutter or react native for app development.

1. Does google use react?

Well the response towards does Google use react, usage is limited. Google mainly used Angular for its app development and build work. Being a complete framework, Angular is preferable.

2. What are the prerequisites for react native?

The prerequisites for react native to enable react native coding and react native development are:

1. Knowledge of Javasacript
2. Proficient in HTML and CSS
3. Awareness of React and working experience in using React

3. What is the limitation of react native?

It is crucial to understand what is the limitation of react native before selecting it. React native is difficult to debug since react native elements use C, C++, Javascript. It is not a good choice when it comes to interactions, multiple screen transitions, and animations. React native is focused on going for rapid pace changes in its framework which poses difficulties to the developers. Since it’s a cross-platform framework, sometimes it becomes tough to find out such experienced developers.

4. What are flutter disadvantages?

The flutter disadvantages are:

1. No web apps can be developed as it does not support browsers
2. It yet does not have a huge resource base and hence many of the flutter UI components and elements need to be written from scratch
3. Dart being not a popular language yet compared to Javascript, developers have to invest in substantial effort to learn it and then leverage it for app development
4. Lastly the apps developed by Flutter are weighty

5. Is Flutter worth learning?

If you want to know is Flutter worth learning, the response is yes, it is definitely worth it. The demand for Flutter is increasing manifold due to its relevance to native app development. The developer community is also building up and along with it the forums.

6. Is react native and reactjs same?

The answer to is react native and reactjs same, is not exactly. Reactjs mainly creates the skeleton of an application whereas react native builds the native mobile applications. Both of them uses reusable components of the framework. React js builds UI for web applications and react native builds UI for mobile applications.

What is the difference between platform vs framework?

Comparison of platform vs framework states that – A platform comprises of software and hardware components where the developers can build and run applications. A framework contains tools, software development kits, libraries and others to operate as a software-only skeleton to build applications

It has become challenging for developers to design applications that can handle multiple blockchains. Users prefer using one or two chains because blockchains are resource-intensive. Interoperability was one of the roadblocks to the widespread use of blockchains. Cosmos and Polkadot have emerged as two frontrunners to address this problem. We will compare Cosmos vs Polkadot, and examine their underlying architectures and technologies and the two native currencies and development frameworks.

Also read: Polkadot vs Cardano | Compare Ecosystems, Features, Prices

What is Cosmos?

Let us understand what is Cosmos. Cosmos is an ecosystem of blockchain that focus on scalability and interoperability. Before it came around, blockchains were isolated from each other and unable to communicate. They were challenging to construct and had a low transaction speed. The network uses a fresh technical perspective to tackle these issues. It is a free platform that facilitates interoperability between various blockchains, like Binance Chain and Terra. 

The ultimate goal of Cosmos is to establish an “Internet of Blockchains,” a decentralized network of blockchains capable of communicating with one another. It provides the ideal solution for several use cases and allows blockchains to preserve their independence, execute transactions rapidly, and communicate with other blockchains in the ecosystem. The cryptocurrency ATOM fuels and protects the network of interconnected blockchains built to scale.

What is Polkadot?

Let us focus on what is Polkadot. Polkadot is a blockchain that connects blockchains, enabling the transfer of data and assets between two incompatible blockchains. It aims to be quick and scalable. The blockchain uses DOT cryptocurrency for governance and staking.

Polkadot is like a network of networks that enables communication between different blockchain designs. It achieves this using parachains or modified blockchains with unique features and tokens.

The network was modeled around the Ouroboros protocol and utilizes the nominated proof-of-stake (PoS) consensus process.

Also read: Solana vs Ethereum | A Comparative study of top 2 blockchains

1. Blockchain architecture – Cosmos vs Polkadot

Let us compare the difference between Cosmos vs Polkadot based on their architecture, in Polkadot the relay chain is the main, and all other chains are known as parachain, while in Cosmos the main chain is the hub, and all other connected chains are called zones. The Polkadot architecture uses the principles of the sharding concept, and it aims to address the scalability and interoperability issues that plague many current blockchain-based applications. The Cosmos blockchain architecture follows replicated deterministic state machine.

Cosmos Architecture

In Cosmos Architecture, the central chain is called a hub and it connects to other blockchains knowns as zones. Each zone has its validator community to preserve its state. When a zone, wants to communicate with another, it uses Cosmos IBC to send packets. The hub tracks the balances in multi-token ledger balances.

The zones use light status to keep an eye on the Hubs status, but the hub does not monitor the zone status. Zones implement the IBC interface and use a deterministic finality algorithm (Tendermint) to send messages to other chains through the hub.

Cosmos uses peg zones (similar to bridged parachains) to communicate with external chains.

The architecture of Polkadot Blockchain

Polkadot architecture has a Relay Chain that serves as the system’s main chain on Polkadot, and all validators are on the relay chain. The collators build and suggest parachain blocks to validators. They do not have any security obligations and hence do not need a strong incentive structure. Collators can submit one parachain block for every Relay Chain block every six seconds. Before committing a block to the main chain, validators do several availability and validity tests before committing to the final chain.

Since parachain slots are limited, the parachain candidates bid in an auction to secure a spot for two years. Polkadot has the option of parathreads for parachains that lack resources or the requirement to run in a six-second block of time and operate on a pay-as-you-go model where you pay to execute a block when required.

Polkadot uses bridge parachains that provide two-way interoperability to communicate with chains that want to use their finalization method.

Also read: How to write a smart contract? | Step-by-Step Guide

2. Consensus mechanism on Cosmos and Polkadot

The consensus mechanisms of Cosmos and Polkadot are different. Cosmos supports the consensus algorithm that complies with the ABCI specification (Only Tendermint follows this specification). It gives validators the option to accept or reject a single block. Block production and finalization follow the same pattern, and only one block can be finalized at a time.

Polkadot uses The BABE (Blind Assignment for Blockchain Extension) hybrid consensus algorithm, which combines the verifiable random function and Ghost-based Recursive Ancestor Deriving Prefix Agreement (GRANDPA) (VRF). Grandpa’s consensus method moves swiftly and accepts several validators ( 1000 validators). Every block does not require a vote from the validators. The algorithm accounts for the block with the largest number of votes before passing it on to the ancestors. The supermajority vote block’s decisions become binding. 

Cosmos consensus 

The Cosmos consensus or Tendermint consensus is a round-robin technique that enables finality and is used by the hub and zones. The algorithm creates and finalizes the blocks one at a time because block production and finalization follow the same path. It has quadratic transport complexity because it is a PBFT-based method (like GRANDPA), yet it can only finalize one block at a time. The Tendermint algorithm creates finality for each block by having each validator communicate with one another to approve or reject each given block. The algorithm has undergone live stress testing with 200 validators and 6-second block durations during Game of Stakes, and it is quick.

Polkadot consensus mechanisim

Polkadot uses a hybrid consensus protocol that has two subprotocol: BABE and Grandpa collectively called Fast Forward. BABE (Blind Assignment for Blockchain Extension) uses a verifiable random function (VRF) to assign slots to validators and a fallback round-robin pattern to guarantee each slot has an author. Grandpa (Ghost-based Recursive Ancestor Deriving Prefix Agreement) votes on chains rather than individual blocks.

This has several advantages – it reduces the complexity of transport for block production and finalization. Due to BABE’s linear complexity, scaling to thousands of block producers is simple and involves little networking overhead. GRANDPA is quadratic in complexity, but its execution time, or the number of blocks it finalizes in a batch, reduces it by a factor.

Second, it gives the flexibility to add unfinished blocks to the chain and lets additional validators carry out thorough availability and validity checks to ensure that no invalid state transitions enter the final chain.

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3. Governance & Membership – Polkadot vs Cosmos

If we compare Polkadot vs cosmos blockchain, we find they have different governance and membership criteria. Polkadot follows multicameral governance i.e. a proposal passes through multiple houses before it becomes a rule. Cosmos has coin-vote signaling and uses coin-vote for passing referenda. In Polkadot, validators are not given any voting power based on who nominated them.

Cosmos Governance

The Cosmos governance model uses coin-vote signaling to pass referenda. Similar to other blockchains, governance implementation occurs via a protocol split. All token holders are eligible to vote and in case a delegator chooses not to vote, the validator they designate will act in the capacity. There is no single governance process in the Cosmos network. There is no single set of guidelines governing the network of blockchains, and each hub and zone has its governance procedure. Anyone may submit a text proposal to the Cosmos Hub under a set of regulations, and Atom holders can vote on it, and the number of Atoms they own influences the weight of the vote.

Governance & Membership on Polkadot

The governance and membership on Polkadot have a multicameral governance structure and have several ways to enact legislation. Every proposal must pass by popular vote, and results are decided by a majority of tokens. Polkadot uses adaptive quorum biasing for a referendum with low turnout. The referendum includes proposals like fund distribution from an on-chain treasury. On-chain decisions are autonomous and legally binding, and Polkadot has many on-chain permissionless bodies. The main body is the Council which comprises a set of accounts chosen in a Phragmén-style election. A proposal with universal support from the Council, and representing minority concerns is more likely to pass a public referendum. A Technical Committee exists to provide technical advice (e.g. emergency runtime upgrade to fix a bug).

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4. Security Models on Cosmos and Polkadot

Cosmos and Polkadot have different approaches to tackling security. Polkadot uses shared security measures, which entail two parachains with two different levels of security- a uniform security level and a fishing technique to reverse the entire transaction if an incorrect block is found. The validators in Cosmos ensure that malicious activity in a zone does not negatively impact the network.

Cosmos Security model

The Cosmos security model is as follows every blockchain in the Cosmos Network is independent and secures itself instead of employing a local/global architecture for security. Each blockchain operates under its consensus, and only that blockchain’s validators maintain its security. The network’s interoperability is due to the hub-and-zone paradigm, in which zones (separate blockchains) can send tokens to other zones by traveling through a hub (also an independent blockchain). This method of communicating between chains to depict token transfers is known as IBC (Inter-Blockchain Communication). The IBC protocol is a work-in-progress that initially supports token transfers before expanding to include other messages sent between blockchains.

Polkadot Security model

The Polkadot security model uses shared security or pooled security in certain documents, which is one of the distinctive value propositions for chains thinking about joining the Polkadot network as a parachain. Shared security means that the financial security offered by the Relay Chain validators is shared by any parachains connected to the Polkadot Relay Chain by renting a parachain slot.

In bridge protocols, each chain is considered to be independent and upholds its own validator set and financial stability. Another security measure used by Polkadot is using fishermen to reverse the entire transaction if they find an incorrect block. Polkadot uses a cross-consensus message passing style to send random messages to each other (XCM).

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5. Blockchain interoperability – Cosmos vs Polkadot

Data accessibility via a variety of networks is known as interoperability. One use case would be to execute avalanche transactions on the Polkadot network using an Ethereum contract. In Cosmos vs. Polkadot, the inter-blockchain communication design is very different. Polkadot makes data and asset transfers between several blockchain networks possible by facilitating communication between two parachains using transfer tokens and other methods.

Cosmos, on the other hand, concentrates on moving resources between chains. The Tendermint consensus, a round-robin technique that enables instant finality, is used by Cosmos (both the Hub and the zones). The algorithm creates and finalizes blocks one at a time since block production and finalization follow the same path. It has quadratic transport complexity because it is a PBFT-based method (like GRANDPA), yet it can only finalize one block at a time.

Cosmos blockchain interoperability

Cosmos blockchain interoperability uses a consensus algorithm that complies with the ABCI spec and can be used by any blockchain on the Cosmos Network. Only the Tendermint algorithm complies with this specification as of right now. Cosmos is working on developing other consensus algorithms to improve interoperability. The Tendermint algorithm creates finality for each block by having each validator communicate with one another to approve or reject each given block. The algorithm has undergone live stress testing with 200 validators and 6-second block durations during Game of Stakes, and it is quick. The Tendermint algorithm is available right out of the box thanks to the software development kit that the Cosmos team offers.

Polkadot blockchain interoperability

Polkadot blockchain interoperability approach can support a number of validators and allows the Relay Chain to quickly finish multiple blocks from all the parachains (over 1000). This can be explained simply by the fact that not all validators are required to vote on every block; instead, validators can vote on the highest block they believe to be legitimate, and the algorithm transitively applies the vote to all of that block’s ancestors. The program then determines the set of blocks with the supermajority vote and treats those as the final set. Polkadot is still developing GRANDPA, and its performance in the actual world is unknown.

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6. Scalability and upgrading on Polkadot and Cosmos

If we compare the scalability of Polkadot and Cosmos, we find Polkadot has more robust scalability options. Polkadot can reach 1 million TSP with parachains, while Cosmos can start its network that runs parallel to the main network. On the upgrades features, Polkadot offers upgrades without hard forks, while Cosmos upgrades are possible with hard forks.

Cosmos Scalability

Cosmos interoperability ensures the scalable system will function properly. With the help of the Cosmos interoperability model of shared communication standards, any sovereign blockchain can connect and improve the protocol design. Cosmos achieves greater scalability by separating the applications into many application-specific blockchains or replicating a blockchain to reduce congestion. Token transfers between chains let these several chains function as one network. Developers can start their new blockchains and use the SDK to launch a parallel network to the main network.

Polkadot Scalability

Parachains reduce the number of transactions that could clog up on Polkadot’s native chain because they are their distinct blockchains processing their transactions. Instead of the vertical scalability present on a network like Ethereum, Polkadot scalability is horizontal. On the Polkadot network, parachains are designed for use in particular scenarios. The majority of the computation on Polkadot can be handled by the parachains because they are inherently scalable. This resolves the scalability problems that other blockchains, like Ethereum, have and some of the centralization problems that blockchains like Binance Smart Chain have (BSC).

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7. Staking Mechanics on Cosmos and Polkadot

Comparing the stalking mechanism on Cosmos and Polkadot we see the Cosmos (ATOM) and Polkadot (DOT) tokens have similar functions in their respective ecosystems. Holders can use their stake to influence governance or stake their tokens to validate transactions either alone or alongside other validators. Polkadot validators are chosen using the nominated proof-of-stake g a nominated proof-of-stake (NPoS) procedure, while Cosmos Hub uses bonded proof-of-stake (a variant of delegated PoS) to select validators. 

Staking on Cosmos

Staking on Cosmos uses bonded proof-of-stake model. for each validator they wish to delegate to, holders must bond funds and submit a delegate transaction along with the appropriate number of tokens. Up to 300 validators will be supported by the Cosmos Hub.

In Cosmos, both consensus voting and rewards are based on stakes. Instead of 2/3 of the validators, more than 2/3 of the stake must commit in a consensus vote. A validator that contributes 10% of the total stake will also receive 10% of the prizes.

Polkadot staking

Polkadot Stalking utilizes the Nominated Proof of Stake (NPoS) model to choose validators with the help of the sequential Phragmén algorithm. The governance determines the size of the validator set (1000 validators are intended), and stakeholders who do not wish to manage the infrastructure for validators can propose up to 16 validators. Phragmén’s algorithm helps choose the optimal stake allocation, where optimal refers to having the most evenly staked out. Phragmén’s algorithm chooses the optimal stake allocation, where optimal refers to the most evenly staked set.

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Polkadot’s consensus mechanisms give equal weight to all validators. In other words, rather than 2/3 of the stake, more than 2/3 of the validators must back a chain to obtain greater than 2/3 of support. The same is true for validators, who are rewarded based on their activity, principally block production and finality justifications, rather than their stake. Given that they will receive greater returns on their staked tokens, validators who are nominated with lesser stakes will be encouraged to do so.