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This article was published as a part of the Data Science Blogathon.

Decentralized Applications (dapps) are applications built on a blockchain. A decentralized application can be of many types such as an NFT marketplace, or a decentralized exchange (DEX). Using a DEX, users are not required to deposit their assets on the exchange (whereas in a centralized exchange, users deposit their assets on the exchange). Due to this, users have complete control of their assets. Thus, decentralization puts the power in the hands of the people.

In this guide, we will go through how to use QuickSwap, a decentralized application built on Polygon that allows users to swap, farm, and stake in a decentralized, secure, and low-cost manner.

QuickSwap is a decentralized application built on the Polygon blockchain. It is a layer-2 DEX (Decentralized Exchange) and AMM (Automated Market Maker). QuickSwap is the second largest decentralized application on the Polygon blockchain having ~$250m of assets in TVL (Total Value Locked). QuickSwap offers features such as swapping, liquidity, yield farming, and much more.

QuickSwap allows users to earn additional rewards for adding liquidity to the platform. Once users stake their LP tokens, they earn LP rewards in addition to the additional rewards in the form of $QUICK tokens (QuickSwap’s native token).

To execute any transaction on a decentralized application, some fees are associated with it. This fee is paid in the native token of the underlying blockchain. Since QuickSwap is built on the Polygon blockchain, users must pay gas fees in $MATIC tokens.

Wallet Used in QuickSwap

QuickSwap supports multiple Web3 wallets. In this guide to QuickSwap, I will be using the MetaMask wallet. Since QuickSwap is built on the Polygon blockchain, your MetaMask should be connected to the Polygon blockchain.

MetaMask is a Web3 crypto wallet that enables users to store, send, receive, buy and swap their crypto assets in a secure and decentralized way.

Connect MetaMask to QuickSwap

To interact with any decentralized application, we first have to connect our wallet to the application. You can do so by following the steps below:

Select the wallet you want to connect the dapp with.

Select which account of your wallet you want to connect QuickSwap to.

Your wallet is now connected to the application. Hence you can start interacting with the dapp. Once your wallet has been connected, it will direct you to the swap page.

What is Swapping?

Swapping is exchanging (trading) one asset for the other without the need for an intermediary. Intermediaries refer to as middlemen. When we want to exchange crypto on a centralized exchange, the exchange acts as the middleman between the buyer and seller. On a decentralized exchange such as QuickSwap, there’s no middleman required.

QuickSwap allows two kinds of swapping – Swap and Limit Swap.


In this method, assets get swapped (exchanged) at the current market price. To swap your tokens, follow the steps below:

Select the token you want to swap.

Enter the amount of the token you want to swap.

The token you have swapped is now reflected in your wallet.

Limit Swap

In a limit swap, users can specify the price at which you want their asset to be swapped. For example, the current price of MATIC = $1. In a limit swap, you may set the limit amount as $0.50. This means the assets will get swapped (traded) only when the price of MATIC = $0.5.

To swap your tokens using the limit feature, follow the steps below:

Select the token you want to swap.

Enter the amount of the token you want to swap.

Enter the price at which you want the transaction to take place.

Scroll down and in the Open section, you will see your open orders.

Your order will be executed when your specified asset’s price is met.

Cancel Limit Order

In the above section, we saw how to set a limit order. In this section, we will see how to cancel the order which we have already placed.

You can cancel a limit order by following the steps below:

You can notice the open section is now empty, signifying there are no current open orders.

Here you can see the past orders which you have canceled.

Bonus Tip!

In the Swap section, you must have seen a Pro Mode slider. The pro mode section will show you the market depth of the current asset pair you want to swap.

You can enable the pro mode by following the steps below:

You can now see the price chart of the respective asset pairs.

Furthermore, you can also view the market depth of the respective asset pair.


Liquidity refers to adding funds to the protocol. Orders will easily get executed on protocols that have high liquidity. With QuickSwap, as specified above, users can earn additional rewards in the form of $QUICK tokens for staking their LP tokens. By adding liquidity, users earn fees equal to the pool’s share.

To add liquidity, you are required to add liquidity for two tokens. The amount of these two tokens is required to be in a 50-50 weightage. For example, if you are adding liquidity for the USDC/USDT pair, you are required to add equal amounts of USDT and USDC since they are of the same price i.e. $1.


In the pool section, you will receive LP tokens as proof that you have added liquidity to the protocol.

Add Liquidity

To add liquidity (get LP tokens), follow the steps below:

Select the tokens to which you want to add liquidity.

You have successfully added liquidity to the respective two token pairs.

After providing liquidity, the LP tokens will be reflected in your wallet.

Remove Liquidity

To remove liquidity (sell LP tokens), follow the steps below:

Adjust the slider depending on the amount of liquidity you want to remove.

You will notice both the tokens which you added as liquidity to the protocol have been credited back to your wallet.


In the farm section, your LP tokens can be put to use. Users can earn additional yield by staking their LP tokens. This yield is variable as it depends on the supply and demand of the underlying tokens.

How to Stake LP tokens?

Users can farm their LP tokens by following the steps below:

This is the farm page.

Select the pool with the same token pair as your LP tokens.

Enter the amount of LP tokens you want to stake.

Your LP tokens are now staked and are earning interest.

How to Unstack LP tokens?

Users can unstack their staked LP tokens by following the steps below:

Select the pool with the same token pair as your LP tokens.

Enter the amount of LP tokens you want to unstack.

Your LP tokens are now unstacked from the pool.

Dragon’s Lair

In the Dragon’s lair section, users can earn a good yield in the form of $QUICK tokens by staking their $QUICK tokens. The APY for staking $QUICK is 27%.

You can stake $QUICK tokens and earn interest by following the steps below:

Enter the amount of $QUICK tokens you wish to stake.

Your $QUICK tokens have now been staked and are earning yield.


Initially, the $QUICK token had a maximum supply of 1 million tokens. The community decided to change this and voted for a 1:10000 token split to make $QUICK more appealing. The total supply of the new $QUICK token is 1 billion tokens.

In the convert section, users can convert their old $QUICK tokens for the new $QUICK tokens. Due to the split, 1 QUICK(OLD) = 1000 QUICK(NEW).

To convert your old $QUICK tokens to the new $QUICK tokens, follow the steps below:

Enter the amount of old $QUICK tokens you have.

Your old $QUICK tokens are now converted to new $QUICK tokens.


Voila, you now know how to use a decentralised application (dapp) on Polygon. A decentralised exchange may have associated risks; therefore, please conduct intensive research before connecting your wallet to any dapp. Avoid investing in something too good to be true.

Key Takeaways

QuickSwap is a DEX built on the Polygon blockchain.

Users can earn additional yield for staking their LP tokens on QuickSwap.

QuickSwap offers features such as swapping, liquidity, yield farming, and much more.

Avoid investing in something too good to be true.

Make sure the link of the dapp is correct. To ensure this, head to the official social media accounts of the protocol and use the links from there.

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Finally A Good Tv Guide Application

I’ve been waiting for this since June 29, 2007; a good, useful, TV application for iPhone! It seems that chúng tôi [iTunes Link] made my wish come true, and even more…

Before chúng tôi (pronounce “i dot tv”), I used the mobile version of chúng tôi and although it served its purpose, it was a very unpractical web app. Then came What’s On, for which I had big hopes. My hopes just remained hopes as What’s On wouldn’t have some basic features such as setting channels as “Favorites”.

All my past frustrations are now long gone thanks to chúng tôi I downloaded this FREE app this morning from the App Store and instantly fell in love with it. According to the company’s website:

i.TV is a TV and movie guide for the iPhone and iPod touch.

i.TV helps users discover entertainment options by providing up-to-date information on television shows and movies. Users also benefit from feedback and information provided by other chúng tôi users who utilize i.TV’s community-focused features. These features enable customers to write reviews and give star ratings to visual media. In addition to this, chúng tôi allows users to directly access entertainment such as television previews and movie trailers through their iPhone or iPod Touch.

Let’s see how it works…

When launching chúng tôi it will ask you if you want to create an account. You don’t have to create an account but doing so will give you access to more features. After giving some basic information about you, it will automatically detect your location, and provide you a list of TV programs available in your area. The Zip Code detected by chúng tôi was not correct for me (it gave 92116 instead of 92106) and it wouldn’t show my Cable provider. So I manually edited my Zip Code and chúng tôi automatically refreshed the list of providers. Cox Communications was now showing up! Sweet! I chose my provider and it then loaded the TV listings of programs that were playing now.

I was overwhelmed by the amount of channels listings available so I went directly to the “settings” to create my list of favorite channels. I have about 100 channels but I only watch maybe 10 of them… I used the “hide channel” feature to hide the channels I didn’t want (goodbye MTV and other VH1 crap) and went back to the TV listing screen and here were my favorites.

I have to say that’s all I expect from a good TV listing application. I don’t need more than being able to set up my favorite channels but chúng tôi does much more than that. I’ve explored the app for a few minutes and found out about other cool features. You can rate and recommend shows and channels, write reviews, create email alerts and more.

As if that wasn’t enough, chúng tôi also allows you to browse through movies that are currently playing in theaters around you. You can search by movie title or by theater and even watch movie trailers

Although this first version offers more than I could expect, future developments in the work will bring even more with remote DVR programming, DVD rentals, movie ticket purchases and even the ability to watch full TV shows streamed directly from the source.

Alright, why are you still reading this? Go the App Store and download chúng tôi for free!

A Comprehensive Guide To Pandasai


Generative AI and Large Language Models (LLMs) have brought a new era to Artificial Intelligence and Machine Learning. These large language models are being used in various applications across different domains and have opened up new perspectives on AI. These models are trained on a vast amount of text data from all over the internet and can generate text in a human-like manner. The most well-known example of an LLM is ChatGPT, developed by OpenAI. It can perform various tasks, from creating original content to writing code. In this article, we will look into one such application of LLMs: the PandasAI library. Guide to PandasAI can be considered a fusion between Python’s popular Pandas library and OpenAI’s GPT. It is extremely powerful for getting quick insights from data without writing much code.

Learning Objectives

Understanding the differences between Pandas and PandasAI

PandasAI and its Role in data analysis and Visualization

Using PandasAI to build a full exploratory data analysis workflow

Understanding the importance of writing clear, concise, and specific prompts

Understanding the limitations of PandasAI

This article was published as a part of the Data Science Blogathon.


PandasAI is a new tool for making data analysis and visualization tasks easier. PandasAI is built with Python’s Pandas library and uses Generative AI and LLMs in its work. Unlike Pandas, in which you have to analyze and manipulate data manually, PandasAI allows you to generate insights from data by simply providing a text prompt. It is like giving instructions to your assistant, who is skilled and proficient and can do the work for you quickly. The only difference is that it is not a human but a machine that can understand and process information like a human.

In this article, I will review the full data analysis and visualization process using PandasAI with code examples and explanations. So, let’s get started.

Set up an OpenAI Account and Extract the API Key

To use the PandasAI library, you must create an OpenAI account (if you don’t already have one) and use your API key. It can be done as follows:

Sign in to your account.

Select View API keys from the dropdown.

Create a new secret key.

Copy and store the secret key to a safe location on your computer.

If you have followed the above-given steps, you are all set to leverage the power of Generative AI in your projects.

Installing PandasAI

Write the command below in a Jupyter Notebook/ Google colab or a terminal to install the Pandasai package on your computer.

pip install pandasai

Installation will take some time, but once installed, you can directly import it into a Python environment.

from pandasai import PandasAI

This will import PandasAI to your coding environment. We are ready to use it, but let’s first get the data.

Getting the Data and Instantiating an LLM

You can use any tabular data of your liking. I will be using the medical charges data for this tutorial. (Note: PandasAI can only analyze tabular and structured data, like regular pandas, not unstructured data, such as images).

The data looks like this.

Now with the data in place, we will need our Open AI API key to instantiate a Large Language Model. To do this, type in the below-given code:

# Use your API key to instantiate an LLM from pandasai.llm.openai import OpenAI llm = OpenAI(api_token=f"{YOUR_API_KEY}") pandas_ai = PandasAI(llm)

Just enter your secret key created above in place of the YOUR_API_KEY placeholder in the above code, and you will be all good to go. Now we can analyze our data and find some key insights using PandasAI.

Analyzing Data with PandasAI

PandasAI mainly takes 2 parameters as input, first the dataset and second a prompt which is the query or question asked. You might be wondering how it works under the hood. So, let me explain a bit.

Executing your prompt using PandasAI sends a request to the OpenAI server on which the LLM is hosted. The LLM processes the request, converts the query into appropriate Python code, and then uses pandas to calculate the answer. It returns the answer to PandasAI, then outputs it to your screen.


Let’s start with one of the most basic questions!

Question: What is the size of the dataset? prompt = "What is the size of the dataset?" pandas_ai(data, prompt=prompt) Output: '1338 7'

It’s always best to check the correctness of the AI’s answers to ensure it understands our question correctly. I will use Panda’s library, which you must be familiar with, to validate its answers. Let’s see if the above answer is correct or not.

import pandas as pd print(data.shape) Output: (1338, 7) Output

The output matches PandasAI’s answer, and we are off to a good start. PandasAI is also able to impute missing values in the data. The data doesn’t contain any missing values, but I deliberately changed the first value for the charges column to null. Let’s see if it can detect the missing value and the column it belongs to.

prompt = '''How many null values are in the data. Can you also tell which column contains the missing value''' pandas_ai(data, prompt=prompt) Output: '1 charges'

This outputs ‘1 charge’, which tells that there is 1 missing value in the charges column, which is absolutely correct. Now let’s try imputing the missing value.

prompt = '''Impute the missing value in the data using the mean value. Output the imputed value rounded to 2 decimal digits.''' pandas_ai(data, prompt=prompt) Output: 13267.72

It imputes the missing value in the data and outputs 13267.72. Now the first row looks like this.

Age Average  BMI06432.97613615232.93603425832.71820036132.54826146232.342609. Generally, BMI values greater than 30 falls in the range of the obese category. Therefore, the data shows that people in their 50s and 60s are more likely to be obese than other age groups.

Question: Which region has the greatest number of smokers? prompt = '''Which region has the greatest number of smokers and which has the lowest? Include the values of both the greatest and lowest numbers in the answer. Provide the answer in form of a sentence.''' pandas_ai(data, prompt=prompt) Output: 'The region with the greatest number of smokers is southeast with 91 smokers.' 'The region with the lowest number of smokers is southwest with 58 smokers.'

Let’s increase the difficulty a bit and ask a tricky question.

Question: What are the average charges of a female living in the north?

The region column contains 4 regions: northeast, northwest, southeast, and southwest. So, the north should contain both northeast and northwest regions. But can the LLM be able to understand this subtle but important detail? Let’s find out!

prompt = '''What are the average charges of a female living in the north region? Provide the answer in form of a sentence to 2 decimal places.''' pandas_ai(data, prompt=prompt) Output: The average charges of a female living in the north region are $12479.87

Let’s check the answer manually using pandas.

north_data = data[(data['sex'] == 'female') & (data['region'] == 'northwest'))] north_data['charges'].mean() Output: 12714.35

The above code outputs a different answer (which is the correct answer) than the LLM gave. In this case, the LLM wasn’t able to perform well. We can be more specific and tell the LLM what we mean by the north region and see if it can give the correct answer.

prompt = '''What are the average charges of a female living in the north region? The north region consists of both the northeast and northwest regions. Provide the answer in form of a sentence to 2 decimal places.''' pandas_ai(data, prompt=prompt) Output: The average charges of a female living in the north region are $12714.35

This time it gives the correct answer. As this was a tricky question, we must be more careful about our prompts and include relevant details, as the LLM might overlook these subtle differences. Therefore, you can see that we can’t trust the LLM blindly as it can generate incorrect responses sometimes due to incomplete prompts or some other limitations, which I will discuss later in the tutorial.

Visualizing Data with PandasAI

So far, we have seen the proficiency of PandasAI in analyzing data; now, let’s test it to plot some graphs and see how good it can do in visualizing data.

Correlation Heatmap

Let’s create a correlation heatmap of the numeric columns.

prompt = "Make a heatmap showing the correlation of all the numeric columns in the data" pandas_ai(data, prompt=prompt)

That looks great. Under the hood, PandasAI uses Python’s Seaborn and matplotlib libraries to plot data. Let’s create some more graphs.

Distribution of BMI using histogram prompt = prompt = "Create a histogram of bmi with a kernel density plot." pandas_ai(data, prompt=prompt)

The distribution of BMI values somewhat resembles the normal distribution plot with a mean value near 30.

Distribution of charges using boxplot prompt = "Make a boxplot of charges. Output the median value of charges." pandas_ai(data, prompt=prompt)

The median value of the charges column is roughly 9382. In the plot, this is depicted by the orange line in the middle of the box. It can be clearly seen that the charges column contains many outlier values, which are shown by the circles in the above plot.

Now let’s create some plots showing the relationship between more than one column.

Region vs. Smoker prompt = "Make a horizontal bar chart of region vs smoker. Make the legend smaller." pandas_ai(data, prompt=prompt)

From the graph, one can easily tell that the southeast region has the greatest number of smokers compared to other regions.

Variation of charges with age prompt = '''Make a scatterplot of age with charges and colorcode using the smoker values. Also provide the legends.''' pandas_ai(data, prompt=prompt)

Looks like age and charges follow a linear relationship for non-smokers, while no specific pattern exists for smokers.

Variation of charges with BMI

To make things a little more complex, let’s try creating a plot using only a proportion of the data instead of the real data and see how the LLM can perform.

prompt = "Make a scatterplot of bmi with charges and colorcode using the smoker values. Add legends and use only data of people who have less than 2 children." pandas_ai(data, prompt=prompt)

It did a great job creating a plot, even with a complex question. PandasAI has now unveiled its true potential. You have witnessed the true power of Large Language Models.


The responses generated by PandasAI can sometimes exhibit inherent biases due to the vast amount of data LLMs are trained on from the internet, which can hinder the analysis. To ensure fair and unbiased results, it is essential to understand and mitigate such biases.

LLMs can sometimes misinterpret ambiguous or contextually complex queries, leading to inaccurate or unexpected results. One must exercise caution and double-check the answers before making any critical data-driven decision.

It can sometimes be slow to come to an answer or completely fail. The server hosts the LLMs, and occasionally, technical issues may prevent the request from reaching the server or being processed.

It cannot be used for big data analysis tasks as it is not computationally efficient when dealing with large amounts of data and requires high-performance GPUs or computational resources.


We have seen the full walkthrough of a real-world data analysis task using the remarkable power of the PandasAI library. When dealing with GPT or other LLMs, one cannot overstate the power of writing a good prompt.

Here are some key takeaways from this article:

PandasAI is a Python library that adds Generative AI capabilities to Pandas, clubbing it with large language models.

PandasAI makes Pandas conversational by allowing us to ask questions in natural language using text prompts.

Despite its amazing capabilities, PandasAI has its limitations. Don’t blindly trust or use for sophisticated use cases like big data analysis.

Thank you for sticking to the end. I hope you found this article helpful and will start using PandasAI for your projects.

Frequently Asked Questions (FAQs)

Q1. Is PandasAI a replacement for pandas?

A. No, PandasAI is not a replacement for pandas. It enhances pandas using Generative AI capabilities and is made to complement pandas, not replace them.

Q2. For what purposes can PandasAI be used?

A. Use PandasAI for data exploration and analysis and your projects under the permissive MIT license. Don’t use it for production purposes.

Q3. Which LLMs do PandasAI support?

A. It supports several Large Language Models (LLMs) such as OpenAI, HuggingFace, and Google PaLM. You can find the full list here.

Q4. How is it different from pandas?

A. In pandas, you have to write the full code manually to perform data analysis while PandasAI uses text prompts and natural language to perform data analysis without the need to write code.

Q5. Does PandasAI always give the correct answer?

A. No, it can occasionally output wrong or incomplete answers due to ambiguous prompts provided by the user or due to some bias in the data.

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Run A Java Application As A Service On Linux

A simple service in Linux is a program that runs in the background and performs a specific function or set of functions. These programs can be started automatically at boot time, and can be controlled using the command line or through a system service manager such as systemd or Upstart.

To create a simple service in Linux, you would first write the program that performs the desired function. This program should be designed to run in the background and to continue running even after the terminal window or SSH session is closed.

Next, you would create a script that can start, stop, and check the status of the program. This script is usually placed in the /etc/init.d/ directory and should be made executable. It would then be registered with the system service manager using command like systemctl or service .

Service file

A service file, also known as a unit file, is a configuration file used by the system service manager (such as systemd or Upstart) to control a service in Linux. It describes how the service should be started, stopped, and managed, and contains various settings and options that can be used to customize the behavior of the service.

A service file typically has a simple structure and is written in a declarative language. It consists of a series of sections, each of which contains specific configuration options. The most important sections are [Unit], [Service], and [Install].

[Unit] − contains information about the service, such as its name, description, and dependencies.

[Service] − contains information about how the service should be executed, such as the command to start and stop the service, and the user and group under which the service should run.

[Install] − contains information about how the service should be installed and activated, such as the runlevels at which the service should be started and stopped.

Here is an example of a simple service file for a service named “myservice” −

[Unit] Description=My Service [Service] User=myservice Group=myservice ExecStart=/usr/bin/myservice Restart=always [Install] Forking Service

A forking service is a type of service that starts multiple instances of a program, each of which runs in its own process. In the context of a Java application, a forking service would involve starting multiple Java Virtual Machines (JVMs) to run the same application, with each JVM running in its own process.

Here’s one example of how you could create a forking service for a Java application −

Write a script that starts the Java application and forks a new process for each instance. This script should take command-line arguments to specify the number of instances to fork, as well as any other necessary configuration options.

Use the nohup command to run the script in the background and prevent it from being terminated when the terminal is closed.

Use a for loop to start n number of instance specified in the command line arguments

for ((i=1;i<=$instance_count;i++)); do nohup java -jar chúng tôi & done

Create a service file and register it with the system service manager using systemctl or service command

[Unit] Description=My Java Forking Service [Service] ExecStart=/usr/local/bin/ Restart=always User=myuser [Install]

Note that this is just one example of how you could create a forking service for a Java application, and there are other ways to achieve the same result depending on the specifics of the application and the environment it is running in.

Registering and Running the Service

Once you have created a service file for your Java application, you can register it with the system service manager to make it automatically start at boot time and allow you to control it using standard commands.

The process of registering and running the service will depend on which system service manager you are using.

For example, with systemd, you can use the following commands to register and run the service −

Copy the service file to the /etc/systemd/system directory.

Reload systemd to read the service file

systemctl daemon-reload

Enable the service to start automatically at boot time

systemctl enable myservice

Start the service

systemctl start myservice

Check the status of the service

systemctl status myservice

Stop the service

systemctl stop myservice

With sysvinit based distro like Ubuntu 14.04 and earlier uses update-rc.d

update-rc.d myservice defaults

With Upstart, the process is similar, but you will use the initctl command instead of systemctl .

It’s worth noting that if you’re running your application in a container like docker, you don’t have to worry about this process of registering and running the service. you should handle the container to start at boot.

Run a Java Application as a Service on Linux

Running a Java application as a service on Linux involves creating a service file that describes how the application should be started, stopped, and managed, and then registering that service file with the system service manager.

Here is an example of how you could run a Java application as a service on Linux −

Create a script that starts your Java application. This script should use the nohup command to run the application in the background and prevent it from being terminated when the terminal is closed.

nohup java -jar chúng tôi &

Create a service file with the desired configuration for your service. The service file should contain information such as the name of the service, the user and group to run the service as, and the command to start and stop the service.

[Unit] Description=My Java Application Service [Service] User=myuser Group=myuser ExecStart=/usr/local/bin/ Restart=always [Install]

Register the service file with the system service manager. On systems using systemd, you can use the systemctl command to register the service, such as

systemctl enable myservice

start the service

systemctl start myservice

Check the status of the service

systemctl status myservice

Stop the service

systemctl stop myservice

It is worth noting that the above steps and examples are for a system that uses systemd as service manager. The process for other service managers (like SysV or Upstart) is slightly different, but the general idea is the same.


Running a Java application as a service on Linux involves creating a service file that describes how the application should be started, stopped, and managed, and then registering that service file with the system service manager.

The process involves creating a script that starts your Java application using the nohup command to run the application in the background and prevent it from being terminated when the terminal is closed.

A Complete Guide To Joomla Version

Introduction to Joomla Version

Web development, programming languages, Software testing & others

Different Joomla Versions

As we know, Joomla was a result of the dispute of Mambo, which was formed as a non-profit organization with the intention of protecting the project from lawsuits. It is used widely to create both simple and complex web applications. Joomla is a spelling of Swahili “jumla”, which means “all together”. In September 2024, the first official version of Joomla was released with some major bug fixes. It was basically a rebranding version. Over 3 years, many new versions with so many updates and new changes were released.

1. Joomla 1.x Series

Joomla 1.0: It was the first-ever release of Joomla on 22 September 2005. It is basically for the rebranding release of Mambo. The basic motive of this release is rebranding with major bug fixes. By voting in the community members, the company decided on the logo of Joomla on 29 September 2005.

Joomla 1.5: It was the first major version of Joomla, which was released on 22nd January 2008. This version has got new features and new power levels in the world of CMS (Content Management Systems). It has got full support and enthusiasm from the public. In this version, Joomla has got APIs as well, which helped it to become international CMS. This is the version of Joomla, which has got long term support from the public. Joomla 1.5 was officially ended in April 2012 with the release of Joomla 3.0. The last version of Joomla 1.5v was 1.5.26, which was released on 27th March 2012.

Joomla 1.7.0: This version of Joomla was released in July 2011. This version added some new and important features related to the security and improved migration tools. Features added in this version were very quite important from the user perspective.

2. Joomla 2.x Series

Joomla’s 2.x version was released in 2012 and was one of the major revisions announced after the version 1.x release.

It has got some necessary improvements like:

Making new APIs making it easier for the users to use the application.

One of the major features of multi-language was added in this release.

Bug fixes like registration form on the login page, display of currencies, light box for product images, etc.

SSL features for different pages like login, register, title, etc., were added.

Image resizing and adding new events for plugins were added.

This version of Joomla is an LTS (Long Term Support) release.

3. Joomla 3.x Series

This version focuses on some of the important features like:

The more intuitive backend of the website.

Mobile-friendly websites for the users.

Fixing some major bugs of the 2.x release.

After six months of the release of Joomla 3.0, Joomla 3.1 was released for the users. It included one of the important features of tagging for the users. With the release of Joomla 3.2 and 3.3, which released in November 2013 and April 2014, respectively, it became one of the most popular versions of Joomla making. Other versions obsolete as it helps in the ease of access of websites for the users.

Some features given below were introduced in this version of Joomla:

Ease of navigation in the website.

Editing the content of the website.

A search was added for the various website pages.

Checking the products in stock before ordering.

Ability to unlock an unfinished item on the website.

4. Joomla 4.x Series

With all the chaos that happened in the Joomla 3.x series, the team decided to develop a stable build to improve the relationship between the members. After long discussions on the current situations and the changes that needed to be done, the team was more productive and made many changes in the project.

Some of the features that were added in this release were:

Focused on the usability of the backend interface of Joomla 4.

Support for PHP5 was removed.

Support for SQL server has been removed.

The team members made a new strategy for the development.

Active engagement of team member in testing rather than criticizing the previous releases.

Admin code refactoring was done, along with some major bug fixes.

Conclusion – Joomla Version

The above description clearly explains the full version history of Joomla. It is one of the most popular and widely used systems for content management and designing of software. It is used worldwide and is considered one of the most reliable, easy to use and secure applications to use. It has got wide support and a lot of enthusiasm from the people in such a short period of time.

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Apple Patent Application Describes A Simple Way To Boost Battery Life

For years now, we’ve been promised exciting new battery technologies that claim to dramatically boost battery life, and none of them ever seem to materialize. But a new Apple patent application describes a simple way to achieve a worthwhile increase in battery life with nothing more high-tech than a metal housing.

The invention is a little ironic, as it solves a problem Apple originally created back in 2024 with the batteries in the 12-inch MacBook, before subsequently adopting the same tech in the rest of the MacBook lineup …

In the 12-inch MacBook, Apple switched from rigid rectangular batteries to pouches that can be shaped to fit the available space. At the time, the company hailed that as a breakthrough tech, as it allowed batteries to fill all the free space inside the machine.

That offers a lot of flexibility, but the approach also creates problems, as Apple explains.

Lithium-polymer batteries are commonly used as rechargeable batteries to provide power to a variety of electronic devices, including laptop computers, tablet computers, mobile phones, personal digital assistants (PDAs), digital music players and cordless power tools. Lithium-polymer batteries can often include electrodes and electrolyte sealed in an aluminized laminated pouch. These pouch batteries can be used in space-constrained portable electronic devices such as mobile phones, laptop computer, and/or wearable devices.

The sealed edges of the pouch battery can result in excess pouch material and the pouch can have a positive voltage that requires the exterior surface of the pouch to be isolated from conductive surfaces in the electronic device. To accommodate the excess pouch material and isolate the conductive surface of the pouch battery, the pouch battery needs to be smaller than the area provided in the electronic device, resulting in wasted space. This is especially important in space-constrained portable electronic devices, where space is at a premium and the devices are commonly designed to accommodate the largest batteries possible.

To solve this, Apple proposes to stick to the irregular shapes that enable the batteries to make full use of the space, but swap a soft pouch for a rigid metal housing.

This disclosure describes various embodiments that relate to an improved battery for reducing space between the battery and electronic components in an electronic device. In some embodiments, a battery can include a rigid or semi-rigid housing contacting components in an electronic device.

For example, in some embodiments the battery includes an electrode and cathode surrounded by a metal housing. The battery can be sized to optimize the available space in the electronic device without the need for spacing between the battery housing and other components in the electronic device. In some embodiments, the metal housing can be connected to a common ground, to allow other components to contact the battery housing without causing a short circuit or corroding the components.

Additionally, the metal housing can be used as a structural element in the electronic device. For example, brackets can be attached to the metal housing or a flange between two housing pieces can be using as an attachment point. 

In that way, we’d get the best of both worlds.

In principle, that would allow increased battery capacity without increasing the thickness of the MacBook. This being Apple, however, one might suspect the company will take the opposite approach, and maintain the existing battery capacity while making the laptops thinner…

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