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Methods for scraping web pages include off-the-shelf web scrapers, web scraping APIs, and in-house web scrapers. Each data extraction method would be beneficial depending on your specific data collection requirement. In-house web scrapers are the best option if the website you want to scrape doesn’t support API or you don’t need to outsource the development of web scraping infrastructure.

Cheerio and Puppeteer are two of the most popular Nodejs libraries used by developers to create web crawlers that efficiently extract data from web sources.

In this article, we will examine Cheerio and Puppeteer, including their main features, pros, and cons, and outline the key differences between Cheerio and Puppeteer. This way, we aim to help developers choose the most suitable web scraping library for their data collection projects.

Cheerio vs Puppeteer: A detailed comparison

Cheerio and Puppeteer are chúng tôi libraries that can be used for web scraping and browser automation. There are major differences between these two libraries; the following table outlines the main differences between Cheerio and Puppeteer.

Here’s a quick comparison of Cheerio and Puppeteer ; we will go into more detail about each library in the following sections:

Cheerio evaluation

Cheerio is a chúng tôi framework for parsing and modifying HTML and XML documents.

Traversing DOM is the act of selecting one element from a neighboring component of  a document. Traversing a copy  enables you to select and manipulate elements within the document easily. You can traverse in three directions DOM tree using Cheerio:




Cheerio enables developers to manipulate elements within a document based on their specific requirements. You can modify element attributes, add and remove classes, and modify an element’s text content.

You can load HTML documents and parse them into a DOM structure using various methods, such as “load”, “loadBuffer”, “stringStream”, “fromUrl”, etc.,

Figure 3: An example of a  CSS selector to select elements from a document

Source: MDN Web Docs

Cheerio enables users to select HTML document elements using CSS selectors. You can select elements based on their tag name, attribute values, etc. Cheerio provides two different parsers based on the source and code of data.

For parsing HTML documents: parse5

For parsing XML documents: htmlparser2

Cheerio installation: You must have chúng tôi installed on your device to install Cheerio. Available operating systems include macOS, Linux, and Windows. You can install chúng tôi via the package manager as well

npm install

cheerio yarn add cheerio


Does not include features such as screenshot capture or PDF generation.

Does not support Javascript parsing.

Incapable handling scraping dynamic pages.

Puppeteer evaluation

Puppeteer is a chúng tôi library designed for browser automation in particular. It is an open-source Node library, similar to Cheerio. Some of the main features of Puppeteer include:

Puppeteer has an event-driven architecture. Event-driven architecture (EDA) is a software architecture that enables independent and interoperable operation of decoupled services. For example, if one service fails, the others will continue functioning. It allows for asynchronous communication between decoupled services.

Puppeteer runs in headless mode. Developers and test automation engineers use headless mode to run tests. It reduces the time of testing. Headless mode is also beneficial for web scraping. Web scraping benefits from headless mode as well. Headless browsers collect data from web pages without rendering entire web pages. You are not required to wait for whole web pages to load visual elements.

Puppeteer is a JavaScript Web Scraping Libraries for chúng tôi Javascript rendering enables users to scrape dynamic web pages like single-page applications (SPAs).

Puppeteer installation: Puppeteer requires no setup; you can use it in your project by executing the command below.

Figure 3: Puppeteer installation script

Source: Puppeteer

When you install Puppeteer, a recent version of Chromium is automatically downloaded.


Puppeteer does not support video playback. Because Puppeteer is included with Chromium, it inherits all of Chromium’s media-related restrictions.

Puppeteer is not compatible with HTTP Live Streaming (HLS).

Cheerio or Puppeteer: which is better for web scraping?

If you intend to scrape well-protected websites such as Amazon, you need to integrate Cheerio and Puppeteer with a proxy solution to avoid being blocked. Bright Data offers various proxy server solutions for different web scraping use cases. To learn how to set up Puppeteer proxy settings and integrate with Bright Data’s Proxy servers, check out their guide on the topic.

Figure 4: A diagram of Bright Data’s proxy network

Further reading

Feel free to Download our whitepaper for a more in-depth understanding of web scraping:

If you have more questions, do not hesitate contacting us:

Gulbahar Karatas

Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.





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Synthetic Data Vs Real Data: Benefits, Challenges In 2023

 In recent years, there has been a growing interest in the use of synthetic data for various applications, such as machine learning and data analytics. According to Gartner, by 2030, synthetic data use will outweigh real data in AI models.

In this article, we will explore:

what is synthetic data and how it is created

what are the benefits of synthetic data over real data

what are some of the challenges with using synthetic data

which type of data should be used for specific applications

What is synthetic data? How is it created?

Synthetic data is data that has been artificially created by computer algorithms, as opposed to real data that has been collected from natural events.

Although there are other ways to generate synthetic data, AI-generated synthetic data is produced by AI that is trained on complex real-world data, by the power of deep learning algorithms. The merit in using generative AI is that it is capable of automatically detecting patterns, structures, correlations, etc. within real data, and then learning how to generate brand new data with the same patterns. You can see the structural similarity in Figure 1 below.

Figure 1. (Source: UK Government)

One popular method is to generate data using a computer algorithm that mimics the behavior of real-world data. This approach can be used to create synthetic data sets that are similar to real data sets in terms of their distribution and variability. Another common method for creating synthetic data is to use a random number generator to create data that is uniform and has no correlation.

For more on what is synthetic data and its benefits, you can check our article.

The benefits of synthetic data over real data

There are several benefits of using synthetic data over real data. Below we listed 8 ways synthetic data can be useful.

1. Overcomes regulatory restrictions: The most important benefit of synthetic data over real data is that it avoids regulatory restrictions on real data. Synthetic data can replicate all important statistical properties of real data without exposing the latter, eliminating any concern about privacy regulations. This feature thus further enables:

Privacy preservation: It is hard to sustain privacy in classic anonymization methods while preserving the usefulness of the real dataset. You have to choose either protecting the privacy of the people  while diminishing the effectiveness  of that data or getting usefulness while renouncing privacy. With synthetic data, the privacy/usefulness dilemma is resolved since there is no real data that you must protect against leaking.

Resistance to reidentification:  Real data removes certain information to satisfy anonymization. Yet, reidentifying the data source is still highly possible. As a study shows, sharing only 3 bank transaction information per customer, with the merchant and the date of the transactions, makes 80% of customers identifiable.

Aptitude for Innovation and Monetization: As there are no privacy concerns for synthetic data, it is possible to share these datasets with third parties for innovation research and to use them as a monetisation tool.

2. Streamlines simulation: Synthetic data enables the creation of data to simulate conditions that have not yet been encountered. Where real data does not exist, synthetic data is the only solution. For instance, automotive firms may not gather real data for all possible situations to train smart cars.

3. Avoids statistical problems: Synthetic data is immune to some common statistical problems. These can include item nonresponse, skip patterns, and other logical constraints. For example, a synthetic data generation program could be designed to ensure that all items in a survey are answered, and that there are no skip patterns in the responses. This can be done by specifying the rules for generating the data, such as the possible response options for each item and the dependencies between items. By carefully designing these rules, the synthetic data can be generated in a way that avoids common statistical pitfalls.

4. Speeds up the process: Synthetic data can be generated much faster than real data can be collected, saving time and ensuring agility and competitiveness in the market.

5. Achieves higher consistency: Synthetic data can be more uniform and consistent than real data, which can be variable due to its natural origins. This uniformity makes synthetic data more suitable for  performing accurate analyses on synthetic datasets.

6. Ensures easy manipulation: Synthetic data can be more easily manipulated than real data in a controlled way, which can be difficult to alter without compromising accuracy. Therefore, it allows for more precise and controlled testing and training of machine learning models, and it can be generated in large quantities with specific characteristics and biases. This can be useful for improving the performance of machine learning algorithms in a variety of applications.

7. Increases cost-effectiveness: Synthetic data can be more cost-effective than real data. Of course, creating synthetic data is not free. The main cost of synthetic data is an upfront investment in building the simulation. However, real data enforce timely and financial costs every time a new data set is required or an existing one is revised.

8. Facilitates AI/ML training:  Synthetic data is more enriching for teaching AI/ML models as it has no regulations restricting real data. Also, it has a higher  capacity to create more data, feeding AI much more to learn. For more detail, check our article on the use of synthetic data to improve deep learning models.

Some challenges with using synthetic data against real data

Besides a variety of benefits, there are some challenges with using synthetic data.

Biased or deceptive results: Synthetic data can be misleading, limited or discriminatory  due to its lack of variability and correlation. 

Lack of accuracy: Another challenge with synthetic data is that it is often created using a computer algorithm, which may not always be accurate. As a result, synthetic data can occasionally  produce inaccurate results.

Time-consuming steps: Relatedly, synthetic data requires additional verification steps, such as comparing model results with human-annotated, real-world information. Such efforts take time to complete and prolong the projects. 

Losing outliers: Synthetic data may not cover some of the outliers present in the original dataset because it can only mimic but not replicate real data.  However, outliers can be relevant for some research. 

Dependency on the real data: Synthetic data quality often depends  on the real model and the dataset that have been developed for creating synthetic data. Without a desirable and qualitative real dataset, various synthetic datasets that are generated in huge amounts by using the original dataset will end up functioning ineffectively and sometimes even incorrectly.

Consumer skepticism: As synthetic data use increases, businesses can face consumer skepticism, such as questioning the credibility of the data for reaching conclusions and making products. Consumers might demand assurance for the transparency of the data generation techniques and the privacy of their information. 

Despite these challenges, synthetic data remains an important tool for data analysis. When used correctly, synthetic data can provide valuable insights into the behavior of real-world data.

Which type of data should be used for specific applications? Synthetic or Real?

As we discussed in the section on the benefits of synthetic data, there are various application areas it can be used, while it is impossible to use real data.

For example, synthetic data can be used in radioactive data sets. The term “radioactive” is often used to describe data that is constantly changing and difficult to keep track of. This can be due to a variety of factors, such as the rapid growth of the dataset, the frequent addition of new data points, or the dynamic nature of the data itself. It is highly difficult to keep track of such data in a real data method. 

On the other hand, it is better to use real data rather than synthetic data in cases where the goal is to reproduce the exact distribution of a real-world dataset. In such cases, it is often preferable to use the original dataset rather than a synthetic version.

In cases where the goal is to study the correlation between different variables in a dataset, it is often better to use real-world data instead of synthetic data, which typically does not exhibit any correlation.

Additionally, synthetic data can be difficult to interpret and may not accurately reflect the behavior of real-world data.

Ultimately, the type of data that should be used for a particular application depends on the specific needs of the analysis. When accuracy is key, then real-world data should probably be used. However, in cases where speed or consistency is more important than accuracy, then synthetic data may be a better choice.

For more on synthetic data

If you want to gain more insight on synthetic data, its benefits, use cases, tools, you can check our other articles on the topic:

If you have questions regarding synthetic data and real data, feel free to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Grammarly Vs. Word: Which One Is Better In 2023?

We all make spelling and grammar mistakes. The trick is picking them up before it’s too late. How do you do that? You might ask someone else to check your work before you send or publish it, use Word’s spell check, or better still, use an app that specializes in proofreading.

Grammarly is one of the most popular of these. It’ll check your spelling and grammar for free. The Premium version will also help you improve your document’s readability and check for potential copyright violations. A plug-in is available to run it inside Microsoft Word on Windows and Mac. Read our full Grammarly review here.

Microsoft Word needs no introduction. It’s the world’s most popular word processor and includes basic spell and grammar checking. But compared with Grammarly, those checks are basic indeed.

Microsoft Editor is new and a direct competitor of Grammarly. It uses artificial intelligence to help improve your writing. Its free features include spelling and basic grammar. A paid subscription gives you access to clarity, conciseness, formal language, vocabulary suggestions, plagiarism checking (“similarity”), and more.

Editor’s features are being integrated into Word. Depending on which version and subscription you have, you may already be able to access Editor’s features from within the word processor. I was able to test many of them using the online version of Word.

So, which is better? Grammarly, the world’s OG online editor, or Microsoft Editor, the big-budget new kid in town? Let’s find out.

Grammarly vs. Microsoft Word: How They Compare

1. Word Processing Features: Word

Grammarly is a quality grammar checker, but it offers a basic word processor. You can do some basic formatting—including bold, italics, underline, headings, links, and lists—get a word count, and choose your language.

If you’re a Word user, none of that will impress you. There’s no question which is the better word processor. What’s interesting is that Grammarly can run in Word as an add-in, providing additional proofreading features. That means the real questions are: How much better is Grammarly compared to Word’s own grammar checker? Is it worth installing? Is it worth the potential additional cost?

Winner: Word. There’s no question which app is the better word processor. For the rest of this article, we’ll explore whether Word users should consider installing Grammarly as a plug-in.

2. Context-Sensitive Spelling Corrections: Grammarly

Traditionally, spell checks have operated by ensuring that all of your words are in the dictionary. That’s helpful, but not infallible. Many proper nouns, such as company names, are not found in the dictionary. Even though you may use a dictionary word, it still may be the wrong spelling in context.

I had both apps check a test document that’s riddled with spelling mistakes:

“Errow,” an actual spelling mistake

“Apologise,” UK spelling when my Mac’s localization is set to US English

“Some one,” “any one,” and “scene,” which are all spelling errors in context

“Gooogle,” a misspelling of a well-known company name

The free version of Grammarly successfully identified every error and suggested the correct word in each case.

Word’s grammar checker identified four errors and missed three. “Errow” was flagged, but the first suggested correction was “arrow.” “Error” was the second. “Some one,” “Gooogle,” and “scene” were also identified and successfully corrected.

“Apologise” and “any one” were not identified as errors. Word hadn’t picked up my Mac’s localization settings and was checking for Australian English. Even after changing the language to US English, the errant word remained unflagged. One final experiment: I manually corrected them to “apologize” and “anyone.” Those spellings weren’t flagged as errors either.

I opened the online version of Word that has Microsoft Editor installed, then checked again. This time, all of the errors were found.

However, the suggested corrections were not as accurate as Grammarly’s. For example, the correct suggestion for “apologise” and “errow” were listed second in both cases. Choosing the first suggestion would have resulted in a nonsensical sentence.

Winner: Grammarly. It successfully identified and corrected every error. Word identified four out of seven. Its first suggestions were not always the correct ones. Editor did identify each mistake, though the right correction still wasn’t always listed first.

3. Identifying Grammar and Punctuation Errors: Grammarly

I also included a bunch of grammar and punctuation errors in my test document:

“Mary and Jane finds the treasure,” a mismatch between the number of the verb and subject

“Less mistakes,” which should be “fewer mistakes”

“I would like it, if Grammarly checked,” which includes an unnecessary and incorrect comma

“Mac, Windows, iOS and Android” leaves out the “Oxford comma,” which is often considered better grammar, but is a debatable error

Again, the free version of Grammarly successfully identified and corrected each error. Word only found one—the most blatant one about Mary and Jane.

By default, Word doesn’t check for the Oxford comma. Even after checking that option, it still didn’t flag the error in this instance. Finally, it didn’t correct the incorrect quantifier, “less mistakes.”

In my experience, Word’s grammar checker is far less reliable when trying to ensure your document is error-free. If that’s important to you, you should seriously consider using the Grammarly add-in, especially since it will make corrections like this for free.

Checking again using Microsoft Editor was much more accurate: every error was identified except one. “Less mistakes” still was not flagged.

Winner: Grammarly successfully identified a range of grammar errors. Word missed most of them, while Editor found all but one.

4. Suggesting How to Improve Your Writing Style: Grammarly

We’ve seen how successful Grammarly is at identifying and correcting spelling and grammar errors. Reminder: it does all of that for free. The Premium version goes further by suggesting how you can improve your writing style in terms of clarity, engagement, and delivery.

I had Grammarly Premium check a draft of one of my older articles to see what sort of feedback it gave and how helpful I found it. Here are some of the suggestions it gave:

I overused the word “important” and could use the word “essential” instead.

I overused the word “normal” and could possibly use “standard,” “regular,” or “typical” as a replacement.

I frequently used the word “rating” and could use “score” or “grade” instead.

There were a few places where I could say the same thing using fewer words, such as using “daily” instead of “on a daily basis.”

There were a few places where Grammarly suggested I split a long, complex sentence into two simpler ones.

I certainly wouldn’t make every change that Grammarly suggested, but I appreciated the input. I found the warnings about frequently-used words and complex sentences particularly helpful.

Microsoft Word doesn’t offer a readability check. However, several grammar checking settings aren’t enabled by default, such as showing readability statistics and enabling “Grammar & Refinements” instead of just “Grammar.”

I was curious about any extra input Word could give me about my writing, so under Grammar Settings, I enabled these additional options:

Double Negation


Passive Voice

Passive Voice with Unknown Actor

Words in Split Infinitives


Informal Language


Gender-Specific Language


I then checked the same draft article using Word’s grammar checker. Very few additional suggestions were made. The most helpful was flagging a missing comma after “if necessary.”

I couldn’t find a way to manually show the readability statistics. However, they’re displayed automatically after running a spell check.

Finally, I checked the document online where Microsoft Editor went to work. It had a lot more to say about my writing.

“Different designs” could be more specific. “Assorted designs,” “distinctive designs,” or “unique designs” may work better.

“Similar to” could be more concise by replacing it with “like.”

A missing Oxford comma was flagged, as were several other missing and unneeded commas.

“Purchasing” could be replaced with a simpler word, such as “buying.”

“Read through” could be more concise—“read” was suggested.

It listed some uncommon words—“tactile,” “constricted,” and “tether”—and offered replacements that are more commonly used.

Editor’s readability suggestions are different from Grammarly’s but still helpful. Choosing a winner is somewhat subjective, but I give Grammarly the edge here.

Winner: Grammarly. It offered dozens of helpful suggestions on how I can improve the clarity and engagement of my writing. Word doesn’t claim to help improve your writing style. Even with all of the grammar checking options enabled, it made very few suggestions. Editor offers a much more competitive experience.

5. Checking for Plagiarism: Grammarly

Grammarly Premium will warn you of plagiarism. It does this by comparing your text with billions of web pages and ProQuest’s academic database. It then alerts you when there is a match. I checked two different documents to evaluate the feature. One contained a few quotes, and the other didn’t. The check took less than a minute in both cases.

The second document was cleared of being free of plagiarism. The first was reported as being virtually identical to an article found on the web—and that was where my article was published on SoftwareHow.

The sources of the seven quotes in the article were also correctly identified.

Grammarly’s checker isn’t foolproof, though. In one experiment, I checked an article full of text I blatantly copied from other websites. Grammarly found it 100% original.

Microsoft Word does not currently check for plagiarism, but will soon when Editor’s Similarity Checker is added. This feature uses Bing Search to check for online documents with the same or similar content and should be able to identify plagiarism from online sources.

This feature is not yet available in the Mac and online versions of Word I’m currently using, even after joining the Office Insider Program. I was unable to test the feature, unfortunately.

Winner: Grammarly. It compares your text with online sources and an academic database to identify potential plagiarism. In the near future, Microsoft Word will offer similar functionality using Editor, but will only check online sources via Bing Search.

6. Ease of Use: Tie

Winner: Tie. Both apps make it easy to identify potential errors and correct them.

7. Pricing & Value: Tie

Assuming you already have access to Word, there are many ways to check your spelling and grammar for free. The simplest way is to use Word’s built-in features, though you’ll get better results using a plug-in. Grammarly and Microsoft Editor identify a wider range of errors for free.

Grammarly Premium adds additional checks. It will make suggestions to improve your writing’s readability, clarity, and engagement and warn you of potential copyright infringements. In my experience, Grammarly offers a discount of at least 40% every month, potentially bringing the cost down to $84 or less.

Microsoft Premium Editor offers similar features. In my opinion, they are not as helpful or full-featured. For example, Editor only checks online sources for plagiarism, while Grammarly also checks an academic database. It costs $10/month, which is a little cheaper than Grammarly’s regular price. It’s my understanding that in the future, these features will be included in Word, presumably at no additional cost.

Winner: Tie. There’s currently not a huge difference in price between the two service’s Premium plans. In the future, Microsoft Editor’s premium features may be included in Word at no extra cost. At that point, Microsoft might offer better value than Grammarly.

Final Verdict

Sending out correspondence with spelling and grammar errors can cost you your reputation. Even sending an error-filled email to a friend is embarrassing. When checking for mistakes, you need a tool you can trust: one that will identify as many problems as possible and help you make needed corrections.

Microsoft Word comes with a basic spelling and grammar checker. In my tests, it missed too many errors to be reliable. Grammarly and Microsoft Editor are much better. Grammarly consistently identified virtually all mistakes and suggested the right corrections. Microsoft’s tool wasn’t as consistent.

Both options offer premium services that are priced competitively. Both of them promise to improve your writing quality and identify potential copyright infractions. If those features are important to you, both services are worth paying for. Again, I feel that Grammarly has the edge between the two.

The value proposition will change in the near future, though. Microsoft Editor’s features are being integrated into Word—they may already be available in your version. At that point, you’ll get excellent proofreading features (presumably) for free. At that point, you’ll need to evaluate for yourself whether Grammarly’s greater consistency and more stringent checks are worth the subscription price.

Into Vs. In To

Into and in to are pronounced the same, but they have different grammatical functions.

In to is a combination of two separate words: the prepositions “in” and “to.” The words should remain separate when the sense is separate. For example, in the phrase “call in to see you,” the phrasal verb “call in” is separate from the infinitive verb phrase “to see you.”

Examples: Into in a sentence Examples: In to in a sentence

Una turned her hobby into a business. She turned the report in to her boss.

The principal stormed into the classroom.

Amanda is really into stamp collecting.

Everyone chipped in to pay for gas.

My grandmother tunes in to the news at 6 p.m. every day.

Check commonly confused words for free

Fix mistakes for free

How to use “into”

Into is a preposition used to indicate that something is moving inside of (or colliding with) something else. It’s also used to refer to mathematical division.

Examples: How to use intoThe protagonist snuck into the castle to warn the prince.

George accidentally ran into a wall.

How many times does 4 go into 20?

It can also be used to refer to a transformation or to indicate that someone is interested in something.

Examples: Other uses of intoAndy turned his attic into a home office.

Sophie used to be really into skateboarding.

How to use “in to”

In and to are two separate words. They can end up beside each other when “in” is part of a phrasal verb and “to” is part of an infinitive verb phrase. In these instances, it’s wrong to use “into.”

Examples: How to use in to

Amir dropped into borrow a book.


dropped in

to borrow a book


The thief broke into steal the gems.

The thief

broke in

to steal the gems


The choice sometimes has a major effect on your meaning, especially when similar phrasal verbs exist, some of which use “in,” while others use “into.”

Examples: How to use in toThe thief broke in to steal the gems [broke in, in order to steal the gems].

She broke into a run [started running].

Worksheet: In to vs. into

You can test your understanding of the difference between “in to” and “into” with the worksheet below. Fill in either “in to” or “into” in each sentence.

Practice questions

Answers and explanations

Sarah put the oranges into the fruit bowl.

“Into” is a preposition used to indicate that something is entering something else.

Ann and Linda turned their house into a bed and breakfast.

The phrase “turn into” is used to indicate a transformation. It shouldn’t be confused with “turn in,” which has various meanings (e.g., to go to bed, to hand over, to produce) that are unrelated to transformation.

I let the electrician in to install a new air conditioner.

“In” and “to” are two separate words. In this instance, “in” is part of the phrasal verb “let in” and “to” is part of the infinitive verb phrase “to install a new air conditioner.”

Farrah and Daniel are into filmmaking.

“Into” is also used to indicate that someone is interested in something.

You must log in to submit your application.

“In to” is correct here. In this instance, “in” is part of the phrasal verb “log in” and “to” is part of the infinitive verb phrase “to submit your application.”

Other interesting language articles

If you want to know more about commonly confused words, definitions, and differences between US and UK spellings, make sure to check out some of our other language articles with explanations, examples, and quizzes.

Frequently asked questions Cite this Scribbr article

Ryan, E. Retrieved July 19, 2023,

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Fun Fact Generator Web App In Python

Flask offers a number of features, such as database access, processing of user input, and dynamic data delivery. An effective and user-friendly online application may be made using HTML and simple Python coding. Python gives us the ability to work with data and provide consumers tailored experiences, while Flask makes it easier to create web applications. The data item is also shown in the browser using HTML. You’ll have a working Fun Fact Generator web application at the conclusion of this lesson.


Make sure we have the necessary frameworks and libraries installed before we start. The only requirements are Flask and Python 3.x for this project. With pip, the Python package installer, you can set up Flask. Now begin constructing the app when you have installed Python and Flask.

pip install flask

The Fun Fact Generator Web App can be used in a variety of settings. For example, it can be integrated into a trivia game or used as a conversation starter at social gatherings. It can also be extended to include more categories of facts, such as science, history, or literature. The possibilities are endless!

The folder structure will look like this −

Project Folder/ ├── └── templates/ └── index.html Algorithm

Import modules needed: Flask, render template, and random.

Make a Flask class instance, then assign it to a variable.

Make a list of fascinating facts, then put it in a variable.

Use the @app decorator to define a route for the web application’s home page’s route.

Make a function that utilizes the random as a starting point. Choose a random fact from a list of facts using the choose() function, then save the result in a variable.

To show the “index.html” template and provide the random fact variable as an input, use the render template() function.

Launch the web app using the script with flask run

The fact variable will be shown on the HTML page using Jinja2 template syntax.

Use a text editor to create an “index.html” file, and then save it there. The “templates” directory will be generated in the same location as the Python code file where the Flask app code is located. To give the web page the structure you want, add HTML code. To show the random fact on the HTML page using Jinja2 template syntax, use double curly brackets with the variable name. Run the Flask app after saving the file

Example from flask import Flask, render_template import random app = Flask(__name__) facts = [ "A group of flamingos is called a flamboyance.", "The longest English word is 189,819 letters long and takes more than 3 hours to pronounce.", "The shortest war in history was between Britain and Zanzibar in 1896. Zanzibar surrendered after just 38 minutes.", "There are more possible iterations of a game of chess than there are atoms in the known universe.", "The first webcam was created to check the coffee pot at Cambridge University.", "Bananas are berries, but strawberries are not." ] @app.route("/") def home(): fact = random.choice(facts) return render_template("index.html", fact=fact) if __name__ == "__main__":

Index.html [must be saved templates/ folder]


On Refreshing, a different fact will be generated as seen below

This code sets up a Flask web application to generate random fun facts. The code imports the Flask module and the render_template function, which allows the use of HTML templates to generate web pages. The facts are stored in a list, and the home() function generates a random fact from this list using the random.choice() method. Then these facts are passed to the chúng tôi template using the render_template() function, and the resulting web page displays the fact along with some text. The index.html file should be saved in the “templates” folder, and it contains HTML code to display the fun fact along with some header and paragraph text. When the app is executed, Flask runs a local server on your local machine , and the user can visit the URL displayed in the console to view the web page.


In this article, we examined how to use Python and Flask to build a web application that creates entertaining facts. The setup of the required libraries and frameworks, as well as the syntax, file format, and coding standards involved, were all updated. Overall, it included detailed instructions for utilizing Python and Flask to create a fully working online application.

Big Tech Vs Web3: Which Industry Are Techies Choosing In 2023?

Though web3 is comparatively new, aspirants are still choosing Web3 Jobs over Big Tech

Hundreds of recent graduates have been negatively affected by Big tech’s slow hiring practices, leaving them high and dry in a job market with few openings. Earlier, Techgig reported that despite receiving selection letters, new hires at large Indian Big tech companies like Infosys, Capgemini, and Tech Mahindra had to wait months before receiving their actual offer letters. An applicant will first receive a selection letter after an interview, followed by an offer letter and an onboarding date from the IT firm. While many of these layoffs are likely due to an economic downturn, this has resulted in an overwhelming amount of talent flocking to early-stage Web3 companies. For example, Andrew Masanto, a serial entrepreneur who has founded a number of startups, told Cointelegraph that he recently launched Nillion, a startup specializing in decentralized computation, to help ensure privacy and confidentiality for Web3 companies. Web3 serves decentralization as the answer to many traditional setbacks in the use of the internet. Though web3 is comparatively new, aspirants are still choosing Web3 Jobs over Big Tech. New work opportunities are the biggest reason why Tech talent migrated to Web3.

Recent market analysis indicates that the Big tech in India has seen a slowdown in hiring of 18% from the previous month and 16% from the previous year. Bengaluru, Hyderabad, and Pune all saw a drop in recruiting activity as the IT industry contracted.

As inflation continues to grow, coupled with a looming recession, many tech firms are having to cut portions of their staff. To put this in perspective, data from chúng tôi found that over 700 tech startups have experienced layoffs this year, impacting at least 93,519 employees globally. It has also been reported that tech giants like Google, Netflix, and Apple are undergoing massive job cuts.

Popular Jobs in Web3 companies

Some of the jobs offered by Web3 companies that are likely to attract a lot of traction and be in huge demand are:

1.Blockchain Core Developer – The blockchain Core Developer creates Blockchain architecture, defines its protocol and consensus mechanism, and determines and implements high-level Blockchain network choices.

2.Blockchain software developer – Similar to how a conventional web developer builds web apps utilizing the protocols and design structure established by a core web architect, Blockchain Software Developers build decentralized applications or Dapps using protocols established by Blockchain Core Developers. This is accomplished by creating smart contracts and deploying them on the Blockchain.

3.UI & UX designer – Responsible for communication with developers to put your product live out there in the market. Design the product and make it user friendly

4.Solidity developer – Responsible for creating smart contracts on the specific computer language ‘Solidity’.

5.Front-end Developer – responsible for creating visually appealing and functional user interfaces as well as producing well-tested and dependable code. You also have to collaborate closely with UX/UI designers to provide the greatest end-user experience possible.

6.Back-end Developer – Responsible for successful extraction and delivery of data to service providers and establishing direct communication with Blockchain.

7.DevOps – Responsible for ensuring the timely delivery of high-quality products and upgrades to end consumers. You will assist developers in coding while ensuring that the correct code is placed in the correct location. You will also be responsible for infrastructure maintenance, monitoring, process automation, CI/CD, and deploying software from Github to servers.

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