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Dev platform Stack Overflow axes 28% of staff as AI competition grows

The technology Q&A forum has seen declines in web traffic since the launch of ChatGPT in 2022.

Developer and programmer platform Stack Overflow is cutting the company’s headcount by approximately 28% amid a rise in the popularity of artificial intelligence (AI) chatbots. 

On Oct. 16, Stack Overflow CEO, Prashanth Chandrasekar, made the announcement citing the challenges of macroeconomic pressures impacting the entire tech industry.

The firm is on a “path to profitability” and “continued product innovation,” said Chandrasekar who added, “This year we took many steps to spend less.”

Stack Overflow is a 15-year-old tech-focused question-and-answer forum for millions of developers, coders, and enthusiasts. It doubled its headcount in 2022 to 540, according to reports, so this week’s layoffs account for around 150 employees.

In August, Stack Overflow noted that its web traffic has seen a small decline compared to 2022, falling by an average of 5%.

"Conversely, in April of this year, we saw an above-average traffic decrease (~14%), which we can likely attribute to developers trying GPT-4 after it was released in March," it added.

The firm also said it expected generative AI to cause "some rises and falls in traditional traffic and engagement over the coming months."

Meanwhile, technology outlets such as Ars Technica have attributed the rise of AI chatbots to declines in the traffic and usage of traditional social knowledge-sharing platforms such as forums.

“Chatbots can offer more specific help than a 5-year-old forum post ever could,” it stated on October 17. ChatGPT and the like can also correct code, provide optimization suggestions, and explain what each line of code is doing.

New York University Leonard N. Stern School of Business Professor Panos Ipeirotis also made a similar suggestion in an X post on Oct. 17. 

Screenshot from X post by professor Panos Ipeirotis on Oct. 17. Source: X/@ipeirotis

However, Stack Overflow is working on its own answer to OpenAI’s ChatGPT in the form of “Overflow AI,” announced in July.

The goal is to introduce new features to leverage Stack Overflow's community knowledge to power AI that provides developers with personalized, trustworthy solutions. Chandrasekar concuded.

“As we refine our focus, priorities, and strategy it's to better meet the demands of our users, customers, and partners as part of this commitment to product innovation and the continued momentum of OverflowAI.”

Cointelegraph contacted Stack Overflow for comment but was referred back to the Oct. 16 announcement.

Related: How AI is changing crypto: Hype vs. reality

In related news, the Coinhouse crypto exchange has also axed 15% of its workforce according to reports.

The 2015-founded French exchange has laid off 10 of its 70 employees citing “reduced enthusiasm for Web3 and a fragile global economic environment.”

Earlier this month French hardware wallet provider announced a 12% staff reduction.

Magazine: ‘AI has killed the industry’: EasyTranslate boss on adapting to change

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Compiler vs. interpreter: Key differences

Learn about the crucial differences between compilers and interpreters and how they affect code execution and development.

Compilation and interpretation are the two primary methods for executing code in the realm of programming. In order for computers to comprehend and execute high-level programming languages, compilers and interpreters are essential tools. Although they have this in common, how they go about achieving it and how it affects program performance are very different. 

To better understand their respective functions in the software development process, this article will examine the main distinctions between interpreters and compilers.

What are compilers and interpreters?

Compiler

The complete source code of a program produced in a high-level programming language is simultaneously translated into machine code (binary code) by a compiler, a specialized tool. There are numerous phases of the compilation process that are typical, including lexical analysis, syntactic analysis, semantic analysis, optimization and code creation. A standalone executable file produced by a compiler can be run directly by the operating system.

Interpreter

An interpreter, on the other hand, is a program that reads a program’s source code line by line and runs it immediately without first creating a file of intermediate machine code. The interpreter quickly executes each statement after translating it into machine code or intermediate code, rather than translating the entire program at once. This means that when a program is running, the source code is read and translated in real time.

Comparing compilation and interpretation

Execution process

The execution process is one of the key distinctions between interpreters and compilers. Before running, a compiler converts the complete source code into machine code, creating a standalone executable file. As a result of the conversion being completed beforehand, the built software often runs faster. But the initial compilation procedure can take a while, particularly for big programs.

An interpreter, on the other hand, does not create a standalone executable. Instead, it runs the source code line by line while reading and executing it immediately. As a result, development input may be given more quickly because changes can be tested right away without having to recompile. However, because of the potential cost of the interpretation process, interpreted programs are typically slower than compiled ones.

Related: Top 10 most famous computer programmers of all time

Portability

Compiled applications are closely tied to a particular operating system and hardware architecture because compilers produce machine code unique to the target platform. A program compiled for one platform may not be able to execute on another without modification or recompilation due to this lack of portability.

Instead of creating machine code files, interpreters immediately execute the source code, making them often platform-neutral. This makes it simpler to port interpreted programs between several systems, provided that the right interpreter is accessible for each target platform.

Error detection

The methods used for error detection in compilation and interpretation are also different. Before producing machine code, a compiler thoroughly examines the entire source code. As a result, it may detect a variety of mistakes during compilation, including logical flaws, type issues and syntax errors. The compiler will produce an error message with a list of all the errors found in the code, making it easier for the developer to find and fix them.

Related: 10 emerging technologies in computer science that will shape the future

An interpreter, on the other hand, halts operation when it comes across the first error in the code. As a result, error detection with interpreted languages is quicker. However, because the interpreter only reports the first fault it finds, further problems with the code could go unreported until the initial error has been repaired and the code has been run again.

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How to land a high-paying job as an AI prompt engineer

Discover the essential steps, skills and strategies needed to land a lucrative career in the rapidly growing field of AI customization and fine-tuning.

The field of AI is rapidly expanding, and one niche area that has gained significant attention is prompt engineering. As the demand for artificial intelligence (AI) applications and customization grows, the need for skilled AI prompt engineers is on the rise. This article will explore the steps and strategies to land a high-paying job as an AI prompt engineer, including the necessary skills, educational background and job market context.

Understanding the role of an AI prompt engineer

An AI prompt engineer specializes in designing effective prompts to guide the behavior and output of AI models. They deeply understand natural language processing (NLP), machine learning and AI systems.

The AI prompt engineer’s primary goal is to fine-tune and customize AI models by crafting precise prompts that align with specific use cases, ensuring desired outputs and enhanced control.

Developing the necessary skills

To excel as an AI prompt engineer, some skills are crucial:

NLP and language modeling

A strong understanding of transformer-based structures, language models and NLP approaches is required. Effective prompt engineering requires an understanding of the pre-training and fine-tuning procedures used by language models like ChatGPT.

Programming and machine learning

Expertise in programming languages like Python and familiarity with frameworks for machine learning, such as TensorFlow or PyTorch, is crucial. Success depends on having a solid understanding of data preprocessing, model training and evaluation.

Related: How to write effective ChatGPT prompts for better results

Collaboration and communication

Prompt engineers will frequently work with other teams. Excellent written and verbal communication skills are required to work with stakeholders effectively, explain urgent requirements, and comprehend project goals.

Educational background and learning resources

A strong educational foundation is beneficial for pursuing a career as an AI prompt engineer. The knowledge required in fields like NLP, machine learning, and programming can be acquired with a bachelor’s or master’s degree in computer science, data science, or a similar discipline.

Additionally, one can supplement their education and keep up-to-date on the most recent advancements in AI and prompt engineering by using online tutorials, classes, and self-study materials.

Getting practical experience

Getting real-world experience is essential to proving one’s abilities as an AI prompt engineer. Look for projects, research internships, or research opportunities where one can use prompt engineering methods.

An individual’s abilities can be demonstrated, and concrete proof of their knowledge can be provided by starting their own prompt engineering projects or contributing to open-source projects.

Networking and job market context

As an AI prompt engineer, networking is essential for seeking employment prospects. Attend AI conferences, get involved in online forums, go to AI-related events and network with industry experts. Keep abreast of employment listings, AI research facilities, and organizations that focus on NLP and AI customization.

Related: How to use ChatGPT like a pro

Continuous learning and skill enhancement

As AI becomes increasingly ubiquitous, the demand for skilled AI prompt engineers continues to grow. Landing a high-paying job in this field requires a strong foundation in NLP, machine learning, and programming, along with practical experience and networking.

Aspiring prompt engineers can position themselves for success and secure a high-paying job in this exciting and evolving field by continuously enhancing skills, staying connected with the AI community, and demonstrating expertise.

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How to learn JavaScript using ChatGPT

Discover how to learn JavaScript effectively using ChatGPT, an AI-powered language model.

Imagine yourself as a future programmer eager to learn JavaScript, one of the most popular languages for web development. If you are looking for an interactive and collaborative way to enhance your understanding of JavaScript concepts and improve your coding skills, ChatGPT, a language model, may help you in this endeavor.

One can converse with ChatGPT as their virtual learning partner, asking questions and looking for explanations about essential JavaScript concepts, such as variables, data types, operators and control flow diagrams. This article will explain how to learn JavaScript using ChatGPT.

Start with the basics

One can ask ChatGPT questions on the basics of JavaScript. Users can inquire about variables, data types, operators and control flow structures, such as loops and conditionals. Users can effectively understand these topics by looking for clarifications and illustrations.

Request code snippets

One can ask ChatGPT for code snippets or examples if they’re attempting to figure out how to implement a certain feature or resolve a problem with JavaScript. This enables people to research and evaluate the offered code to understand how it functions.

Seek explanations for error messages

Ask ChatGPT for help if you’re having trouble understanding error messages or other problems with JavaScript code. Users can ask for assistance in comprehending the issue and locating viable solutions by explaining the error message or offering pertinent code samples.

Related: How to solve coding problems using ChatGPT

Explore JavaScript libraries and frameworks

There is a sizable ecosystem of libraries and frameworks for JavaScript. Users can query ChatGPT about specific libraries or frameworks they are interested in, and it will provide information, best practices and illustrative code. Users can use this to better understand how to use outside tools in JavaScript applications.

Learn JavaScript concepts and patterns

Users can gain a deeper understanding of JavaScript as a programming language and learn about industry best practices by having discussions with ChatGPT on JavaScript design patterns, architectural ideas or advanced techniques.

Share and review code

Users can send ChatGPT their JavaScript code for evaluation and comment. They might ask for general code improvement advice or specify the exact issue they are seeking to tackle. ChatGPT can offer advice, point out areas for improvement and suggest different strategies.

Collaborate with ChatGPT on projects

If users are working on a JavaScript project, they can discuss their project with ChatGPT. They can explain the requirements, ask for guidance on project structure or seek suggestions for specific features. ChatGPT can act as a collaborative partner, offering insights and helping users brainstorm solutions.

Related: How to learn Python with ChatGPT

Explore additional resources

Although ChatGPT can be a great help, users should also use other resources to round out their education. Users can practice coding and solidify their knowledge by using online lessons, documentation, coding challenges and interactive platforms.

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9 Tech YouTube channels to follow

Discover nine tech-focused YouTube channels covering topics such as programming, machine learning, cybersecurity, blockchain and Web3.

Learning tech via YouTube channels can be a great way to supplement traditional learning methods, as it provides a more interactive and engaging experience. Many YouTube channels dedicated to tech provide in-depth tutorials and explanations of complex concepts in a way that is easy to understand, making it accessible to learners of all skill levels.

Additionally, YouTube channels often provide access to industry experts, giving learners the opportunity to learn from individuals with real-world experience and knowledge. For instance, Cointelegraph’s YouTube channel provides news, interviews and analysis on the latest developments in the cryptocurrency and blockchain industries. The channel’s content is well-produced and features engaging visuals, making it an accessible and entertaining way to learn about these topics.

Here are nine other YouTube channels to follow and learn beyond cryptocurrencies.

Ivan on Tech 

Ivan on Tech is a popular YouTube channel focused on blockchain technology, cryptocurrencies and decentralized applications (DApps). The channel is hosted by Ivan Liljeqvist, a software developer and blockchain expert.

Liljeqvist offers educational material on his YouTube channel on a range of subjects relating to blockchain technology, such as crypto trading, the creation of smart contracts, decentralized finance (DeFi) and more. Also, he offers updates on the most recent events and trends in the sector.

Liljeqvist also maintains an online school called Ivan on Tech Academy in addition to his YouTube channel. This school includes classes on blockchain development, cryptocurrency trading and other relevant subjects.

Andreas Antonopoulos

Andreas Antonopoulos’ YouTube channel is an invaluable resource for anyone seeking in-depth knowledge and insights into Bitcoin (BTC) and cryptocurrencies, featuring a wealth of informative talks, interviews and Q&A sessions.

Antonopoulos is a renowned advocate, speaker and author in the field of Bitcoin and cryptocurrencies. He is widely regarded as a leading expert on blockchain technology and has written several books on the subject, including Mastering Bitcoin and The Internet of Money.

He is renowned for his fervent defense of decentralized systems and his capacity to concisely and clearly convey difficult ideas. Since the beginning of cryptocurrencies and blockchain technology, Antonopoulos has been a vocal proponent of their development and use.

Crypto Daily 

Crypto Daily is a popular YouTube channel dedicated to providing daily news, analysis and commentary on the world of cryptocurrencies. With over 500,000 subscribers, the channel covers a broad range of topics, from the latest developments in cryptocurrencies to initial coin offerings and blockchain technology.

James, the host of the channel, makes his insights interesting for both inexperienced and seasoned crypto aficionados by combining wit, humor and intellect in his delivery. The channel also offers interviews with industry leaders, product reviews and educational content, making it a well-rounded resource for anybody interested in the world of cryptocurrency.

Cybersecurity Ventures 

Cybersecurity Ventures is a YouTube channel focused on providing educational content on cybersecurity, cybercrime and cyberwarfare. The channel offers in-depth analyses of new trends and technology, news updates on the most recent cyber threats and assaults, and interviews with top industry experts.

The channel, which has over 20,000 members, offers guidance and best practices for people and businesses wishing to safeguard themselves against online risks, making it a useful tool for both inexperienced and seasoned cybersecurity professionals.

Related: Top 10 most famous computer programmers of all time

Machine Learning Mastery

Machine Learning Mastery also has a YouTube channel that complements its website by providing video tutorials on machine learning topics. The channel, which is hosted by Jason Brownlee, provides a range of content, including lessons, interviews with business leaders, and discussions of the most recent developments and difficulties in the field of machine learning.

The videos are well-made and very educational, covering everything from the fundamentals of machine learning to more complex subjects, such as neural networks and computer vision. The channel, which complements the substantial materials already offered on the Machine Learning Masters website, has a growing subscriber base and is a great resource for anybody wishing to learn about machine learning in a visual format.

Two Minute Papers 

Two Minute Papers is a popular YouTube channel that summarizes and explains complex research papers in the fields of artificial intelligence, machine learning and computer graphics in two minutes or less. 

The channel, hosted by Károly Zsolnai-Fehér, provides an easy way to stay up-to-date on the most recent developments and discoveries in these areas. The professionally made videos include simple visual explanations and can help viewers understand even the most challenging studies.

In order to personalize the information, Two Minute Papers also includes interviews with researchers and subject-matter experts. Two Minute Papers, a popular and useful resource for people interested in cutting-edge research and advancements in AI and related subjects, has more than 1.5 million subscribers.

 Web3 Foundation

The Web3 Foundation is a nonprofit organization dedicated to supporting and building the decentralized web, also known as Web3. Its YouTube channel provides educational content and updates on the latest developments in Web3 technology, including blockchain, distributed systems and peer-to-peer networks.

Related: What are peer-to-peer (P2P) blockchain networks, and how do they work?

The channel offers talks by prominent authorities in the field, including programmers, researchers and businesspeople, as well as discussions and interviews on subjects pertaining to Web3 technology. Also, it provides updates on the progress of the Polkadot network, an open-source platform for constructing interoperable blockchain networks. Overall, the Web3 Foundation YouTube channel is a great resource for anyone interested in the decentralized web’s future because it has over 20,000 followers.

Dapp University 

Dapp University’s YouTube channel complements its educational platform by providing video tutorials on blockchain development, smart contracts and decentralized application (DApp) development. Hosted by developer and entrepreneur Gregory McCubbin, the channel features clear and concise explanations of complex topics in blockchain technology, making it accessible to beginners and experts alike.

The videos cover a wide range of topics, including Ethereum, Solidity and other blockchain tools and technologies. With over 300,000 subscribers, the Dapp University YouTube channel is a valuable resource for individuals looking to learn how to develop decentralized applications on the blockchain.

Tech With Tim

Tech With Tim is a popular YouTube channel dedicated to teaching programming and computer science concepts to beginners and intermediate learners. The channel offers tutorials on a range of programming languages, including Python, Java and C++, as well as web development, game development and machine learning.

It is hosted by Tim Ruscica, a software engineer and seasoned tutor. The well-produced videos have straightforward explanations and examples of programming topics, making them understandable to a variety of students. Tech With Tim is a great resource for anybody wishing to learn programming and computer science skills, with more than 800,000 members.

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OpenBazaar marketplace says it’s set to ‘grow again from the ashes’

After shutting down in 2020 due to financial issues and poor user growth, the decentralized marketplace appears set to rise again.

The decentralized marketplace OpenBazaar appears set for a comeback after it was shut down over two years ago, according to a number of social media and GitHub updates.

A GitHub repository on the collaborative software development site shows progress as recent as April 12 on building a new version of the marketplace which was shut down in 2020.

Brian Hoffman, the former project lead at OpenBazaar and CEO of OB1 — the for-profit company which developed its software — tweeted on April 9 of the progress made on a “new” version of the marketplace saying it is “getting more interesting by the day.”

In the replies, Hoffman was asked how the marketplace would be different this timegiven that due to financial issues and poor user growth, OpenBazaar was forced to shut down.

Hoffman replied speaking of “freedom of exploration” and inferred that outside influence had contributed to its initial downfall.

The first hints that OpenBazaar would be launching a comeback came in a tweet from Hoffman on March 28, where he linked OpenBazaar’s GitHub page that showed he’d been working on a new version of the marketplace in the programming language Rust.

Just hours later OpenBazaar’s official account also posted a Tweet, which said “it is now time to grow again from the ashes,” and that “work has begun.”

Adding to the evidence that the marketplace appears likely to relaunch, the OpenBazaar website currently bears the message “openbazaar 3.0 - coming soon.”

Related: 5 programming languages to learn for AI development

After the exchange had shut down in 2020, Hoffman tweeted that a future iteration of OpenBazaar would require more independence from OB1, but provided no more information about how this might work.

OpenBazaar has been hailed as the decentralized eBay alternative and was first launched back in 2014. It allowed users to interact directly with each other to make transactions using Bitcoin (BTC).

The marketplace initially had the name “DarkMarket,” but changed it to OpenBazaar following community input in an attempt to improve its public image.

Cointelegraph contacted Hoffman and OpenBazaar for comment but did not immediately receive a response.

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UN Experts Say North Korea Laundered $147,500,000 in Stolen Crypto via Tornado Cash in March: Report

ETHGlobal Hackathon Kicks Off in Tokyo With First Ever Pragma Summit

ETHGlobal Hackathon Kicks Off in Tokyo With First Ever Pragma SummitAs previously announced by ETHGlobal, the first-ever Pragma summit kicked off the wider ETHGlobal Tokyo hackathon on Thursday as a “hub for high-quality talks and as a forum of discussion for builders and leaders from the Ethereum ecosystem and beyond.” The event, emceed by Kartik Talwar of ETHGlobal, featured on-stage interviews with Aya Miyaguchi of […]

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How to solve coding problems using ChatGPT?

ChatGPT, the AI language model, can assist in breaking down complex coding problems and finding efficient solutions.

Here’s how one can use ChatGPT’s abilities to solve coding problems:

  • Identify the problem: The first step is to identify the problem that you need to solve. Once you have identified the problem, you can start thinking about how to solve it.
  • Break the problem down: The next step is to break the problem down into smaller, more manageable pieces. This will help developers or programmers understand the problem better and make it easier to solve.
  • Research: Once you have broken the problem down, you may need to do some research to find out how to solve each part of the problem. So, you can use ChatGPT to search for information about coding algorithms, concepts and programming languages.
  • Create a plan: Once developers or programmers have researched the problem, they can create a plan to solve it.
  • Write the code: With a plan in place, you can start writing the code to solve the problem. And you can use ChatGPT to generate code snippets, check syntax and help debug the code.
  • Test and debug: Once you have written the code, they should test it to make sure it works as expected. In case of any errors, ChatGPT can help them debug the code.
  • Refine and optimize: After developers or programmers have tested their code, they may need to refine and optimize it to make it faster or more efficient, for which they can use ChatGPT.

Here are some examples of coding problems that you could solve using ChatGPT.

What kind of coding problems can be solved using ChatGPT?

Various problems that can be solved using ChatGPT are discussed below:

Syntax error

Syntax errors occur when the code violates the rules of the programming language. For example, forgetting to close a parenthesis or quotation mark can result in a syntax error. The following code shows an example of a syntax error:

This code produces a syntax error because the quotation mark is not closed. To solve this error, you can add the missing quotation mark and closing parenthasis as shown below:

Type error

Type errors occur when you try to perform an operation on a value that is not of the correct type. For example, trying to add a string to an integer can result in a type error. The following code shows an example of a type error:

This code produces a type error because you cannot add a string to an integer. To solve this error, you can convert the string to an integer using the int() function as shown below:

Name error

A name error occurs when the interpreter or compiler cannot find a definition for a particular name (variable, function, class, etc.) that is being used in the code.

This can happen for a variety of reasons, including the name is misspelled or incorrectly capitalized, the name has not been defined yet or has been removed from the code, or the name is defined in a different scope or module than where it is being used. The following code shows an example of a name error:

This code produces a name error because x has not been defined. To solve this error, you can define x and assign a value to it as shown below:

Index error

Index errors occur when you try to access an element of a list or array that does not exist. The following code shows an example of an index error:

This code produces an index error because “my_list” only has three elements, and you are trying to access the fourth element (which does not exist). To solve this error, you can access one of the existing elements of the list as shown below:

Reference error

A reference error occurs due to a variable or function not being declared. The solution is to declare the variable or function before referencing it. For example, let’s say we have the following code that causes a reference error because the variable “myVariable” has not been declared:

To fix this, we need to declare the variable before referencing it:

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Top 10 most famous computer programmers of all time

Computer programming has made the impossible possible. Read about the top 10 computer programmers to date.

For computer programs and mobile applications, programmers must develop code. In order to keep things working properly, they are also involved in maintaining, debugging and troubleshooting software and systems.

Here is a brief overview of the top 10 most famous computer programmers of all time.

Alan Turing

Alan Turing was a British mathematician and computer scientist who contributed significantly to the growth of artificial intelligence, cryptography and computer science. He helped decipher the Enigma code during World War II and introduced the idea of the Turing Machine, a theoretical representation of a computer.

Turing also contributed to the creation of the Manchester Baby, the first stored-program computer and the basis for contemporary computing. He is widely regarded as the father of theoretical computer science and artificial intelligence.

Ada Lovelace

Many people consider Ada Lovelace, an English mathematician and writer, to be the first ever computer programmer. She understood the creative potential of computing and realized that computers could do more than just crunch numbers, creating the first published algorithm designed to be processed by a machine.

Lovelace has motivated countless generations of women to work in the fields of science and technology and is honored today for her contributions to the history of computing.

Bill Gates

Bill Gates is a software developer, businessman and philanthropist most well known for founding Microsoft, the world’s largest personal computer software company. He was crucial to the development of the PC and transformed the computer software market.

Under his direction, Microsoft created several successful lines of software, including the well-known Windows operating system, which eventually overtook other PC platforms. In addition, Gates founded the Bill and Melinda Gates Foundation to help improve global health and education.

Steve Jobs

Steve Jobs co-founded Apple and played a crucial role in developing the Macintosh, iPod, iPhone, and iPad. With his ground-breaking innovations and striking design aesthetics, he changed the PC, music and mobile phone sectors as well as popularized the graphical user interface. Jobs was a dynamic, forward-thinking leader who encouraged and motivated his team to develop and introduce successful products.

Jobs’ technical know-how and love for design and marketing contributed to Apple’s success as one of the world’s most cutting-edge and prosperous technological businesses. Numerous people acknowledge his influence on technology, and his legacy continues to motivate future generations of entrepreneurs and tech enthusiasts.

Linus Torvalds

Linus Torvalds developed the Linux operating system, which is frequently found running servers, supercomputers and mobile devices. He began Linux as a side project, but it has since expanded into an extensive global development collaboration.

In addition, he is the principal architect of the Linux kernel, the foundational element of the Linux operating system. Torvalds has won numerous honors for his contributions to the open-source software movement, and Linux has grown to be one of the most significant, well-known software projects in history.

Mark Zuckerberg

Mark Zuckerberg co-founded Facebook, one of the world’s most widely used social networking sites. He played a crucial role in building its infrastructure and turning the startup into a multibillion-dollar corporation now known as Meta. He has been instrumental in connecting people across the world through the platform, enabling them to share information, news and personal experiences.

Meta is currently working on several projects and initiatives to make its vision of the metaverse a reality, including the Meta Quest (formally Oculus Quest) virtual reality headsets, Horizon Worlds and Meta Horizon. In addition to Meta, Zuckerberg has worked on charitable projects, including the Chan Zuckerberg Initiative, which aims to advance human potential and promote equal opportunity.

Related: What is metaverse in blockchain? A beginner's guide on an internet-enabled virtual world

Guido van Rossum

Computer programmer Guido van Rossum created the Python programming language in 1989. In addition to being the language’s original implementer, he actively participated in its growth and made numerous significant contributions to its functionality, community of users and design.

In July 2018, he left his post as the Python community’s “benevolent dictator for life” (BDFL).

Bjarne Stroustrup

Early in the 1980s, Danish computer scientist and professor Bjarne Stroustrup developed the C++ programming language. C++, one of the most popular programming languages in the world, was created by him to add object-oriented capabilities to the C language.

Stroustrup has made numerous key contributions to the design and features of the C++ language and is still actively involved in its development and progress.

Tim Berners-Lee

British computer scientist Tim Berners-Lee is widely recognized as the creator of the World Wide Web. In the early 1990s, he created the first web browser and server software and expanded on the idea of hypertext, which made it possible to create connected documents and the modern web.

Berners-Lee, who currently serves as the president of the World Wide Web Consortium — the leading international standards body for the Web — has been a significant proponent of the open Web and continues to work on its advancement and accessibility.

Related: What is Web 3.0: A beginner's guide to the decentralized internet of the future

Dennis Ritchie

American computer scientist Dennis Ritchie was instrumental in creating the Unix operating system and the C programming language. While working at Bell Labs in the late 1960s and early 1970s, he co-created Unix, and his contributions to the development of the C programming language helped make it one of the world's most widely used programming languages.

Ritchie is widely considered a pioneer of modern computing, and his work has had a significant impact on the computer science industry.

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How to improve your coding skills using ChatGPT

ChatGPT can generate code snippets and solutions to coding problems quickly and efficiently. Here’s how.

As a language model, ChatGPT is primarily used for natural language processing tasks such as text generation and language understanding. While it can be used to generate code samples, it’s not designed to help improve coding skills. However, here are a few ways ChatGPT can be used to help improve coding skills.

Practice explaining coding concepts

Use ChatGPT to explain coding concepts and algorithms to help solidify one’s understanding of them. This can also help users identify areas where they may need to study further.

For instance, when using ChatGPT to practice explaining coding concepts, one can input a prompt that describes a specific coding concept or algorithm, such as “Explain how a hash table works” or “How does the quicksort algorithm work?”

ChatGPT will then generate a response that explains the concept in a clear and concise manner, using natural language. This can help users understand the concept better by hearing it explained in different ways and also help them identify areas where they may need to do further study.

One can also use this approach to practice explaining coding concepts to others, which can be an important skill for technical communication and teaching. By reviewing the output generated by ChatGPT, users can identify areas where they might need to improve their explanations and practice different ways to present the information.

Generate code snippets

ChatGPT can be used to generate code snippets based on certain inputs. This can be useful as a starting point for one’s coding projects or to help understand how a certain function or algorithm works.

To use ChatGPT for this purpose, users can input a prompt that describes the code snippet they want to generate, such as “generate a Python function to reverse a string” or “generate JavaScript code for a simple calculator.”

Related: 10 ways blockchain developers can use ChatGPT

ChatGPT will then generate a code snippet based on the input prompt, and the output will be coherent and functional code that one can use as a reference or starting point for their project. However, keep in mind that the code generated by ChatGPT may require some modifications and debugging to fit one’s specific use case or project requirements. Additionally, users should always review and test the code before using it in a production environment.

Research and learning

ChatGPT can be used for coding research and learning by inputting prompts that ask for information on a specific technology or programming language. For example, one can input a prompt like “What are the key features of Python 3.0?” or “What are the best practices for writing efficient JavaScript code?”

ChatGPT will then generate a response that summarizes the key concepts and information users need to know about the topic, which they can use as a starting point for their research and learning. Additionally, they can use the generated output as a reference, while they are learning the new technology or language.

Related: How to learn Web3 development for beginners

Nonetheless, while ChatGPT can provide a good starting point, it’s not a substitute for hands-on practice and in-depth learning. It’s essential to supplement the information provided by ChatGPT with additional resources and practice.

Practice coding challenges

By entering prompts that outline a challenge or problem that users desire to tackle, ChatGPT can be used to practice coding problems. For example, one can input a prompt like “Write a function that finds the second largest element in an array” or “Create a script that takes a string and returns the number of vowels in it.” ChatGPT will then generate a response that includes a code snippet that solves the problem or challenge.

One can then use the generated code as a reference and try to implement the solution on their own, comparing their code with the generated one. This can help users practice their coding skills and improve their understanding of specific concepts or algorithms. Additionally, users can modify the generated code to fit their specific needs or to add more complexity to the problem.

It is critical to note that while ChatGPT can generate functional code, it’s not a substitute for hands-on practice and learning. Reviewing the generated code and trying to implement the solution on their own will help users solidify their understanding of the concepts and algorithms used. Additionally, users should always test and debug the code before using it in a production environment.

Collaborate with other developers

ChatGPT can be used to collaborate with other developers by inputting prompts that describe a specific coding problem or challenge and then sharing the generated response with other developers for review and feedback. For example, one can input a prompt like “I am having trouble with this function; can you help me optimize it?” along with the code snippet and share it with other developers. They can then use the generated response to provide feedback and suggestions on how to improve the code.

ChatGPT can also be used to generate detailed explanations of the code, which can be helpful when working on a team or trying to understand the code written by others. Additionally, ChatGPT can be used to generate comments and documentation for the code, which can make it easier for other developers to understand and maintain the codebase.

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