Auburn basketball is one week away from facing Iowa State in the first round of the Maui Invitational in Hawaii. Before the long trip, the Tigers will battle in-state foe North Alabama at Neville Arena on Monday for its lone game of the week. Who has the upper hand in…

ESPN predicts the winner of Auburn vs. North Alabama - Yahoo Sports

The resulting run on Nvidia's chips fueled incredible financial results and sent the stock into the stratosphere.

Should You Buy Nvidia Stock Before Nov. 20? Wall Street Has a Compelling Answer.

Over his career, Vance has been involved in investments across a broad range of business areas. Through Narya, he continues to hold a portfolio of investments in several startups, though he gave up his role as a partner there in 2022 and doesn't actively run or advise any of the companies.

Before politics, JD Vance had a career in venture capital. Here are the startups he's invested in.

The big question investors should be looking to answer is just how much of a tailwind will these increased infrastructure investments represent for Nvidia? This is where management's financial guidance comes into play.

Nvidia Stock Could Soar Between 10% and 28%, According to Select Wall Street Analysts. Is the Stock a Buy Before Nov. 20?

Science Corp bought the technology that powers Prima from another startup, Pixium Vision, earlier this year. Before the acquisition, the future of similarly functioning retinal implants looked uncertain, with promising companies in the space like Second Sight going belly up and leaving its ...

Neuralink Competitor Restores Vision in Blind Patients With Eye Implant

While Elon Musk continues to rail against immigration, it appears that he was staying in the US illegally when he started his first business.

Evidence Grows That Elon Musk Was an Illegal Immigrant Before Deciding He Hated Immigrants

More meals out. More holidays.” And, always, graceless hypocrisy. Before he reminded us that Boris had “needlessly [insulted] our closest allies” and “given a few choice Anglo-Saxon words for business”, SkyNews flashed images of a P&O Ferry next to him.

Mission-driven global language sausage rolls? Welcome to doing business with Labour

October 7 kicks off Nvidia's AI Summit, a big event for the whole industry.

3 Reasons to Buy Nvidia Stock Before October 7

Before

before

The CSS properties are what also known as . They are used to add something before or after the content of an element. There are a lot of great uses for these pseudo elements, and we are here to explore some of them. :before and :after pseudo elements The Syntax If we have an element like this one: welcome to our website < > h2 </ > h2 We can add a pseudo element before it using CSS, like this: { : “Hello“; : blue;
} h2 :before content color The result will be: This is a quite simple principle. You are adding content before or after a certain element. It can be extremely helpful when , and in many other cases. adding icons, clearing floats The property of the pseudo element can be left with empty quotes like this .  This way you can treat the pseudo element like a . If the content property is removed altogether, the pseudo element content empty `content: “”` box with no content will not work. Adding Icons Probably the most popular usage of the before and after pseudo elements is using them to . We can add a simple .png icon. add icons Let’s look at the markup. HTML Weather report < > h2 </ > h2 CSS { : “ ”; : block; : ; : ; : (‘images/icon.png’’); : ; h2 :before content display height 15px width 25px url margin-right 5px The result: Now we have successfully added an icon before the text. Easy, right? Clearing Floats After elements are , another one needs to be added in order to clear that float. You can and simply using a pseudo one. floated avoid adding a new element Let's say we have this situation: We can use the next code to achieve clearing of the floats. HTML Lorem ipsum dolor amet truffaut kombucha roof party iPhone forage farm-to-table. < = > div class "box-container" < = > div class ”box” </ > div < = > div class ”box” </ > div </ > div < > p </ > p CSS , { : ; : block;
} { : both;
} { : ; : ; : ; : ; : left;
} .box-container :before .box-container :after content "" display .box-container :after clear .box height 100px width 100px background-color #C98EED margin 5px float Now, let's take a look at the result. By using this method we are and the paragraph is moved below the two elements. clearing the float Using a Background Image We can also to a pseudo element. This is commonly used when styling a header. add a background image HTML Hello World < > h2 </ > h2 CSS { : “”; : ; : ; : (‘underline.png’) center center no-repeat; : block;
} h2 :after content width 100% height 30px background url display The result achieved: Browser Support As with everything else in CSS, browser support needs to be checked. By consulting the , we see that these pseudo classes have a high browser support ( ) and we won’t have a headache when using them. CanIUse over 98% Summary Here, we have explained of the CSS pseudo elements. The examples illustrate just some of the many possible usages. Don't worry if it's not completely clear in the beginning. A little practice goes a long way. basic principles Hopefully, this article will help with any future projects. Thank you for reading! Previously published at https://kolosek.com/css-before-and-after/

How to use CSS pseudo-classes :before and :after

In RSpec, there are two different ways to write DRY tests, by using or . Their purpose is to create variables that are common across tests. In this post, we will explore differences between before and let and explain . before let why let is preferred by the Ruby community let Let creates lazily-evaluated local variables. This means that let() is not evaluated until the method that it defines is run for the first time. It DRYs up the spec and makes it more readable. $count = describe let( ) { $count += }
  it expect(count).to eq( )
    expect(count).to eq( ) it expect(count).to eq( ) 0 "let" do :count 1 "stores the value" do 1 1 end "is not cached across examples" do 2 end end Let should not be used for local variables, which have to be saved to the database, as they will not be saved to the database unless they have already been referenced. In this case, you should use or . let! before blocks The the tests we are creating are done using . RSpec and Capybara Also, have a let block inside of a before the block, this is what let! is made for! never let! Unlike let, you can . It means that, even if you didn't invoke the helper method inside the example, it will be invoked before your example runs. use let! to force the method's invocation before each example $count = describe invocation_order = []
  let!( ) invocation_order << $count += it invocation_order << expect(invocation_order).to eq([ , ])
    expect(count).to eq( ) 0 "let!" do :count do :let! 1 end "calls the helper method in a before hook" do :example :let! :example 1 end end As with let blocks, if multiple let! blocks are defined with the same name, the most recent one will be executed. The core difference is that will be executed multiple times if used like this, whereas the will only execute the last time. let! blocks let block before(:each) Before(:each) block will run before each example, even if the example doesn't use any of the instance variables defined in the block. This can noticeably slow down the setup of the instance variables. @tests = [] describe User before( ) @user = User.new describe it @user.should have( ).tests it @user.tests << Object.new it @user.should have( ).tests class User def tests || end end do :each do end "initialized in before(:each)" do "has 0 tests" do 0 end "can accept new tests" do end "does not share state across examples" do 0 end end end Depending on your personal preference you could use before blocks when: In nearly every situation, it is better to use let over before blocks. There is a reasonable amount of variables. There are variables that don't need to be referenced directly but are required. There are many commands to be executed because its syntax is more clear when many commands are involved. Creating mocks/stubs. Give it a try and create your own model or ! controller tests before(:all) This block is executed only once, before all of the examples in a group. There are certain situations this can cut down on execution and effort. @tests = [] describe User before( ) @user = User.new describe it @user.should have( ).tests it @user.tests << Object.new it @user.should have( ).tests class User def tests || end end do :all do end "initialized in before(:all)" do "has 0 tests" do 0 end "can get accept new tests" do end "shares state across examples" do 1 end end end It runs outside of transactions, so the data created here will bleed into other specs. Using before(:all) in RSpec will cause you lots of trouble unless you know what you are doing! Conclusion It all depends on what and how you need to make the work or consider using for creating data. Let blocks bring more to the table than before blocks. RSpec tests FactoryGirl Besides being slower, one of the major problems with before blocks is that spelling errors can lead to bugs and false positives, allowing certain types of tests to pass when they shouldn't. before( ) @user = User.find( )
    @user.logout it expect(@usr).to be_nil :each do username: "kolosek" end "should log the user out" do end Since @usr wasn't previously defined, the test will pass, @usr is nil by default. The same test using let would raise NameError because @usr isn't defined. Hope this will help you to better understand the differences between let and before blocks. Thank you for reading! Previously published at https://kolosek.com/rspec-let-vs-before/

RSpec Testing: Let Vs. Before

There is a popular saying that: Data is the new gold, and the web (internet) is the biggest gold mine. As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. Why Do We Collect Data: The Essence of Data Scraping/Collection Before I dive into the challenges faced with data scraping/collection and the wonders of Bright Data solutions, it is important to understand the essence of data scraping in this digital age that we are in. Data scraping is a powerful technique that efficiently navigates the internet to extract specific valuable informations, acting like a personal assistant for data retrieval. Whether you seek to analyze market trends and performance, track competitor prices, or simply satisfy your thirst for knowledge, data scraping is the key to making your life easier. Challenges With Data Scraping: The Limitations Before Bright Data Solutions Like an adventure, data scraping comes with its share of challenges and obstacles. One significant hurdle is the risk of websites feeling under attack when excessive data is scraped too quickly. To counter such attempts, websites deploy defensive measures, such as CAPTCHAs, to determine whether the user is a human or a computer. If the volume of requests persists, the website may resort to blocking users, hindering or halting data scraping altogether. To avoid such unfavorable situations, a more versatile and intelligent solution is required. Bright Data: The Ultimate Solution for Web Data Collection Bright Data comes as the ultimate solution for web data collection tailored specifically for businesses. With its automated online data collection platform, Bright Data offers unlimited access to the vast wisdom of the web without exposing you to any risks. How Bright Data Works The magic behind Bright Data lies in its web unlocker: a unique P2P network composed of millions of genuine residential and mobile IPs from across the globe. These IPs serve as secret identities, making your data requests appear as if they come from real people, making it highly unlikely for websites to block them. Why I Am Using Bright Data: The Power of Using Bright Data 1. Versatility and Ease of Use Bright Data shines with its user-friendly approach. To start scraping vast amounts of data, all you need is a scraping browser — an automated browser that allows you to interact with websites just like a human user. It efficiently handles challenges such as CAPTCHAs, fingerprints, and retries while mimicking the behavior of a regular user. Compatible with popular automation tools like Puppeteer and Playwright and programming languages like Python and Node.js, the scraping browser makes data collection seamless and efficient. 2. Ready-to-Use Data Sets If you ever need ready-to-use data, Bright Data’s data sets tool provides pre-collected and structured data from various domains, including popular platforms like LinkedIn, Amazon, Crunchbase, Indeed, and Twitter. Regularly updated, these comprehensive data sets offer you a treasure trove of information at your fingertips. Practical Examples of What I Have Used Bright Data for 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs In the world of e-commerce, web data plays a crucial role in monitoring competitor prices, ensuring product availability, and understanding customer reviews. For instance, a global Beauty and Personal Care Marketplace efficiently uses web data to check competitor prices in real-time, enabling them to adjust pricing strategies and optimize promotions on the fly, enhancing their market competitiveness. 2. Social Media: Analyzing Consumer Sentiment and Trends Social media platforms offer a gold mine of user-generated content, providing valuable insights into consumer behavior, trends, and sentiment. A global digital marketing agency effectively analyzes consumer sentiment towards their client’s brands using web data from various social media platforms. This deeper understanding empowers them to refine marketing strategies and achieve better results. In Conclusion: The Bright Future of Data Collection As I conclude with Bright Data's powerful web data collection solutions, one thing becomes very clear; data scraping is a game-changer in today’s digital landscape. Whether you are a business owner, entrepreneur, or data professional, harnessing the power of web data can unlock countless opportunities for growth and success. Traditional manual data collection methods are slow, labor-intensive, and error-prone, rendering them ineffective and inefficient in the face of today’s data-driven world. By automating the process with Bright Data, you can extract specific and relevant information from web pages effortlessly, propelling your business to new heights. So, if you find yourself in need of vast amounts of data, fast and easy, look no further than Bright Data. Embrace the power of web data, and let it guide you to new horizons of success in this ever-evolving digital landscape. There is a popular saying that: Data is the new gold, and the web (internet) is the biggest gold mine. Data is the new gold, and the web (internet) is the biggest gold mine. Data is the new gold, and the web (internet) is the biggest gold mine. As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. (if not the most) Why Do We Collect Data: The Essence of Data Scraping/Collection Before I dive into the challenges faced with data scraping/collection and the wonders of Bright Data solutions, it is important to understand the essence of data scraping in this digital age that we are in. Bright Data Data scraping is a powerful technique that efficiently navigates the internet to extract specific valuable informations, acting like a personal assistant for data retrieval. Data scraping is a powerful technique that efficiently navigates the internet to extract specific valuable informations, acting like a personal assistant for data retrieval. Whether you seek to analyze market trends and performance, track competitor prices, or simply satisfy your thirst for knowledge, data scraping is the key to making your life easier. Challenges With Data Scraping: The Limitations Before Bright Data Solutions Bright Data Like an adventure, data scraping comes with its share of challenges and obstacles. One significant hurdle is the risk of websites feeling under attack when excessive data is scraped too quickly. To counter such attempts, websites deploy defensive measures, such as CAPTCHAs, to determine whether the user is a human or a computer. If the volume of requests persists, the website may resort to blocking users, hindering or halting data scraping altogether. To avoid such unfavorable situations, a more versatile and intelligent solution is required. Bright Data: The Ultimate Solution for Web Data Collection Bright Data comes as the ultimate solution for web data collection tailored specifically for businesses. With its automated online data collection platform, Bright Data offers unlimited access to the vast wisdom of the web without exposing you to any risks. Bright Data How Bright Data Works How Bright Data Works The magic behind Bright Data lies in its web unlocker: a unique P2P network composed of millions of genuine residential and mobile IPs from across the globe. These IPs serve as secret identities, making your data requests appear as if they come from real people, making it highly unlikely for websites to block them. Why I Am Using Bright Data: The Power of Using Bright Data 1. Versatility and Ease of Use 1. Versatility and Ease of Use Bright Data shines with its user-friendly approach. To start scraping vast amounts of data, all you need is a scraping browser — an automated browser that allows you to interact with websites just like a human user. It efficiently handles challenges such as CAPTCHAs, fingerprints, and retries while mimicking the behavior of a regular user. Compatible with popular automation tools like Puppeteer and Playwright and programming languages like Python and Node.js, the scraping browser makes data collection seamless and efficient. 2. Ready-to-Use Data Sets 2. Ready-to-Use Data Sets If you ever need ready-to-use data, Bright Data’s data sets tool provides pre-collected and structured data from various domains, including popular platforms like LinkedIn, Amazon, Crunchbase, Indeed, and Twitter. Regularly updated, these comprehensive data sets offer you a treasure trove of information at your fingertips. Practical Examples of What I Have Used Bright Data for 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs In the world of e-commerce, web data plays a crucial role in monitoring competitor prices, ensuring product availability, and understanding customer reviews. For instance, a global Beauty and Personal Care Marketplace efficiently uses web data to check competitor prices in real-time, enabling them to adjust pricing strategies and optimize promotions on the fly, enhancing their market competitiveness. 2. Social Media: Analyzing Consumer Sentiment and Trends 2. Social Media: Analyzing Consumer Sentiment and Trends Social media platforms offer a gold mine of user-generated content, providing valuable insights into consumer behavior, trends, and sentiment. A global digital marketing agency effectively analyzes consumer sentiment towards their client’s brands using web data from various social media platforms. This deeper understanding empowers them to refine marketing strategies and achieve better results. In Conclusion: The Bright Future of Data Collection As I conclude with Bright Data's powerful web data collection solutions, one thing becomes very clear; data scraping is a game-changer in today’s digital landscape. Whether you are a business owner, entrepreneur, or data professional, harnessing the power of web data can unlock countless opportunities for growth and success. Traditional manual data collection methods are slow, labor-intensive, and error-prone, rendering them ineffective and inefficient in the face of today’s data-driven world. By automating the process with Bright Data, you can extract specific and relevant information from web pages effortlessly, propelling your business to new heights. So, if you find yourself in need of vast amounts of data, fast and easy, look no further than Bright Data. Embrace the power of web data, and let it guide you to new horizons of success in this ever-evolving digital landscape.

How Bright Data AI Made Web Data Scraping/Collection Effortless: The Challenges Before Bright Data AI Solutions

Як AI Bright Data зрабіў скрабаванне/збор вэб-даных простым: праблемы перад рашэннямі AI Bright Data

Com Bright Data AI va fer que el raspat/recollida de dades web sigui sense esforç: els reptes abans de les solucions Bright Data AI

Cómo Bright Data AI hizo que la recopilación y el raspado de datos web fueran sencillos: los desafíos que enfrentaban las soluciones de Bright Data AI

Kako je Bright Data AI skrapiranje/prikupljanje web podataka učinio jednostavnim: Izazovi pred Bright Data AI rješenjima

Bright Data AI が Web データのスクレイピング/収集をいかに簡単にしたか: Bright Data AI ソリューションの課題

Cara AI Data Cerah Menjadikan Pengikisan/Pengumpulan Data Web Mudah: Cabaran Sebelum Penyelesaian AI Data Cerah

څنګه روښانه ډیټا AI د ویب ډیټا سکریپینګ/راټولول بې ځایه کړي: د روښانه ډیټا AI حلونو دمخه ننګونې

Bright Data AI විසින් Web Data Scraping/එකතු කිරීම වෙහෙසකින් තොරව සිදු කළේ කෙසේද: Bright Data AI විසඳුම් ඉදිරියේ ඇති අභියෝග

Jinsi Data Angavu AI Ilivyofanya Uchakachuaji/Ukusanyaji wa Data ya Wavuti Bila Jitihada: Changamoto Kabla ya Suluhisho Mkali za Data AI

எப்படி பிரைட் டேட்டா AI இணையத் தரவை ஸ்கிராப்பிங்/சேகரிப்பதை சிரமமின்றி உருவாக்கியது: பிரைட் டேட்டா AI தீர்வுகளுக்கு முன் உள்ள சவால்கள்

کس طرح برائٹ ڈیٹا اے آئی نے ویب ڈیٹا کو اسکریپنگ/جمع کرنے کو آسان بنایا: برائٹ ڈیٹا اے آئی کے حل کے سامنے چیلنجز

Bright Data AI đã giúp việc thu thập/sao chép dữ liệu web trở nên dễ dàng như thế nào: Những thách thức trước khi có giải pháp Bright Data AI

Seven Things Angel Investors Want To See In A Startup Before Investing

Lessons in Development, Hard Work, and Critical Thinking Life before AI, being a developer, was a different world — a harder world in the sense that everything you do requires tremendous focus and incredible persistence. Challenges were real, and so were the triumphs that followed. Honest to God, I sometimes miss parts of that time. I was reminded of those early days vividly. There were no AI-powered tools back in those days that could swoop in and solve your problems for you. Every problem was a mountain which you had to climb alone. If you had bugs in your code, no AI could have run a simulation or suggested a fix. You had to work out by yourself. Countless late nights staring at my screen, combing through lines of code trying to figure out where I did it wrong. Debugging was like detective work. Sometimes, finally, it would come out what I was hunting for, and the relief was almost overwhelming — like just unlocking this huge mystery that had been gnawing at me for hours maybe even days. You’d learn so much in those moments. Not only about the code you’re writing but about the system itself — the inner workings of the technology. I only learned when I needed to know how to do something; it wasn’t because some tool just pointed me in the right direction, otherwise. It was slow and often frustrating, but this slow burn of learning made me a better developer. Without the safety net of AI, I needed to know what I was doing at each step. I think one of the reasons why developers nowadays miss this depth of understanding is that they have these incredibly powerful tools right there at their fingertips. And, of course, I cannot forget the level of “manuals” of everything: everything required a tremendous amount of effort, including writing test cases, for example. Nowadays, AI can write tests for you or suggest improvements, and everything is ready for you; back then, it was all up to me. I had to think of all the ways that my code might break and code for each one. And these weren’t glamorous pieces of work, but they were pretty darn important. Every test that I wrote taught me something about how fragile my code could be, and how I could make my code robust. The same applied to optimizing code. I had no fancy AI tools to analyze my work and suggest ways of efficiency. Instead, I had to spend hours iterating over different versions of a function, trying to shave off milliseconds from execution times or reduce memory consumption. It was boring, but it sharpened my understanding of how systems work under the hood. It also meant knowing about memory management, algorithm efficiency, and how the hardware was going to interpret my code. There’s no autopilot to do it for you. It was different too, as far as collaboration with my team was concerned. We had no AI to turn to quickly for debugging or optimization, so we relied on each other nearly for everything. We brainstormed, troubleshoot, and sometimes argued over the best ways to solve a problem. It was never easy, but there was a camaraderie in just knowing we were all in the same boat, all struggling with the same challenges. The beauty of it was that we were learning together every step of the way. It was tough getting through acquiring new skills back then. There were no shortcuts. So if you wanted to learn a new programming language or technology, you had to dive in deep. There wasn’t an AI assistant to fill in the gaps or give real-time feedback. If you didn’t know how something worked, you figured it out by yourself or through your mentors and peers. That’s how I learned C# — through trial and error and countless mistakes. I shall never forget the headache of trying to get a hold of things like multithreading, exception handling, or memory leaks. There wasn’t a silver bullet-just long hours of frustration followed by satisfaction when things worked. Another was the errors. Every time I hit a bug, it felt like hitting a wall. But with each of those walls lay an opportunity. To debug did not mean that you solved the problem; it was instead, learning why the problem existed in the first place. It is what those experiences shaped me: how to approach problems methodically and patiently. In that world before AI, what mattered above all else were your skills. Knowing how to write code was certainly not sufficient; it required creativity when solving problems and critical thinking. I still remember starting from scratch so many times because I realized my original thought was inefficient or wrong in general. But that was what made me improve. And each time I re-wrote a function or refactored a class, I was improving too, I was getting to know how to make performance and readability decisions, and how to trade-off between speed and stability. Even crafting something as simple as a SQL query was a chore in precision. There was no such thing as AI-crafted databases that could optimize queries on the fly. I spent hours futzing with them, trying to make sure that they ran as well as possible, especially when I was dealing with larger datasets. All these performance tweaks had been hard-won through trial and painful error. Then quietly began AI’s influence in the world of development. At first, it was just little things auto-complete in my IDE getting smarter, code editors catching errors before I even ran the program. I did not realize it at that time, but changes like these were game-changers. Things that took me hours to get done could be completed in a few minutes. Debugging, optimizing-even generating some boilerplate code became easier. It is like having an assistant who could see farther than my tiny horizon, an assistant who could point out all the problems that I had never considered at all. But as easy as it was, so was it strange. I was no longer asked to dig deep into the problems anymore. The AI was already there before I could understand the issue- presenting the solutions for me. It made me much more efficient, yes, but part of me misses the depth of understanding that came with doing things the hard way. There is satisfaction in cracking on your own a tough problem that AI, for all its brilliance, can’t reenact. Learning has changed these days and in this AI-driven era. The hard work isn’t gone; it’s just different. Today, developers do not bother memorizing syntax or spending hours debugging. It is all about how to work with AI. How to use these tools to be more efficient at building faster, more scalable solutions. However, it is still the same problem-solving essence. It can be described as AI suggesting a potential fix, but then, the decision still lies on the side of the developer whether it is right and whether it fits into the overall context of the project. This new chapter has been exciting and challenging for me. The transition to technological leadership allowed me to free up more energy to focus on bigger problems, building complex systems. I will always look back at the days before AI with a certain sort of nostalgia. It was long nights and tough-won victories that made me the developer that I am today. And for that, I appreciate this efficiency brought on by AI, but I will never forget the lessons I learned when it was just me, my code, and that quiet determination to figure things out. Follow on Medium: The AI Guy Lessons in Development, Hard Work, and Critical Thinking Life before AI, being a developer, was a different world — a harder world in the sense that everything you do requires tremendous focus and incredible persistence. Challenges were real, and so were the triumphs that followed. Honest to God, I sometimes miss parts of that time. I was reminded of those early days vividly. There were no AI-powered tools back in those days that could swoop in and solve your problems for you. Every problem was a mountain which you had to climb alone. If you had bugs in your code, no AI could have run a simulation or suggested a fix. You had to work out by yourself. Countless late nights staring at my screen, combing through lines of code trying to figure out where I did it wrong. Debugging was like detective work. Sometimes, finally, it would come out what I was hunting for, and the relief was almost overwhelming — like just unlocking this huge mystery that had been gnawing at me for hours maybe even days. You’d learn so much in those moments. Not only about the code you’re writing but about the system itself — the inner workings of the technology. I only learned when I needed to know how to do something; it wasn’t because some tool just pointed me in the right direction, otherwise. It was slow and often frustrating, but this slow burn of learning made me a better developer. Without the safety net of AI, I needed to know what I was doing at each step. I think one of the reasons why developers nowadays miss this depth of understanding is that they have these incredibly powerful tools right there at their fingertips. And, of course, I cannot forget the level of “manuals” of everything: everything required a tremendous amount of effort, including writing test cases, for example. Nowadays, AI can write tests for you or suggest improvements, and everything is ready for you; back then, it was all up to me. I had to think of all the ways that my code might break and code for each one. And these weren’t glamorous pieces of work, but they were pretty darn important. Every test that I wrote taught me something about how fragile my code could be, and how I could make my code robust. The same applied to optimizing code. I had no fancy AI tools to analyze my work and suggest ways of efficiency. Instead, I had to spend hours iterating over different versions of a function, trying to shave off milliseconds from execution times or reduce memory consumption. It was boring, but it sharpened my understanding of how systems work under the hood. It also meant knowing about memory management, algorithm efficiency, and how the hardware was going to interpret my code. There’s no autopilot to do it for you. It was different too, as far as collaboration with my team was concerned. We had no AI to turn to quickly for debugging or optimization, so we relied on each other nearly for everything. We brainstormed, troubleshoot, and sometimes argued over the best ways to solve a problem. It was never easy, but there was a camaraderie in just knowing we were all in the same boat, all struggling with the same challenges. The beauty of it was that we were learning together every step of the way. It was tough getting through acquiring new skills back then. There were no shortcuts. So if you wanted to learn a new programming language or technology, you had to dive in deep. There wasn’t an AI assistant to fill in the gaps or give real-time feedback. If you didn’t know how something worked, you figured it out by yourself or through your mentors and peers. That’s how I learned C# — through trial and error and countless mistakes. I shall never forget the headache of trying to get a hold of things like multithreading, exception handling, or memory leaks. There wasn’t a silver bullet-just long hours of frustration followed by satisfaction when things worked. Another was the errors. Every time I hit a bug, it felt like hitting a wall. But with each of those walls lay an opportunity. To debug did not mean that you solved the problem; it was instead, learning why the problem existed in the first place. It is what those experiences shaped me: how to approach problems methodically and patiently. In that world before AI, what mattered above all else were your skills. Knowing how to write code was certainly not sufficient; it required creativity when solving problems and critical thinking. I still remember starting from scratch so many times because I realized my original thought was inefficient or wrong in general. But that was what made me improve. And each time I re-wrote a function or refactored a class, I was improving too, I was getting to know how to make performance and readability decisions, and how to trade-off between speed and stability. Even crafting something as simple as a SQL query was a chore in precision. There was no such thing as AI-crafted databases that could optimize queries on the fly. I spent hours futzing with them, trying to make sure that they ran as well as possible, especially when I was dealing with larger datasets. All these performance tweaks had been hard-won through trial and painful error. Then quietly began AI’s influence in the world of development. At first, it was just little things auto-complete in my IDE getting smarter, code editors catching errors before I even ran the program. I did not realize it at that time, but changes like these were game-changers. Things that took me hours to get done could be completed in a few minutes. Debugging, optimizing-even generating some boilerplate code became easier. It is like having an assistant who could see farther than my tiny horizon, an assistant who could point out all the problems that I had never considered at all. But as easy as it was, so was it strange. I was no longer asked to dig deep into the problems anymore. The AI was already there before I could understand the issue- presenting the solutions for me. It made me much more efficient, yes, but part of me misses the depth of understanding that came with doing things the hard way. There is satisfaction in cracking on your own a tough problem that AI, for all its brilliance, can’t reenact. Learning has changed these days and in this AI-driven era. The hard work isn’t gone; it’s just different. Today, developers do not bother memorizing syntax or spending hours debugging. It is all about how to work with AI. How to use these tools to be more efficient at building faster, more scalable solutions. However, it is still the same problem-solving essence. It can be described as AI suggesting a potential fix, but then, the decision still lies on the side of the developer whether it is right and whether it fits into the overall context of the project. This new chapter has been exciting and challenging for me. The transition to technological leadership allowed me to free up more energy to focus on bigger problems, building complex systems. I will always look back at the days before AI with a certain sort of nostalgia. It was long nights and tough-won victories that made me the developer that I am today. And for that, I appreciate this efficiency brought on by AI, but I will never forget the lessons I learned when it was just me, my code, and that quiet determination to figure things out. Follow on Medium: The AI Guy The AI Guy

The World Before AI

Everything You Need to Know Before Building on the Stellar Blockchain

The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 15 of 18. Count V – Violation of 17 U.S.C. § 1202(b)(3) by OpenAI Defendants 146. The above paragraphs are incorporated by reference into this Count. 147. Upon information and belief, the OpenAI Defendants shared copies of Plaintiff’s works without author, title, copyright notice, and terms of use information with Defendant Microsoft in connection with the development of ChatGPT and Copilot. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 15 of 18. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 15 of 18. HackerNoon’s Legal PDF Series here Count V – Violation of 17 U.S.C. § 1202(b)(3) by OpenAI Defendants 146. The above paragraphs are incorporated by reference into this Count. 147. Upon information and belief, the OpenAI Defendants shared copies of Plaintiff’s works without author, title, copyright notice, and terms of use information with Defendant Microsoft in connection with the development of ChatGPT and Copilot. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. motherjones.com

Non-profit Newsroom Claims OpenAI Removed Copyright Before Sharing Works with Microsoft

The Noonification: Five Questions to Ask Yourself Before Creating a Web Project (8/1/2024)

Five Questions to Ask Yourself Before Creating a Web Project

একটি ওয়েব প্রকল্প তৈরি করার আগে নিজেকে জিজ্ঞাসা করার জন্য পাঁচটি প্রশ্ন

Fünf Fragen, die Sie sich vor dem Erstellen eines Webprojekts stellen sollten

Cinco preguntas que debe hacerse antes de crear un proyecto web

Cinq questions à se poser avant de créer un projet Web

वेब प्रोजेक्ट बनाने से पहले खुद से पूछने वाले पांच सवाल

Web プロジェクトを作成する前に自問すべき 5 つの質問

웹 프로젝트를 만들기 전에 스스로에게 물어봐야 할 다섯 가지 질문

Cinco perguntas a se fazer antes de criar um projeto web

Пять вопросов, которые следует задать себе перед созданием веб-проекта

Web Projesi Oluşturmadan Önce Kendinize Sormanız Gereken Beş Soru

Năm câu hỏi để tự hỏi bản thân trước khi tạo một dự án web

创建 Web 项目前要问自己的五个问题

The most important obstacle standing in the way of developing blockchain technology is scalability. Layer 2 solutions represent the leading edge in the effort to address this, allowing for higher transaction throughput on blockchain networks, cost reduction, and better user interface. This article focuses on top trends that determine how Layer 2 technology will evolve, as well as how they affect other aspects within the overall blockchain landscape. Understanding Layer 2 Solutions Layer 2 solutions are protocols that are created on top of an existing blockchain, which acts as the first layer, thereby enhancing what it can do. However, these solutions work by completing transactions through different means, not on the main chain, although it will still possess all the securities inherent from being decentralized while still ensuring that no single transaction is lost throughout its network. This they do by making it highly possible for many blocks to be pushed through the network since each block does not have to be associated with one specific transaction anymore…rather they can all fall under specific types or categories depending upon some criteria determined by those who came up with them…such as Sharding for instance.…so long as there are still some available resources at any given time (whether money or energy). Let us see what is happening now in these quick-developing fields. Scalability Improvements The primary aim of layer 2 solutions is to solve scalability limits in layer 1 blockchains. Different methods have been developed based on different ideas to achieve this. One of the most distinguished traditional solutions for Scalable Systems covering the Layer 2, optimistic rollups operate out of the main chain transactions and simply present the exchange data. When the need arises, optimistic rollups conduct their verification of correctness, taking truth for granted. This method effectively cuts down on the computational work done on the main chain, hence making it easier to handle more transactions per unit of time. ZK-Rollups (Zero-Knowledge Rollups): Zero-knowledge rollups leverage zero-knowledge proofs in verifying transactions off-chain for several transactions to be checked using a single proof translated to the main chain. It increases scalability, meaning developers like it for security reasons. Interoperability It is more important, as the blockchain ecosystem expands that diverse blockchains interoperate smoothly. The crucial role being played in enhancing the interoperability are solutions in Layer 2. Interconnected Bridges can make it easy for people to send money or information across a variety of types of blockchains. By allowing each chain of blocks to be able to interact with one another via them, interconnected bridges in use now allow users access into whole new realms where decentralized applications (dApps) grow best if one’s desires go beyond just making transactions but rather seek systems made from more than a single system. Reduced Transaction Costs Layer 1 networks, especially Ethereum, are more expensive because of expensive gas prices. Therefore, various Layer 2 solutions with substantially decreased transactional costs have been developed. Without this, the use of blockchain technology on a larger scale would not be possible. Payment Channels: Technological advancements in Bitcoin, such as Lightning Network and Ethereum, such as Raiden Network, make the creation of off-chain payment mechanisms between users possible. Whenever these channels are open, there are instant low-fee and off-main chain transaction settlements, only in case of their closure do they become floating-upon the primary blockchain reducing the cost by eliminating some transfers on it. Enhanced Privacy The concern of a lot of blockchain users is about privacy. But technologies for this matter increasingly adopt layer 2 solutions that have privacy-preserving features as an answer. Layer 2 solutions are incorporating protocols like zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) and zk-STARKs (Zero-Knowledge Scalable Transparent Arguments of Knowledge) to provide for various privacy levels. This ensures confidential transactions can occur without any identifiable information of the parties involved being shared. Decentralized Finance (DeFi) Expansion The sector of DeFi has had a massive expansion, with the sustainable momentum requiring Layer 2 solutions to support it. Thus, they offer an opportunity to improve the number of transactions per unit of time that DeFi communities can handle at a given point in the future. Scalable Decentralized Financial Protocols have started migrating onto Layer 2 solutions as a way of getting around some of the limitations associated with Layer 1 blockchains that emerged due to the ever-increasing popularity of decentralized systems.. User Experience Enhancements Layer 2 solutions are mainly focused on enhancing client experience. Two main facets of this trend are quicker transaction finality and less waiting time. Developers are working on enhanced protocols to get near-instant confirmation times with respect to transactions. Such improvement is not only improving client satisfaction but also making blockchain technology more user-friendly. Enterprise Adoption Businesses are turning to Layer 2 solutions, which provide scalable, safe, and affordable blockchain applications that can meet their company requirements. Enterprise Solutions: We are developing customized Layer 2 applications designed for supply chain management, finance, and healthcare in different business sectors to promote the operational efficiency and scalability of large enterprise applications. Security Innovations It is imperative that security for Layer 2 solutions is ensured, resulting in discoveries into security mechanisms meant to safeguard against cyber crimes or fraud. Better mechanisms for fraud proofs and validity proofing that come with optimistic rollups have improved the integrity and security of off-chain transactions significantly. These proofs ensure that transactions are verified without compromising on the security element. Community and Ecosystem Growth The Layer 2 ecosystem is growing quickly, with more community involvement, development activity, and institutional interest.\ Developer Tools and SDKs Developer tools and software kits (SDKs) are being developed to improve the way Layer 2 solutions are created and integrated. They make it simpler for developers to construct and launch scalable blockchain applications. Conclusion Layer 2 solutions are affecting scalability, cost, privacy, and user knowledge hurdles as far as revolutionizing the blockchain landscape goes. Layer 2 solutions are these advancements taken further on to the extent that for blockchain technology adoption and growth, they are projected to serve as highly significant. The stage is being set for increased scalability through optimistic and ZK-rollups, interoperability is being driven by cross-chain bridges, and transaction costs are being decreased; these are some of the features you can find more efficient and accessible blockchain ecosystems or platforms. These are additional proofs that show how transformative these technologies can be; for example, enhanced privacy features, expansion into DeFi, adoption in NFTs, and gaming of Layer 2 solutions. In addition, the corporate adoption, user experience, and security improvements are significant, showing how the focus is on making blockchain technology more useful and safe across a variety of applications. This trend emphasizes the collaborative spirit of the expanding network and environment around Layer 2 solutions. To move on, we need more work done on Layer 2 solutions so that problems faced by existing blockchain networks can be dealt with conclusively; hence, new prospects opened up for decentralized applications. If the important issues of scalability, cost, privacy, and security are tackled, Layer 2 solutions will drive the development of next-level blockchain technologies. If you are to navigate the ever-changing world of blockchain technology, you need to be well-apprised of what is happening in Layer 2 solutions, whether you are in an enterprise or developing apps. There are more to come, and so far, what we have only represents a tip of the iceberg. The most important obstacle standing in the way of developing blockchain technology is scalability. Layer 2 solutions represent the leading edge in the effort to address this, allowing for higher transaction throughput on blockchain networks, cost reduction, and better user interface. This article focuses on top trends that determine how Layer 2 technology will evolve, as well as how they affect other aspects within the overall blockchain landscape. Understanding Layer 2 Solutions Layer 2 solutions are protocols that are created on top of an existing blockchain, which acts as the first layer, thereby enhancing what it can do. However, these solutions work by completing transactions through different means, not on the main chain, although it will still possess all the securities inherent from being decentralized while still ensuring that no single transaction is lost throughout its network. This they do by making it highly possible for many blocks to be pushed through the network since each block does not have to be associated with one specific transaction anymore…rather they can all fall under specific types or categories depending upon some criteria determined by those who came up with them…such as Sharding for instance.…so long as there are still some available resources at any given time (whether money or energy). Let us see what is happening now in these quick-developing fields. Scalability Improvements The primary aim of layer 2 solutions is to solve scalability limits in layer 1 blockchains. Different methods have been developed based on different ideas to achieve this. One of the most distinguished traditional solutions for Scalable Systems covering the Layer 2, optimistic rollups operate out of the main chain transactions and simply present the exchange data. When the need arises, optimistic rollups conduct their verification of correctness, taking truth for granted. This method effectively cuts down on the computational work done on the main chain, hence making it easier to handle more transactions per unit of time. ZK-Rollups (Zero-Knowledge Rollups): Zero-knowledge rollups leverage zero-knowledge proofs in verifying transactions off-chain for several transactions to be checked using a single proof translated to the main chain. It increases scalability, meaning developers like it for security reasons. Interoperability It is more important, as the blockchain ecosystem expands that diverse blockchains interoperate smoothly. The crucial role being played in enhancing the interoperability are solutions in Layer 2. Interconnected Bridges can make it easy for people to send money or information across a variety of types of blockchains. By allowing each chain of blocks to be able to interact with one another via them, interconnected bridges in use now allow users access into whole new realms where decentralized applications (dApps) grow best if one’s desires go beyond just making transactions but rather seek systems made from more than a single system. Reduced Transaction Costs Layer 1 networks, especially Ethereum, are more expensive because of expensive gas prices. Therefore, various Layer 2 solutions with substantially decreased transactional costs have been developed. Without this, the use of blockchain technology on a larger scale would not be possible. Layer 2 solutions Layer 2 solutions Payment Channels: Technological advancements in Bitcoin, such as Lightning Network and Ethereum, such as Raiden Network, make the creation of off-chain payment mechanisms between users possible. Whenever these channels are open, there are instant low-fee and off-main chain transaction settlements, only in case of their closure do they become floating-upon the primary blockchain reducing the cost by eliminating some transfers on it. Enhanced Privacy The concern of a lot of blockchain users is about privacy. But technologies for this matter increasingly adopt layer 2 solutions that have privacy-preserving features as an answer. Layer 2 solutions are incorporating protocols like zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) and zk-STARKs (Zero-Knowledge Scalable Transparent Arguments of Knowledge) to provide for various privacy levels. This ensures confidential transactions can occur without any identifiable information of the parties involved being shared. Decentralized Finance (DeFi) Expansion The sector of DeFi has had a massive expansion, with the sustainable momentum requiring Layer 2 solutions to support it. Thus, they offer an opportunity to improve the number of transactions per unit of time that DeFi communities can handle at a given point in the future. Scalable Decentralized Financial Protocols have started migrating onto Layer 2 solutions as a way of getting around some of the limitations associated with Layer 1 blockchains that emerged due to the ever-increasing popularity of decentralized systems.. User Experience Enhancements Layer 2 solutions are mainly focused on enhancing client experience. Two main facets of this trend are quicker transaction finality and less waiting time. Developers are working on enhanced protocols to get near-instant confirmation times with respect to transactions. Such improvement is not only improving client satisfaction but also making blockchain technology more user-friendly. Enterprise Adoption Businesses are turning to Layer 2 solutions, which provide scalable, safe, and affordable blockchain applications that can meet their company requirements. Enterprise Solutions: We are developing customized Layer 2 applications designed for supply chain management, finance, and healthcare in different business sectors to promote the operational efficiency and scalability of large enterprise applications. Security Innovations It is imperative that security for Layer 2 solutions is ensured, resulting in discoveries into security mechanisms meant to safeguard against cyber crimes or fraud. Better mechanisms for fraud proofs and validity proofing that come with optimistic rollups have improved the integrity and security of off-chain transactions significantly. These proofs ensure that transactions are verified without compromising on the security element. Community and Ecosystem Growth The Layer 2 ecosystem is growing quickly, with more community involvement, development activity, and institutional interest.\ Developer Tools and SDKs Developer tools and software kits (SDKs) are being developed to improve the way Layer 2 solutions are created and integrated. They make it simpler for developers to construct and launch scalable blockchain applications. Conclusion Layer 2 solutions are affecting scalability, cost, privacy, and user knowledge hurdles as far as revolutionizing the blockchain landscape goes. Layer 2 solutions are these advancements taken further on to the extent that for blockchain technology adoption and growth, they are projected to serve as highly significant. The stage is being set for increased scalability through optimistic and ZK-rollups, interoperability is being driven by cross-chain bridges, and transaction costs are being decreased; these are some of the features you can find more efficient and accessible blockchain ecosystems or platforms. These are additional proofs that show how transformative these technologies can be; for example, enhanced privacy features, expansion into DeFi, adoption in NFTs, and gaming of Layer 2 solutions. In addition, the corporate adoption, user experience, and security improvements are significant, showing how the focus is on making blockchain technology more useful and safe across a variety of applications. This trend emphasizes the collaborative spirit of the expanding network and environment around Layer 2 solutions. To move on, we need more work done on Layer 2 solutions so that problems faced by existing blockchain networks can be dealt with conclusively; hence, new prospects opened up for decentralized applications. If the important issues of scalability, cost, privacy, and security are tackled, Layer 2 solutions will drive the development of next-level blockchain technologies. If you are to navigate the ever-changing world of blockchain technology, you need to be well-apprised of what is happening in Layer 2 solutions, whether you are in an enterprise or developing apps. There are more to come, and so far, what we have only represents a tip of the iceberg.

Stories, News & Mentions about BEFORE

Before’s Stories on HackerNoon

Before's News & Mentions