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

The Noonification: We’ve Been Here Before: The Impacts of Today’s Age of AI (4/26/2024)

Accelerated employment. Wage gains. Faster investment, more information and notable gains in productivity. These were all hallmarks of the 1990s, a period that looms large in the memories of those who witnessed this time of innovation and skyrocketing long-term growth. New technologies, like computers and software, gave rise to a greater reliance and utilization of networking. The internet first flashed its true potential. Information was suddenly accessible and storing it became cheaper and cheaper. The combination of personal computing and the internet catalyzed massive changes to industry, trade, finance and services. The economic sluggishness of the 1970s and 1980s finally folded, as technology prospered, inflation fell and poverty gave way to job gains. History certainly harmonizes. In many ways, the 1990s are primed to rhyme with today’s gains in artificial intelligence. Like the entrepreneurs of the early-1990s, we’re standing at the outset of a period that will see massive growth, productivity gains and the rapid transformation of business, creativity and data utilization. Across industries and around the globe, the promises of AI are already apparent. What’s left to us, today, is to ensure that AI operates in collaboration with workers, complementing our abilities and allowing us to make the most of an exciting and singular period in our lives. When AI Booms, Businesses Benefit By the close of 2023, AI had been mentioned more than 30,000 times on earnings calls. Society seemed to embrace AI overnight. Machine learning, technology that was often used to automate routine processes, started giving way to generative AI, technology that outputs specialized knowledge work. Just as the internet democratized access to information, AI is democratizing the context and insights we obtain from data. That adds up to productivity enhancements and demand for new skills. Similarly to how familiarity with personal computers became an expectation for professionals in the 1990s, today’s employers and business owners are beginning to hire workers who know their way around AI. Early adopters are already pushing the boundaries of what’s possible — and acceptable — with AI-generated images, videos, briefs, reports and marketing material. Like any boom, the Age of AI needs an energy source. Data licensing, which provides firms with access to information, will be the in-demand raw material in the years to come. Entrepreneurs and executives are already aware of its potential: AI data licensing was valued at $203 million in Reddit’s recent IPO, for example. What Comes Next? The internet fundamentally changed our world in the 1990s. It changed the language we used, the way we interacted with one another and how we thought about the world. It changed the jobs we coveted and ways we studied. The same is happening today. AI is already leading to productivity gains, which in turn explains why the market is optimistic about stock prices and valuations. Inflation is coming down and output is on its way up. The prospects of high interest rates aren’t as intimidating today as they were two years ago, and the near-zero interest rates of March 2022 seem like ancient history. Cyclical sectors that benefit from a strong economy are outperforming defensive sectors and there’s an appetite for even the riskiest of corporate bonds. In the financial services sector, AI is already generating a positive impact. The disjointed client experiences of yesterday are giving way to smoother, more client-friendly portals, workflows and interactions. Most significantly, AI is enabling financial advisors to collect, organize and retain volumes of information that allow delivery of more targeted and relevant solutions. AI is also helping HR departments, back office personnel and technology vendors, leading to better client service and stronger adherence to rules and regulations, according to a recent report. AI isn’t all hype; it’s not something to be afraid of, either. Our AI Chapter Will be Long and Prosperous On a macro level, we should stand ready to capitalize on the collective benefits we’ll reap from our AI productivity boom. Remember the budget surpluses of the late-1990s? We, too, are poised at the edge of a period of growth. In the years ahead we’ll see lower core inflation, tracking at or below the Fed’s 2% target. We will also see improving corporate profits that will lead to higher equity valuations, enabling millions of investors to retire comfortably and creating a wealth effect that supports consumption, which accounts for 70% of economic activity. Perhaps most importantly, rising equity markets will generate capital gains tax revenue, meaning smaller deficits at both the federal and state level. This will support government spending without the need for tax increases, leading to widespread improvements including social security solvency along with critical investments in healthcare, defense, infrastructure and education. Capital market assumptions and portfolio construction are already changing in anticipation of this new reality. In part, AI is producing smarter investing, even as it brightens the overall economic picture in vivid detail. In the financial services sector, AI adds the most value when used as a collaborative tool within teams of experts. It’s not meant to replace us; rather it’s meant to enable, enliven and strengthen our decision making, the relationships we form and the world we are ambitiously building for tomorrow. We’re yet to see the full impact of AI across industries, but it’s already changing our lives for the better, in much the same way that the internet, mobile devices and mobile payments have in recent decades. Accelerated employment. Wage gains. Faster investment, more information and notable gains in productivity. These were all hallmarks of the 1990s, a period that looms large in the memories of those who witnessed this time of innovation and skyrocketing long-term growth. New technologies, like computers and software, gave rise to a greater reliance and utilization of networking. The internet first flashed its true potential. Information was suddenly accessible and storing it became cheaper and cheaper. The combination of personal computing and the internet catalyzed massive changes to industry, trade, finance and services. The economic sluggishness of the 1970s and 1980s finally folded, as technology prospered, inflation fell and poverty gave way to job gains. History certainly harmonizes. In many ways, the 1990s are primed to rhyme with today’s gains in artificial intelligence. Like the entrepreneurs of the early-1990s, we’re standing at the outset of a period that will see massive growth, productivity gains and the rapid transformation of business, creativity and data utilization. Across industries and around the globe, the promises of AI are already apparent. What’s left to us, today, is to ensure that AI operates in collaboration with workers, complementing our abilities and allowing us to make the most of an exciting and singular period in our lives. When AI Booms, Businesses Benefit When AI Booms, Businesses Benefit By the close of 2023, AI had been mentioned more than 30,000 times on earnings calls . Society seemed to embrace AI overnight. Machine learning, technology that was often used to automate routine processes, started giving way to generative AI, technology that outputs specialized knowledge work. Just as the internet democratized access to information, AI is democratizing the context and insights we obtain from data. 30,000 times on earnings calls on earnings calls access context and insights That adds up to productivity enhancements and demand for new skills. Similarly to how familiarity with personal computers became an expectation for professionals in the 1990s, today’s employers and business owners are beginning to hire workers who know their way around AI. Early adopters are already pushing the boundaries of what’s possible — and acceptable — with AI-generated images, videos, briefs, reports and marketing material. Like any boom, the Age of AI needs an energy source. Data licensing, which provides firms with access to information, will be the in-demand raw material in the years to come. Entrepreneurs and executives are already aware of its potential: AI data licensing was valued at $203 million in Reddit’s recent IPO, for example. What Comes Next? What Comes Next? The internet fundamentally changed our world in the 1990s. It changed the language we used, the way we interacted with one another and how we thought about the world. It changed the jobs we coveted and ways we studied. The same is happening today. AI is already leading to productivity gains, which in turn explains why the market is optimistic about stock prices and valuations. Inflation is coming down and output is on its way up. The prospects of high interest rates aren’t as intimidating today as they were two years ago, and the near-zero interest rates of March 2022 seem like ancient history. Cyclical sectors that benefit from a strong economy are outperforming defensive sectors and there’s an appetite for even the riskiest of corporate bonds. In the financial services sector, AI is already generating a positive impact. The disjointed client experiences of yesterday are giving way to smoother, more client-friendly portals, workflows and interactions. Most significantly, AI is enabling financial advisors to collect, organize and retain volumes of information that allow delivery of more targeted and relevant solutions. AI is also helping HR departments, back office personnel and technology vendors, leading to better client service and stronger adherence to rules and regulations, according to a recent report . AI isn’t all hype; it’s not something to be afraid of, either. a recent report a recent report Our AI Chapter Will be Long and Prosperous Our AI Chapter Will be Long and Prosperous On a macro level, we should stand ready to capitalize on the collective benefits we’ll reap from our AI productivity boom. Remember the budget surpluses of the late-1990s? We, too, are poised at the edge of a period of growth. In the years ahead we’ll see lower core inflation, tracking at or below the Fed’s 2% target. We will also see improving corporate profits that will lead to higher equity valuations, enabling millions of investors to retire comfortably and creating a wealth effect that supports consumption, which accounts for 70% of economic activity. Perhaps most importantly, rising equity markets will generate capital gains tax revenue, meaning smaller deficits at both the federal and state level. This will support government spending without the need for tax increases, leading to widespread improvements including social security solvency along with critical investments in healthcare, defense, infrastructure and education. Capital market assumptions and portfolio construction are already changing in anticipation of this new reality. In part, AI is producing smarter investing, even as it brightens the overall economic picture in vivid detail. In the financial services sector, AI adds the most value when used as a collaborative tool within teams of experts. It’s not meant to replace us; rather it’s meant to enable, enliven and strengthen our decision making, the relationships we form and the world we are ambitiously building for tomorrow. We’re yet to see the full impact of AI across industries, but it’s already changing our lives for the better, in much the same way that the internet, mobile devices and mobile payments have in recent decades.

We’ve Been Here Before: The Impacts of Today’s Age of AI

আমরা আগে এখানে এসেছি: আজকের এআই যুগের প্রভাব

Das haben wir schon einmal erlebt: Die Auswirkungen des heutigen Zeitalters der künstlichen Intelligenz

Ya hemos estado aquí antes: los impactos de la era actual de la IA

Nous sommes déjà venus ici : les impacts de l’ère actuelle de l’IA

हम पहले भी यहां आ चुके हैं: आज के एआई युग का प्रभाव

これまでにも同じようなことが起こっていた: 今日の AI 時代の影響

우리는 전에도 여기에 있었습니다: 오늘날 AI 시대의 영향

Já estivemos aqui antes: os impactos da era atual da IA

Мы были здесь раньше: влияние современной эпохи искусственного интеллекта

Daha Önce Buradaydık: Günümüzün Yapay Zeka Çağının Etkileri

Chúng tôi đã từng ở đây trước đây: Tác động của thời đại AI ngày nay

我们曾经经历过这样的事情：当今人工智能时代的影响

In this new series, we’re talking about remarkable cypherpunks who helped to create decentralized money and more privacy and online freedom tools for everyone. Let’s remember that the group named “ ” was formed by . As you may imagine, a lot of people in the crypto world belong there. cypherpunks computer science, cryptography experts, and Internet activists aiming to create new software to foster privacy and social change Wei Dai is, of course, one of them. Maybe his name is ringing a bell if you ever read the Bitcoin whitepaper. He’s one of the references at the end. We don’t know much about his personal life, because he’s a quite private person —like most cypherpunks, indeed. However, we do know about his career. Probably born , of Chinese origin but living in the USA, Dai went to the University of Washington. He graduated in computer science with a minor in mathematics. According to , his career began at TerraSciences, where he contributed to developing security solutions for the oil and gas industry. Later at Microsoft, he played a crucial role in optimizing encryption algorithms. in 1976 some sources Dai's VMAC algorithm, designed for 64-bit systems, demonstrated high-performance data verification. He also identified critical vulnerabilities in SSH2's Encryption Block Chaining and was part of the team that discovered the BEAST protocol vulnerability in SSL/TLS, posing a significant risk to Internet security. Dai's notable contributions include the widely-used open-source Crypto++ library, employed in projects like Microsoft Office Groove and LastPass. Besides, his work on b-money influenced the core concepts implemented in Bitcoin. This is a very similar system, published before the . Despite speculation, Dai denies being Satoshi Nakamoto, or even having some real influence in the cryptocurrency creation. Bitcoin whitepaper B-money Proposal is considered an important precursor of all cryptocurrencies, even though it was never released. “Fascinated”, in his own words, by Tim May’s with its purpose of making the government unnecessary and the threat of violence impotent, free of governmental intervention. B-money crypto-anarchism ideology Dai designed potentially unregulated money for the crypto-anarchist community to operate, He showed, indeed, two protocols. The first one, described as impractical, establishes a basis for the second, more feasible one. In both, an untraceable network is assumed, where participants are identified solely by digital pseudonyms. The first protocol relies on a synchronous and uninterrupted anonymous broadcast channel, with participants maintaining databases of money ownership tied to pseudonyms. It covers the creation and transfer of money, and enforcement of contracts, including arbitration mechanisms. . Transaction messages remain similar to the first protocol, but participants must verify that messages are received and processed by a randomly selected server subset. To prevent collusion, servers are required to deposit money for potential fines or rewards, periodically publishing and committing to money creation and ownership databases. The second protocol shifts the accountability of the money mint to a subset of participants called servers, connected through a Usenet-style broadcast channel B-Money and Bitcoin These two systems share many similarities. Both of them aim to establish unregulated digital currencies operating in a peer-to-peer (P2P) network without the need for a central authority. The use of Proof of Work (PoW) for the creation of new units of currency is considered as well. In both b-money and Bitcoin, participants can generate new coins by solving computational problems. This PoW process serves the dual purpose of securing the network and preventing abuse. Additionally, both systems employ cryptographic mechanisms to ensure secure and private transactions. . Transactions are signed and encrypted, contributing to the privacy and security of the overall system. B-money, like Bitcoin, uses digital pseudonyms (public keys) to identify participants, providing a level of anonymity The concept of a distributed ledger is another shared element. B-money and Bitcoin utilize a decentralized approach to maintain a ledger of transactions. In b-money, participants maintain separate databases, while Bitcoin realizes these databases as a blockchain—a public ledger that records all transactions across a network of computers. Overall, the conceptual foundations of b-money, particularly the use of PoW, cryptographic techniques, and decentralized ledgers, Even the “b” in the name may result in suspicion. But Dai himself any contribution or involvement. laid the groundwork for the subsequent development of Bitcoin and other cryptocurrencies. denied “I didn't create Bitcoin but only described a similar idea more than a decade ago. And my understanding is that the creator of Bitcoin, who goes by the name Satoshi Nakamoto, didn't even read my article before reinventing the idea himself. He learned about it afterward and credited me in his paper. So my connection with the project is quite limited.” Not that decentralized B-money was only one of the important steps to what we have today, and to the real decentralization we’re still aiming to achieve in crypto. It wasn’t really decentralized, as you may have noticed: the “servers” (middlemen) were in control of the money. Something similar happens with Bitcoin, indeed. Miners, in charge of minting new coins, can cherry-pick or even censor transactions. If more than half of the big miners collude, they could take over the whole network. And they’re not even a lot of parties, that only two mining pools are currently in control of 54% of the network power (hashrate). considering , on the other hand, has solved this issue by completely eliminating the mining system. Instead, its Directed Acyclic Graph (DAG) structure is fully censorship-resistant. There are no powerful parties behind, and only (OPs) are required to order transactions. Their transactions serve as waypoints to order the rest, and nothing else. If someone sends a transaction to the DAG, it can’t be blocked or censored by any party. The total supply is also fixed, protecting against inflation. Obyte Order Providers Interestingly, as Wei Dai described enforcement of contracts and arbitration mechanisms, we also have contracts with arbitration as well as an arbiters store ( ) in Obyte. This is a new decentralized escrow payment solution, specifically designed for international trade. ArbStore Operating on the decentralized , , offering a decentralized escrow that safeguards funds until the conditions of the contract are met. With the added layer of protection from independent arbiters in case of disputes, the parties involved are assured a fair resolution process. Obyte crypto platform contracts with arbitration facilitate safe transactions using smart contracts (without users needing to code anything) and cryptocurrencies We’re in debt to our predecessors. Distinguished cypherpunks like Wei Dai and Satoshi Nakamoto laid the groundwork for the building of in the financial and online world. Let’s continue their mission! real decentralization Featured Vector Image by Garry Killian / Freepik

Cypherpunks Write Code: Wei Dai and B-money — or a Coin Before Bitcoin

সাইফারপাঙ্কস কোড লিখুন: ওয়েই দাই এবং বি-মানি — বা বিটকয়েনের আগে একটি মুদ্রা

Cypherpunks schreiben Code: Wei Dai und B-Geld – oder eine Münze vor Bitcoin

Los Cypherpunks escriben código: Wei Dai y B-money, o una moneda antes de Bitcoin

Cypherpunks écrivent du code : Wei Dai et B-money – ou une pièce avant Bitcoin

साइफरपंक्स कोड लिखें: वेई दाई और बी-मनी - या बिटकॉइन से पहले एक सिक्का

サイファーパンクがコードを書く: Wei Dai と B-money — またはビットコイン以前のコイン

Cypherpunks 작성 코드: Wei Dai 및 B-money — 또는 비트코인 이전의 동전

Cypherpunks escrevem código: Wei Dai e B-money – ou uma moeda antes do Bitcoin

Шифропанки пишут код: Вэй Дай и B-деньги — или монета до биткойна

Şifrepunklar Kod Yazıyor: Wei Dai ve B-money — veya Bitcoin'den Önce Bir Coin

Cypherpunks viết mã: Wei Dai và B-money - hoặc một loại tiền trước Bitcoin

密码朋克编写代码：Wei Dai 和 B-money——或者是比特币之前的代币

1. Validate idea first I wasted at least 5 years building stuff nobody needed. 2. Kill your EGO It's not about me, but the user. I must want what the user wants, not what I want. 3. Don't chase investors Chase users, and then investors will be chasing you. 4. Never hire managers Only hire doers until PMF. 5. Landing page is the least important thing in a startup Pick an average template, edit texts and that's it. 90% of the users will end up on your site coming from a blog article, social media post, a recommendation. Which means they have the intent. No need to "convert" them again. 6. Hire only fullstack devs There is nothing less productive in this world than a team of developers. One full stack dev building the whole product. That's it. 7. Chase global market from day 1 If the product and marketing is good, it will work in the global market too; if it's bad, it won't work on the local market either. So better go global from day 1, so that if it works, the upside is 100x bigger. 8. Do SEO from day 2 As early as you can. I ignored this for 14 years. It's my biggest regret. 9. Sell features, before building them Ask existing users if they want this feature. I run DMs with 10-20 users every day, where I chat about all my ideas and features I wanna add. I clearly see what resonates with me most and only go build those. 10. Hire only people you would wanna hug My mentor said this to me in 2015. And it was a big shift. I realized that if I don't wanna hug the person, it means I dislike them. Even if I can't say why, but that's the fact. Sooner or later, we would have a conflict and eventually break up. 11. Invest all money into your startups and friends Not crypt0, not stockmarket, not properties. I did some math, if I kept investing all my money into all my friends’ startups, that would be about 70 investments. 3 of them turned into unicorns eventually. Even 1 would have made the bank. Since 2022, I have invested all my money into my products, friends, and network. 12. Post on Twitter (or other biz social media) daily I started posting here in March this year. It's my primary source of new connections and traffic. 13. Don't work/partner with corporates Corporations always seem like an amazing opportunity. They're big and rich, they promise huge stuff, millions of users, etc. But every single time none of this happens. Because you talk to a regular employees there. They waste your time, destroy focus, shift priorities, and eventually bring in no users/money. 14. Don't get ever distracted by hype, e.g. crypt0 I lost 1.5 years of my life this way. I met the worst people along the way. Fricks, scammers, thieves. Some of my close friends turned into thieves along the way, just because it was so common in that space. I wish this didn't happen to me. 15. Don't build consumer apps. Only b2b Consumer apps are so hard, like a lottery. It's just 0.00001% who make it big. The rest don't. Even if I got many users, then there is a monetization challenge. I've spent 4 years in consumer apps and regret it. 16. Don't hold on bad project for too long, max 1 year Some projects just don't work. In most cases, it's either the idea that's so wrong that you can't even pivot it or it's a team that is good one by one but can't make it as a team. Don't drag this out for years. 17. Tech conferences are a waste of time They cost money, take energy, and time and you never really meet anyone there. Most people there are the "good" employees of corporations who were sent there as a perk for being loyal to the corporation. Very few fellow makers. 18. Scrum is a Scam If I had a team that had to be nagged every morning with questions as if they were children in kindergarten, then things would eventually fail. The only good stuff I managed to do happened with people who were grownups and could manage their stuff. We would just do everything over chat as a sync on goals and plans. 19. Outsource nothing at all until PMF In a startup, almost everything needs to be done in a slightly different way, more creative, and more integrated into the vision. When outsourcing, the external members get no love and no case for the product. It's just yet another assignment in their boring job. 20. Bootstrap I spent way too much time raising money. I raised more than 10 times, preseed, seed, and series A. But each time it was a 3-9 month project, meetings every week, and lots of destruction. I could afford to bootstrap, but I still went the VC-funded way, I don't know why. To be honest, I didn't know bootstrapping was a thing I could do or anyone does. That's it. All my projects → http://x.com/johnrushx/bio Also appears . here

Things I Wish I Knew Before Starting My 1st Startup

The tumultuous journey of "The Day Before," once one of Steam's most anticipated games of 2023, has taken an unexpected turn. It was originally pitched as an open-world MMO, similar to and OSRS, albeit with a post-apocalyptic skin. Fntastic, the developer behind this game, recently announced that they are shutting down their operations. This announcement comes less than a week after the studio faced accusations of deceiving players by significantly altering the nature of the game from what was initially advertised. World of Warcraft On December 11, Fntastic released a statement on Twitter announcing the closure of their studio. They cited the financial failure of "The Day Before" as the primary reason, stating that all revenue is being used to settle debts with partners. Despite their efforts over five years in developing the game, they didn't raise funds through Early Access, pre-orders, or crowdfunding. The future of "The Day Before" and other online games from the studio remains uncertain, although the servers will remain active for now. The statement from Fntastic expressed regret for not meeting player expectations, acknowledging the immense challenge of game development and admitting to an overestimation of their capabilities. Simultaneously, the studio appears to be distancing itself from responsibility for the issues plaguing "The Day Before." The game, which was one of the most wished-for on Steam earlier in the year, drew significant attention upon release. It attracted over 400,000 viewers on Twitch and 30,000 concurrent players on its first day. However, players quickly realized that the game, built on the Unreal Engine and impressive in trailers, diverged from its original concept as a zombie survival MMO. Instead, it turned out to be a buggy extraction shooter with sparse content. The game, priced at $40, faced harsh criticism in numerous Steam reviews, with players labeling it a "scam" and citing server problems and flawed levels. Many sought refunds. PC The game's launch, which led to a predominantly negative rating, followed months of controversy. This included allegations of exploiting volunteer labor, misleading trailers, asset flips, a legal dispute leading to a temporary removal from Steam, and several delays. These issues fueled speculation about the game's legitimacy. Last week, Fntastic defended its integrity against such speculation, criticizing doubters and expressing commitment to improving the game post-launch. However, just four days after this statement, the studio announced its closure, marking an abrupt end to its promise of continued development and support for the game.

Fntastic, the Studio behind The Day Before, Shutting Down After Tumultuous Launch

You've created a mobile app. And so, what now? Over the past ten years, mobile devices have transformed into an indispensable form of communication. The ubiquitous availability and convenience of mobile devices have resulted in the relentless growth of . Therefore, the market for mobile apps is now among the most rapidly growing sectors. For every conceivable use case, there is a matching tool or app. mobile app development All of these factors have heightened the relevance of testing mobile apps. Today, it is essential that you have an app that works seamlessly and is superior to rival applications. This can only be accomplished through testing. Understanding Mobile App Testing As the name indicates, mobile app testing is the process of verifying an Android or iOS mobile application's usability and functionality before its public release. The purpose of testing mobile applications is to confirm that the app meets the projected business and technological specifications. Every day, people use mobile apps in a variety of scenarios, such as banking, contemporary vehicles, and games, or in smart refrigerators. Yet, most users have faced a very common and annoying situation -- the app didn't work when it needed the most. If mobile apps aren't rigorously tested, there is a major possibility that consumers will on their devices, which can result in a negative user experience, especially among new users. encounter serious bugs Any unexpected app breakdown or functionality defect could result in the app's immediate uninstallation. Severe bugs may even lead to sensitive data loss, which can result in fines and penalties. 7 Elements to Test Before Deploying Your Mobile Application The mobile app testing process is slightly different from testing a web app or a desktop app. Testers have to check the following key elements before the app is ready for deployment: 1. Functioning in Different Software Environments Your consumers will mostly install the app or work on different browsers and operating systems (like iOS and Android). Establish the necessary configurations, and use the test strategy to figure out which tests should be executed first. Software configuration testing usually takes place at the time of the AUT (application under test) phase. The mobile app must be tested across various OS versions, firmware upgrades, etc. 2. Functioning in Different Hardware Environments Hardware testing checks internal processors, core hardware, screen dimensions, resolution, capacity or memory, the camera, radio, Wi-Fi, Bluetooth connectivity, etc. There are scores of unique mobile devices, and end users will run your app on almost all of them. So, it is vital to check this element before launching the application. 3. Working Under Different Network Conditions The network has a major impact on app behavior, and every mobile app behaves uniquely across different network environments. Multiple factors define networks, like physical barriers, network spectrum, separation between devices, radio interference, network usage, or traffic, among others. When testing applications, network latency should be taken into consideration. Before being made available to the public, mobile apps have to undergo a range of evaluations on multiple networks, under various conditions. Remember that bandwidth and data capacities may influence the speed of page loading, which will ultimately affect . user retention 4. Performance on Real Devices and Not Just Emulators Your clients or consumers will use these applications on real devices, such as tablets and smartphones, and not on emulators. It is simple to evaluate the efficacy and general user experience of an app when tested on actual hardware. Emulators can carry out limited tests. Real devices can be deployed to test each and every function, like camera evaluations, geolocation abilities, biometric sensors, and more. However, there may be an additional cost involved. 5. Performance Under Difficult or Strenuous Conditions You must evaluate the app's usability, security, and resource utilization under different conditions and challenges. This comprises: Load testing: The application's response time to various kinds of requests : The application's functional capacity at above average demand, several times over Stress testing : Longevity of the app under normal operating conditions Concurrency testing: The number of concurrent users who can run an application Stability testing 6. The Security Level of the Mobile App The safety aspects of the app should also be evaluated before launch. Assess the dangers posed by viruses, hackers, or unauthorized access to private data. Ensure that all user information (logins, passwords, credit card details) is secure from mechanized attacks on the network. Confirm that the app's security system is protected by a robust password. 7. The App’s Ability to Recover After Failure An excellent mobile app must be ready to withstand and recover from defects resulting from software glitches, hardware malfunctions, or communication issues. Verify the app's ability to recover from unforeseen crises, transaction delays, or potential data loss. Conclusion This list is by no means exhaustive. Launching a fully functional, large bug-free, and sticky app can be difficult, which is why you need to test continuously – even after deployment. The primary elements we've highlighted are critical for a smooth app launch. To make these parts work in your development and testing, establish a strong team and, if necessary, seek assistance from a . software testing services company Ask users for feedback and roll out timely upgrades. With time, you will learn to test for local/cultural user expectations, work in newer OSs, and more.

Key Elements to Test Before Deploying Your Mobile App

আপনার মোবাইল অ্যাপ স্থাপন করার আগে পরীক্ষা করার মূল উপাদান

Wichtige Elemente, die Sie vor der Bereitstellung Ihrer mobilen App testen sollten

Elementos clave para probar antes de implementar su aplicación móvil

Éléments clés à tester avant de déployer votre application mobile

अपने मोबाइल ऐप को तैनात करने से पहले परीक्षण करने योग्य मुख्य तत्व

モバイルアプリを展開する前にテストする重要な要素

모바일 앱을 배포하기 전에 테스트해야 할 주요 요소

Elementos principais para testar antes de implantar seu aplicativo móvel

Ключевые элементы, которые необходимо протестировать перед развертыванием мобильного приложения

Mobil Uygulamanızı Devreye Almadan Önce Test Edilecek Temel Unsurlar

Các yếu tố chính cần kiểm tra trước khi triển khai ứng dụng di động của bạn

部署移动应用程序之前要测试的关键要素

BEFORE

before-inc.com

-- employees

Since n.d.

COMPANY RANKING

BEFORE

EVERGREEN INDEX #2510

Representing this company? Click here to claim and customize this page!

Before WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

BEFORE

before-inc.com

-- employees

Since n.d.

COMPANY RANKING

BEFORE

EVERGREEN INDEX #2510

RELATED COMPANIESRANK

STORIES ABOUT BEFORE

Representing this company? Click here to claim and customize this page!

Before WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!