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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

When most companies talk about expanding into new markets, the first instinct is to crank up performance marketing, localize the website, or spin up a regional sales team. But here’s the truth: you can’t scale what you don’t understand. you can’t scale what you don’t understand. In one of my most pivotal roles, we were tasked with launching into two new regions — without wasting months (or millions) on unvalidated assumptions. The twist? We didn't lead with marketing. We turned business development into our market discovery engine — and it changed how we scale forever. business development market discovery engine forever This article is about how we used BD to explore, test, and adapt before making any big bets. Think of it as a blueprint for market validation — one conversation, signal, and small partnership at a time. explore test adapt Step 1: Redefine the Role of BD — From Seller to Scout In most orgs, business development is tasked with creating pipeline or closing partnerships. But in an expansion scenario, we flipped the script. Instead of asking, “How can we sell here?” — we asked, “What can we learn here?” “What can we learn here?” We created a temporary role called Market Discovery BD. The KPIs weren’t revenue. They were: Market Discovery BD Number of qualitative conversations with ICPs
Signals about market pain, behavior, and maturity
Evidence of potential GTM fit (tools, processes, budget owners) Number of qualitative conversations with ICPs Signals about market pain, behavior, and maturity Evidence of potential GTM fit (tools, processes, budget owners) Our BD lead wasn’t armed with a pitch deck. They were armed with a discovery script, focused on learning: discovery script What local alternatives are already dominant?
How are buying decisions made?
What are the most urgent problems no one's solving? What local alternatives are already dominant? How are buying decisions made? What are the most urgent problems no one's solving? Within three weeks, we had 42 interviews across 3 verticals — and some assumptions completely unraveled. Step 2: Prototype Offers Through Conversational GTM Rather than launching a new pricing page or funnel, we started by prototyping offers directly through conversations. prototyping offers Here’s how it worked:In week 4, we began proposing lightweight pilots to high-signal accounts. Not via campaigns — via BD calls. lightweight pilots We’d say: “Based on what you shared, we can spin up a pilot focused just on [pain point X], using [feature Y]. Would you be open to trying that over 2 weeks?” “Based on what you shared, we can spin up a pilot focused just on [pain point X], using [feature Y]. Would you be open to trying that over 2 weeks?” The response rate? 60%+ interest. Why? Because the offer came after deep understanding — not before. This helped us answer: What positioning resonates most?
What objections are we hearing early?
What kind of results move the needle for these customers? What positioning resonates most? What objections are we hearing early? What kind of results move the needle for these customers? Instead of writing case studies months later, we were generating real outcomes during discovery — and feeding those insights into product and marketing in real time. real outcomes Step 3: Use BD Feedback Loops to Design Scalable GTM By week 6, we had real traction. A handful of customers had completed fast pilots, shared testimonials, and even introduced us to other local players. But more importantly, our BD team had collected first-hand, structured intelligence that shaped everything: first-hand, structured intelligence Messaging → We dropped “efficiency” in favor of “speed to ROI”
Objections → We created specific assets to answer local procurement issues
Ideal Customer Profile → We narrowed our focus to mid-sized tech teams with internal tooling challenges Messaging → We dropped “efficiency” in favor of “speed to ROI” Objections → We created specific assets to answer local procurement issues Ideal Customer Profile → We narrowed our focus to mid-sized tech teams with internal tooling challenges We fed all of this into a GTM readiness doc, co-created by BD, marketing, and product. It was clear: GTM readiness doc Where to focus
How to position
What was still missing Where to focus How to position What was still missing Only then did we greenlight regional ad spend, build local landing pages, and begin hiring. Conclusion Using BD as a market exploration tool — not just a channel — helped us move faster, de-risk expansion, and build real conviction before we scaled. In a world obsessed with automation and dashboards, it’s easy to forget the power of high-signal, human conversations. But if you treat business development as a learning engine, you get more than leads. You get clarity. market exploration tool So before you launch your next region, ask yourself: Are you ready to sell — or do you still need to listen? Are you ready to sell — or do you still need to listen?

Before You Burn Millions on Ads, Try Talking to People

AI Gone Wrong? This Guy's Job Is to Catch It Before It Happens

According to CoinMarketCap (CMC), there are over 10,000 coins and tokens available to trade in different markets worldwide. Among them, we can find all kinds of things, from old-established cryptocurrencies like Bitcoin and Obyte to experimental DeFi platforms and memecoins. However, you might not know that, in addition to a vast array of tokens, there’s also a diverse selection of free crypto-related tools —ranging from practical to quirky and entertaining. CMC and CoinGecko are, for instance, two quite popular and useful crypto tools, but they’re far from being the only ones. We can discover some interesting things if we just look around a bit. Let’s see some platforms in this category. When Lambo? Memecoins are quite the thing now, but the crypto community always had its own internal memes and jokes without related tokens. “When Lambo” is an old one and a very popular one too. It implies a momentous question: when will we be able to afford an expensive Lamborghini car with our current crypto holdings? It’s known that prices change rapidly in the crypto space, so can you maybe get richer tomorrow with the same number of coins? We can dream, at least. “When Lambo” is so widespread around here that some years ago CMC changed all the prices from USD to LAMBOs in April’s Fools. And the question has its own calculator, to help you find out once and for all how much of a Lambo you can buy with your current holdings in different coins. The site Crypto to Lambo allows you to type the amount, choose between 100 coins and three Lamborghini models, and calculate, well, when Lambo? CMC changed CMC changed The site Crypto to Lambo allows you to type the amount, choose between 100 coins and three Lamborghini models, and calculate, well, when Lambo? Crypto to Lambo Crypto to Lambo The results will include what Lambo you can buy with your coins (at least a toy Lambo?), how many more tokens you need to buy it, the needed price per coin you’ll need to afford it, and the current percentage you have in your wallet. It’ll also lead you to trade on the exchanges Binance or Bitvavo, but that’s an additional option. Do your own research first! Wallet Scrutiny Cryptocurrencies are mostly decentralized, which means you have full control over your holdings if you have a private key. But wait a second, because there might be a trick there. The coins and their ledger are decentralized, yes, but the wallet you use to interact with crypto networks can be a completely different story. Wallets are often offered by middlemen (companies and developer teams), who generally have good intentions —but that’s not always the case. Some awful things (backdoors and other traps) could be hidden in their codes, beyond what we can see. Even if the wallets are open-source software, which means anyone can check their codes, not everyone knows how to. And worse: cybercriminals are designing fake wallets with the same name and branding as legitimate ones, aiming to trick users into downloading this malicious software from official app stores to steal their private keys. Some awful things (backdoors and other traps) could be hidden in their codes, beyond what we can see. Even if the wallets are open-source software, which means anyone can check their codes, not everyone knows how to Now, of course, there’s a way to avoid these bad scenarios, and we have several easy and free tools available for it. WalletScrunity is one of them, focused primarily on Bitcoin wallets, but basic information about others is available too. On this site, anyone can just type the name of their preferred wallet (software or hardware) and discover if it passed the tests and questions from their developers. Among those tests, they verify who’s the team behind the brand, if it’s open-source or not, if it’s non-custodial (offer private keys) or not, and if it’s the original product. They also offer safe links to download or buy the correct version. WalletScrunity WalletScrunity Crypto Bubbles With all their detailed data and long lists, sites to check cryptocurrency market caps, like CMC and CoinGecko, can be a tad overwhelming for beginners. Crypto Bubbles offers a more visual, dynamic experience to check the same data, using colorful bubbles and user-friendly interfaces to do it. The first thing you’ll see on this site is a lot of crypto logos and tickers floating around in bubbles of different sizes and colors, indeed. Crypto Bubbles offers a more visual, dynamic experience to check the same data, using colorful bubbles and user-friendly interfaces to do it. Crypto Bubbles Crypto Bubbles Green bubbles mean price increases —the larger the bubble, the higher the percentage change or market cap. Red bubbles imply negative percentages, and they also change in size, depending on the figures. It’s possible to order them per period (hour, day, week, month, year) or market cap size. In the Settings, they’ll let you change the currency for the prices (USD, EUR, CAD, etc.), languages, colors, and watchlists. If you click a bubble, more specific data, charts, and links about that currency will appear, summed up in a very graphical manner. Below the bubbles, a more traditional list of coins is also available to order by market cap, price, volume, or time changes. Or you can use their search bar to find your favorite coins. An app for iOS and Android can be downloaded too. TxCity Network explorers aren’t exactly the most colorful and user-friendly thing in the world. And let’s not talk about those long, incomprehensible alphanumeric strings that represent transactions and addresses. TxCity was created to see crypto transactions in a new, clearer light. By representing each transaction as a person attempting to board a bus—where buses symbolize blocks in a blockchain— this platform offers users a unique, real-time perspective on the available crypto networks. By representing each transaction as a person attempting to board a bus—where buses symbolize blocks in a blockchain— this platform offers users a unique, real-time perspective on the available crypto networks. Obviously, those networks are only blockchains — they are the kind of networks that have buses and bus drivers. This approach demystifies on-chain data, effectively converting mere transactional networks into visual ecosystems. https://x.com/tastycrypto/status/1887589349677744200?embedable=true https://x.com/tastycrypto/status/1887589349677744200?embedable=true By using TxCity, anyone can visualize transactions across multiple networks, including Bitcoin, Ethereum, Bitcoin Cash, Monero, and Litecoin. The platform also plans to introduce features like bridge visualization between Ethereum and LUKSO, providing users with a real-time experience of their cross-chain transactions. Additionally, TxCity aims to represent wallets as characters or avatars, simplifying user interactions. TxCity TxCity To begin using TxCity, simply visit their website and select the chain you wish to explore. The platform will display live transactions as animated characters attempting to board buses, offering an engaging way to observe how transactions are processed and included in blocks. This visual representation not only makes it easier to understand crypto operations but also serves as an educational tool for those new to this space. Order a Pizza in Obyte Did you know you can order a Hawaiian, Pepperoni, or Mexican pizza using an Obyte chatbot? Well, at least as a role-play! The Obyte Testnet Wallet offers a fun and risk-free way to explore cryptocurrency transactions, including interacting with chatbots. By following simple steps, users can simulate making a purchase and experience how payments work within the Obyte ecosystem. It’s an engaging way to see how digital payments function while learning about Obyte’s capabilities in a relaxed and playful environment. The Obyte Testnet Wallet offers a fun and risk-free way to explore cryptocurrency transactions, including interacting with chatbots. Obyte Testnet Wallet Obyte Testnet Wallet A testnet is a duplicate version of a crypto network designed for learning, development, and testing. Unlike the mainnet, where transactions involve real assets, the testnet allows users to practice without any financial risk. The Obyte Testnet Wallet is a safe space where developers can refine their applications and everyday users can experiment with different features. To start, users can claim free testnet coins—tokens without market value—to send transactions, explore smart contracts, and even test advanced security measures like two-factor authentication (2FA) or multi-signature accounts. A testnet A testnet free testnet coins free testnet coins Beyond simulated pizza purchases, the testnet wallet offers a variety of features to explore. Users can try chatbots from the Bot Store, set up conditional payments, and experiment with wallet settings. It’s also possible to add new devices and test secure account recovery options. Everything is recorded on the testnet explorer, providing insights into how transactions are processed. Since no real money is involved, users can experiment freely and gain confidence before switching to the actual Obyte wallet. testnet explorer testnet explorer Obyte wallet Obyte wallet Featured Vector Image by Freepik Featured Vector Image by Freepik Freepik Freepik

5 Quirky and Useful Crypto Tools You Didn’t Know Before

The ability to control electronics without touching them or paying with a swipe of the hand may seem like sci-fi, but it’s a growing reality. Microchip implants have existed in some form for decades, and technology is now advanced enough to make them practical. Still, this field carries significant security concerns. The ability to control electronics without touching them or paying with a swipe of the hand may seem like sci-fi, but it’s a growing reality. Microchip implants have existed in some form for decades, and technology is now advanced enough to make them practical. Still, this field carries significant security concerns. The Growing Field of Implanted Microchips The Growing Field of Implanted Microchips Veterinarians have implanted radiofrequency identification chips in pets since the 1990s. Human microchips got FDA approval in 2004 for medical use, though the technology did not catch on at the time. got FDA approval in 2004 got FDA approval in 2004 Fast-forward to today, and several leading tech companies and startups are researching microchip implants. Most famously, Elon Musk’s Neuralink has begun trials for brain-computer interfaces, which let users control electronic devices with their thoughts. Simpler chips that use RFID or near-field communication to store information or manage electronic payments are already available for commercial use. These chips can stand in for debit cards, give doctors access to vital medical information or work as keycards to access digital locks. Though they may not necessarily be widespread yet, they’re becoming increasingly prevalent. Cybersecurity Risks of Microchip Implants While chip implants can be highly convenient, they also open the door to unique cybersecurity risks. People should consider these potential dangers before proceeding with the technology. RFID Skimming Contactless payments are one of the most practical ways to use an implanted microchip. However, this application means users are theoretically always vulnerable to skimming. Cybercriminals could intercept financial information if they get close enough to the implant, and because it’s essentially part of the user’s body, physical protections are minimal. Thankfully, while skimming has garnered much attention from RFID-blocking wallets, it’s not a common threat. NFC is even more secure, as it can only happen within four centimeters of the chip, making it difficult for criminals to get close enough. Still, the possibility may worry some consumers. only happen within four centimeters only happen within four centimeters Malware A more concerning threat is the risk of cybercriminals installing malware onto an implanted chip. Researchers have proved that it’s possible to transfer malware through NFC, even when the chip does not connect to an actively running device. Attackers may still need to get close to do so, but the damage might go far beyond skimming card details. it’s possible to transfer malware through NFC it’s possible to transfer malware through NFC Malware could cause a chip to malfunction. Depending on what people use their microchips for, that could leave them locked out of rooms or unable to access some electronics. The malware could also spread to anything that connects to the implant, including smart home devices or users’ phones. Data Privacy Concerns Finally, storing sensitive data in a wirelessly accessible chip in people’s bodies raises privacy questions, especially when it comes to BCIs. A BCI could theoretically give companies access to brain data, even including users’ thoughts if technology advances enough. give companies access to brain data give companies access to brain data A breach at a business storing its customers’ brainwaves could give criminals access to a massive amount of highly sensitive information. Even outside such an event, targeted advertising based on brain data may feel like a privacy violation. The possibility of government bodies accessing this information further complicates the ethical implications. How Can Microchip Implants Become More Secure? Given these risks, implanted chips should implement a few extra security measures to protect their users. More best practices will emerge as the technology develops, but there are still a few must-have options to include. Zero-Trust Security The companies offering microchip implants must adhere to zero-trust policies. Government agencies began requiring zero trust in 2021, and private businesses would be wise to adopt the same standards. began requiring zero trust in 2021 began requiring zero trust in 2021 Zero-trust architecture treats every transaction as a possible breach, verifying all data transmissions before allowing them. It also restricts access permissions as much as possible. That level of attention and responsiveness is crucial for anything involving as much sensitive information and potential complications as a chip in users’ bodies. Tighter Access Controls Implementing stronger defenses for accessing the microchip and its connected services is also critical. Even a PIN is better than nothing. Hackers can brute-force through a four-digit code instantly, but they would still have to get within centimeters of an implanted NFC chip to do so. brute-force through a four-digit code instantly brute-force through a four-digit code instantly Setting and changing a PIN and managing the information and permissions of an implanted chip should require more robust protections. Strong passwords and multi-factor authentication are essential. Services should also restrict access attempts to stop unauthorized users from changing what it takes to get into an implant-based system. Functionality Limits Finally, microchip implants will be more secure when they do less. While some organizations may not want to sacrifice functionality, limiting what a chip can do minimizes the ways an attacker can take advantage of it. Restricting long-range communications is vital for protection. The most robust defense implanted chips have today is that they only work within a few centimeters. A microchip with remote accessibility or Wi-Fi functionality suddenly becomes vulnerable to a much wider range of attacks and potential entry points. Maintaining a relative air gap by relying on NFC or RFID and restricting the kinds of data a chip can access and transmit make them far safer. Microchip Implants Are Exciting But Potentially Risky Despite being around since the ‘90s, implanted microchip technology is still in its infancy. As this field grows and develops, its possibilities will expand, but so might its potential dangers. Cybersecurity considerations must come before expanded functionality and adoption. When these systems follow a security-by-design approach, people can use them confidently and safely. Failing to consider such risks in design and implementation could lead to catastrophic consequences.

Think Before You Link: Security Risks of Microchip Implants

Caching is a technique in software development to temporarily store application resources, data at a nearby accessible location for efficient retrieval when required. Generally, the resources or data that are frequently used but doesn’t change often are considered good candidates for caching in software design. Caching is implemented to achieve several benefits in software system such as Reduce latency in response of a request
Reduce number of network calls thereby using less network bandwidth
Reduce chances of failure in software system by serving data from cache instead of making call to actual backend system Reduce latency in response of a request Reduce number of network calls thereby using less network bandwidth Reduce chances of failure in software system by serving data from cache instead of making call to actual backend system There are several articles in the web that talks about caching and it’s various implementation strategy. This article doesn’t reiterate those in some other fashion. Instead, this article presents a holistic picture of different type of caching we implement in a large software system, conceptually how do we accomplish that for each types and how is it typically done in a system that is hosted on-premise in dedicated hardware vs how is it done in modern cloud platforms. General Caching Strategy The below diagram(Diagram 1) represents a typical web application architecture hosted in on-premise data center of an organization. Diagram 2 represents the specific type of caching that each of the architecture components contributes in caching implementation. Before we start with explanation of the above diagram(Diagram 2), let’s first talk about categorization of caching in a web application. It can be done based on two different dimensions — what is cached and where is it cached. What is cached? Application resource — By application resource caching, we primarily mean static contents such as CSS, JavaScript, images etc. which are used to design the look & feel of a webpage. Application resource also includes media files though such as audio, video.
Application data — By application data caching, we mean caching data that primarily resides in backend databases of an application Application resource — By application resource caching, we primarily mean static contents such as CSS, JavaScript, images etc. which are used to design the look & feel of a webpage. Application resource also includes media files though such as audio, video. Application data — By application data caching, we mean caching data that primarily resides in backend databases of an application Where is it cached? Generally application resources are cached at the infrastructure level(e.g. CDN, reverse proxy etc.) or on the client side(e.g. browser, device).


Whereas application data is cached on the server side(e.g. memory, disk or dedicated caching tool). Generally application resources are cached at the infrastructure level(e.g. CDN, reverse proxy etc.) or on the client side(e.g. browser, device). Generally application resources are cached at the infrastructure level(e.g. CDN, reverse proxy etc.) or on the client side(e.g. browser, device). Whereas application data is cached on the server side(e.g. memory, disk or dedicated caching tool). Whereas application data is cached on the server side(e.g. memory, disk or dedicated caching tool). Let’s decompose Diagram 2 which shows the different layers of caching and explain how in a traditional on-premise application caching is implemented. A typical web application is composed of 3 different tiers — a web tier hosting the frontend web application running in an web server, a backend services tier running in application server and a database tier. Traffic from internet through a web browser doesn’t hit the web and application server directly. Typically a Web Application Firewall(WAF)/DDoS mitigation tool sits in front of the network. This tool often also work as content delivery network(CDN). Traffic from the WAF/DDoS/CDN tool hits the reverse proxy of the network. The reverse proxy layer works as the interface to accept all traffic from the internet, hides identity of the actual backend servers from the external world where application is running. The reverse proxy server finally routes the traffic to appropriate backend server based on the resource path in the request URL. If the request from the browser is for the web application then reverse proxy layer forwards the traffic to the web server. If the request is for a backend service, then traffic is routed to the application server. Application Resource Caching When the request is to render a webpage, the static resources such as JavaScript, CSS, images used to render the webpage can be cached in all 3 layers — the reverse proxy, CDN and browser. What can be cached and can’t be cached are determined by the various HTTP headers as set by the web application such as — last-modified-since/is-modified-since, cache-control etc. When the browser makes another request for the same webpage or for a different webpage which is reusing some of the static assets(JavaScript, images, CSS) of an already accessed webpage, the browser first checks if the assets have been changed in the application or not(using last-modified-since/is-modified-since header). If no change is found, then the cached asset in the browser is used to render the webpage. Otherwise the browser makes a request to the application to download the updated asset. Once browser makes request to the backend, the request is first intercepted by the CDN/DDOs/WAF layer. This layer does the exact same thing that the browser did i.e. it will check if the copy of the asset it has in it’s cache is same as the asset in backend, if yes, it will not forward the request for the resource to the reverse proxy but return the cached copy of the resource to the browser. If the asset is not cached in CDN layer, then CDN layer does the same thing that browser did i.e. it forwards the request of the asset to the reverse proxy layer. The reverse proxy layer will serve the asset from cache if it hasn’t been updated in the application, otherwise will reach out to the backend web server to retrieve the fresh copy of the asset. The fresh copy of the asset returned by the web server is then cached by all 3 caching layer — reverse proxy, CDN and browser. If the end user decides to clear the browser cache for some reason, then even though the browser will reach out to backend to fetch the asset, the asset will actually be returned by the CDN and/or reverse proxy instead of the backend web application server. As a result of that the end client still experiences improved response time as the resource is served by some upstream infrastructure layer of the application instead of the actual backend server. Needless to say, this also reduces the amount of traffic that hits the actual backend server. The diagram below(Diagram 3) demonstrates the flow of application resource caching in different layers as described above. Application Data Caching When we talk about application data caching, it would typically mean caching data which generally doesn’t change frequently and therefore are good candidate of caching to reduce number of calls that goes to the database. One good example of that could be list of countries or list of states in a country that are displayed in a webpage. These are lookup table type of data and can be cached safely after retrieving once to avoid calling database every time for data that is mostly static. Another example we can think of is modern day day social media application where we may want to cache the home page feed of a user so that when the user launches the home page, the user sees the home page feed right away instead of waiting for several seconds. Until the advent of cloud based applications or emergence of modern caching tools like Memcached, Redis, on-premise applications used to rely on session caching in application server. That typically would mean data which are candidate of caching would live in session of the user that gets created after login but they would not be available for reuse during their next login. Some application servers like WebSphere had a separate caching solution to get around the session caching limitation called Dynacache — a distributed map. Dynacache would allow an application to cache data as key-value storage which is not tied with an user session. WebSphere used to keep this data in server memory and also had the provision of offloading the data into a disk if needed. It worked well given the lack of modern day caching tool we had at that moment but it had the limitation of cache not surviving a restart of the server(unless offloaded into disk). There were also challenges of maintaining consistency of data in cache in a clustered environment as each application server in the cluster would have it’s own copy of cache. Technically there was a provision to replicate cache between application servers in a cluster to address the consistency problem but that wasn’t something easy to implement and would come at a cost of performance. However as the Diagram 2 above demonstrates, caching data at the application server layer is one of the options to cache data on server side. Databases and object-relation-mapping(ORM) tool like Hibernate also provides option to cache data using something called query cache. Databases can cache the result as well the entire SQL query in memory and serves data from memory as long as the data hasn’t been updated. Pretty much most of the common databases like MySQL, Oracle, PostgreSQL, SQL Server etc. supports query cache. In modern day applications, the capability of data caching has improved significantly with advent of caching specialized technologies like Redis or Memcached. On top of that, modern day API Gateway layers that works as reverse proxies(along with providing tons of other features such as orchestration, rate limiting etc.) also provide ability to cache data for API calls. Though API Gateway(e.g. Apigee) and caching tool like Redis/Memcached can be implemented in an on-premise solution to power data caching, in reality, these toolsets are more popular for use in applications running in cloud platforms(e.g. AWS, Azure etc.). The following pair of diagrams, demonstrates usage of API Gateway and caching tool(e.g. Redis, Memcached) for on-premise as well as cloud hosted applications. Caching strategies When the combination of database and caching tool is in place in architecture(common scenario in modern day applications), there are several techniques available to implement caching. There is no right or wrong techniques as all come with trade-offs. Depending on the nature of the use case, appropriate technique needs to be chosen. Cache aside — application reads from cache, if data not available in cache, then application reads from database and update cache. This is the caching pattern I have used mostly for the use cases I have personally encountered.
Read through cache — application reads from cache and if data is not available in cache, then caching tool reads from database, updates cache and returns response.
Write through cache — applications writes into cache, cache sends an acknowledgement immediately to reduce latency in client response and asynchronously writes data into database after that. This is a use case that can be adopted for a social media application where a post by a user can be made available for view to other users with sub-second latency. The actual operation of storing the data in a persistent store(database) happens asynchronously.
Write back cache — data is written into cache only. Right before cache expires(typically after time-to-live aka TTL setting) data is written into the database for persistence. This technique can be used for a ride sharing system like Uber. Generally in a ride sharing system, a vehicle sends it’s current location every 30 seconds or so that a rider can get a real-time location update of the driver they are waiting for ride. In this caching pattern, the location of a car will only be updated in cache and before the cache expires the location data will be written to the database. This caching technique allows user/rider get to know the location of the driver instantaneously in the Uber app. If due to any reason the write to the database fails it results in a data loss. However generally this is acceptable if application doesn’t have history of location data for a car for a small interval of time(e.g. 5mins) in it’s persistent storage. Cache aside — application reads from cache, if data not available in cache, then application reads from database and update cache. This is the caching pattern I have used mostly for the use cases I have personally encountered. Read through cache — application reads from cache and if data is not available in cache, then caching tool reads from database, updates cache and returns response. Write through cache — applications writes into cache, cache sends an acknowledgement immediately to reduce latency in client response and asynchronously writes data into database after that. This is a use case that can be adopted for a social media application where a post by a user can be made available for view to other users with sub-second latency. The actual operation of storing the data in a persistent store(database) happens asynchronously. Write back cache — data is written into cache only. Right before cache expires(typically after time-to-live aka TTL setting) data is written into the database for persistence. This technique can be used for a ride sharing system like Uber. Generally in a ride sharing system, a vehicle sends it’s current location every 30 seconds or so that a rider can get a real-time location update of the driver they are waiting for ride. In this caching pattern, the location of a car will only be updated in cache and before the cache expires the location data will be written to the database. This caching technique allows user/rider get to know the location of the driver instantaneously in the Uber app. If due to any reason the write to the database fails it results in a data loss. However generally this is acceptable if application doesn’t have history of location data for a car for a small interval of time(e.g. 5mins) in it’s persistent storage. Conclusion This wraps up the comprehensive exploration of various types of caching, encompassing where data is cached, the contrasting approaches employed in on-premise applications versus modern-day ones, and the diverse techniques utilized in this domain. As technology evolves, caching strategies will continue to adapt, offering new ways to balance speed, scalability, and consistency. Whether optimizing a small-scale application or architecting large distributed systems, leveraging caching effectively remains a key aspect of building high-performance solutions.

Why Your Browser Knows What You Want Before You Do: A Deep Dive Into Caching

Not that long ago, the experience in managing networks and data centers was one of humans doing the effort, as admins entered commands, configured routers, balanced loads, and hunted down outages. Fast forward to today, and the AI revolution is a game changer — automating fast decision-making, predicting failures before vibrations cause them, and optimizing infrastructure in ways that humans never could. But how much of this is real? And how much is simply marketing hype? Slicing it—and slicing it again—let’s take a look at AI in networks and data centers today, who’s using it and where it makes a difference, as well as what it needs to do better. ⚡ Transforming Networks From Reactive To AI-Driven Infrastructure ⚡ Transforming Networks From Reactive To AI-Driven Infrastructure Network management has traditionally been a reactive job. Something breaks? An engineer fixes it. Latency spikes? Someone troubleshoots it. However, AI-enabled networking is moving us away from the reactive firefighting and toward the proactive self-healing system. Artificial Intelligence Is Redefining Networking. Traffic Optimization Automatically: AI-driven SD-WANs and intent-based networking systems allow bandwidth to be dynamically provisioned, traffic routed dynamically, and loads balanced across data centers, in real time, based on current demand.


Predictive Maintenance: ML algorithms can be trained on historical network data that assist AI in predicting hardware failures (e.g. switches, routers, fiber links) before they happen, minimizing failures and downtime that can be expensive.


Anomaly Detection & Security: AI can spot anomalous traffic patterns that might indicate a cyberattack, misconfiguration, or insider threat — all common occurrences that traditional rule-based systems tend to miss.


Dynamic QoS: Artificial intelligence (AI) — based network monitoring tools dynamically prioritize traffic based on real-time business needs, followed by ensuring that data-heavy applications can get precedence over other not-so-important traffic. Traffic Optimization Automatically: AI-driven SD-WANs and intent-based networking systems allow bandwidth to be dynamically provisioned, traffic routed dynamically, and loads balanced across data centers, in real time, based on current demand. Traffic Optimization Automatically: AI-driven SD-WANs and intent-based networking systems allow bandwidth to be dynamically provisioned, traffic routed dynamically, and loads balanced across data centers, in real time, based on current demand. Traffic Optimization Automatically: Predictive Maintenance: ML algorithms can be trained on historical network data that assist AI in predicting hardware failures (e.g. switches, routers, fiber links) before they happen, minimizing failures and downtime that can be expensive. Predictive Maintenance: ML algorithms can be trained on historical network data that assist AI in predicting hardware failures (e.g. switches, routers, fiber links) before they happen, minimizing failures and downtime that can be expensive. Predictive Maintenance: Anomaly Detection & Security: AI can spot anomalous traffic patterns that might indicate a cyberattack, misconfiguration, or insider threat — all common occurrences that traditional rule-based systems tend to miss. Anomaly Detection & Security: AI can spot anomalous traffic patterns that might indicate a cyberattack, misconfiguration, or insider threat — all common occurrences that traditional rule-based systems tend to miss. Anomaly Detection & Security: Dynamic QoS: Artificial intelligence (AI) — based network monitoring tools dynamically prioritize traffic based on real-time business needs, followed by ensuring that data-heavy applications can get precedence over other not-so-important traffic. Dynamic QoS: Artificial intelligence (AI) — based network monitoring tools dynamically prioritize traffic based on real-time business needs, followed by ensuring that data-heavy applications can get precedence over other not-so-important traffic. Dynamic QoS: 🔗 Cisco AI Networking Overview Cisco AI Networking Overview 🏢 AI and the Data Center of the Future 🏢 AI and the Data Center of the Future So, modern data centers are a logistical hellscape — you’re managing power, cooling, security, storage, compute, and network, all at the same time. AI is coming to optimize everything to efficiency, scale, and autonomy. Data Centers Are Getting Smarter Thanks to AI. Cooling Optimization & Energy Efficiency: AI reads on temperature, load distribution, and airflow to passively adjust the cooling power accordingly, which reduces power consumption. (One industry example: Google cut its data center cooling costs to 40% by adopting DeepMind AI.)


Smart Resource Allocation: This includes workload orchestration with AI, which can intelligently distribute workloads of jobs over multiple servers to decrease resource waste and increase efficiency. Predictably, Artificial Intelligence, on the other hand, makes possible scaling that is at a granular level as opposed to a resource allocation of fixed resources, significantly reducing waste of power and cost.


Self-Healing Infrastructure: And AI detects early-stage hardware degradation before faults occur and initiates proactive mitigation (e.g., moving workloads from nodes in failure). Hyperscalers are experimenting with replacing hardware with AI-driven robotics.


Automatic Service Establishment: AI coordinates what are often complex, parallel changes to the network so that if many changes are needed, then less human labor is involved and the changes get deployed faster. Business intent-based configuration of BGP peering, VLAN tagging, or cloud interconnects instead of scripts by hand is done via AI for example. Cooling Optimization & Energy Efficiency: AI reads on temperature, load distribution, and airflow to passively adjust the cooling power accordingly, which reduces power consumption. (One industry example: Google cut its data center cooling costs to 40% by adopting DeepMind AI.) Cooling Optimization & Energy Efficiency: AI reads on temperature, load distribution, and airflow to passively adjust the cooling power accordingly, which reduces power consumption. (One industry example: Google cut its data center cooling costs to 40% by adopting DeepMind AI.) Cooling Optimization & Energy Efficiency: Smart Resource Allocation: This includes workload orchestration with AI, which can intelligently distribute workloads of jobs over multiple servers to decrease resource waste and increase efficiency. Predictably, Artificial Intelligence, on the other hand, makes possible scaling that is at a granular level as opposed to a resource allocation of fixed resources, significantly reducing waste of power and cost. Smart Resource Allocation: This includes workload orchestration with AI, which can intelligently distribute workloads of jobs over multiple servers to decrease resource waste and increase efficiency. Predictably, Artificial Intelligence, on the other hand, makes possible scaling that is at a granular level as opposed to a resource allocation of fixed resources, significantly reducing waste of power and cost. Smart Resource Allocation: Self-Healing Infrastructure: And AI detects early-stage hardware degradation before faults occur and initiates proactive mitigation (e.g., moving workloads from nodes in failure). Hyperscalers are experimenting with replacing hardware with AI-driven robotics. Self-Healing Infrastructure: And AI detects early-stage hardware degradation before faults occur and initiates proactive mitigation (e.g., moving workloads from nodes in failure). Hyperscalers are experimenting with replacing hardware with AI-driven robotics. Self-Healing Infrastructure: Automatic Service Establishment: AI coordinates what are often complex, parallel changes to the network so that if many changes are needed, then less human labor is involved and the changes get deployed faster. Business intent-based configuration of BGP peering, VLAN tagging, or cloud interconnects instead of scripts by hand is done via AI for example. Automatic Service Establishment: AI coordinates what are often complex, parallel changes to the network so that if many changes are needed, then less human labor is involved and the changes get deployed faster. Business intent-based configuration of BGP peering, VLAN tagging, or cloud interconnects instead of scripts by hand is done via AI for example. Automatic Service Establishment: 🔗 AI Data Center Efficiency — Google AI Data Center Efficiency — Google 🏙️The Significance of Edge AI in Networking and Data Center Environments The Significance of Edge AI in Networking and Data Center Environments As AI workloads are on the hike, putting everything on the cloud is no more a smart option. Enter edge computing. Why AI is Moving to the Edge. Reduced Latency: Processing AI workloads closer to the user results in timely decisions — autonomous vehicles, industrial robotics, smart cities, etc.
Lower Bandwidth Costs: Not every application requires returning the data to a centralized data center — edge AI performs this task locally.
Real-Time Inference: Applications like security, fraud detection, and machine vision are all AI-powered and must respond in real time, so tasks at the edge are ideal.
Example: Tesla’s Autopilot doesn’t stream raw video to a cloud data center—its on-device edge AI processes it in the moment. Reduced Latency: Processing AI workloads closer to the user results in timely decisions — autonomous vehicles, industrial robotics, smart cities, etc. Reduced Latency: Lower Bandwidth Costs: Not every application requires returning the data to a centralized data center — edge AI performs this task locally. Lower Bandwidth Costs: Real-Time Inference: Applications like security, fraud detection, and machine vision are all AI-powered and must respond in real time, so tasks at the edge are ideal. Real-Time Inference: Example: Tesla’s Autopilot doesn’t stream raw video to a cloud data center—its on-device edge AI processes it in the moment. Example: How Networks Need to Adapt. Provision of high-speed low-latency connections between devices, edge nodes, and central data centers is AI at the Edge. 5G and next-gen networking is going to play a huge role in making AI-enabled edge computing practical. 🔗 Edge AI & Networking Edge AI & Networking 🛑 AI is NOT the Solution: Challenges and Risks 🛑 AI is NOT the Solution: Challenges and Risks And even while AI is coming to revolutionize networking and data centers, it’s not a sure thing. Here are some of the most significant roadblocks: AI Still Needs Quality Data. AI models are only as good as the data they’re trained on. So garbage data = garbage predictions.
For example: AI-based Network Monitoring has too many false positives, where normal traffic is flagged as an anomaly.
Overhead & Complexity. AI-powered automation can occasionally introduce an additional level of complexity, and so that when things go awry, it’s even harder for engineers to diagnose the problem. “AI misconfiguration” is a real threat — perhaps an AI auto-optimizes a network that somehow adds congestion.
Security Risks. Attackers are already leveraging adversarial attacks to evade security systems powered by AI. A poorly trained AI could even create new vulnerabilities instead of shutting them down. AI Still Needs Quality Data. AI models are only as good as the data they’re trained on. So garbage data = garbage predictions. AI Still Needs Quality Data. For example: AI-based Network Monitoring has too many false positives, where normal traffic is flagged as an anomaly. For example: Overhead & Complexity. AI-powered automation can occasionally introduce an additional level of complexity, and so that when things go awry, it’s even harder for engineers to diagnose the problem. “AI misconfiguration” is a real threat — perhaps an AI auto-optimizes a network that somehow adds congestion. Overhead & Complexity. Security Risks. Attackers are already leveraging adversarial attacks to evade security systems powered by AI. A poorly trained AI could even create new vulnerabilities instead of shutting them down. Security Risks. 🔮 How AI Will Change Networks and Data Centers 🔮 How AI Will Change Networks and Data Centers We aren’t there just yet — not fully at the AI-driven, self-optimizing network/data center — but we are moving in that direction. Some trends to watch: AI-Native Networking: Companies like Juniper, Cisco, and Arista are using AI-native network controllers so you don’t even have to adjust anything by hand.


AI-Optimized Networking Chips: Custom AI accelerator chips will pinpoint packet routing and conduct deep packet inspection faster than ever before.


AI for Carbon-Neutral Data Centers: Look for carbon-aware AI scheduling—to move workloads to the server farms with the greenest energy mix in real time. AI-Native Networking: Companies like Juniper, Cisco, and Arista are using AI-native network controllers so you don’t even have to adjust anything by hand. AI-Native Networking: Companies like Juniper, Cisco, and Arista are using AI-native network controllers so you don’t even have to adjust anything by hand. AI-Native Networking: AI-Optimized Networking Chips: Custom AI accelerator chips will pinpoint packet routing and conduct deep packet inspection faster than ever before. AI-Optimized Networking Chips: Custom AI accelerator chips will pinpoint packet routing and conduct deep packet inspection faster than ever before. AI-Optimized Networking Chips: AI for Carbon-Neutral Data Centers: Look for carbon-aware AI scheduling—to move workloads to the server farms with the greenest energy mix in real time. AI for Carbon-Neutral Data Centers: Look for carbon-aware AI scheduling—to move workloads to the server farms with the greenest energy mix in real time. AI for Carbon-Neutral Data Centers: 🔗 Juniper Mist AI: AI-Powered Networking Juniper Mist AI: AI-Powered Networking TL;DR TL;DR AI is helping networks and data centers to be more clever, speedy, and efficient. Self-optimizing fabric networks, AI-powered cooling, and predictive maintenance are just some of the ways the industry is working towards fully autonomous infrastructure. But AI is not a cure-all — bad data, hard-to-reduce errors, and security risks all need human supervision. The future? AI-driven networking architecture, AI-first chipsets, true self-healing. The future? Thanks for Reading! Thanks for Reading! What impact will there be from AI on networking and data centers in the future? Leave your comments below!

AI Wants to Fix Your Network Before It Breaks—But Can You Trust It?

SaaS product design is the process of creating user-friendly software/applications that give customers a solid reason to keep returning. Thomas Watson, Jnr, a retired IBM CEO rightly pointed out; “good design is good business”. That’s to say if you don’t have a good design for your product, I’m sorry to say, you may have killed your SaaS product even before its launch date. From my research, I realised that top companies invest heavily in product design. They spare no cost to understanding what makes their target market tick – no assumptions, 100% verified data. Then they create a product design in line with their findings; and voila! As we proceed, I’ll share my unique perspective on how to build a SaaS product design that will engage and retain customers, with an appealing ROI. Build a Dream Life for Your SaaS Customers – Know Your Customers You’ve heard it before: “Know your customers” – that’s the only way you can build a dream SaaS product design for them. This reminds me of when I was a teenager, I disagreed with my parents a lot, mostly because I didn’t know any better. The older I get, the more that I see… (yeah yeah) that my parents knew better. All they did was “try” to build a dream life for me because they knew me (as their child) and what was best for me in anticipation of a dream future. When it comes to business, you (the founder) are the “parent”, your product is your “parenting effort”, and your customers are your “kids”. You need to spend quality time and resources knowing your customers. That way, you can create a product design that can connect with them at a deeper level, making their lives easier. Victor Papanek, designer, educator, and author, said: “The only important thing about design is how it relates to people.” Get the Flow Before the Aesthetics. Do you know TikTok? Silly question, right? Who doesn’t? Well, TikTok has undoubtedly ranked as one of the most downloaded apps in the whole world for four years and counting. Do you know why? Because it has a functional interface – full-screen videos, uncluttered grid, and seamless navigation – the design is just perfect. Do not sacrifice a perfect design on the altar of aesthetics. Your focus should be on designing a functional product as opposed to a visually appealing one. Former CEO of Apple, Steve Jobs, once said: “Most people make the mistake of thinking design is what it looks like. People think it’s this veneer – that the designers are handed this box and told, ‘Make it look good!’ That’s not what we think it is. It’s not just what it looks like and feels like. Design is how it works.” Unique Tips on How Not to Kill Your SaaS Product with a Terrible SaaS Product Design Here are some unique tips that can help you build an irresistible product design. What’s the problem? If you can’t recognise the problem, from a peculiar perspective (if I may add), then you have no business creating a product design. This also applies to already existing products – they need constant redesign, update, and evolution to get users to keep coming back. Take Vine for instance; founded in 2012, and shut down in 2017. This social media app had great potential and made a great impact. It shut down partly because it failed to evolve beyond its core six-second looping video format – and that is a design error. Pro tips: Use surveys, interviews, and industry insights to find details about the problem in question.
Refer to your customer service records for any complaints or compliments that may be in line with your idea.
Collaborate with cross-functional teams to get their perspectives on the problem. “A problem shared is half-solved.”
Have a visual representation of the problem to help with in-depth details about the problem. When you are well aware of what the problem is, then you can confidently approach your product design from a unique and well-informed perspective. Analyze the problem: Step back a bit and take a look at your “SaaS idea” again in light of your recent findings from stage one above. The picture should begin to form by now. Compare your findings to your competition and take notes. Pro tips: Compare your findings to similar products in the market and take note of any shortcomings or strengths.
Brainstorm possible design solutions and explore every alternative.
Collaborate with cross-functional teams to refine and enhance your analysis. This is the ideal stage to study brands with similar ideas but did not succeed. Learning from their mistakes can give you an edge. Create a user journey map: Now that you’ve analysed the problem in line with your idea, the next step is to factor in the usability of your product. To do that, you need to create a detailed user journey map. A user journey map is like a navigating tool for your customers, showing their first point of contact with the product, their level of interaction, and the point of exit. Pro tips: Note down how users feel about your existing product and similar products in the market. Find out how often and how long they interact with these products.
Next, show how your idea can solve current pain points and increase usability without sacrificing seamless user experience.
Check for practicality and demand. Ensure your product design has integrated features that fit into your customer’s daily routine.
Create a service blueprint showing every skill and role required to bring your idea to life. A user journey map is basically anticipating customer experience from top to bottom. Design a Prototype: Now you’re almost halfway there. The key thing at the prototype stage is not to put all your eggs in one basket. Try different approaches before making a final decision. Pro tips: Don’t stress over aesthetics just yet – functionality comes first.
Do not be scared to fail fast – the earlier you fail, the faster you get to your best option. The prototype stage allows you to, not just create, but also to experiment and tweak your product layout and interactions. Test and Iterate Information: The LA school system ran into a $1.3 billion problem in 2013 all because of a design error. The school system planned to give each school kid and teacher an iPad, geared towards a massive expansion of classroom technology. This problem would have been averted if Apple and Pearson had taken their time to test and iterate the iPad applications before releasing them to the schools. That way, teachers wouldn’t have struggled so badly to teach with the iPads. Pro tips: Set up usability testing with real users to find out if they have any issues with your prototype.
Pay attention to the human-to-system interactions and try to identify any gaps using the Human Factor principles.
Every test is a step towards perfection, embrace user feedback and craft an exceptional product. SaaS product design is the process of creating user-friendly software/applications that give customers a solid reason to keep returning. Thomas Watson, Jnr, a retired IBM CEO rightly pointed out; “good design is good business”. That’s to say if you don’t have a good design for your product, I’m sorry to say, you may have killed your SaaS product even before its launch date. From my research, I realised that top companies invest heavily in product design. They spare no cost to understanding what makes their target market tick – no assumptions, 100% verified data. Then they create a product design in line with their findings; and voila! As we proceed, I’ll share my unique perspective on how to build a SaaS product design that will engage and retain customers, with an appealing ROI. Build a Dream Life for Your SaaS Customers – Know Your Customers You’ve heard it before: “Know your customers” – that’s the only way you can build a dream SaaS product design for them. This reminds me of when I was a teenager, I disagreed with my parents a lot, mostly because I didn’t know any better. The older I get, the more that I see… (yeah yeah) that my parents knew better. All they did was “try” to build a dream life for me because they knew me (as their child) and what was best for me in anticipation of a dream future. (yeah yeah) When it comes to business, you (the founder) are the “parent”, your product is your “parenting effort”, and your customers are your “kids”. You need to spend quality time and resources knowing your customers. That way, you can create a product design that can connect with them at a deeper level, making their lives easier. Victor Papanek, designer, educator, and author, said: “The only important thing about design is how it relates to people.” Get the Flow Before the Aesthetics. Do you know TikTok? Silly question, right? Who doesn’t? Well, TikTok has undoubtedly ranked as one of the most downloaded apps in the whole world for four years and counting. Do you know why? Because it has a functional interface – full-screen videos, uncluttered grid, and seamless navigation – the design is just perfect. Do not sacrifice a perfect design on the altar of aesthetics. Your focus should be on designing a functional product as opposed to a visually appealing one. Former CEO of Apple, Steve Jobs, once said: “Most people make the mistake of thinking design is what it looks like. People think it’s this veneer – that the designers are handed this box and told, ‘Make it look good!’ That’s not what we think it is. It’s not just what it looks like and feels like. Design is how it works.” Unique Tips on How Not to Kill Your SaaS Product with a Terrible SaaS Product Design Here are some unique tips that can help you build an irresistible product design. What’s the problem? What’s the problem? If you can’t recognise the problem, from a peculiar perspective (if I may add), then you have no business creating a product design. This also applies to already existing products – they need constant redesign, update, and evolution to get users to keep coming back. Take Vine for instance; founded in 2012, and shut down in 2017. This social media app had great potential and made a great impact. It shut down partly because it failed to evolve beyond its core six-second looping video format – and that is a design error. Pro tips: Pro tips: Use surveys, interviews, and industry insights to find details about the problem in question. Refer to your customer service records for any complaints or compliments that may be in line with your idea. Collaborate with cross-functional teams to get their perspectives on the problem. “A problem shared is half-solved.” Have a visual representation of the problem to help with in-depth details about the problem. Use surveys, interviews, and industry insights to find details about the problem in question. Refer to your customer service records for any complaints or compliments that may be in line with your idea. Collaborate with cross-functional teams to get their perspectives on the problem. “A problem shared is half-solved.” Have a visual representation of the problem to help with in-depth details about the problem. When you are well aware of what the problem is, then you can confidently approach your product design from a unique and well-informed perspective. Analyze the problem: Analyze the problem: Step back a bit and take a look at your “SaaS idea” again in light of your recent findings from stage one above. The picture should begin to form by now. Compare your findings to your competition and take notes. Pro tips: Pro tips: Compare your findings to similar products in the market and take note of any shortcomings or strengths. Brainstorm possible design solutions and explore every alternative. Collaborate with cross-functional teams to refine and enhance your analysis. Compare your findings to similar products in the market and take note of any shortcomings or strengths. Brainstorm possible design solutions and explore every alternative. Collaborate with cross-functional teams to refine and enhance your analysis. This is the ideal stage to study brands with similar ideas but did not succeed. Learning from their mistakes can give you an edge. Create a user journey map: Create a user journey map: Now that you’ve analysed the problem in line with your idea, the next step is to factor in the usability of your product. To do that, you need to create a detailed user journey map. A user journey map is like a navigating tool for your customers, showing their first point of contact with the product, their level of interaction, and the point of exit. Pro tips: Pro tips: Note down how users feel about your existing product and similar products in the market. Find out how often and how long they interact with these products. Next, show how your idea can solve current pain points and increase usability without sacrificing seamless user experience. Check for practicality and demand. Ensure your product design has integrated features that fit into your customer’s daily routine. Create a service blueprint showing every skill and role required to bring your idea to life. Note down how users feel about your existing product and similar products in the market. Find out how often and how long they interact with these products. Next, show how your idea can solve current pain points and increase usability without sacrificing seamless user experience. Check for practicality and demand. Ensure your product design has integrated features that fit into your customer’s daily routine. Create a service blueprint showing every skill and role required to bring your idea to life. A user journey map is basically anticipating customer experience from top to bottom. Design a Prototype: Design a Prototype: Now you’re almost halfway there. The key thing at the prototype stage is not to put all your eggs in one basket. Try different approaches before making a final decision. Pro tips: Pro tips: Don’t stress over aesthetics just yet – functionality comes first. Do not be scared to fail fast – the earlier you fail, the faster you get to your best option. Don’t stress over aesthetics just yet – functionality comes first. Do not be scared to fail fast – the earlier you fail, the faster you get to your best option. The prototype stage allows you to, not just create, but also to experiment and tweak your product layout and interactions. Test and Iterate Information: Test and Iterate Information: Test and Iterate Information: The LA school system ran into a $1.3 billion problem in 2013 all because of a design error. The school system planned to give each school kid and teacher an iPad, geared towards a massive expansion of classroom technology. The LA school system ran into a $1.3 billion problem This problem would have been averted if Apple and Pearson had taken their time to test and iterate the iPad applications before releasing them to the schools. That way, teachers wouldn’t have struggled so badly to teach with the iPads. Pro tips: Pro tips: Set up usability testing with real users to find out if they have any issues with your prototype. Pay attention to the human-to-system interactions and try to identify any gaps using the Human Factor principles. Every test is a step towards perfection, embrace user feedback and craft an exceptional product. Set up usability testing with real users to find out if they have any issues with your prototype. Pay attention to the human-to-system interactions and try to identify any gaps using the Human Factor principles. Every test is a step towards perfection, embrace user feedback and craft an exceptional product.

How Not to Kill Your SaaS Product Before it Even Launches

Data migration isn’t just about moving data from one system to another. It’s about preserving business continuity, ensuring financial accuracy, and maintaining customer trust. One wrong move can result in corrupted records, compliance violations, or even financial losses. When dealing with sensitive financial systems, the stakes are higher. I’ve led multiple high-stakes migrations, but the most intense one was at Prosper Marketplace Inc. Prosper is a peer-to-peer marketplace for loans, savings, and earnings. There was a transition from Experian to TransUnion. During this full-scale migration, every transaction, every credit history, and every compliance metric had to be preserved flawlessly. Migrations like this teach you hard lessons. Always plan for migration to avoid problems. The Right Questions to Ask Before touching a single transaction, I always ask three key questions. What’s being moved? What kind of data is being moved? Is it structured customer records, unstructured logs, or a combination?


Where is it going? Can the new system handle legacy formats, large data volumes, and compliance requirements?


What can’t be lost? Some data is critical for operations, while other records are just digital baggage. The Foundation of a Smooth Migration Governance is the foundation of a smooth migration. Without proper governance, data migration turns into chaos. I’ve seen projects where teams assumed someone else was responsible for a dataset, only to realize no one owned it. To prevent this, clear governance protocols were established. 1. Defined Roles & Responsibilities The first significant aspect was to define roles and responsibilities. IT teams managed operational data. Compliance teams handled regulatory records. Finance teams ensured transaction histories were accurate. 2. Decision-Making Workflows When conflicting data formats arose, there was a structured escalation process to resolve issues before they became showstoppers. 3. Risk Management Protocols Every potential failure scenario—including data loss, system downtime, and security breaches—was mapped out with a contingency plan in place. I ran mock migrations to test every step before the actual migration. One of these tests caught a major issue: a script was stripping leading zeros from customer account numbers. Had this gone live, thousands of accounts would have mismatched. That single test saved us from a major operational disaster. Avoiding Dirty Data Nightmares One of the biggest challenges I faced was dealing with inconsistent date formats. The old system stored dates in three different formats: MM/DD/YYYY
DD-MM-YY
An unrecognizable custom format These discrepancies would have caused reporting errors and transaction mismatches. The Solution I used Tableau Prep to visualize inconsistencies before migration.


Automated validation rules to enforce a uniform format.


Engaged stakeholders—people who use the data daily know its quirks best. By migration day, thousands of duplicate records were cleaned in standardized formats. This ensured data integrity. Choosing the Right Migration Strategy I’ve seen migrations fail simply because companies rushed the process. One organization attempted an overnight migration of all its financial records. By morning, the system had crashed under the weight of incomplete, corrupted records. Their team spent weeks rolling back changes and recovering lost data. Why I Prefer a Phased Approach Migrate historical data first, then recent transactions.
Test migrations with small datasets before scaling up.
Use an incremental cutover instead of a risky “big bang” migration. At Prosper, key migrations were scheduled over long weekends to minimize downtime. But here’s a pro tip: always have a rollback plan. System snapshots were taken at every stage so we could immediately revert if something went wrong. Protecting Data in Transit Financial data is a prime target for hackers, and a migration window is the perfect time for an attack. Why? Because systems are temporarily vulnerable. These processes were implemented to secure the migration. Role-based access controls (RBAC) to restrict who can access sensitive data.
End-to-end encryption (TLS, VPNs) to protect data in transit.
Real-time monitoring using OSSEC to flag suspicious activities immediately. Security isn’t just about blocking hackers. It’s also about compliance. The migration was aligned with__PCI DSS__ regulations to avoid legal trouble. Post-Migration: The Work Doesn’t Stop There Once the migration is complete, the real work begins. Data Reconciliation – We cross-checked migrated records against the original dataset to catch any discrepancies.


Compliance Reviews – Auditors verified that all regulatory requirements were met.


Performance Monitoring – These three parameters were tracked.System speed to identify slow queries, error rates to catch data corruption, and downtime occurrences to ensure seamless operations. One of the best tools implemented was a visual dashboard that tracked migration progress, error rates, and performance in real-time. It gave executives immediate insights into how everything was running. Lessons Learned I’ve led multiple migrations since my Prosper Marketplace project, but the core lessons remain the same for high-stakes data migration. Know your data before you touch it.
Governance prevents disaster. Clear accountability is key**.**
Clean before you move. Messy data creates long-term problems**.**
Use a phased migration strategy. There’s no need to rush it**.**
Test everything. Catching minor errors early prevents significant failures**.**
Secure your data. Hackers love migration windows**.**
Validate post-migration. Assume nothing is perfect until proven otherwise**.** Migration isn’t just about moving data. It’s about business continuity, financial accuracy, and customer trust. Migration doesn’t just preserve data, it transforms businesses. Data migration isn’t just about moving data from one system to another. It’s about preserving business continuity, ensuring financial accuracy, and maintaining customer trust. One wrong move can result in corrupted records, compliance violations, or even financial losses. When dealing with sensitive financial systems , the stakes are higher. financial systems financial systems I’ve led multiple high-stakes migrations, but the most intense one was at Prosper Marketplace Inc. Prosper is a peer-to-peer marketplace for loans, savings, and earnings. There was a transition from Experian to TransUnion. During this full-scale migration , every transaction, every credit history, and every compliance metric had to be preserved flawlessly. Migrations like this teach you hard lessons. Always plan for migration to avoid problems. migration migration The Right Questions to Ask The Right Questions to Ask Before touching a single transaction, I always ask three key questions. Before touching a single transaction, I always ask three key questions. What’s being moved? What kind of data is being moved? Is it structured customer records, unstructured logs, or a combination? Where is it going? Can the new system handle legacy formats, large data volumes, and compliance requirements? What can’t be lost? Some data is critical for operations, while other records are just digital baggage. What’s being moved? What kind of data is being moved? Is it structured customer records, unstructured logs, or a combination? What’s being moved? What kind of data is being moved? Is it structured customer records, unstructured logs, or a combination? What’s being moved? Where is it going? Can the new system handle legacy formats, large data volumes, and compliance requirements? Where is it going? Can the new system handle legacy formats, large data volumes, and compliance requirements? Where is it going? What can’t be lost? Some data is critical for operations, while other records are just digital baggage. What can’t be lost? Some data is critical for operations, while other records are just digital baggage. What can’t be lost? The Foundation of a Smooth Migration Governance is the foundation of a smooth migration. Without proper governance, data migration turns into chaos. I’ve seen projects where teams assumed someone else was responsible for a dataset, only to realize no one owned it. To prevent this, clear governance protocols were established. 1. Defined Roles & Responsibilities 1. Defined Roles & Responsibilities The first significant aspect was to define roles and responsibilities. IT teams managed operational data. Compliance teams handled regulatory records. Finance teams ensured transaction histories were accurate. 2. Decision-Making Workflows 2. Decision-Making Workflows When conflicting data formats arose, there was a structured escalation process to resolve issues before they became showstoppers. 3. Risk Management Protocols 3. Risk Management Protocols Every potential failure scenario—including data loss, system downtime, and security breaches—was mapped out with a contingency plan in place. I ran mock migrations to test every step before the actual migration. One of these tests caught a major issue: a script was stripping leading zeros from customer account numbers. Had this gone live, thousands of accounts would have mismatched. That single test saved us from a major operational disaster. Avoiding Dirty Data Nightmares Avoiding Dirty Data Nightmares One of the biggest challenges I faced was dealing with inconsistent date formats. The old system stored dates in three different formats: MM/DD/YYYY DD-MM-YY An unrecognizable custom format MM/DD/YYYY DD-MM-YY An unrecognizable custom format These discrepancies would have caused reporting errors and transaction mismatches. The Solution The Solution I used Tableau Prep to visualize inconsistencies before migration. Automated validation rules to enforce a uniform format. Engaged stakeholders—people who use the data daily know its quirks best. I used Tableau Prep to visualize inconsistencies before migration. I used Tableau Prep to visualize inconsistencies before migration. Tableau Prep Tableau Prep Automated validation rules to enforce a uniform format. Automated validation rules to enforce a uniform format. Engaged stakeholders—people who use the data daily know its quirks best. Engaged stakeholders—people who use the data daily know its quirks best. By migration day, thousands of duplicate records were cleaned in standardized formats. This ensured data integrity. Choosing the Right Migration Strategy Choosing the Right Migration Strategy I’ve seen migrations fail simply because companies rushed the process. One organization attempted an overnight migration of all its financial records. By morning, the system had crashed under the weight of incomplete, corrupted records. Their team spent weeks rolling back changes and recovering lost data. Why I Prefer a Phased Approach Why I Prefer a Phased Approach Migrate historical data first, then recent transactions. Test migrations with small datasets before scaling up. Use an incremental cutover instead of a risky “big bang” migration. Migrate historical data first, then recent transactions. Test migrations with small datasets before scaling up. Use an incremental cutover instead of a risky “big bang” migration. At Prosper, key migrations were scheduled over long weekends to minimize downtime. But here’s a pro tip: always have a rollback plan. System snapshots were taken at every stage so we could immediately revert if something went wrong. Protecting Data in Transit Protecting Data in Transit Financial data is a prime target for hackers, and a migration window is the perfect time for an attack. Why? Because systems are temporarily vulnerable. These processes were implemented to secure the migration. Role-based access controls (RBAC) to restrict who can access sensitive data. End-to-end encryption (TLS, VPNs) to protect data in transit. Real-time monitoring using OSSEC to flag suspicious activities immediately. Role-based access controls (RBAC) to restrict who can access sensitive data. Role-based access controls (RBAC) End-to-end encryption (TLS, VPNs) to protect data in transit. End-to-end encryption (TLS, VPNs) Real-time monitoring using OSSEC to flag suspicious activities immediately. Real-time monitoring using OSSEC Security isn’t just about blocking hackers. It’s also about compliance. The migration was aligned with__ PCI DSS __ regulations to avoid legal trouble. PCI DSS Post-Migration: The Work Doesn’t Stop There Post-Migration: The Work Doesn’t Stop There Once the migration is complete, the real work begins. Data Reconciliation – We cross-checked migrated records against the original dataset to catch any discrepancies. Compliance Reviews – Auditors verified that all regulatory requirements were met. Performance Monitoring – These three parameters were tracked.System speed to identify slow queries, error rates to catch data corruption, and downtime occurrences to ensure seamless operations. Data Reconciliation – We cross-checked migrated records against the original dataset to catch any discrepancies. Data Reconciliation – We cross-checked migrated records against the original dataset to catch any discrepancies. Data Reconciliation – Compliance Reviews – Auditors verified that all regulatory requirements were met. Compliance Reviews – Auditors verified that all regulatory requirements were met. Compliance Reviews Performance Monitoring – These three parameters were tracked.System speed to identify slow queries, error rates to catch data corruption, and downtime occurrences to ensure seamless operations. Performance Monitoring – These three parameters were tracked.System speed to identify slow queries, error rates to catch data corruption, and downtime occurrences to ensure seamless operations. Performance Monitoring One of the best tools implemented was a visual dashboard that tracked migration progress, error rates, and performance in real-time. It gave executives immediate insights into how everything was running. Lessons Learned Lessons Learned I’ve led multiple migrations since my Prosper Marketplace project, but the core lessons remain the same for high-stakes data migration . high-stakes data migration high-stakes data migration Know your data before you touch it. Governance prevents disaster. Clear accountability is key**.** Clean before you move. Messy data creates long-term problems**.** Use a phased migration strategy. There’s no need to rush it**.** Test everything. Catching minor errors early prevents significant failures**.** Secure your data. Hackers love migration windows**.** Validate post-migration. Assume nothing is perfect until proven otherwise**.** Know your data before you touch it. Know your data before you touch it. Governance prevents disaster. Clear accountability is key**.** Governance prevents disaster. Clean before you move. Messy data creates long-term problems**.** Clean before you move. Use a phased migration strategy. There’s no need to rush it**.** Use a phased migration strategy. Test everything. Catching minor errors early prevents significant failures**.** Test everything. Secure your data. Hackers love migration windows**.** Secure your data. Validate post-migration. Assume nothing is perfect until proven otherwise**.** Validate post-migration. Migration isn’t just about moving data. It’s about business continuity, financial accuracy, and customer trust. Migration doesn’t just preserve data, it transforms businesses.

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