The HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today! Tell us about yourself! Hi! I'm Joana! I'm a programmer, hobbyist game developer, and  technical writer at ! Distantjob I've worked primarily on Software and Videogame Development! I've also freelanced in art and UI Design. Videogames are my passion. Both making and playing them! Why did you choose this field in the first place? I had the luck of growing up in a household where tech was always present and it became a part of my life. My father, although not formally educated in any tech field, always had a passion for it, and we had access to a computer fairly early. I became an avid user of our home computer and fell in love with the possibilities. I was playing computer games when I was as young as 5. I became interested in how they worked and decided as time went by that I wanted to make them too! At the time, although we had a computer, internet access was almost non-existent, so I didn't have much to go by in terms of resources where I could learn on my own. So I decided to pursue IT and programming at University so I could learn how to do it! And the rest is history! What tech are you most excited/passionate about right now and why? I'd say a combination of VR and AR. I've tried it before, and it wasn't exactly ground-breaking, but I can definitely recognize the potential it can have as a tool for interaction with all sorts of media. What tech are you most worried about right now and why? Definitely NFTs. I just don't see the need or appeal. Their recent adoption by major corporations, as well as their effects on the environment, are really worrying. What are your hobbies and interests outside of tech? Oh, I have a ton! I find it hard to make time for them all, but I try! I dabble in digital art and animation, play Tabletop RPGs with several groups of friends, do DIY crafts, write fiction, and read about history! That's not all, but it's the ones I engage with most frequently. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? I guess the biggest challenge was finding a place as a woman in a world where men are far more prevalent. It was hard at first. Thankfully, I've met great people over the course of my life and never met much pushback, but the reality is that even in that scenario, there's still a chance for harm. At university, I was seen as "one of the guys" by my group of friends. In 10+ people, I was the single female member, so it felt nice to be welcomed into a group of men despite your gender, but at the same time, it disconnected me from the rest of the female community. Being young and foolish, I looked at other women that weren't "the guys" (and there weren't many that would fit that description in my eyes at the time) and found myself avoiding them in order to preserve my status among my friends. Eventually, I realized how silly this all was and stopped doing it altogether. Thankfully, I think our global society is also evolving past this sort of mentality. But in a way, it wasn't an easy thing to get over. Any interesting misogynistic story/situation you faced/handled, and you want to share with the HackerNoon Fam? I've had an instance or two of peers looking down at me because of who I was, and it was hard at first convincing my biggest friends in my community that it was happening. To them, it seemed like I was just seeing things where they didn't exist. I had the fortunate position of being able to ignore the people looking down on me and move on past them. Let my work do my talking for me, as they say. Eventually, my friends in the community saw those people for what they were and what I was talking about. So I guess it worked out in the end. What was the biggest setback/failure that you faced, and how did you manage it? I've been developing a videogame project with a few of my best friends for a few years. The project was about to receive funding, only for the negotiations to take an unexpected turn and the project going on standby. It was extremely difficult at the time, and we're all still recovering from the blowback. We're trying other venues right now, but none of them are certain. Hopefully, I can say in the future that I managed to overcome it! What's your biggest achievement that you're really proud of? The biggest one so far was a short game I developed, named . Even though it's a little barebone in terms of progression, I managed to make a small RPG (with help from my great friend Tiago Rodrigues for the sounds and music) in two weeks’ time! It was a great experience that I'm hoping I can replicate sometime soon! The Summit In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? There are many historical reasons as to why women were discouraged out of tech, but nowadays I think it's just a matter of making sure they feel represented and welcome in their chosen field. I think that if girls and women grow that passion for tech (much like it happened to me over my childhood), and see that they have a place in it with the help of other pioneering women, they will eventually retake their space in dictating the future of tech and that in turn will open the way to even more women joining the fray. Who is your tech idol? Why? One of the games that changed my life was Aquaria. One of its developers, Derek Yu, is a huge inspiration. He went on to make the Spelunky games, and they were (and still are!) a huge success. I've actually met him once in passing, and he was very kind! I hope I get to meet him again in some less rushed scenario next time. To this day he remains a huge inspiration to me. I hope I get to experience just a fraction of his career's notes. Do you have any advice for aspiring girls who want to join the field? I guess the best advice I can give anyone is to find a subject you're passionate about and start working on it. In any way possible! Nowadays it's really easy to get into anything you put your mind to with the help of the internet. Picking something you enjoy and love makes it harder to give up and let go. That is really the most important thing, maintaining the momentum. Sometimes you find out what you're working on isn't what you really want after all, and that's ok! Just pay attention and keep exploring related topics as you go, and you'll eventually find the one thing you're really good at and enjoy doing at the same time!

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'I Made a Small RPG in Two Weeks' An Interview w/Game Developer Joana Almeida

Back in November 2021, a record voluntarily left their jobs. Yet despite the pressure created by an exiting employee’s absence, companies shouldn’t rush to fill their position right away. When a business rushes to fill an open spot on their team, they can miss crucial details about an applicant and end up with a bad fit for their company. Nearly 3 in every 4 employers say they have hired the wrong person for a position before. When hiring mistakes happen, companies ought to be quick to rectify them, even if it means firing people. Now more than ever, businesses need to remember to hire slow and fire fast. 4.5 million people Many have heard the phrase before. But what does it really mean? hire slow, fire fast As mentioned above, companies shouldn’t rush to replace an employee that just left. First, they need to clarify their needs and expectations for a potential new hire. Job descriptions should be revised to fit current business objectives, and skills listed on postings should be what candidates need for long-term success at a company. Beyond technical skills, consider how well candidates will complement existing team dynamics. Hiring slow doesn’t mean conforming to an arbitrary time frame. It means taking steps to ensure new hires are built to last at the company. On the other side of the coin, firing fast is not meant to be harsh or extreme. Occasional mistakes from new employees deserve to be forgiven. Just don’t keep an employee who’s a bad fit for the organization. If an employee truly is a bad fit, they won’t be happy staying. The team won’t benefit from their continued inclusion either. Focus on long-term benefits for the people involved. Don’t be cruel, but don’t beat around the bush either. Think of firing as the consequence of a hiring mistake and confront it head-on. Throughout the process, remember that selective hiring is a luxury not afforded to all businesses. In order to have overly exacting expectations, the company needs to be a desirable place to work. Use hiring to maintain corporate culture.

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What it Really Means to Hire Slow, Fire Fast

Not Financial Advice.  Just the opinion of a couple of Apes. https://twitter.com/MBApesNFT Section 1: Summary $JOE is the token of the Trader Joe decentralized finance platform on the Avalanche blockchain. The platform offers decentralized trading, borrowing / lending, staking, yield farming and soon an NFT marketplace. I believe $JOE is undervalued as the market Executive Summary: 1.) does not fully recognize the potential of the Avalanche blockchain; 2.) underappreciates the project’s strong moat and innovative team; 3.) overestimates the risk of customer departure as reward emissions decline. Through my analysis, I arrive at a market cap of $422.5 million utilizing the average of a DCF and market approach, representing 124.3% upside from the current price. Section 2: Overview Trader Joe is a one-stop-shop defi platform on the Avalanche blockchain. The company earns protocol revenue through three distinct revenue streams — 1.) Swap Fees — 0.05% of every swap is converted to USDC and distributed to staked holders; 2.) Lending through Banker Joe platform; 3.) Protocol Owned Liquidity — Trader Joe owns 27.5% of its JOE-AVAX Liquidity Pool and receives 0.25% of swap fees on its holdings. Furthermore, the protocol plans to launch an NFT marketplace in Q1 2022 and will earn an estimated 3.0% of all sales on the platform. The project was founded by Cryptofish and 0xMurloc, both full-stack and smart contract engineers that have experience working in big tech and running startups in the crypto space. Company Overview: Trader Joe incentivizes users to hold its token $JOE by offering Modular Staking (multiple staking options). This means that you can stake $JOE in any of the three pools outlined below to earn rewards: Tokenomics: 1. Rocket Joe — Innovative launchpad that facilitates fair market price discovery. Holders can stake $JOE to earn rJoe. Users require rJOE to participate in the launch event and rJoe is non-transferable. In short, if you want to invest in new, exciting projects on the platform (I hear $YETI is launching soon…) then you need to buy and stake $JOE. 2. sJOE — All swap fee revenue is converted into stablecoins and then distributed to sJOE holders. 3. veJOE — Users stake $JOE to accrue veJOE overtime. The more veJOE you accrue, the higher your $JOE yield in selected farms. veJOE will also provide future governance power. Check out Trader Joe’s Website to learn more — https://docs.traderjoexyz.com/main/trader-joe/staking Trader Joe’s Modular Staking structure provides incredible long-term value and utility for its holders. Analysis: First, Rocket Joe is a unique launchpad that decreases initial volatility by its fair market price discovery mechanism. Every new project launching on Avalanche will want to partner with Trader Joe, cementing Trader Joe as the center of the Avalanche universe. Furthermore, you need to own and stake $JOE to accrue rJOE. Therefore, to participate in new launches users must acquire and hold $JOE. Second, sJOE allows users to take advantage of the platform’s revenues. Every dollar of the protocol’s swap fees is paid out directly to sJOE holders in the form of stablecoins — users can now receive a daily dividend in USD! This will remove downward sell pressure from $JOE rewards and encourage users to reinvest in the Avalanche ecosystem (promoting more swaps, activity, etc.). In fact, ~50% of users that harvest their $USDC rewards daily are compounding it back into sJOE by buying more $JOE. Finally, veJOE (which will be launched this week) rewards the most bullish $JOE advocates to hold for the long-term. The staking option allows users to boost their yield on selected farms and will offer voting rights on future governance proposals. Furthermore, it offers unique economics that will benefit the entire community — veJOE is accrued over-time, is non-transferrable and is lost when the underlying $JOE is unstaked. Therefore, veJOE incentivizes users to hold long-term (diamond hands) and may lead to an accumulation war (i.e. the $JOE Wars) between large bag holders. Similar locking structures including Curve / veCurve and more recently PTP / vePTP (Platypus Finance) have resulted in massive price appreciation for the underlying token as users compete for more rewards and voting rights. In fact, new businesses have launched to capitalize on the innovative tokenomic design. Echidna and Vector are two such examples (both launched via Rocket Joe), the two protocols are solely focused on acquiring and locking PTP / vePTP to create lucrative stablecoin yield farms. In the first week of launch, Echidna and Vector have combined to acquire and stake (forever!) over 15% of the circulating PTP supply. Trader Joe has a larger community than Platypus, is a cornerstone of the Avalanche community and is perfectly positioned to capitalize off the new tokenomics design of veJOE. There are already several businesses that profit off Trader Joe yield farming that are primed to compete in the future $JOE Wars including Avalanche staples such as Yield Yak and Penguin Finance. Others like Echidna and Vector will also most likely compete for $JOE and build profitable businesses on top of Trader Joe’s platform. The business model is simple, 1. Accumulate veJOE to boost your yields, 2. Offer users higher yields than they currently earn, and 3. Take a percentage of each reward. Users will receive the benefit of higher yield (even net of fees) and the protocols will earn steady revenue. Win-Win-Win! The new staking option will benefit the entire community and launch a new wave of growth for $JOE. Overall, Trader Joe’s innovative Modular Staking approach provides utility through Rocket Joe launchpad and voting rights, offers direct revenue participation with sJOE and incentives long-term holders with veJOE. Section 3: Investment Research Potential of Avalanche Blockchain Avalanche is a decentralized, scalable blockchain solution that focuses on low costs, fast transactions, and eco-friendliness. The efficient blockchain currently ranks 4th in Total Value Locked (TVL) and is only 1 of 2 blockchains in the top 8 to grow over the past month, driven by the introduction of liquid staking on Benqi and Platypus Finance’s innovative stablecoin yield farm. In the next few weeks, Yeti Finance will also launch its platform. Yeti will allow users to leverage up to 11x against their entire portfolio and will unlock billions of additional capital on the blockchain. Furthermore, Avalanche is achieving record user growth as well, from 20,194 monthly active users in July 2021 to 800,614 in January 2022. As the blockchain continues to add functionality, users will continue to flock to Avalanche for lower fees and faster transactions. Finally, the next stage of growth is here, and subnet season has finally arrived! With the integration of subnets, Avalanche can scale horizontally and allow protocols to create application-specific blockchains (customized gas fees, decentralization, time to finality, etc.). In March 2022, the Avalanche Foundation announced a $290 million incentive program to accelerate the growth of subnets. Several exciting projects including DeFi Kingdoms (play-to-earn world to explore) and Shrapnel (AAA First-Person shooter) have signed on, as well as established institutional capital including Golden Tree Assets, Jump Capital and Valkyrie Funds that will build the future of Institutional DeFi with native KYC functionality. This will usher in the next wave of growth as users flock to play the most exciting games and institutions begin to move finance onto the blockchain. Ultimately, as Avalanche continues to add users and institutional capital, Trader Joe will benefit as the prominent defi platform on the chain. Learn more about the subnet initiative here Trader Joe’s Dominance Trader Joe is the leading decentralized exchange (DEX) in terms of trading volume and total value locked on a fast-growing blockchain (Avalanche). The protocol currently averages 8 times more daily trading volume than closet competitor Pangolin with 5 times more users over the last 30 days. After launching in August, Trader Joe has already surpassed $1B trading volume in a single day and temporarily reached over $2.0B in TVL on the protocol. The project is embraced by the Avalanche community and its brand serves as a strong barrier to entry for future competitors. Furthermore, Trader Joe’s forward-thinking team will continue to adapt to the changing environment and implement new projects that add utility to its token, bring in new revenue streams and engage its community. The Trader Joe team has a track record of success. In just six months since launch, the team has already added a suite of products including Banker Joe (a borrowing / lending protocol), created lucrative partnerships with other key players on the blockchain and started using revenues to acquire Protocol Owned Liquidity to improve price stability and open an additional revenue stream. Furthermore, the team has revamped its tokenomics with modular staking (get ready for the $JOE Wars) and will launch an NFT marketplace by the end of Q1 2022. The leadership and engineering team have a great understanding of the market and constantly look to innovate, which is essential in an industry that is changing at exponential rates. Overall, between the growth of the blockchain, Trader Joe’s dominate position, the potential of institutional capital and the launch of new revenue streams, Trader Joe has multiple avenues to maintain its high growth into the future. The Market Overestimates Departure Risk In the defi community there is a current narrative (given the rapid decline of Sushiswap) that users are agnostic to exchanges and will simply follow the highest rewards; therefore, as reward emissions decline then projects like Trader Joe will lose its customer base. However, it’s unfair to compare Sushiswap to other exchanges due to several project specific problems within the protocol. First, Sushiswap lost key members of its leadership and engineering team that chose to pursue other interests. Trader Joe’s team is eager and hungry to innovate, the founders are dedicated to the community and take a long-term view. Secondly, Sushiswap is behind Uniswap in terms of trading volume and TVL on its native blockchain and could not offer a best-in-class product. On the other hand, Trader Joe dominates Avalanche in both categories and therefore, can offer a superior service and better prices than its competitors. As more resources, including institutional money, enter Avalanche they will flock to Trader Joe as the safest, most reliable DEX on the chain. Finally, Trader Joe understands the limitations of operating solely as a DEX and has started to add additional revenue streams. By acquiring Protocol Owned Liquidity (POL), Trader Joe is in control of its own destiny. The protocol can become self-sustaining and allocate valuable resources to other areas. Furthermore, Banker Joe and the NFT Marketplace will provide supplementary income to sustain operations and fund further growth projects. Overall, Cryptofish and 0xMurloc are taking a long-term approach, adding functionality, revenue, and liquidity and ultimately, building a company that will last. Section 4: Key Risks and Assessments Competitive Landscape The Trader Joe platform is a fork of Sushiswap and therefore, easy to replicate for competitors. Furthermore, decentralized finance encourages openness and interoperability between dapps, making switching costs negligible. Therefore, competitors (i.e. Pangolin and Hurricane Swap) can emerge offering better reward incentives and taking Trader Joe’s market share on the Avalanche blockchain. Long-term Underperformance of Digital Asset Industry Overall, Trader Joe and any DeFi platform relies on the growth of the cryptocurrency industry. Fees are determined as a percentage of total use and therefore, its growth will directly correlate with the health of the entire ecosystem and Avalanche blockchain moving forward. Cryptocurrency Regulation There’s a lot of uncertainty moving forward about the regulation of the cryptocurrency industry. Changes in rules related to taxation and the structure of decentralized autonomous organizations (DAOs) will have a major impact on profitable in the future. Section 5: Valuation Discounted Cash Flow I derived my base case valuation by forecasting the next 7 years. I utilized a step down from 4.0% monthly growth for total trading volume on Avalanche in 2022 to 0.25% in 2027 (3.0% annual growth rate), which also served as the perpetual growth rate (in line with inflation expectations — insert inflation joke here). Furthermore, I haircut Trader Joe’s market share of total trading volume from 85% in 2022 to 70% in 2027 to consider the potential of new competition. I maintained Banker Joe Revenue flat throughout the forecast, given the limited growth over the past month. Banker Joe Revenue calculated by (Borrowed Total x Borrowing APY) — (Deposited Total x Deposit APY) for each digital asset. Rewards are not included given they are released as part of the emissions schedule. Protocol Owned Liquidity considers the current 30-day average trading volume of the liquidity pool as a percent of total trading volumes (7.5%), in addition to the protocol’s ownership percentage (27.5%) and swap fees (0.25%). Protocol’s ownership of pool increased from 27.5% in 2022 to 50.0% by the end of the forecast given the project’s commitment to building own liquidity pools. Finally, the NFT Marketplace utilizes total NFT sales on Avalanche and a market share of 50.0% given Trader Joe’s dominant brand and strong user experience and an industry standard 3.0% transaction fee on sales. (Note: NFTs are still early on Avalanche blockchain and have significant potential to outperform this forecast moving forward) …. Plus, I hear there’s a pretty cool NFT launching soon, learn more at http://mbapesacademy.com/ Discount Rate: The Beta of 3.0x considers the inherent volatility in the cryptocurrency markets at this time. The Market Rate estimate for a small tech company considers that Trader Joe is already profitable and growing month over month. Note: Token inflation related to emissions won’t impact market cap, just price per coin. 1. Most costs are considered in token emission schedule (i.e. pay engineers, incentivize leadership); utilized $5.0m to consider additional costs that might arise related to hiring / marketing. 2. No information available; Protocol currently has at least $5.0 million in assets based on normal course of business Note: DCF for early-stage businesses (especially in crypto) are very difficult. A Sensitivity Analysis provides more insight into the wide range of potential outcomes. The concluded market cap of $410.6 million represents a 117.9% increase from the current market cap of $188.4 million. Market Analysis For this section I utilized a sum of parts analysis to consider Trader Joe’s multiple revenue streams — Decentralized Exchange (Swap Fees), Banker Joe (Borrowing / Lending), Protocol Owned Liquidity and an NFT Marketplace. Decentralized Exchange Note: Trader Joe Protocol Revenue above does not include Banker Joe, NFT Marketplace or POL revenue streams. Data via Token Terminal as of March 8, 2022. Annualized Protocol Revenue based on most recent 30-day period. Prior period reflects valuation as of December 7, 2021. Since the Q4 2021 valuation, the total cryptocurrency market cap has declined by 18.7%, leading to lower annualized protocol revenue across the board. Avalanche, specifically, has experienced a further decline in trading volume as evidenced by a 61.1% decline in Protocol Revenue for Trader Joe and a 66.9% decline for Pangolin. SpookySwap is the one exception as it increased its Protocol Revenue by 208.5% since the prior valuation, driven by an 86.8% increase in total value locked on its native blockchain (Fantom). Trader Joe is trading at a noticeable discount to its peers (including Pangolin) in terms of MC / TVL and MC / Revenue, despite offering a superior product and having a stronger brand. Trader Joe should trade at a premium to other players in the space given its rapid growth and domination of a promising blockchain. Trader Joe utilizing a MC / Protocol Revenue equal to the mean of the comparable company set would imply a market cap of $328.1 million. Banker Joe To keep this section simple, I utilized the closest comparable company — Benqi — as the focal point of my analysis. Benqi is a liquidity market on the Avalanche blockchain that offers a similar product to Banker Joe. Users can utilize the platform to supply, borrow and earn interest on their digital assets. According to Token Terminal, Benqi currently trades at a multiple of 18.21x Annualized Protocol Revenue (excluding revenue from liquid staking). Banker Joe utilizing a MC / Protocol Revenue equal to the closest comparable company would imply a market cap of $61.4 million. Note: Banker Joe Revenue calculated (Borrowed Total x Borrowing APY) — (Deposited Total x Deposit APY) for each digital asset. Rewards are not included given they are released as part of the emissions schedule. Protocol Owned Liquidity There are no projects that solely offer POL. The most similar concept would be a decentralized exchange given that the revenue is still generated from swap fees. The main difference here is that POL earns 0.25% of each swap fee versus a DEX that earns 0.05%. Therefore, I will simply utilize the same MC / Protocol Revenue multiple that I used to value the decentralized exchange revenue steam. Trader Joe’s POL Revenue as a standalone entity, utilizing a MC / Protocol Revenue equal to the mean of the comparable company set would imply a market cap of $42.4 million. NFT Marketplace Finally, the NFT Marketplace is set to launch by the end of Q1 2022 and therefore, should be included in the analysis. There is no data available for Kalao or NFT Trade (the two current NFT Marketplaces on Avalanche) and therefore, I utilized LooksRare for my analysis. It’s also worth noting that Opensea does not have a token or Protocol Revenue. LooksRare operates on the Ethereum blockchain and offers a similar service. According to Token Terminal, LOOKS currently trades at 1.0x MC / Protocol Revenue. The NFT Marketplace utilizing a MC / Protocol Revenue equal to the closest comparable company would imply a market cap of $2.6 million. Sum of Parts Analysis The sum of parts analysis outlined above concludes a market cap of $434.5 million, which represents a 130.6% increase from the current market cap of $188.4 million. Section 6: Conclusion Overall, through the analysis present above, I have concluded a market capitalization of $422.5 million for Trader Joe ($JOE), representing 124.3% upside from the current price. Trader Joe’s new tokenomics will serve as a catalyst to unlock the next stage of value for the underlying token and propel $JOE to trade more in line with its peers. Given Trader Joe’s robust suite of products, strong brand and innovative team, Trader Joe is poised for a bright future. Section 7: Sources https://stats.avax.network/dashboard/c-chain-activity/ www.dappradar.com/rankings/protocol/avalanche www.defillama.com www.tokenterminal.com www.coinmarketcap.com/ https://medium.com/@traderjoe-xyz https://docs.traderjoexyz.com/main/welcome/master I/we own $JOE. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company that is mentioned in this article. Disclosure: First Published here

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A Research Report on the Trader $JOE DeFi Platform

What are Core Web Vitals? Google announced its algorithm update of core web vitals in May 2020 and also informed that this update will be applicable from June 2021. Core web vitals are that identify the user experience of the website and track how seamlessly users are able to navigate through your site and get what they are looking for. page experience signals The author is a contributing writer for Blurbpoint The important components of web core vitals include; load time, interactivity, and visual stability. Various factors determine the overall user experience of your site and if you are unsure whether it is good or not, there are different tools that will help you find out about your site experience and how you can improve it. Google has made it easier for search engine optimizers and site owners to measure web core vitals through a range of tools available online. We will be talking about the tools later in this article but we need to identify whether the user experience of your site is good or not. Let’s talk about the important core web vitals metrics on which the user experience is reliant. Why are Core Web Vitals Important? Google measures the user experience of your site which can up your Google ranking at least by one position.  There are various metrics of Google core web vitals, which if improved, can significantly increase the traffic on your website and help you establish digitally. A poor user experience can result in a higher bounce rate. To make effective use of tools, here are some important metrics you need to know about. Most Important Metrics of Core Web Vitals - This metric measures how long the content takes to appear on the web page for users. Google recommends the LCP should be under 2.5 seconds. Largest Contentful Paint (LCP) - This metric measures the number of unexpected layout shifts which cause problems while reading webpage content and interacting with the website.  Google recommends a CLS score of 0.1 seconds or less. Cumulative Layout Shift( CLS) -  This metric measures your web page response time when the user interacts with your webpage for the first time and also measures how quickly the website browser provides results to the users. Google recommends the speed to be less than 100 milliseconds. First Input Delay(FID) Tools to Measure Core Web Vitals Google Search Console helps site owners to evaluate pages across the entire site.  This tool helps you thoroughly analyze the pages that may require improvements and fixes. You can use the Chrome UX report to determine the performance of your site. Google Search Console The URLs are segregated as per their status; Poor, Needs Improvement, and Good. Google Search Console states how your webpages are performing based on Core web vitals. Google Lighthouse is an automated website auditing tool that allows developers to audit and assess their sites and find out which areas need improvement. These areas include; performance, accessibility, SEO, and best practices. Lighthouse Lighthouse measures for user experience include; Cumulative Layout Shift (CLS),  (Total Blocking time )TBT, Largest Contentful Paint (LCP), and Time To Interactive (TTI). Chrome UX Report (CrUX) is not a tool but a dataset that consists of real-world user experience data from different sites. This dataset measures field data on all core web vital metrics. Chrome UX report As a developer, you can make optimum use of this dataset and identify which areas of your website need work. PageSpeed Insights is capable of measuring core web vitals in lab and field sections of the reports on both mobile and desktop. This tool gives you an idea about how the users experience your page and what improvements you can make to improve the page experience for your customers. PageSpeed Insights This tool also allows you to test whether your website meets all the user-friendly core web vital metrics or not. Chrome DevTools is a tool available on Google Chrome that can be accessed in two clicks. Right Click on the desktop, choose ‘Inspect’ and the Chrome DevTools will show up. Chrome DevTools This tool helps you analyze core web vital metrics whereas the experience section present inside will help you detect unexpected layout shifts. Overall, you can find and fix the issues associated with visual instability that contributes to Cumulative Layout Shift( CLS). Web Vitals Extension This is a new that measures three web core vital metrics easily. chrome extension This extension is available on the Chrome Web Store for installation. These are some of the tools that will help you identify the aspects you need to improve for a better user experience. Improving the core web vitals can positively increase your rankings and deliver the desired results. Apart from utilizing tools, you can also implement some important tips to improve the user experience of your website. They are as follows Tips to Improve Core Web Vitals Here are 6 tips that improve your website Core Web Vitals scores. Let’s know them: Reduce JavaScript (JS) Execution If you notice a poor FID score on the report, it means the page takes more than 300 milliseconds to interact with users. Reducing the time between your browser execution JS code and page which in short is also called JS execution. Also, make sure to use as little memory as possible because whenever your site’s code requests the browser, it occupies memory that stops JavaScript and possibly slows down the page. Pay attention to attributes or dimensions of your media or ads This is very important to improve your CLS scores. One must ensure that their image or any media source’s height or width is proper. If you plan to place any display ad make sure to set your ads with proper dimensions because improper ads can reduce the CLS score. Thus, keep an eye on other media sources as well if you wish to place any. Image Optimization Images are one of the most influencing factors in web core vitals. With the plenty of lazy load packages out there you can use one to lazy load all the images on your page. This will make your page lighter and require less loading time for rendering your page in the browser. One more important thing, define your image resolution based on the device type. The resolution for different devices should be different. Improve the server response time A long-serving response time is harmful to your SEO and negatively affects your UX. You can use specialized tools to measure the server response time. However, before starting, compile the data of your current performances to identify how you are doing. To compile the data: Check the speed of web hosting Use (Content delivery Network) CDN. Pick only necessary plugins for your site as each comes with an additional weight that can affect your site’s performance. Based on the report, work on your site’s improvements and make your response server time faster. A faster server time improves page load metrics and LCP as well. Reduce the overall page/document size A heavy landing page contributes badly to the LCP score. To avoid this only use the data that is necessary for your rendering page and eliminate all the useless data. Use fonts wisely Fonts can drop your performance scores if the browser takes time to load the custom font used on your site. The good thing to do here is to load your site with the fonts that are by default supported by the browser. These tips will enhance the page experience for your users and have positive effects in both the long-term and short term. Final Words Improving page experience as a part of your SEO strategy can improve the user experience of your site and possibly improve the ranking of your site. Google supports the site that delivers valuable and comprehensive content along with a good user experience. So make sure not to miss on creating good content for your readers along with delivering a good user experience. The author is a contributing writer for Blurbpoint

Google

Improve Your Website's Core Web Vitals: A How-To Guide

“If you can’t measure, you can’t improve it” - some famous person Understanding the differences — the state of the system, based on gathering predefined sets of metrics or logs. Monitoring understand — the state of a system, based on exploring properties and patterns not defined in advance. Observability infer Why do we need monitoring? Monitoring should address two questions: what’s broken, and why? vs. is one of the most important distinctions in doing good monitoring with maximum signal and minimum noise. What why Why do we need observability? Basically, monitoring relies on capturing and displaying the data providing a restricted view of the system, whereas observability can anticipate the system's health based on the data it generates (logs, metrics, traces). Lots of software jobs (especially SRE) include different monitoring tech stacks, one might argue that you can make a living only from mastering those specific technologies. The tooling landscape might seem daunting. And at a first glance, it looks overwhelming, especially since each technology comes with a specific nomenclature like forwarder, indexer, exporter, data-source, controller, etc. When navigating through all these matters, we need to know the basics. System metrics vs application metrics Usually, system metrics capture infrastructure-related metrics such as CPU and memory consumption, disk I/O, network I/O, whereas application metrics refer to error rates, requests per minute, average response times. Agent vs agentless At times it might be needed that some kind of agent to be deployed on your system (e.g. Splunk forwarder, AppDynamics app agents), and in some cases there’s no need for an agent, for example, Prometheus which uses an HTTP pull model to populate a time-series database. Push vs. Pull monitoring Push model, the agents push their data to the monitoring system whereas pull model the system pulls data from the agents. The key difference is that in the push-based approach (Nagios, Zabbix) the central monitoring system knows quite a lot about the metrics whereas in the pull-based approach (Prometheus, Datadog) the main monitoring system knows nothing or very little about the metrics which are coming in. Tooling landscape Metric collection: , , Prometheus Stackdriver InfluxDB Log aggregation: , Fluentd Logstash Tracing: , , OpenTelemetry Jager Zipkin Performance monitoring: , , AppDynamics NewRelic Dynatrace Dashboarding and visualization: , Grafana Kibana Monitoring can mean a lot of things As a piece of advice, it’s important to understand that monitoring might be different from one company to another, nothing is written in stone. One way to measure the in an organization is to check the following aspects: observability Alerting: How many alerts are generated per week? What percentage of alerts are handled “out of hours”? Monitoring system configuration: Is the monitoring system under version control? How many Pull Requests/Change Requests are made to the repository containing the monitoring system? On-call rotation: Are the alerts fairly distributed and addressed by all teams ( )? Guide to understand your OPS “The phone should not ring” - M.B, because ” Paging a human is a quite expensive use of an employee’s time. If an employee is at work, a page interrupts their workflow. If the employee is at home, a page interrupts their personal time, and perhaps even their sleep” - Google SRE book

Stacks

2022 - HackerNoon Contributor of the Year - Linux

2022 - HackerNoon Contributor of the Year - Microservices

2022 - HackerNoon Contributor of the Year - Philosophy

📚 our subjective awareness of decisions is quite unreliable...just click

Nominated for 2022 - HackerNoon Contributor of the Year - Linux

Nominated for 2022 - HackerNoon Contributor of the Year - Microservices

Nominated for 2022 - HackerNoon Contributor of the Year - Philosophy

Observability and Monitoring in a nutshell

is one of the most popular open-source JavaScript libraries. It implements a lot of functional programming helpers that make everyday coding easier. It's something I use often in my own development work, and so I wanted to share with you some of the methods that I find myself using a lot. Lodash In this short post, we'll go over 15 everyday methods that, when used properly, can speed up development and make your code more readable. 1. shuffle Creates an array of shuffled values (using a version of the ). Fisher-Yates shuffle import shuffle from 'lodash/shuffle';

const result = shuffle([1, 2, 3, 4, 5]);

// result => [2, 5, 1, 4, 3] 2. orderBy Creates an array of elements, sorted in the specified order by the results of running each element in a collection thru each iteratee. import orderBy from 'lodash/orderBy';

const items = [
  { name: 'Item A', price: 2 },
  { name: 'Item B', price: 8 },
  { name: 'Item C', price: 4 },
  { name: 'Item D', price: 4 }
];

const result = orderBy(items, ['price', 'name'], ['desc', 'asc']);

/*
result => [
  { name: 'Item B', price: 8 },
  { name: 'Item C', price: 4 },
  { name: 'Item D', price: 4 },
  { name: 'Item A', price: 2 }
]
*/ 3. chunk Creates an array of elements split into groups of a specified size (if the array can't be split evenly, the final chunk will only contain the remaining elements). import chunk from 'lodash/chunk';

const array = [ 1, 2, 3, 4 ];

const result = chunk(array, 2);

// result => [[1, 2], [3, 4]] 4. take Creates a slice of the array with elements taken from the beginning. n import take from 'lodash/take';

const result = take([1, 2, 3], 2);

// result => [1, 2] 5. difference Creates an array of the values not included in the other given arrays. The order and references of result values are determined by the first array. import difference from 'lodash/difference';

const result = difference([1, 2, 3], [2, 3, 4]);

// result => [1] 6. intersection Creates an array of unique values that are included in all given arrays. The order and references of result values are determined by the first array. import intersection from 'lodash/intersection';

const result = intersection([1, 2, 3], [2, 3, 4]);

// result => [2, 3] 7. isEmpty Checks if the value is an empty object, collection, map, or set (objects are considered empty if they have no own enumerable string keyed properties). import isEmpty from 'lodash/isEmpty';

isEmpty({});
// => true

isEmpty({ name: 'John Doe' });
// => false 8. throttle Creates a throttled function that only invokes the passed function at most once per every interval, specified in milliseconds. import throttle from 'lodash/throttle';

const throttled = throttle(() => {
  console.log('Throttled after 50ms!');
}, 50);

window.addEventListener('resize', throttled); 9. debounce Creates a debounced function that delays invoking the passed function until after the specified wait time has elapsed since the last time the debounced function was invoked. import debounce from 'lodash/debounce';

const debounced = debounce(() => {
  console.log('Debounced after 400ms!');
}, 400);

window.addEventListener('resize', debounced); 10. merge Recursively merges own and inherited enumerable string keyed properties of source objects into the destination object. import merge from 'lodash/merge';

const firstObject = { 'A': [{ 'B': 1 }, { 'C': 2 }] };

const secondObject = { 'A': [{ 'B': 3 }, { 'D': 4 }] };

const result = merge(firstObject, secondObject);

// result => { A: [{ B: 3 }, { C: 2, D: 4 }] } 11. cloneDeep Creates a deep clone of a specified value. import cloneDeep from 'lodash/cloneDeep';

const items = [
  { name: 'Item A' },
  { name: 'Item B' },
];

const result = cloneDeep(items);

// result[0] === items[0] => false 12. startCase Converts a string to (the first letter of each word capitalized). start case import startCase from 'lodash/startCase';

startCase('--foo-bar--');
// => 'Foo Bar'

startCase('fooBar');
// => 'Foo Bar'

startCase('__FOO_BAR__');
// => 'FOO BAR' 13. kebabCase Converts a string to (punctuation is removed, the text is converted to lowercase, and spaces are replaced by single dashes). kebab case import kebabCase from 'lodash/kebabCase';

kebabCase('Foo Bar');
// => 'foo-bar'

kebabCase('fooBar');
// => 'foo-bar'

kebabCase('__FOO_BAR__');
// => 'foo-bar' 14. snakeCase Converts a string to (punctuation is removed, the text is converted to lowercase, and spaces are replaced by single underscores). snake case import snakeCase from 'lodash/snakeCase';

snakeCase('Foo Bar');
// => 'foo_bar'

snakeCase('fooBar');
// => 'foo_bar'

snakeCase('--FOO-BAR--');
// => 'foo_bar' 15. camelCase Converts a string to (spaces and punctuation are removed and the first letter of each word is capitalized). camel case import camelCase from 'lodash/camelCase';

camelCase('Foo Bar');
// => 'fooBar'

camelCase('--foo-bar--');
// => 'fooBar'

camelCase('__FOO_BAR__');
// => 'fooBar' That’s all. Thanks! I hope this can help you the next time you write a JavaScript application. Lodash is a very popular open-source library that's worth checking out if you're looking for ways to build web applications faster.

Diogo Capela

15 Lodash Methods for Everyday Coding

Cross-site scripting(XSS) and Cross-Site Request Forgery(CSRF) are likely to occur if a JSON Web Token(JWT) is not properly stored in the browser. In this article, I will share how we can avoid those 2 attacks when using JWT in our web application. I value your time, so I will start off with how to accomplish this in a summary. The user fills up the login form and hit the submit button from the Frontend. Once the user is authenticated from the Backend, a JWT will be sent and a will be set in an . access_token refresh_token HTTP-Only cookie Frontend stores the in-memory access_token var access_token = data.access_token; When the user refreshes the page, the stored in memory will be gone. But we can still retrieve the by making an API call via the stored in HTTP-Only cookie (Step 2). Then FE will have the again, which is used to communicate with Backend. access_token access_token refresh_token access_token I will walk you through the tutorial from the Backend and then the Frontend. I hope this will be easy to for you to understand the article. Backend I will use as my HTTP routing framework in this entire tutorial. echo 1. Create a routing framework Start your backend router with which sets in the response header. This configuration tells browsers whether to expose all responses to the frontend JavaScript code when the request's credentials mode (Request.credentials) is . AllowCredentials: true Access-Control-Allow-Credentials include e := echo.New()
e.Use(middleware.CORSWithConfig(middleware.CORSConfig{
   AllowCredentials: true,
})) 2. Create Login API In this Login API, you should perform authentication based on the credentials sent by the user before you proceed. Once the user is authenticated, the Backend should generate an and a to send back to the Frontend in the response body, along with an HTTP-Only Cookie. access_token refresh_token type User struct {
   Name string `json:"name"`
}

func Login(c echo.Context) error {
   u := new(User)
   if err := c.Bind(u); err !=nil {
      log.Errorf("error in bind: %s", err)
      return err
   }
   accessToken, exp, err := generateAccessToken(u)
   if err != nil {
      return err
   }

   refreshToken, exp, err := generateRefreshToken(u)
   if err != nil {
      return err
   }
   setTokenCookie(refreshTokenCookieName, refreshToken, exp, c)

   return c.JSON(http.StatusOK, echo.Map{
      "message": "login successful",
      "access_token": accessToken,
   })
} 3. Create Refresh API When the user refreshes the page in the browser or the token in the Frontend has expired, the Frontend can call this API to retrieve a new set of and . access_token refresh_token func RefreshToken(c echo.Context) error {
   cookie, err := c.Cookie(refreshTokenCookieName)
   if err != nil {
      if err == http.ErrNoCookie {
         return c.NoContent(http.StatusNoContent)
      }
      log.Errorf("err is : %s", err)
      return err
   }

   token, err := jwt.ParseWithClaims(cookie.Value, &jwtCustomClaims{}, func(token *jwt.Token) (interface{}, error) {
      return []byte(GetRefreshJWTSecret()), nil
   })
   if err != nil {
      log.Errorf("err is : %s", err)
      return err
   }

   if !token.Valid {
      return errors.New("error")
   }

   claims := token.Claims.(*jwtCustomClaims)
   log.Infof("claims.Name : %+v", claims.Name)

   u := &User{Name: claims.Name}

   accessToken, exp, err := generateAccessToken(u)
   if err != nil {
      return err
   }

   refreshToken, exp, err := generateRefreshToken(u)
   if err != nil {
      return err
   }
   setTokenCookie(refreshTokenCookieName, refreshToken, exp, c)

   return c.JSON(http.StatusOK, echo.Map{
      "access_token": accessToken,
   })
} 4. Create Get User API The frontend can call this API to get the user information based on the provided by the Login API or Refresh API. access_token func UserAPI(ctx echo.Context) error {
   accessToken := ctx.Request().Header.Get("Authorization")

   token, err := jwt.ParseWithClaims(accessToken, &jwtCustomClaims{}, func(token *jwt.Token) (interface{}, error) {
      return []byte(jwtSecretKey), nil
   })
   if err != nil {
      log.Errorf("err is : %s", err)
      return err
   }

   claims := token.Claims.(*jwtCustomClaims)
   log.Infof("claims.Name : %+v", claims.Name)

   if !token.Valid {
      return ctx.JSON(http.StatusUnauthorized, echo.Map{
         "message": "Unauthorized",
      })
   }
   return ctx.JSON(http.StatusOK, echo.Map{
      "name": claims.Name,
   })
} The above-mentioned APIs (Login, Refresh and Get User API) are the only APIs required for Backend. The Frontend components will be introduced in the following parts. Frontend 1. Refresh and Get User API During page load, we will make an API call to the Backend refresh API with . The HTTP-Only cookie will be sent to the Backend from the browser. If a valid exists in the HTTP-Only cookie, the Backend will respond with a new to the Frontend, which is used to make an API call to retrieve the user information from the Backend. credentials: 'include' refresh_token access_token // Use this token as Authorization token to communicate with the other API.
  const [token ,setToken] = React.useState<string>('');
  // Retrieve the user information from the AccessToken.
  const [username ,setUsername] = React.useState<string>('');

  useEffect(() => {
    async function refresh() {
      // You can await here
      const resp = await fetch('http://localhost:8080/refresh', {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
        },
        credentials: 'include',
      });
      console.log("status is " + resp.status);
      if (resp.status === 204) {
        console.log("exit")
        return
      }

      const d = await resp.json();
      if(resp.ok) {
        console.log('Login Success:', d);
        setToken(d.access_token);
        user(d.access_token);
      }
    }
    refresh();
  }, []);

  const user = async (token: string) => {
    const resp = await fetch('http://localhost:8080/user', {
      method: 'GET',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `${token}`,
      },
      credentials: 'include',
    });

    const d = await resp.json();
    if(resp.ok) {
      console.log('User Success:', d);
      setUsername(d.name);
    }
  } 2. OnFinish function This function will be triggered once the user submits the login information from the browser. If the user credential is valid, the Backend will respond with a new and in the response body and HTTP-Only cookie respectively. access_token refresh_token const onFinish = async (values: any) => {
    const resp = await fetch('http://localhost:8080/login', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({'name': values.username}),
      credentials: 'include',
    });
    const {data, errors} = await resp.json();
    if (resp.ok) {
      setToken(data.access_token);
      setUsername(values.username);
    } else {
      console.error(errors);
    }
  }; 3. Render function This UI contains only one input. When the user hits the submit button, will be triggered with a parameter of the value in the . Username onFinish input return (
    <div className="App">
      <header className="App-header">
        <img src={logo} className="App-logo" alt="logo" />
        <p>
          Welcome { username }.
        </p>
        <Form
          name="basic"
          labelCol={{ span: 8 }}
          wrapperCol={{ span: 16 }}
          initialValues={{ remember: true }}
          onFinish={onFinish}
          autoComplete="off"
        >
          <Form.Item
            label="Username"
            name="username"
            rules={[{ required: true, message: 'Please input your username!' }]}
          >
            <Input />
          </Form.Item>
          
          <Form.Item wrapperCol={{ offset: 8, span: 16 }}>
            <Button type="primary" htmlType="submit">
              Login
            </Button>
          </Form.Item>
        </Form>
      </header>
    </div>
  ); Conclusion As you can see, to avoid the security issues mentioned in the title, the safest way is to store the JWT token in memory. There is nothing to be worried about for the in the HTTP-Only cookie, because the attacker can’t use the to perform any action from their website. refresh_token refresh_token You can download the full source code . here That‘s all for my sharing, I hope you will like it.

Fetch

2022 - HackerNoon Contributor of the Year - Golang

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Avoid XSS and CSRF Attacks in JWT (React + Golang): A Tutorial

As more and more brands have been launching their affiliate marketing programs, dozens of various tools and platforms have emerged to ease the task and boost the efficiency of marketing efforts. Such solutions are capable of solving most affiliate-related tasks, including searching for webmasters, providing them with materials, adding integrations, making payments, performing analytics, and more. is an affiliate marketing platform for the tourism industry with GMV over $1 billion and a multinational team. The platform connects over 100 trusted travel brands with clear statistics with 300k+ of inspired travel content creators and solo entrepreneurs from all over the world. Travelpayouts I've asked Ivan Baidin, Head of Travelpayouts, to elaborate on some important issues businesses face when launching an affiliate marketing program. What emerging trends do you see in the affiliate marketing market? How has it changed over the past couple of years? What were the main factors (like COVID, etc.)? Just a few years ago, it was really difficult and expensive to implement a fully functional affiliate program, but today affiliate marketing is no longer limited to big brands that can afford to invest $100k for the development of the program and a team. The market is now ready to offer software even for small brands—ranging from SaaS solutions, like Affise for about $1,000 a month, to advanced marketing platforms, like Travelpayouts, with payment only for actual results achieved. In terms of trends, affiliate marketing trends are not as important as global trends. Businesses either support them or become outsiders. One of the trends today is toward local travel and local service providers. Because of the accessibility of entry into affiliate marketing, even small local companies can effectively operate an affiliate program. The second trend has to do with the long-term changes on the market. Online shopping significantly and continues to grow. Consumers of goods and services make more purchases online than ever before. There are still people who buy airline tickets at an offline agency, but the potential reach of the audience that is willing to buy travel services online is now a lot wider than before. The number of online purchases has grown and is continuing to grow. Even with the easing of restrictions and the post-covid market recovery, people are staying online—it's convenient, fast and safe. Affiliate marketing is therefore a viable income source for content creators and businesses alike. has grown In my opinion, the most important global trend is More Focus on People, Make Brands Human. 84% of consumers prefer to be treated as a person and not a number. By creating memories, brands strengthen the personal connection between product and emotion which increases brand buy-in and conversions. Through partner marketing, brands can reach their target audiences without aggressive PPC advertising, but through influencers that they already trust. Since the business relies on trust between all participants in the process, I prefer to refer to it as partner marketing rather than affiliate marketing. Influencer marketing is on the rise these days. Can this tool be bundled with affiliate marketing, is this something common or unique now? Influencer marketing is getting extremely popular these days, whereas affiliate marketing is a way for businesses to reach thousands of influencers at once. Affiliates are influencers interested in offering your products and services to their audience. Unlike classic influencer marketing, where the brand pays for placement and coverage, with affiliate marketing you get placement, coverage and sales. Managing influencers isn't a walk in the park too as it takes a lot of time and paperwork. In affiliate marketing, partners promote your brand on their own—their motivation is to get a percentage of revenue from sales and a recommendation of a brand that they really love. For example, GetYourGuide—one of the most popular excursion search engines in the world—pays affiliates 9% of the cost of a tour. There are influencers of all scales, from local bloggers with an audience of a few thousand, to celebrities and opinion leaders. Through Travelpayouts, a travel brand can reach out to influencers with a combined reach of over 5,000,000 people. In your opinion, what types of businesses benefit from using affiliate marketing the most today, and how has that changed over time? The short answer is: virtually anyone who sells goods or services online. Obviously, these include services selling airline tickets, hotels, tours or car rentals, but there are also other types of businesses both outside and within travel. Here are just a few examples: E-commerce—the share of online purchases has increased significantly, to the UN; according Online education—also increased due to the COVID-19, to the World Economic Forum; according Legal services helping to get compensation for flight delays; SIM cards for travelers; Bank cards for travelers; and so on. There is also one common misconception, which is that if the structure of a business implies different margins for different goods and services (for example, one service generates income of 10%, and another just 1%), there is a fear that it is impossible to offer partners a solution. But there is a way out, if you don’t pay a % of the cost but share a portion of the income. EconomyBookings, for example, do this by sharing with partners 60% of their income rather than the cost of the service. What are the main issues that brands are facing when they deal with affiliate marketing? The first one is partners. Even having solved the technical problem, businesses face the issue of attracting partners. In this matter, the decision to connect to a network that already has a large pool of partners wins a lot. The second challenge is a transparent record of actions. The basic model is that the brand should account for all actions taken by the affiliate's audience, so correct and stable accounting is the cornerstone of affiliate marketing. No matter which technical solution you choose, the stability and transparency of accounting are important factors in developing the trust of bloggers. Third, there are violations by affiliates. Although the affiliate channel involves low risks—you only pay for results—affiliates can unintentionally or deliberately violate the rules. For example, buying PPC ads with brand requests, if this is prohibited. It is important to prescribe complete rules that take into account all the specifics and to monitor the execution of the rules. Or trust a service with an anti-fraud solution like Travelpayouts. Can you name some key mistakes brands make when they start working with the affiliate channel and scaling it? One of the common mistakes is setting too strict rules for traffic. A brand chooses its own types of traffic sources, and it can significantly reduce potential reach by setting excessively strict traffic rules. Everything that fits within the company's traffic policy should be allowed, and if something is controversial, it can always be handled by using prior approval. Another trivial but not uncommon mistake is starting an affiliate program and not investing in developing it further. Like any marketing channel, an affiliate program requires attention. Even though affiliates work with their own audiences and develop their own projects, your job is to help affiliates with their work, such as conducting training webinars, providing advanced tools, unique solutions for large affiliates, etc. Are there any new technology solutions that can solve these problems? Certainly. There are SaaS solutions like Affise, and platforms like Travelpayouts. The latter are solving all the issues with launching, developing, and operating. That doesn't mean a brand won't need to work with the channel at all, but of all the options available, it's probably the most efficient and easiest solution. For example, the Travelpayouts platform solves every basic point: Launch: no development required; Partners: no need to look for them on your own, there is an existing base; Growth: 10+ years of team expertise; Anti-fraud protection: artificial intelligence + automatic notifications of any anomalies. What is the main idea behind your First Digital Partnership Platform for Travel Brands? How does this work? Travelpayouts Digital Partnership Platform gives travel brands access to 300,000 travel influencers worldwide. You can reach new customers, pay in a single transaction. Our mission is simple—we help people travel more, show the world and earn money from it. We unite travel, experience, freedom, honesty, technology and money driven people and brands to benefit. The platform connects those who have an audience with those who have a product or service for that audience. This is the ultimate win-win story, where a brand gets sales and a creator of travel content earns money by offering relevant brand products or services to their audience. More than 300,000 travel content creators around the globe share their favorite travel brands and inspire travelers to get to know the world in a profitable and qualitative way. It benefits all: people get high-quality content and travel a lot; brands get a new channel to reach new audiences; and bloggers get a way to monetize their passion. What is your advice to those travel and non-travel brands that are starting their efforts in affiliate marketing? Launching an affiliate program is just the beginning of a long journey. And in order to get through it as efficiently as possible, you need to think about how you’re going to develop the program at the very beginning. There is also competition in affiliate marketing—big brands or older solutions are already on the market. And even if you have the resources to develop your own affiliate program, it's worth connecting to one or more platforms. These are not mutually exclusive options. Walking the path with a market expert is what will allow you to jumpstart in affiliate marketing.

Target

Travelers

"Affiliate Marketing is NO Longer Limited to Big Brands with Budgets over $100k" - Ivan Baidin 

In this article, I will tell you how to write a telegram bot that integrates with the real world via a microcontroller (Arduino). Everything is remembered better in practice. So, I will explain everything through a practical project from my experience. The project code is published on my GitHub . This article will explain how to create a telegram bot, process messages, and respond to users. Also, in the end, will be given some ideas for future projects for you. ‘Smart Queue’ project page Shortly about project I did a project for controlling the queue to our office gaming room by Arduino-based Telegram. The Arduino with servo motor was installed to lock and unlock the door. People could take a queue and check how many people were in the queue via telegram bot. In this project, NodeMCU has been used but the same logic works for Arduino with ESP32/ESP8266. Telegram Telegram is a messenger with mobile applications (iPhone and Android) and desktop applications (Mac, Windows, and Linux). In Telegram, we have an opportunity to create a bot. We will control it with our microcontroller by processing messages from users. Creating Telegram Bot To create a Telegram Bot, we need to request it from the “botfather” telegram bot. All you need is to find the bot by search or get it by the link: . The bot is user-friendly and as you open the chat with the bot, our next steps should be clear for you. https://t.me/botfather Send /start command to start the bot. Send /newbot command to create a new bot. We will be asked to enter the name and username of the new bot. After the successful creation of the bot, the botfather will send us a token. This one will be required to receive all messages sent to the bot and send messages from the bot to users. Connection We will use a servo motor to lock and unlock the door and an LED to indicate if the door is locked. Servo motors have 3 pins: orange, red and brown: Red - 5V, Brown - GND, Orange - 14. LED connected to pin 2. Code First of all, we should import all required libraries: #ifdef ESP32
  #include <WiFi.h>
#else
  #include <ESP8266WiFi.h>
#endif
#include <WiFiClientSecure.h>
#include <UniversalTelegramBot.h>  
#include <ArduinoJson.h>

#include <Servo.h>
Servo myservo; Write credentials of wi-fi to which microcontroller will be connected: const char* ssid = "";      //wifi name
const char* password = "";  //wifi password Write token of our bot, which we received after its creation: // Initialize Telegram BOT
#define BOTtoken "XXXXXXXXX:XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"  // your Bot Token (Get from Botfather) Define the outputs and other required variables: // Checks for new messages every 0.1 second.
int botRequestDelay = 100;
unsigned long lastTimeBotRan;

int servo_pin = 14;//servo pin
int servo_locked_value = 180;
int servo_unlocked_value = 30;

const int ledPin = 2;

String queue[10] ={};//array of queue
int queue_array_len = 0;//len of queue
bool room_is_free = true;//initially the room is free
bool door_is_locked = true;// the door is locked Create a new wi-fi client and a bot with the token and the client: WiFiClientSecure client;
UniversalTelegramBot bot(BOTtoken, client); In the setup section we will initialize led and servo, begin serial and connect to wi-fi: void setup() {
  Serial.begin(115200);

  #ifdef ESP8266
    configTime(0, 0, "pool.ntp.org");      // get UTC time via NTP
    client.setTrustAnchors(&cert); // Add root certificate for api.telegram.org
  #endif

  pinMode(ledPin, OUTPUT);
  digitalWrite(ledPin, HIGH);
  
  // Connect to Wi-Fi
  WiFi.mode(WIFI_STA);
  WiFi.begin(ssid, password);
  #ifdef ESP32
    client.setCACert(TELEGRAM_CERTIFICATE_ROOT); // Add root certificate for api.telegram.org
  #endif
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to WiFi..");
  }
  // Print ESP32 Local IP Address
  Serial.println(WiFi.localIP());

  //Servo - close:
  myservo.attach(servo_pin);
  myservo.write(180);
} In the loop section we will do three main things: check if there are any new messages to the telegram bot; check if the room is free and if so, invite the next person in the queue; indicate if door locked and open/close the door void loop() {
  if (millis() > lastTimeBotRan + botRequestDelay)  {
    int numNewMessages = bot.getUpdates(bot.last_message_received + 1);

    while(numNewMessages) {
      Serial.println("got response");
      handleNewMessages(numNewMessages);
      numNewMessages = bot.getUpdates(bot.last_message_received + 1);
    }
    lastTimeBotRan = millis();

    //check if the room free
    if(room_is_free ==true && queue[0]!=""){
      room_is_free = false;
      //invite to the room:
      Serial.println("Invinting to the room: "+queue[0]);
      bot.sendMessage(queue[0], "The room is free! \n Please come and send /unlock_the_door_from_outside to unlock the door.", "");
    }

    //Indicating and opening/closing the door:
    digitalWrite(ledPin, door_is_locked);//ledState
    if(door_is_locked){
      myservo.write(servo_locked_value);//locked
    }else{
      myservo.write(servo_unlocked_value);//unlocked
    }
    
  }
} Here is how we will handle new messages and respond to /start message: void handleNewMessages(int numNewMessages) {
  Serial.println("handleNewMessages");
  Serial.println(String(numNewMessages));

  for (int i=0; i<numNewMessages; i++) {
    // Chat id of the requester
    String chat_id = String(bot.messages[i].chat_id);

    // Print the received message
    String text = bot.messages[i].text;
    Serial.print("From: ");Serial.println(chat_id);
    Serial.print("Message: ");Serial.println(text);

    String from_name = bot.messages[i].from_name;

    if (text == "/start") {
      String welcome = "Welcome, " + from_name + ".\n";
      welcome += "By this bot you can stand in line to the Gaming room.\n";
      welcome += "Please see my /options.\n\n";
      bot.sendMessage(chat_id, welcome, "");
    }    
  }
} Here is how we respond to /options message and send buttons for easy interactions: if (text == "/options") {
  String keyboardJson = "[[\"/Stand_in_line\"],[\"/cancel\", \"/status\"]]";
  bot.sendMessageWithReplyKeyboard(chat_id, "Here is my options:", "", keyboardJson, true);
} When we receive /Stand_in_line message we need to check if person already in the queue and if not add the person to the queue and inform it: if (text == "/Stand_in_line") {
  //check if person already in the queue:
  if(if_chat_id_not_in_queue(chat_id)){
    //add person to the queue
    queue[queue_array_len] = chat_id;//array of queue
    queue_array_len++;
    bot.sendMessage(chat_id, "You joined the queue.", "");
  }else{
    bot.sendMessage(chat_id, "You are already in the queue.", "");
  }
      
  //printing the queue
  print_queue();
} ** ** When we receive /cancel message, we need to remove the person from the queue by checking if he is there. Also if the person was already invited to the room, we will set the room status back to free. if (text == "/cancel") {
  //check if person already in the queue:
  if(if_chat_id_not_in_queue(chat_id)){
    bot.sendMessage(chat_id, "You were not in the queue.", "");
  }else{
    //checking if the person first one to set room to free again.
    if (queue[0]=chat_id){
      room_is_free = true;
    }   
    //remove person from the queue
    for(int i = 0; i < queue_array_len; i++){
      if(queue[i]=chat_id){
        queue[i] = "";
        //move items after the deleted one:
        for(int j = i;j < queue_array_len; j++){
          queue[j] = queue[j+1];
        }
      }
    }
    queue_array_len--;
    bot.sendMessage(chat_id, "You are no longer in the queue.", "");

    //printing the queue
    print_queue();
  }
} When we receive /status message, we will send how many persons in the queue and if person in it, we will also send person's position in the queue: if (text == "/status") {
  //printing the queue
  print_queue();
      
  int current_person_id_in_queue = -1;
  for(int i = 0; i < queue_array_len; i++){
    if(queue[i]==chat_id){
      current_person_id_in_queue = i+1;
    }
  }
  String message = "The queue consist of " + String(queue_array_len ) + " person(s).\n";
  if(current_person_id_in_queue != -1){
        message +="You are #"+String(current_person_id_in_queue)+ " in line.\n";
  }
      
  bot.sendMessage(chat_id, message, "");
} When person queue comes, we will send an invitation message: When we receive /unlock_the_door_from_outside message we need to check if the person is first in the queue and if so, unlock the door and send response: if (text == "/unlock_the_door_from_outside") {
  //checking if this first person in queue:
  if(chat_id == queue[0]){
    door_is_locked = false;
    myservo.write(servo_unlocked_value);//unlocked
    bot.sendMessage(chat_id, "The door is unlocked! \nCome in and lock the door by sending /lock_the_door_from_inside.", "");
  }else{
    bot.sendMessage(chat_id, "Something went wrong. \nPlease try to check your status in queue by /status command.", "");
  }
} In the next /lock_the_door_from_inside and /unlock_the_door_from_inside commands as before we need to check if the person is the first one in the queue. We do this to make sure the door is locked or unlocked by the right person. For /lock_the_door_from_inside message, we need to just lock the door: if (text == "/lock_the_door_from_inside") {
  //checking if this first person in queue:
  if(chat_id == queue[0]){
    door_is_locked = true;
    myservo.write(servo_locked_value);//locked
    bot.sendMessage(chat_id, "The door is locked! \nTo unlock the door send /unlock_the_door_from_inside.", "");
  }else{
    bot.sendMessage(chat_id, "Something went wrong. \nPlease try to check your status in queue by /status command.", "");
  }
} As we receive /unlock_the_door_from_inside message, we will unlock the door for three seconds and lock it back. Also, we will remove the person from the queue and set the room status back to free. This required to invite next person from the queue: if (text == "/unlock_the_door_from_inside") {
    //checking if this first person in queue:
    if(chat_id == queue[0]){
    door_is_locked = false;
    myservo.write(servo_unlocked_value);//unlocked
    bot.sendMessage(chat_id, "The door is unlocked! We will lock it in 3 sec", "");
    delay(3000);
    door_is_locked = true;
    room_is_free = true;
    myservo.write(servo_locked_value);//locked
        
    //remove the person from the queue
    for(int i = 0; i < queue_array_len; i++){
        if(queue[i]=chat_id){
        queue[i] = "";
        //move items after the deleted one:
        for(int j = i;j < queue_array_len; j++){
            queue[j] = queue[j+1];
        }
        }
    }
    queue_array_len--;
    bot.sendMessage(chat_id, "The door is locked. Bye!", "");

    }else{
    bot.sendMessage(chat_id, "Something went wrong. \nPlease try to check your status in queue by /status command.", "");
    }
} Here are our helper functions. These two parts of the code are used many times. So, I decided to separate them for convenience of the code: //check if person already in the queue:
bool if_chat_id_not_in_queue(String chat_id){
  for(int i = 0; i < queue_array_len; i++){ 
    if(chat_id == queue[i]){
      return false;
    }
  }
  return true;
}

//printing the queue
void print_queue(){
  Serial.print("In queue - ");Serial.println(queue_array_len);
  for(int i = 0; i < queue_array_len; i++){
    Serial.println(queue[i]);
  }
} Conclusion Through this tutorial you’ve learned how to interact with your microcontroller with Telegram Bot. The microcontroller can process messages sent to the bot and send messages back. This project is an example and showcase for you. The possibilities are limited by your imagination. Here are some ideas for you: make a Motion Detector which will send a message as motion will be detected; connect a camera module and send image by request; control LED strip or get any sensor indication. Also, there could be done such as making a big led matrix and giving control of it to everyone by telegram bot. global project The great thing about integrated Telegram bot projects is that they can be controlled from anywhere in the world. This gives your projects the opportunity to reach a new level.

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Integrate Your Telegram Bot With Real World via ESP32/ESP8266 Arduino Microcontroller

APIs are essential when it comes to software development. It allows software to communicate with each other to exchange information. In the beginning, when learning to make API calls, it can be a challenge using tools such as cURL in the terminal, in the frontend or backend option such as . Postman is a great tool for understanding the process of making API calls and getting feedback on the response of those calls. It provides a visual interface and allows developers to get a sense of the workflow which can provide easier transitions to using other tools such as cURL or Express. FETCH Express In this tutorial, we will be using to make API requests to to get a better understanding on building an application similar to Twitch or YouTube. The Livepeer Video Services API consists of the standard features for users such as creating new live streams, recording the streams and getting a list of their streams. By leveraging for transcoding, it will be easier and cost effective to scale for in the future. Postman Livepeer Video Services Livepeer’s decentralized network Get access to Postman Step 1: Go to and sign up for a free account. Postman’s website You can use their web browser tool or download the desktop version. For this tutorial, we will use the desktop version with , but it is also available for and . Mac OS Windows Linux Get access to Livepeer Video Services Step 2: Go to and sign up for a free account. Livepeer Video Services Now that we have access to these two services, let’s start interacting with them. Get anAPI key Step 3: In your Livepeer dashboard, tap the developers tab and select API keys Click the button that says Create Key Then give your key a name and click Create With the API key, we will start going through the section of Livepeer Video Services' . The reference uses the cURL command but we will do this using Postman instead. Stream API Reference To learn more about streaming Livepeer Video Services with the cURL command, visit the tutorial How To Stream With Livepeer Video Service’s RTMP API Step 4: Launch the Postman desktop app Click the icon under the Reports tab to start a new request or select under the file menu + New Tab Create a stream Step 5: Select the option and the paste in Livepeer Video Services’ base URL for streaming POST https://livepeer.com/api/stream Then underneath the URL, click on the tab, and select as the type Authorization Bearer Token Copy and paste your API key from Livepeer Video Services Now select the tab and then underneath select the radio button Body raw Then click the the drop down menu and select the option Text JSON In the text box field below, paste the following code which is from the . You can change the to whatever you want as this is for the name of your stream Livepeer Video Services' API reference docs name { "name": "First Stream", "profiles": [ { "name": "720p", "bitrate": 2000000, "fps": 30, "width": 1280, "height": 720 }, { "name": "480p", "bitrate": 1000000, "fps": 30, "width": 854, "height": 480 }, { "name": "360p", "bitrate": 500000, "fps": 30, "width": 640, "height": 360 } ] } Now click on the button and you should receive a confirmation that it went through with a 201 status Send Scroll down in the response and copy the for the stream that was just created. It should be under id renditions Check the Livepeer Video Services’ dashboard and the stream that you just created should come up Now let’s get the stream we just created Retrieve a stream Step 6: In the Postman app, we will change from to , and in the URL paste in and replace with the id we copied from our response earlier. POST GET https://livepeer.com/api/stream/{id} {id} Click the blue send button and you should get back a 200 status with the response including the parameters of the stream Get a list of streams Step 7: In order to get a list of streams, follow to create a second stream so that there is more than one stream Step 5 Give it a name of your choice, but for this example, it will be named Second Stream Go back to Livepeer Video Services’ dashboard and you should see the appear Second Stream Now that we have multiple streams, go back to the Postman app and retrieve them Similar to we are changing to and insert the base URL for retrieving multiple streams Step 6 POST GET https://livepeer.com/api/stream?streamsonly=1 Remember that the body should have the selected since we are not passing in any data none radio button Click the blue send button and it should confirm with a response of status meaning that the request went through and a response back with a list of your streams 200 Toggling a stream Step 8: Replace with and insert the URL for for toggling recording and replace with the one you have at the end of GET Patch https://livepeer.com/api/stream/{id}/record {id} Step 5 Now we will toggle the recording on by putting the following in body, just as we did in Step 5 { “record”: true } Click the blue send button and it should confirm with a response of status meaning that the request went through 204 Update a stream Step 9: Select and insert the URL for updating a stream and replace with the one you have at the end of Patch https://livepeer.com/api/stream/{id} {id} Step 5 Now we will not have the recording on and suspend the stream by putting the following in body, just as we did in Step 5 { “record”: false, “suspend”: true } Click the blue send button and it should confirm with a response of status meaning that the request went through 204 Delete a stream Step 10: We will delete by replacing with and insert the URL for deleting a stream and replace the you have at the end of Second Stream Patch Delete https://livepeer.com/api/stream/{id} {id} Step 5 Click the blue send button and you should get a status as a response to confirm the stream has been deleted 204 Go back to Livepeer Video Services' dashboard to confirm that the stream has been deleted Now that you are able to use Postman to interact with Livepeer Video Services' API, take your skills to another level using cURL. To learn more, check out the tutorial How to Stream With Livepeer Video Service’s RTMP API. How to Stream with RTMP API First Published here

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It's no secret that having access to pre-trained Deep Learning models is crucial to contemporary Deep Learning applications. With State-of-the-Art models growing larger and larger into the trillions of parameters, it no longer makes sense to train advanced models from scratch in a large number of areas, especially fields like . Automatic Speech Recognition Given the importance of pre-trained Deep Learning models, which Deep Learning framework - PyTorch or TensorFlow - has more of these models available to users is an important question to answer. In this article, we'll explore this topic quantitatively so you can stay informed about the current state of the Deep Learning landscape. Why do Pre-trained Deep Learning Models Matter? Especially for intricate applications like Natural Language Processing, where the size and complexity of models hinder the engineering and optimization process, the ability to build state-of-the-art (SOTA) models from scratch is an impossible task for all but the largest budgets and organizations. OpenAI's paradigm-shifting GPT-3 has over 175 billion parameters; and, if that weren't enough, its successor is slated to be orders of magnitude larger. GPT-4 is expected to have over 100 trillion parameters. Because of this model growth, small-scale ventures rely on pre-trained Deep Learning models in their workflow, either for direct out-of-the-box inference, fine-tuning, or transfer learning. In the arena of model availability, PyTorch and TensorFlow diverge sharply. Both PyTorch and TensorFlow have their own official model repositories, as we’ll explore below in the Official Resources sections, but practitioners may want to utilize models from other sources. Let’s take a quantitative look at model availability for both Deep Learning frameworks to get an idea of how they compare. HuggingFace The demand for access to pre-trained Deep Learning models is evident by the meteoric rise of , which recently raised recently in a Series B funding round. HuggingFace's popularity and rapid growth can be attributed to its ease-of-use, providing access to SOTA models in just a few lines of code. HuggingFace $40 million When we segment the number of total and top Deep Learning models on HuggingFace by framework (PyTorch or TensorFlow), we begin to see a trend that will continue throughout our discussion. Total Models Below you can see a graph comparing the total number of models on HuggingFace by framework. A cursory inspection of this graph allows you to easily glean the superior framework on this front. Key takeaways: 1. Almost 85% of Deep Learning models are PyTorch exclusive 2. In contrast, only about 8% of models are TensorFlow exclusive 3. Only 16% of all models are available in TensorFlow 4. Of the roughly 16% of models that are not PyTorch exclusive, roughly half of them are still available in PyTorch Top Models To play Devil's Advocate, one might say that, although there are astronomically more models available in PyTorch, the best models are surely still available in both frameworks - that PyTorch's numbers are padded by thousands of uploads of low-quality Deep Learning models given that so many beginners choose PyTorch. A fair point - let's dive into the numbers to see if this argument holds up by considering only the 30 most popular models on HuggingFace: We find that the above argument does not hold up. The key takeaways from the graph are that: 1. All top 30 models are available in PyTorch 2. Not even two-thirds of the top 30 models are available in TensorFlow 3. There are zero top 30 models that are TensorFlow exclusive Conclusion PyTorch dominates the pre-trained Deep Learning model landscape on HuggingFace. Papers with Code Another popular source for Deep Learning models is - a website whose mission is to create a free and open resource with Machine Learning papers, code, datasets, etc. Papers with Code Papers over Time If we look at a plot of the percentage of papers with associated repositories that utilize PyTorch, TensorFlow, or another framework over time, we observe a steadily growing fraction of papers which utilize PyTorch: - Data aggregated quarterly, from Q4 2017 to Q4 2021 Data source Conversely, we see the continuing, near-monotonic decline of TensorFlow, even after the release of TensorFlow 2 in 2019. Fraction of Papers Last Quarter If we consider just the most recent quarter of data, Q4 2021, in which 4,500 repositories were created, we find that 60% of them are implemented in PyTorch, with just 11% implemented in TensorFlow. Conclusion When considering the availability of Deep Learning models on Papers with Code, we again see another clear victory for PyTorch. It should be noted that not all of these papers offer pre-trained models, we simply use the aggregate to note the general trend. PyTorch Official Resources Beyond HuggingFace and Papers with Code, PyTorch has several official sources for pre-trained Deep Learning models. Hub is a research-oriented platform for sharing repositories with pre-trained models that cover a wide range of applications. PyTorch Hub TorchVision is PyTorch's official Computer Vision library and includes a selection of models. It includes everything you need for your Computer Vision projects, including and popular . TorchVision pre-trained Deep Learning model architectures datasets For more pre-trained vision models, you can check out (pyTorch IMage Models). TIMM TorchText If your area of expertise is Natural Language Processing rather than Computer Vision, then you might want to check out . It contains a few , including an instance of RoBERTa. It also contains datasets frequently seen in the NLP domain, as well as data processing utilities to operate on these and other datasets. TorchText pre-trained Deep Learning models SpeechBrain is an open-source speech toolkit for PyTorch. It supports ASR, , and more, and comes with some . SpeechBrain speaker diarization pre-trained Deep Learning models TensorFlow Official Resources TensorFlow, just like PyTorch, has several official sources for pre-trained Deep Learning models. Hub is a repository of trained Machine Learning models ready for fine-tuning. This resouce allows you to utilize a large Deep Learning model like BERT with just a few lines of code. TensorFlow Hub The models in Hub cover a wide range of use cases, with models available for image, video, audio, and text problem domains. There are models available in TensorFlow, TensorFlow Lite, and TensorFlow.js models. You can see a list of the available models . here MediaPipe is a framework for building multimodal, cross-platform applied Machine Learning pipelines. MediaPipe offers which can be used for face detection, multi-hand tracking, object detection, and more MediaPipe pre-trained models Final Words While both frameworks have their share of official tools to distribute pre-trained Deep Learning models, it is obvious from our above analysis that PyTorch dominates most, if not all, non-affiliated model sources. With the ever-growing popularity of PyTorch and its Pythonic, easy-to-use approach to Deep Learning, we expect this trend to continue into at least the near future. While PyTorch beats out TensorFlow on this front, the conversation on which framework is better in toto is quite nuanced, and most information on the subject is outdated. You can check out this analysis comparing for an up-to-date, in-depth look into when each framework should be used. Pytorch vs Tensorflow

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A test automation framework is a collection of rules or guidelines for generating and designing test cases. It includes a combination of practices and tools that are developed to assist the QA professionals in testing more effectively. Moreover, these set of guidelines may encompass coding standards, object repositories, test-data handling techniques, processes for securing tests results and also data on how to access external resources. These rules are mandatory, and testers have the flexibility to design or record tests without adhering to them. However, accessing an organized framework offers more benefits that professionals would miss out on otherwise. In this blog, we shed light on some of the prominent types of automated testing frameworks: #1 Modular-Centric Testing Framework A modular testing framework requires testers to divide the application into separate testing categories like functions, units, sections, etc. Each of the classes is tested individually. Once the application is divided, testers create a test script for every part; subsequently, they are combined to create more extensive tests hierarchically. Additionally, the larger test sets work to represent different test applications. In the modular form of a testing framework, it is recommended to build an abstraction layer. This way, the sections won’t influence the overarching module. #2 Linear Automation Framework In a linear framework, testers are not required to write code or generate functions. Moreover, the steps in it are written chronologically, and the process is also known as the record-and-playback framework. The tester records every step like checkpoints, user input, navigation, etc. and then it is played back to conduct the test automatically. Considering there is no need for custom code, this framework can be executed without any expertise in test automation. test automation #3 Data-Driven Framework The data-driven framework breaks up script logic and test data. This allows the testers to store data in the framework externally. At times, testers need to test a particular feature or function different times with multiple data sets. In such situations, testers cannot rely just on the data coded into the script. This framework will allow the testers to store and pass various parameters to test scripts from different sources of data like text files, excel spreadsheet, ODBC repositories, SQL tables, etc. #4 Hybrid Testing Automation Framework A hybrid test automation framework amalgamates other forms of testing automation frameworks. This allows testers to leverage the potentials of other frameworks and eliminate their weaknesses as well. Each application is different, and the process of testing these applications needs to be distinctive as well. And leveraging a hybrid framework allows testers to be more adaptable and agile to get better test results. #5 Library Architecture Testing Framework This testing framework is focused on a modular framework but provides additional benefits. Rather than dividing the application under various tests, it identifies similar tasks within the scripts and groups them based on the function. This allows the application to be grouped by the common objectives. Moreover, functions are stored in the library that they access whenever needed. In Conclusion When selecting an automated testing framework, testers should consider presenting flexibility and supporting multiple applications and languages. This allows them to contribute to the testing process irrespective of their skills and background. The hybrid framework is one such adaptive testing approach that efficiently works with different applications and processes. First Published here‍

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