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Alongside

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All product-led are faced with the development optimization question in one way or another: how to build processes, outsourcing or in-house, how many people to hire, what kind of specialists to hire, and what to do with all this while growing a business. technology companies In practice, managers, founders, and investors ask these questions at every significant stage of project development: who to hire first, what stack to use, how to scale the team and increase productivity, moving from an MVP to a full-fledged market launch, and what to do with the growing payroll expenses. In this article, I will try to provide useful tips for each stage of product development. It is based on my and my colleagues' experiences, including successful and unsuccessful business decisions we made while working in various companies. Starting the project and beginning the development process In general, I see three paths that a founder or CEO can take in the early stages: Outsourcing MVP development Start assembling your in-house team Starting with no code tools How do you make the right decision? The nature of a product will determine your first step. What if a product is not supposed to use new rocket science technologies? For example, suppose you want to introduce a new business model to a specific market niche (XaaS product). Outsource developers will also be able to quickly implement such a project because many of them have been in the industry for a long time and have experience developing standard solutions. If the main value of your product is in technology (all types of AI, IoT, AR/VR/XR, etc., or just an unusual solution), then the no-code option is out because it is impossible to implement technologically complex projects using these tools. Furthermore, if you need an MVP to essentially test a hypothesis to determine whether there is market demand for your product, working with an outsourcing team is probably the best option. If there are market analogs for the product, the likelihood of failure increases, so it is better not to waste primary resources. However, if you are familiar with the market and want to introduce your technologies, it is best to begin forming your team. This will give you more leeway in defining how your product will look from the start. In case you present an outsourced team with a new product idea that significantly alters an MVP during the development process, it will additionally bill you and will demand more time for analysing new requirenments. In this case, your development team will complete the task faster, though introducing new ideas midway will likely prompt team members to roll their eyes. Following that, I will primarily focus on working with an in-house team, with a few exceptions for the cases where an MVP was implemented differently. Forming a team Who should be hired right away? If the product necessitates the use of specific technologies, such as AI, it is critical to find the best professional you can afford in the market. The skills and experience that such a professional will bring to the team, as well as the speed with which they will ensure development, will more than compensate for your salary expenses. The most qualified specialists should be in charge of your cornerstone technologies because these are the professionals who will give you an advantage over your competitors while also innovating. After choosing a technical lead and a stack, you can hire a couple more developers. Again, hiring someone with good experience saves time in the middle of trying to implement solutions that seniors have already implemented multiple times. There is no need to assemble a full-fledged team of , QA engineers, and system analysts right away; if we are building an MVP, team expansion should be gradual. Ensure that developers do not overlook unit tests, as they prevent the project from stalling in later stages of development. There are many testers on the market, and you can find one quickly, which is why you can hire them once the product is usable. DevOps In the development process, there is no need to reinvent the wheel. If your application contains logic that every other product on the market has (notifications, monitoring, billing, etc.), there is a 90% chance that you will find a ready-made SaaS service or Open-Source solution that will cover your needs for a low or no cost. Concentrate on what matters most: creating a solution that distinguishes your product. Later, if necessary, you can implement your specialized notification systems or the same billing. There is no need to construct complex infrastructure. Most major cloud providers' tools enable you to launch containers with code and load balance in a few clicks using serverless solutions, eliminating the need to set up and deal with managed clusters like Kubernetes. At this point, you can invest in simple CI/CD to accelerate the process of delivering code to the cloud. At the very beginning, when you have written 0 lines of code, it is usually pointless to implement Scrum or any Agile approach with sprints. In reality, the initial stages of development require the completion of basic tasks such as deploying the framework, selecting libraries, creating basic entities, and implementing minimal business logic. Such tasks can be difficult to evaluate because the team has no tangible results to be able to evaluate. Establish fundamental domains, entities, and processes for your product with your team, and then create a Kanban board with cards containing these basic tasks and employees assigned to complete them. Most likely, these cards will randomly merge and divide during the work process; there is nothing wrong with that; let the developers structure them in a way that is convenient for them - bureaucracy is not required at this stage. Sprints are useful and convenient; they can be implemented after the developers have laid the groundwork for the future product. At the moment, it’s critical that your small team communicates constantly - there's no documentation, the requirements are vague, and no one knows what kind of system you'll get. Make up for it with daily meetings and timely communication. A small life hack that I've been using successfully for the last four years is to conduct all work communication regarding tasks, emerging problems, technologies, deadlines, and so on in the main team chat. Even if a developer believes there are only two people involved in the task, he should communicate in the main team chat by tagging another person. Direct messaging should only be used to discuss personal matters. This method keeps everyone on the small team up to date on the product's progress. Completing the MVP The closer the team gets to finishing the MVP, the better they understand which product components require more and which require less resources. When the product has its primary functions, it is time to consider QA specialists. Testing during the development process is a necessity, not a luxury; it's not just about potential bugs, but also about product owners having confidence that the team is creating exactly the kind of product they need. Now that developers can be assigned more specific tasks and the system foundation allows them to assess their complexity, it's time to consider development methodology. In my practise, we've always used "near-Scrum" processes because they allow for good planning while also allowing for quick changes in product requirements. You can hire additional performers as needed. For example, if the project involves a server load that is disproportionate to the number of users (complex calculations, processing large amounts of data), use DevOps to begin building a scalable infrastructure. Once the MVP works, carry on with product development So your MVP is up and running and has found a market niche. You're increasing your marketing budget and learning about what your customers want. You must now focus on several things at once: business clients want convenient tools, consumers require engagement mechanisms, and so on. It makes sense to scale the team further for new tasks for each product subsystem separately. For example, one team should deal with your core technology, another with billing, a third with internal tools, and so on. This method allows you to keep small teams with minimal internal bureaucracy while also preventing developers from stepping on each other's toes when working on the same code. It would be preferable to separate the manufacturer team from the producers of subproducts (or services), with separate code. In the case of a marketplace, for example, keep a personal account separate from a storefront. Make the developer who does the most work in the back office the team leader (but only if he wants it), and hire new people in a specific team. If it is possible to assign/hire a separate product manager for a new team, do so; this will allow you to manage backlogs regardless of basic functionality. In general, the rule is as follows: if there are an increasing number of tasks in a specific development area that are difficult to prioritise, form a separate team with its own backlog, priorities, and sprints to complete these tasks. Each team can develop its own internal processes, such as changing the release cycle and task estimation techniques. However, it would be preferable if some general principles were followed everywhere. Simultaneously, it becomes difficult to build communication between teams when one team is tasked with maintaining the work of another. This is where documentation comes in handy. Draw diagrams to describe and support interaction interfaces between services. You can hire a systems analyst or architect to do this as well as act as a mediator in contentious issues. Add in regular cross-team meetings. At MyGig, for example, we hold all-teams big retrospective meetings that allow all participants to voice their opinions, including those on other teams and the systems they control. Create architectural committees where team leaders can discuss technical solutions, overall technical debt, and interaction patterns. Small meetings for synchronization on this project can be added if more than one team is involved in its implementation. Finally, product managers from all teams meet on a regular basis to synchronize backlogs. Develop communication - each individual team operates as a single organism in a 100% single information field, and more bureaucratic procedures involving documentation, described contracts, and so on can be used to interact with other teams. The DevOps team, the analytics team, and the "R&D special forces" are all examples of departments whose resources are used by everyone else. Form them separately and consider them to be service ones. Create a request system that allows everyone to use these service teams' services. Every business is unique Of course, the process I describe may differ in different companies. However, in the three companies where I served as CTO, these general principles enabled the synchronization of three components: product growth, development costs, and the required level of planning flexibility. New products were successfully launched into the market in a short period of time and at a low cost. In contrast, existing ones were allowed to develop more quickly in various directions.

Scaling the Development Team Alongside Business Growth: From the Ground Up to Market Entry

A term that’s a popular theme of discussion among people today is ‘ (AI)’. There’s no doubt that AI has played the role of a catalyst in transforming industries and reshaping how people interact with technology in today’s world. artificial intelligence As the horizons of artificial intelligence expand, the distinctions between conventional AI and ground-breaking Generative AI become more pronounced, signaling a trans-formative era in intelligent computing. AI enables machines to model or make improvements as per the abilities of the human mind. Right from self-driving cars to entry of generative AI tools like , AI has become a part of daily life and something companies spanning diverse industries are investing in. ChatGPT and Google’s Bard In essence, both General and Generative AI bring formidable strengths to the table, but they’re not without their challenges. Recognizing these strengths and limitations allows for a balanced perspective, ensuring that as we harness AI’s capabilities, we remain cognisant of its potential pitfalls. So, let’s explore the way the two are different from each other and reshaping the future technological landscape. Generative AI vs. AI: Key Differences Generative AI Artificial Intelligence (AI) Meaning It’s a subset of AI and generates content (text, code, and image, audio, video and other data) instantly in response to a prompt. AI are computer systems than can perform simple to highly complex tasks requiring human intelligence, logical reasoning, and speech-recognition, problem-solving and decision-making. Function It dives into massive repositories of existing content and uses that information for generating new content. It identifies consistent trends in the data they are trained on to make similar patterns as per prompts provided by the user. AI combines large amounts of data and uses algorithms and computational power to recognize patterns, improve decision-making and bolster business outcomes. It uses machine learning to train algorithms with data enabling systems to continue learning and improve performance on their own as per experience. Use Professionals like writers, graphic designers, software developers, artists and musicians utilise it for augmenting their productivity. It has shown immense potential in areas like creative writing, sound editing, image editing and suggesting variations to existing designs. AI applications are endless across various sectors and industries. It is used as an expert system in the healthcare industry to diagnose diseases, identify treatments, and suggest drug dosages. Financial industries are also deploying AI to offer financial advice, decide whether or not to approve a loan application and detect and flag activities in banking and finance for fraud detection. Applications ChatGPT, Midjourney, DALL-E, DeepDreem, StyleGAN, Midjourney, etc. and Bard. AI is also used in smartphones that uses facial recognition for unlocking and voice assistants like Alexa, Google Assistant and Siri. Methodology Leverages neural networks and often employs machine learning techniques like Generative Adversarial Networks (GANs). Uses various algorithms, such as machine learning, deep learning, natural language processing (NLP), and computer vision. Limitations Can create a variety of content types. Capable of understanding and responding to human language. Future prediction Identifies patterns and uses this knowledge to create unique new forms. Leverages historical and current data to identify patterns and make predictions. Generative AI vs. AI: Use Cases There are numerous current and potential use cases for both generative AI and AI (traditional) across various industries and domains. Here are some examples of their applications. Generative AI It can produce contextually relevant and easy-to-understand creative and academic content on various topics. It can compose emails, create structure of articles and prepare annual reports. Text Generation: It can create new realistic images and also enhance or completely transform existing images. Image Generation and Editing: It can write lyrics, compose original music tracks and produce new melodies. Music Composition: It can be used for generating realistic videos of people acting. Video Synthesis: It can spot underlying patterns in the dataset and create new designs. Fashion Design: Traditional AI AI can be used to forecast weather and finances based on historical data. Predictive Analytics: Depending on user’s behavior and preferences, AI-based systems can recommend products or content in sync with users’ interests. Product recommendation: Voice assistants like use AI. is used by these applications to interact with customers and offer assistance. Chatbots and Voice Assistants: Siri, Alexa, Chatbots, and Google Assistant Natural language processing Video games use AI for controlling non-player characters (NPCs). These characters can make decisions within the gaming environment. Video Games: Summary The two technologies, Generative AI and Traditional AI, symbolize two unique approaches in the AI domain. While Generative AI excels in creativity, handling uncertainty, and new applications, Traditional AI excels in efficiency, interpretability, and specific task-solving. The two approaches have their inherent strengths and weaknesses, however, their futuristic role in the AI landscape displays immense potential for major advancements and changing applications. The advances in technology have made the two technologies compelling and critical. AI being mature and flexible is the backbone of several industries, Generative AI breaks the limits of machine capability permitting them to make, innovate, and copy in a manner that was earlier believed to be only in the hands of humans. Right from identifying patterns to producing new content, from specific task solutions to the rise of machine creativity, AI is on a path which is filled with promise and challenges. Similar to many changing technologies, the solution lies in making use of its strengths and at the same time addressing its weaknesses.

Exploring the Creative Frontiers of Generative AI Alongside Robust Traditional AI

I recently spoke with the Co-founder of , Austin Diamond, about the backstory behind the company and the benefits of investing in a crypto market index. Austin shares his passion for finance and how it led him to create Alongside, which is designed to offer exposure to the entire cryptoasset market with a ERC-20 Token. He also discusses the potential impact of index products on the cryptoasset market and how they can change the way retail investors get exposure to markets. Alongside Alongside recently from a seed round led by a16z Crypto with participation from Coinbase Ventures, Franklin Templeton, and more. raised $11 million Thank you for joining us today. To begin, could you share with us how you came up with the idea for Alongside and the backstory behind it? Alongside comes full circle and was over a decade in the making for me (Austin)- I was an incredibly weird kid that was obsessed with finance as a teenager. I got really lucky and had an opportunity to work (read: fetch coffee) at a global investment bank with a group that was managing a portfolio of equity index funds. It was there I learned the open secret that is indexing- despite hogging the CNBC airwaves and charging customers insane fees, the overwhelming majority of active managers don’t beat a simple index that tracks the market. I realized pretty early on I didn't want to work in finance and later spent five years working in tech on marketplaces, most recently with a company called Omni where I met my co-founders Jun and Gauthier (Rai would join just after), which was later acqui-hired by Coinbase. Rai worked on Ethereum as a Core Dev for three years and we've followed crypto markets since 2016-ish- seeing everything evolve from theory to working application layer products in 2020 made us want to get off the sidelines. We also realized we’re not smart enough to pick individual winners, hence what we're building at Alongside. What is the potential impact of index products on the cryptoasset market? Index products secure tens of trillions in equity markets and changed the way retail investors get exposure to markets- they’ve saved retail investors tens of billions in fees, and lowered the barrier to entry such that more people have ownership in the stock market. We think the same dynamic can play out in cryptoasset markets. How would you describe your work to someone who is completely unfamiliar with Alongside, and also to someone who is familiar with it? To someone who is completely unfamiliar, I would describe it as buying the entire crypto market with just one token. To someone who is familiar with the industry, I would describe it as a broad-based index that offers exposure to the entire cryptoasset market with a single asset. Where can you invest and get broad exposure to crypto currently? Any insights into the current TAM among these competitors? The average investor has largely not been able to get indexed exposure to cryptoassets- what’s available on traditional exchange venues are products like BITQ that track crypto stocks, not coins. We don’t believe the best way to get exposure to the space is via a bundle of dodgy bitcoin mining stocks or centralized exchanges. Others are available via private placement to accredited investors and generally aren’t redeemable or liquid (so they trade at wild discounts or premiums). For those more familiar with DeFi, we’re fans of on-chain products like those from Index Coop or Indexed Finance, but they can generally only give you exposure to assets in the Ethereum ecosystem, so their products are largely thematic and get you exposure to categories vs. the entire market. How do you determine which cryptoassets are included? One of the hidden secrets in markets is that the most esteemed professional investors don’t actually know better than the market. We don’t either. Our core product gives holders exposure to the top 25 cryptoassets (ex. stablecoins/securities) by circulating market cap which historically speaking has tracked the total market cap of the category with ~97% tracking efficiency- we don’t try to put our thumbs on the scale. Why are coins like DOGE and SHIBA included? DOGE and SHIB are some of the highest returning assets of all time, and one reason we’re so bullish on indexing is because no reasonable person would have predicted that in a million years. Our core thesis is that we’re not in a position to try to pick winners and losers. If history is any indication, you’re better off “buying the market” vs trying to predict which assets will drive returns. With time, we expect assets like DOGE and SHIB to become a smaller portion of the index as more high quality assets emerge and grow to have large market caps. With the markets falling and a recession likely on the way, is now a good time to attempt to level the playing field with a crypto market index? Are there negative repercussions to that mission you foresee? We joke that we are borderline degenerate long-termists. We try to be as up front with the risks involved in our product as possible. In the short term (and maybe always), crypto markets are volatile and we could see major declines like we have in the past. We feel now is a better time than ever to start to explore the space- prices (and gas fees) are low and some of the most talented people in the world are building new products at a faster rate than anytime in history. We think the most negative repercussions to come out of the crypto space have been the extent to which they’ve encouraged “degen” trading, a behavior we hope our product is somewhat of an antidote to.

Changing the Crypto Market with Index Investing: An Interview with Alongside's Austin Diamond

What does Social SEO mean? And how can it help your business? Although it may sound complicated, Social SEO can make for a user-friendly and straightforward way of helping your business. Fundamentally, Social SEO refers to the idea that social media links and interaction play a key role in a website’s search rankings. As content stand out, this refers to applying your optimization tactics in the wide world of social media. SEO is all about making What is Social SEO? Even if you don’t consider yourself to be an expert when it comes to SEO, let’s assume for the purpose of this post that you understand the and the importance of optimizing your content to rank on search engines for those keywords. basic elements of keyword research Let’s also assume that you’re familiar with the basics of social media and how to drive traffic from your social media pages to your website or landing page. When it comes to Social SEO however, you may be feeling a little less sure of things. It’s no doubt a term you’ve heard around digital marketing circles, but what exactly is it and how should you be using it? The good news is that you’re likely already using it - it’s just that nobody ever really assigned it a term before. The other piece of good news is that you won’t have to go back to the drawing board and learn something new from scratch - this isn’t a new type of SEO, it’s basically a sub-section that already exists. For example, if you have a Facebook page for your business or post content on any social media platform regularly that contains links back to your site, or that people are sharing to their own pages, then you’re already utilizing Social SEO. However, as with all aspects of your , specifically content, how you do things, and why you do them really matters. digital marketing strategy So, in this guide, I want to break down Social SEO for you in a clear way and give you actionable strategies you can implement by using social media and SEO to boost your search rankings organically and on your website. identify weak spots Ready? Let’s dive in. Why Social SEO Matters to have and likely always will be. They’re the place where new prospects come to check out your services, subscribe to your newsletter, and take other actions that ultimately lead them towards becoming a customer. Websites are hugely important for a business You also have control over your website in the sense that it’s an asset for your business that you own - unlike social media pages that can be shut down without warning when the platform decides you’ve done something it doesn’t like. However, this doesn’t mean that social media and Social SEO isn’t a hugely important part of your business, and in fact, how you rank on the search engines. It really comes down to making the two work together as they’re meant to. Having a website should always be a priority for your business, but if you think about your own behaviors when looking for stuff online, you don’t always go straight to Google. You might look a brand up on Instagram or Facebook to see what they sell, or even just to see what their engagement levels are like. Often when you type a name into Google, the first results are for their social media accounts, so as you can see, Social SEO plays a huge and important role in SEO overall. How To Create Your Social SEO Strategy As mentioned above, since social media and SEO are so intertwined, it can sometimes be difficult to see where one ends and the other begins in terms of developing a Social SEO strategy for your business. So, if you’re wondering where to start, then here are some tips to help you when deciding which things to include in your strategy and what to focus on: Start with the end in mind: Just like when building a new strategy for anything, you should always be clear on what you want to achieve and why this is important to you. Knowing this from the start will help you decide which tactics you need to be focused on and help you eliminate anything that doesn’t move you forward. Choose which metrics to measure: When you want to know where your sales or even your subscribers are coming from, it’s a lot easier to decide which metrics you’ll rely on for this information. With search rankings, it’s a little trickier. If you’ve spent any amount of time trying to figure out which social media metrics are affecting your , then you know that the jury is well and truly out on the issue of whether social media directly impacts SEO rankings or not. SEO rankings The issue has been discussed by top marketers and SEO experts like for over 10 years now, and although Google themselves remain adamant that the answer is no, looking specifically at click-through rates (CTR) and shares have told a different story. Of course, your ability to get good results with these will always depend on how valuable and relevant your content is to the target audience. Neil Patel many first-hand studies (Image: ) Databox According to a recent Databox survey, as much as 66% of respondents believed that there was in fact a correlation between the number of social shares a page receives and its overall search ranking. Optimize your social media profiles: When planning your content and thinking about SEO, you spend time and making sure you’ve chosen the right keywords, so the same thing applies to social media. optimizing your content For example, if you’re using Pinterest for your business, although it’s technically not a social media platform in the same sense as Facebook or Twitter, it’s used and treated as one, and is one of the best platforms for creating high value links back to your site and . driving traffic (Image: ) Hubspot When using relevant keywords in the description of each Pin and the board, you’ll boost your authority on Pinterest as you gain more followers, which in turn will help you rank higher on Google when people come searching these particular keywords or phrases - just like would happen on your website or blog. This approach can also be used to great effect on more traditional social media platforms like Twitter, Facebook and Instagram through the use of hashtags. As we can see from the example above from Caffe Nero, hashtags can be used to great effect to engage the right audience as they browse. Choose your content: A key component of your social SEO strategy is going to be the content you create. In terms of the actual format, this is something that you’ve likely been doing for a while, so there’s no need to change up the format unless it’s something you want to do - for example, if you’ve only been doing written posts until now, but would like to add video into the mix. That’s totally fine as long as you enjoy it and it fits in with your overall digital marketing strategy. can be especially effective when it comes to social SEO, for many reasons. Firstly, they can be great for leveraging greater levels of engagement, due to making posts on social networks more eye catching and being more favourable in the eyes of users, as the chart below shows: Multimedia content (Image: ) Martech Zone Secondly, videos can help companies to appear more prominently in SERPs. Because video content is rarer than text, there can often be fewer competitors ranking for the same search terms. However, the main things you need to take into consideration when it comes to your content is, how relevant it is to your audience, how valuable it is, and how likely it is to be shared and stay relevant over time so that you can rank higher on as well as the social . both traditional SERPs SERPs Measuring success: As we mentioned above, there’s a lot of conflicting information when it comes to whether social media plays a direct role your SEO rankings, but overall, it’s been found that when you optimize your content correctly and using the right keywords, as well as how often your content is shared, then it does make a big difference in results. Of course, you can’t just rely on this being the case, so you need to be able to see the numbers for yourself, and the best way of doing this is to use analytics platforms like or that can show you exactly which platforms your best traffic is coming from, and then once you’re able to look at the results of everything you post on that site, for example, Facebook, then you can see which posts are performing the best and contributing to your high traffic volumes and search ranking results. Google Analytics Finteza The Relationship Between Social and Traditional SEO: When many people hear the term Social SEO, they tend to think of it as a complete separate entity from traditional SEO, but this isn’t the case at all - the two have been working in tandem for years now and it’s important to view them this way. You can certainly have one without the other, but let’s be honest, this is only going to work if you either have no website and only use social media or the other way around, and that’s not a likely scenario for most marketers today. If you have individual teams who work on , then it’s hugely important that you start bringing them together to collaborate, as one aspect directly influences the other and what’s needed to make social SEO work is full integration with traditional SEO so that there’s consistency across the board. When you do this is when you’ll really start to notice results in terms of how the two work together and on your . social media and SEO search ranking The Future of Social SEO Neither social media or SEO are new, in fact, in terms of marketing, they’re both relatively old, but at the same time, social media is still in its infancy and SEO is growing in ways that we don’t really see yet, so the future of these two is exciting, but it’s not overly clear how it’s going to look. One thing is for sure, neither is going anywhere anytime soon, and this is important for you, because even if you’re a bit late to the party, now is the perfect time to jump in and start adapting your business for Social SEO.

How Businesses Are Adapting to Social SERPs Alongside Traditional SERPs

The Pokémon Company unveiled two new games as part of its Pokémon day celebrations. The first game revealed, , are remakes of the original Pokémon Diamond and Pearl Games. They are set to release worldwide in late 2021. The second game is one everyone is hyped about: Pokémon Legends: Arceus. Pokémon Brilliant Diamond and Shining Pearl Pokémon Legends: Arceus, An Open-World Pokémon Adventure The Pokémon Company has revealed Pokémon Legends: Arceus, a single-player set to release worldwide in early 2022. open-world Pokémon RPG This new Pokémon game is a different direction for the franchise, taking classic Pokémon gameplay and open-world RPG mechanics to create a brand new series of games. Pokémon Legends: Arceus is set in ancient Sinnoh, the region where Pokémon Brilliant Diamond and Shining Pearl's events occur. Image Credit Nintendo The game has a Legend of Zelda: Breath of the Wild inspired open world where you can catch and battle Pokémon seamlessly. It is your mission to complete Sinnoh's first Pokédex by catching and observing Pokémon in the wild, aided by your starter Pokemon. While we don’t know a whole lot about the game yet, we do know that the Pokémon God, Arceus, has a prominent role in the story and ties into your character's journey. The Pokémon Diamond and Pearl Remakes The Brilliant Diamond and Shining Pearl games are developed by Ilka inc., the developers behind Pokémon Home. Yuichi Ueda is directing the games alongside Game Freak director Junichi Masuda. The games are a faithful 1-1 remake of the original games. Brilliant Diamond and Shining Pearl have an updated graphical chibi style that gives the games a fresh new look. The games also take inspiration from Pokémon Let's Go with battle sequences graphically similar to the Let's Go games. Image Credit Nintendo Interestingly, Diamond and Pearl Remakes have an updated regional Pokédex that include Pokémon Platinum’s updated roster. Thus, we can expect some Pokémon Platinum features to make their way over to these new Pokémon games. Finally, the new Pokémon games have four-player multiplayer options, meaning you and your friends can battle online just like in the original games. Final Thoughts Interestingly, the release of Pokémon Legends: Arceus in 2022 could indicate that a with updated graphics and power could be coming soon. new Nintendo Switch It would make sense for Nintendo to release a new console before or around the release of this game to show off its new console. In the next few months, The Pokémon Company will release further information about these new Pokémon games. Also, check out: 5 Best PokémonGames of All Time Ranked 7 Nioh 2 Character Creation Codes on PC What the Acquisition of Bethesda Means for Xbox

Pokémon Legends: Arceus Revealed Alongside Diamond and Pearl Remakes

What if your digital product could not only look amazing but fuel your startup's success? At Meliorar, we believe that there’s more to design than meets the eye; there’s growth, a lot of growth! We use a combination of research-backed design methodology and user psychology to create intuitive digital experiences. If you want to keep users active for a longer period of time, increase the rate of collecting conversions, or simply design a product that will be adored, we work to fulfill the aims. The Meliorar Difference: Growth Hacking for UX At Meliorar, we don’t just design beautiful interfaces; we ensure growth in them. We analyze user behavior, conduct A/B testing, and track key metrics to ensure every design decision drives measurable results. Our growth hacking process involves User Research, Data Analysis,  UX Strategy, and UI Design. This iterative approach allows us to optimize for conversions and user engagement continuously. However, we differentiate ourselves with our global understanding and appreciation for cultural diversity. We practice cultural customization: creating designs, that appeal to a particular audience in Europe, the MENA area, and further. Meliorar: From Pixels to Performance and Everything In-Between Meliorar was born out of a desire to help startups harness the true power of design. Like many, we began with a simple idea and a lot of passion. We believe that design could be a powerful tool for growth, and we were determined to help other startups unlock that potential. At the very beginning, we remember concentrating on UI design only creating graphically appealing designs. We quickly understood though that looking good was not everything. There are also concepts like user behavior, data, and most importantly conversions. This is where the growth hacking ideology came into the picture and the transformation of Meliorar from just another UI/UX design to construction of beautiful yet effective designs happened. We've faced challenges along the way, learning valuable lessons about the importance of clear communication, agile methodologies, and building strong client partnerships. But through it all, we've remained committed to our core values: user-centered design, data-driven decision-making, and a relentless pursuit of growth. Milestones and achievements Since launching Meliorar in December 2020, we've been on a mission to help businesses unlock growth through user-centered design. And we've hit some pretty awesome milestones along the way: Global expansion: We've worked with incredible clients across Europe, the MENA region, North Africa, and even Canada. Fintech frenzy: We helped a fintech solution generate 1500 subscribers on their opening night! E-commerce magic: We teamed up with an e-commerce company to reduce their cart abandonment rate by 12% Conclusion We're grateful to Hacker Noon for this opportunity to share our story and connect with the startup community. Being nominated for Startup of the Year is a huge honor, and we're excited to be recognized alongside so many other incredible companies. But we need your help!  If you believe in the power of user-centered design and want to support a passionate team of growth hackers, cast your vote for Meliorar today! We believe that design has the power to transform businesses, and we're dedicated to empowering startups with the tools they need to achieve their goals. Every vote counts, and we appreciate your support. Vote for us ! Thank you for being part of our journey! What if your digital product could not only look amazing but fuel your startup's success? What if your digital product could not only look amazing but fuel your startup's success? At Meliorar, we believe that there’s more to design than meets the eye; there’s growth, a lot of growth! We use a combination of research-backed design methodology and user psychology to create intuitive digital experiences. If you want to keep users active for a longer period of time, increase the rate of collecting conversions, or simply design a product that will be adored, we work to fulfill the aims. The Meliorar Difference: Growth Hacking for UX At Meliorar, we don’t just design beautiful interfaces; we ensure growth in them. We analyze user behavior, conduct A/B testing, and track key metrics to ensure every design decision drives measurable results. Our growth hacking process involves User Research, Data Analysis,  UX Strategy, and UI Design. This iterative approach allows us to optimize for conversions and user engagement continuously. User Research, Data Analysis,  UX Strategy, and UI Design. However, we differentiate ourselves with our global understanding and appreciation for cultural diversity. We practice cultural customization: creating designs, that appeal to a particular audience in Europe, the MENA area, and further. Meliorar: From Pixels to Performance and Everything In-Between Meliorar was born out of a desire to help startups harness the true power of design. Like many, we began with a simple idea and a lot of passion. We believe that design could be a powerful tool for growth, and we were determined to help other startups unlock that potential. We believe that design could be a powerful tool for growth, and we were determined to help other startups unlock that potential. At the very beginning, we remember concentrating on UI design only creating graphically appealing designs. We quickly understood though that looking good was not everything. There are also concepts like user behavior, data, and most importantly conversions. This is where the growth hacking ideology came into the picture and the transformation of Meliorar from just another UI/UX design to construction of beautiful yet effective designs happened. We've faced challenges along the way, learning valuable lessons about the importance of clear communication, agile methodologies, and building strong client partnerships. But through it all, we've remained committed to our core values: user-centered design, data-driven decision-making, and a relentless pursuit of growth. Milestones and achievements Since launching Meliorar in December 2020, we've been on a mission to help businesses unlock growth through user-centered design. And we've hit some pretty awesome milestones along the way: Global expansion: We've worked with incredible clients across Europe, the MENA region, North Africa, and even Canada. Global expansion: Fintech frenzy: We helped a fintech solution generate 1500 subscribers on their opening night! Fintech frenzy: E-commerce magic: We teamed up with an e-commerce company to reduce their cart abandonment rate by 12% E-commerce magic: Conclusion We're grateful to Hacker Noon for this opportunity to share our story and connect with the startup community. Being nominated for Startup of the Year is a huge honor, and we're excited to be recognized alongside so many other incredible companies. But we need your help!  If you believe in the power of user-centered design and want to support a passionate team of growth hackers, cast your vote for Meliorar today! We believe that design has the power to transform businesses, and we're dedicated to empowering startups with the tools they need to achieve their goals. Every vote counts, and we appreciate your support. Vote for us ! Vote for us ! Vote for us Thank you for being part of our journey! Thank you for being part of our journey!

SOTY 2024 Nominee: Meliorar, More than just a Design Agency

AWS Athena: Everything You Need To Know

There's something special about that moment when technology truly surprises you. For me, that moment came recently when I first heard about Codeium's Windsurf Editor. As a developer who has spent countless hours in VSCode, I wasn't actively looking for a change. But sometimes change finds you. I remember the first time I settled into VSCode as my main editor. It felt like finding the perfect pair of shoes – comfortable, reliable, and just right. Over the years, I've customized it, learned its quirks, and built my workflow around it. So when I heard about yet another code editor entering the market, I'll admit I was skeptical. But Windsurf caught my attention in a way other editors haven't. It's not just another IDE with AI features bolted on as an afterthought. Instead, it feels like someone looked at the future of coding and built backward from there. The team at Codeium asked themselves what magic would look like for a software engineer using AI, and their answer is compelling. What really drew me in was their concept of AI flows. While I've enjoyed using AI copilots for quick suggestions and code completion, they've always felt limited to small, contained tasks. Windsurf's approach is different. They've managed to create something that feels both collaborative and independent – like having a coding partner who can either work alongside you or take initiative when needed. The heart of this experience is Cascade, which replaces the traditional chat interface. It's hard to describe how natural it feels – imagine having a senior developer looking over your shoulder, but one who has perfect memory of your entire codebase and can instantly understand the context of what you're trying to achieve. It's not just responding to questions; it's actively participating in your development process. Then there's Supercomplete, which feels like it's reading my mind. Instead of just predicting the next line of code, it's anticipating my next move. The first time it suggested a multi-cursor edit that was exactly what I was about to do manually, I had one of those "okay, this is different" moments. What makes this transition feel less daunting is that Windsurf is built on VSCode's foundation. It's like moving into a new house that has the same layout as your old one, but with all sorts of magical upgrades. My muscle memory for keyboard shortcuts still works, but now every keystroke seems to unlock new possibilities. The future possibilities are what really excite me. The team's vision includes features like understanding semantic relationships between files based on developer behavior, and having AI participate in day-to-day collaborations between team members. It's not just about writing code faster; it's about fundamentally changing how we interact with our development environment. I've always believed that great tools don't just make you more productive – they change how you think about the problem you're solving. Windsurf feels like it has the potential to do just that. While I'll always be grateful for my years with VSCode, I'm ready to catch this new wave. The team at Codeium is offering two weeks of the full experience for free, and I'm diving in. Sometimes the best decisions are the ones that feel both exciting and a little nerve-wracking. This feels like one of those moments. Here's to riding the wave of tomorrow's development, today. After all, isn't this what we love about technology? Those moments when something new comes along and makes you rethink what's possible. I'm excited to see where this journey leads, and I have a feeling I'm not the only one ready to grab a board and catch this wave. There's something special about that moment when technology truly surprises you. For me, that moment came recently when I first heard about Codeium's Windsurf Editor. As a developer who has spent countless hours in VSCode, I wasn't actively looking for a change. But sometimes change finds you. I remember the first time I settled into VSCode as my main editor. It felt like finding the perfect pair of shoes – comfortable, reliable, and just right. Over the years, I've customized it, learned its quirks, and built my workflow around it. So when I heard about yet another code editor entering the market, I'll admit I was skeptical. But Windsurf caught my attention in a way other editors haven't. It's not just another IDE with AI features bolted on as an afterthought. Instead, it feels like someone looked at the future of coding and built backward from there. The team at Codeium asked themselves what magic would look like for a software engineer using AI, and their answer is compelling. What really drew me in was their concept of AI flows. While I've enjoyed using AI copilots for quick suggestions and code completion, they've always felt limited to small, contained tasks. Windsurf's approach is different. They've managed to create something that feels both collaborative and independent – like having a coding partner who can either work alongside you or take initiative when needed. The heart of this experience is Cascade, which replaces the traditional chat interface. It's hard to describe how natural it feels – imagine having a senior developer looking over your shoulder, but one who has perfect memory of your entire codebase and can instantly understand the context of what you're trying to achieve. It's not just responding to questions; it's actively participating in your development process. Then there's Supercomplete, which feels like it's reading my mind. Instead of just predicting the next line of code, it's anticipating my next move. The first time it suggested a multi-cursor edit that was exactly what I was about to do manually, I had one of those "okay, this is different" moments. What makes this transition feel less daunting is that Windsurf is built on VSCode's foundation. It's like moving into a new house that has the same layout as your old one, but with all sorts of magical upgrades. My muscle memory for keyboard shortcuts still works, but now every keystroke seems to unlock new possibilities. The future possibilities are what really excite me. The team's vision includes features like understanding semantic relationships between files based on developer behavior, and having AI participate in day-to-day collaborations between team members. It's not just about writing code faster; it's about fundamentally changing how we interact with our development environment. I've always believed that great tools don't just make you more productive – they change how you think about the problem you're solving. Windsurf feels like it has the potential to do just that. While I'll always be grateful for my years with VSCode, I'm ready to catch this new wave. The team at Codeium is offering two weeks of the full experience for free, and I'm diving in. Sometimes the best decisions are the ones that feel both exciting and a little nerve-wracking. This feels like one of those moments. Here's to riding the wave of tomorrow's development, today. After all, isn't this what we love about technology? Those moments when something new comes along and makes you rethink what's possible. I'm excited to see where this journey leads, and I have a feeling I'm not the only one ready to grab a board and catch this wave.

Riding the Wave: My Journey from VSCode to Windsurf

The rise of artificial intelligence (AI) has sparked one of the most hotly debated topics in the tech industry: Will AI take away our jobs? For developers, engineers, and data scientists, the concern hits close to home. After all, if AI can code, test, and even debug, where does that leave us? But here’s the truth: AI isn’t here to steal your job it’s here to change how we work, enhance what we do, and help us be better at it. Let’s dive into why AI isn’t something to fear, and how it’s more likely to complement your work than replace it. 1. AI Enhances Productivity Instead of Replacing Humans Let’s start with the basics. AI isn’t about replacing people it’s about giving us the tools to do more and do it faster. Tools like GitHub Copilot, for example, are there to help developers by suggesting code snippets. It doesn’t take the place of a developer it allows developers to skip past repetitive, mundane tasks and focus on solving real problems. In software testing, AI-powered tools identify bugs much quicker than manual testing ever could. This doesn’t mean quality assurance engineers are obsolete; it means they can spend less time on tedious tasks and more time improving the product and tackling higher-level issues. By handling the small stuff, AI frees up your brainpower for the big ideas. 2. Technological Advancements Create New Job Opportunities If we look back through history, one thing is clear: every new wave of technology creates new roles. The internet gave rise to web developers, cybersecurity analysts, and digital marketers. In much the same way, AI is creating entirely new job categories like Machine Learning Engineers, AI Trainers, and AI Ethicists. The World Economic Forum reports that by 2025, AI and automation will create 97 million new roles globally. These new jobs won’t just be about coding—they’ll also involve things like overseeing AI systems, ensuring fairness in algorithms, and managing the collaboration between humans and machines. 3. AI Requires Human Oversight and Ethical Direction One thing that AI cannot do is make moral or ethical decisions. AI is only as good as the data it’s trained on and even then, it can’t understand the deeper context or nuances that humans bring. For example, AI tools used for hiring can sometimes reflect biases from the data they’re trained on, which means we need human intervention to identify and correct these biases. That’s where you, as a techie, come in. AI needs your guidance to ensure it’s applied ethically. Whether it’s curating data or fixing unintended outcomes, your role in overseeing AI systems is crucial. Machines might be able to process data at lightning speed, but they’ll always need humans to guide them in the right direction. 4. Upskilling: The Key to Thriving Alongside AI AI doesn’t spell the end of jobs it’s a call to evolve. Upskilling is the bridge that connects you to the future of work. If you stay ahead of the curve, you can adapt and thrive in an AI-driven world. It’s not just about learning how to use AI tools it’s about mastering new skills. Skills like cloud computing, AI ethics, and data analytics are becoming increasingly valuable. A USA Today report estimates that around 73 million jobs in the U.S. could be at risk of automation by 2030, which highlights how essential reskilling is. Fortunately, platforms like Coursera, edX, and Udemy make it easy to dive into new fields, from AI and machine learning to natural language processing and ethical AI. By investing in your learning today, you’re not just securing your future—you’re ensuring that you’re indispensable in a world where AI only enhances what you do. 5. Creativity and Empathy: Human Traits AI Can’t Replace AI is great at data, patterns, and tasks that can be boiled down to algorithms. But creativity? Empathy? Critical thinking? Those are still very much human strengths. AI can’t replace the imaginative spark that leads to groundbreaking software architecture, or the empathy required for a top-tier UX design. In fact, as AI takes over routine tasks, it gives tech professionals more space to tap into their creative and emotional intelligence. Think about it: with AI handling the repetitive parts of the work, you have more freedom to focus on the design, the vision, and the strategy that truly drives innovation. 6. AI as a Tool, Not a Threat At the end of the day, AI is a tool that’s designed to make your life easier, not harder. Think of it like your new assistant: tireless, efficient, and always ready to help you get more done. Take DevOps professionals, for example. They’re using AI-powered platforms to monitor systems, predict maintenance needs, and optimize processes in real time. AI isn’t replacing them it’s empowering them to deliver faster and more efficiently. And the same goes for most other fields in tech: AI enhances the work we do, giving us more time to focus on high-value tasks and creative problem-solving. 7. AI Adoption in the Tech Industry: Real-World Examples We’ve seen how AI is empowering professionals rather than replacing them. Look at these real-world examples: GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development.
AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs.
Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. In all of these cases, AI isn’t a replacement for human expertise; it’s an enhancement that helps professionals do their jobs more effectively. 8. Challenges and How to Overcome Them Of course, there are challenges when it comes to AI adoption. A lot of the fear about AI replacing jobs stems from misconceptions or a lack of understanding. But businesses and professionals can tackle these issues by: Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged.
Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes.
Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. By addressing these challenges head-on, we can build a future where AI and humans work together seamlessly. 9. The Future of Jobs in an AI-Driven World The future of work isn’t about humans versus machines it’s about humans and machines working together. As AI continues to evolve, it will redefine roles, but it won’t eliminate them. For tech professionals, this means shifting focus from routine tasks to more strategic, creative, and high-level responsibilities. For example: Software developers will focus on system design and integration, not just writing code.
Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers.
IT managers will guide AI-driven processes while ensuring alignment with business objectives. The change may seem intimidating, but it opens up new, more meaningful career opportunities that blend technology with creativity and problem-solving. 10. Proven Points to Remember Here’s a quick recap of why AI isn’t going to steal your job: AI boosts productivity by automating repetitive tasks.
Technological advancements create new roles and opportunities.
AI requires human oversight and ethical direction.
Creativity, critical thinking, and empathy remain irreplaceable.
Upskilling and adaptability are key to staying relevant.
AI is a tool that complements human expertise. By understanding these points, you can shift your mindset and see AI as an ally rather than a threat. Final Thoughts: Embracing the Change AI isn’t here to take away your job it’s here to transform it. As tech professionals, the key to thriving in an AI-powered world lies in adaptability, continuous learning, and leveraging AI to amplify your abilities. The most successful careers will belong to those who view AI as a partner in innovation, not a competitor. In the broader context of Digital Transformation in the Age of AI, AI is reshaping industries, but it’s not about replacing the workforce. It’s about creating new roles, improving efficiencies, and empowering workers to do more and do it better. Instead of fearing change, let’s embrace it and see how humans and machines can work together to build a brighter future. The rise of artificial intelligence (AI) has sparked one of the most hotly debated topics in the tech industry: Will AI take away our jobs? For developers, engineers, and data scientists, the concern hits close to home. After all, if AI can code, test, and even debug, where does that leave us? But here’s the truth: AI isn’t here to steal your job it’s here to change how we work, enhance what we do, and help us be better at it. Let’s dive into why AI isn’t something to fear, and how it’s more likely to complement your work than replace it. 1. AI Enhances Productivity Instead of Replacing Humans Let’s start with the basics. AI isn’t about replacing people it’s about giving us the tools to do more and do it faster. Tools like GitHub Copilot, for example, are there to help developers by suggesting code snippets. It doesn’t take the place of a developer it allows developers to skip past repetitive, mundane tasks and focus on solving real problems. In software testing, AI-powered tools identify bugs much quicker than manual testing ever could. This doesn’t mean quality assurance engineers are obsolete; it means they can spend less time on tedious tasks and more time improving the product and tackling higher-level issues. By handling the small stuff, AI frees up your brainpower for the big ideas. AI-powered tools 2. Technological Advancements Create New Job Opportunities If we look back through history, one thing is clear: every new wave of technology creates new roles. The internet gave rise to web developers, cybersecurity analysts, and digital marketers. In much the same way, AI is creating entirely new job categories like Machine Learning Engineers, AI Trainers, and AI Ethicists. The World Economic Forum reports that by 2025, AI and automation will create 97 million new roles globally. These new jobs won’t just be about coding—they’ll also involve things like overseeing AI systems, ensuring fairness in algorithms, and managing the collaboration between humans and machines. World Economic Forum reports World Economic Forum reports 3. AI Requires Human Oversight and Ethical Direction One thing that AI cannot do is make moral or ethical decisions. AI is only as good as the data it’s trained on and even then, it can’t understand the deeper context or nuances that humans bring. For example, AI tools used for hiring can sometimes reflect biases from the data they’re trained on, which means we need human intervention to identify and correct these biases. That’s where you, as a techie, come in. AI needs your guidance to ensure it’s applied ethically. Whether it’s curating data or fixing unintended outcomes, your role in overseeing AI systems is crucial. Machines might be able to process data at lightning speed, but they’ll always need humans to guide them in the right direction. 4. Upskilling: The Key to Thriving Alongside AI AI doesn’t spell the end of jobs it’s a call to evolve. Upskilling is the bridge that connects you to the future of work. If you stay ahead of the curve, you can adapt and thrive in an AI-driven world. It’s not just about learning how to use AI tools it’s about mastering new skills. Skills like cloud computing, AI ethics, and data analytics are becoming increasingly valuable. A USA Today report estimates that around 73 million jobs in the U.S. could be at risk of automation by 2030, which highlights how essential reskilling is. USA Today report USA Today report Fortunately, platforms like Coursera, edX, and Udemy make it easy to dive into new fields, from AI and machine learning to natural language processing and ethical AI. By investing in your learning today, you’re not just securing your future—you’re ensuring that you’re indispensable in a world where AI only enhances what you do. 5. Creativity and Empathy: Human Traits AI Can’t Replace AI is great at data, patterns, and tasks that can be boiled down to algorithms. But creativity? Empathy? Critical thinking? Those are still very much human strengths. AI can’t replace the imaginative spark that leads to groundbreaking software architecture, or the empathy required for a top-tier UX design. In fact, as AI takes over routine tasks, it gives tech professionals more space to tap into their creative and emotional intelligence. Think about it: with AI handling the repetitive parts of the work, you have more freedom to focus on the design, the vision, and the strategy that truly drives innovation. 6. AI as a Tool, Not a Threat At the end of the day, AI is a tool that’s designed to make your life easier, not harder. Think of it like your new assistant: tireless, efficient, and always ready to help you get more done. Take DevOps professionals, for example. They’re using AI-powered platforms to monitor systems, predict maintenance needs, and optimize processes in real time. AI isn’t replacing them it’s empowering them to deliver faster and more efficiently. And the same goes for most other fields in tech: AI enhances the work we do, giving us more time to focus on high-value tasks and creative problem-solving. 7. AI Adoption in the Tech Industry: Real-World Examples We’ve seen how AI is empowering professionals rather than replacing them. Look at these real-world examples: GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development. AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs. Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development. AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs. Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. In all of these cases, AI isn’t a replacement for human expertise; it’s an enhancement that helps professionals do their jobs more effectively. 8. Challenges and How to Overcome Them Of course, there are challenges when it comes to AI adoption. A lot of the fear about AI replacing jobs stems from misconceptions or a lack of understanding. But businesses and professionals can tackle these issues by: Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged. Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes. Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged. Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes. Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. By addressing these challenges head-on, we can build a future where AI and humans work together seamlessly. 9. The Future of Jobs in an AI-Driven World The future of work isn’t about humans versus machines it’s about humans and machines working together. As AI continues to evolve, it will redefine roles, but it won’t eliminate them. For tech professionals, this means shifting focus from routine tasks to more strategic, creative, and high-level responsibilities. For example: Software developers will focus on system design and integration, not just writing code. Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers. IT managers will guide AI-driven processes while ensuring alignment with business objectives. Software developers will focus on system design and integration, not just writing code. Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers. IT managers will guide AI-driven processes while ensuring alignment with business objectives. The change may seem intimidating, but it opens up new, more meaningful career opportunities that blend technology with creativity and problem-solving. 10. Proven Points to Remember Here’s a quick recap of why AI isn’t going to steal your job: AI boosts productivity by automating repetitive tasks. Technological advancements create new roles and opportunities. AI requires human oversight and ethical direction. Creativity, critical thinking, and empathy remain irreplaceable. Upskilling and adaptability are key to staying relevant. AI is a tool that complements human expertise. AI boosts productivity by automating repetitive tasks. Technological advancements create new roles and opportunities. AI requires human oversight and ethical direction. Creativity, critical thinking, and empathy remain irreplaceable. Upskilling and adaptability are key to staying relevant. AI is a tool that complements human expertise. By understanding these points, you can shift your mindset and see AI as an ally rather than a threat. Final Thoughts: Embracing the Change AI isn’t here to take away your job it’s here to transform it. As tech professionals, the key to thriving in an AI-powered world lies in adaptability, continuous learning, and leveraging AI to amplify your abilities. The most successful careers will belong to those who view AI as a partner in innovation, not a competitor. In the broader context of Digital Transformation in the Age of AI, AI is reshaping industries, but it’s not about replacing the workforce. It’s about creating new roles, improving efficiencies, and empowering workers to do more and do it better. Instead of fearing change, let’s embrace it and see how humans and machines can work together to build a brighter future. Digital Transformation in the Age of AI, Digital Transformation in the Age of AI,

AI Isn't Here to Steal Your Job, Techies—Proven Points to Understand

Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes. Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes.

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