There's a lot of things that can wreck your chances to do well. We live in a rather dangerous world. So, you got to be not only wise, you got to be careful. And attitude diseases are deadly. I mean, they'll destroy all the good things you start. So, we'll go through those attitude diseases. How to spot them, how to look for them, what they are, and the cure. And I'm a pro on these because I've had them all. So, I can give you excellent advice on these.<hr><h2>Disease #1: Indifference</h2>The shrug of the shoulder. The guy's not even concerned. He's just drifting. There's one problem with drift. You cannot drift to the top of the mountain.The guy says, "Well, I can't see getting all that worked up." Well, to be any kind of winner, you got to get worked up. Here's the key to the good life: Learn to put everything you've got into everything you do.Whatever you are doing, pour it on. It will quickly open up into opportunity or quickly disclose to you that you ought to be doing something else.The delusion is if I had a better job, I'd really pour it on. See, that's delusion. Wherever you are, pour it on. See, that'll help change your life. Get rid of this disease.<hr><h2>Disease #2: Indecision</h2>Mental paralysis. The guy can't make up his mind and it becomes a disease. Pretty soon he knows he's got it. The guy says, "Well, I know I'm on the fence."But he says, "What if I get off on the wrong side?" Listen, after a while, it doesn't matter. Just get off. Any side will do. Here's the best way to live: One way or the other, that's best. Pick a direction and go with everything you got. Just pick one and go.Somebody says, "Yeah, but what if it's the wrong direction?" You'll find out quicker.A life full of adventure is a life full of many decisions. The ones that turn out to be wrong give you better experience to make better decisions. So don't see how many decisions you can get out of. See how many you can get into. That's where the adventure is. So shake off this disease, indecision.<hr><h2>Disease #3: Doubt</h2>Doubt's like a plague. And one of the worst is self-doubt. There are many, but that's one of the worst. The guy doubts himself. Doubts if it'll last that long for him. Doubts if he can do that well. Doubts if he can make that much. Doubts if he can accomplish all that. A chronic excellent self-doubter.You can imagine what damage that does to your future. So, here's the key: Turn this coin over and become a believer. And there's many things to believe in. One of the majors is yourself. The understanding of self-worth is the beginning of progress.<hr><h2>Disease #4: Worry</h2>Now, if those three don't get you, this one will. Worry, that's a devastating disease. Worry causes health problems, social problems, personal problems, family problems. It's devastating. Worry long enough, it'll drop you to your knees. It could reduce you to begging.I know how bad this one is. I used to have it bad. I used to be known as a super worrier. Not a super warrior. No, super worrier. My family wished I'd have been a warrior. I got those years to make up for.But I tell you what, my advice to you is do what I finally did on worry: Give it up. Who needs it? I'm not saying it's easy. I'm saying it's worth it.It took me almost one year to kick the worry habit. And it was not an easy year. It was one of the toughest years I ever spent. But I finally got that monkey off my back. And I discovered you could live the most incredible life free of worry. Not free of challenge, not free of difficulty, free of worry. I learned how to do it. And you can too.<hr><h2>Disease #5: Overcaution</h2>Some people never will have much. They're too cautious. Now you can also be too reckless, but you can also be too cautious. This is called the timid approach to life.And my caution was always the risk. Risk used to drive me right up the wall. I used to say, "What if this happens?" It's called the language of the poor. "What if this happens? And on top of that, if this was to happen, look at the fix I'd be in. I better not try." I could always ace myself out.Then I'll tell you what changed my whole life when I finally discovered: It's all risky.The minute you were born it got risky. If you think trying is risky, wait till they hand you the bill for not trying. If you think investing is risky, wait till you get the tab for not investing. See, it's all risky. Getting married is risky. Having children is risky. Going into business is risky. Investing your money is risky. It's all risky.I'll tell you how risky life is: You're not going to get out alive. That's risky.The Englishman says, "Well, if that's the way it's going to work out, let's give it a go." Right? That's what it's for. Give it a go.Somebody says, "Yeah, but I'm looking for safety and security." Fine, then huddle in a corner. We'll cover you with a sheet, bring you three meals a day, and we'll protect you, feed you, look after you, care for you, we won't let anything happen to you, and you'll probably live to be 100.The guy said, "Well, yeah, I'd live to be 100, but what a way to live, right? What a way to live safe and secure. Don't ask for security. Ask for adventure. Better to live 30 years full of adventure than 100 years safe in the corner."<hr><h2>Disease #6: Pessimism</h2>Pessimism. The deadly disease of always looking on the bad side, the problem side, the difficult side, checking all the reasons why it can't be done. The poor pessimist leads an ugly life.He doesn't try to figure out what's right. He tries to figure out what's wrong. He doesn't look for virtue. He looks for faults. And when he finds them, he's delighted. How ugly.This is the poor guy who looks through the window, doesn't see the sunset. He sees the specks on the window.And this is the poor guy who rushes up and he says, "I've got five good reasons why it won't work." He's so dumb. He doesn't know all he needs is one. He's got five.To the pessimist, the glass is always half empty. To the optimist, the glass is half full. Why would the same measure affect people two different ways? Answer: It all depends on how you look at it. Our lives are mostly affected by the way we think things are, not the way they are. The way we think they are affects us most.<hr><h2>The Power of Thinking</h2>There's a subject we don't have time to get into tonight called better thinking habits. One of the major things Mr. Shoaf taught me when I met him, he said poor thinking habits keeps most people poor. Not poor working habits. Most people work hard but they don't think hard.And Shoaf taught me that the mind is like a factory, a mental factory. And whatever you think about all day long pours ingredients into this mental factory. And that's what builds the economic, social, financial fabric of your life. As you think, so you become. How awesome.When he talked about poor thinking habits, he had me. I used to start the day reading the morning newspaper. I mean, you can believe that or not. I'd get a cup of coffee and read the paper. I'd load up on wars and riots and murders and stabbings and killings and bank robberies and muggings and car wrecks and tragedies. I'd even read the back pages. I seem to like that stuff for some weird reason. I'd load up on all that and then I'd start the day. You can imagine the kind of days I used to have. You walk around on your financial knees. They call you economic pee-wee.The guy says, "I want to be a great leader." Wonderful. The first thing we do is follow him to his house. When we get there, we walk in and check his library. Number one, somebody says, "Well, why check his library?" The reason is because what a man reads pours massive ingredients into his mental factory and the fabric of his life is built from those ingredients.You would not believe what some people have got in their house to read. You would not believe. One of the best dressed up words I know for a lot of it is trash. Can you imagine dumping a barrel of trash into this mental factory every day and coming out with a rich, dynamic, positive life? It can't be done. You might as well try making a cake with cement.<hr><h2>Building the Good Life</h2>The kids back in Danbury, Connecticut high school, they're asking me questions one day. I'm talking to the kids. Kids got good questions these days. One of them said to me, "Mr. Rohn, how do you build the good life?"I said, "It's simple. It's not easy, but it's simple. Here's how you build anything: Select the right ingredients. Keep out the wrong ingredients. And it starts with thought.Everything starts with thought. So, you must be wise and careful what you think about because that starts everything."I asked the kids, "What would happen if somebody dropped sugar in my coffee?" They said, "Well, you'd be okay." I said, "What if somebody dropped strychnine in my coffee?" They said, "Well, you'd be dead." I said, "Correct. Lesson one: Life is both sugar and strychnine. You got to be careful."I said, "What if my worst enemy drops in the sugar?" They said, "You'll be okay?" I said, "What if my best friend, even by accident, drops in the strychnine?" They said, "Well, you'd be dead." I said, "Correct. Lesson two: Watch your coffee."You got to be careful. See, it doesn't matter who hands you the bad stuff. It doesn't matter where you get the bad stuff. It'll still do its damage on your bank account wherever you get it.Mr. Shoaf gave me one of the greatest phrases when I first met him when he said, "Jim, every day, stand guard at the door of your mind." How important. Stand guard at the door of your mind and you decide what goes into your mental factory. Don't let anybody just dump anything they want to in your mental factory because you've got to live with the results.<hr><h2>Disease #7: Complaining</h2>Okay, here's the last disease and we're through with this list. The last subject is very brief. The last disease, but this one is deadly. Engage in this or indulge in it even slightly and you might as well forget the future because it's going to forget you.Complaining, crying, whining, griping—a Bible word called murmuring. See, that'll ace your future.Spend five minutes complaining and you have wasted five. And you may have begun what's known as economic cancer of the bone. Surely they will soon haul you off into a financial desert and there let you choke on the dust of your own regret.I hope I said that well so you won't forget. It's a deadly disease.Just be on the lookout for the things that can destroy all the good you start. The war is on. And this evening, tomorrow, mentally, personally, socially, economically, you got to make sure you're winning the war. And this is part of it.

Attitude Diseases - by Jim Rohn

<h4>Shortly</h4>In this article, I’ll explore the evolution of automation — from the early days of rule-based RPA to the modern, AI-driven systems reshaping workflows today.The question we’re really asking is: is automation still evolving, or has it reached a turning point?<h4>So, what is RPA really&nbsp;first?</h4>Robotic Process Automation (RPA) uses software “bots” to handle repetitive, rule-based tasks across enterprise systems, freeing people to focus on higher-value work.<blockquote>According to Wikipedia: &nbsp;Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents. RPA should not be confused with artificial intelligence, as it is based on automation technology following a predefined workflow. It is sometimes referred to as software robotics (not to be confused with robot software).</blockquote>Originally celebrated for its rapid deployment and clear ROI, RPA is now evolving alongside advances in AI, orchestration, and cloud-native platforms.This evolution raises an important question: what role will RPA play in the age of intelligent automation?<hr><h4>Brief history&nbsp;lesson</h4>Most of us deal with tasks that could easily be automated, whether through AI-driven tools or simple rule-based systems.While exploring this topic (to be transparent, it was more of a deep Google dive than formal research), I came across eight different sources.Strangely enough, they all felt almost identical — like a copy-and-paste chain passed from one website to another, just under different names.Still, I’ve shared them as references at the end of this article.Interestingly, most of these sources trace the roots of RPA back to the 1990s.<blockquote>But there’s one exception: <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="https://www.nandan.info/history-of-robotic-process-automation-rpa/">https://www.nandan.info/history-of-robotic-process-automation-rpa/</a></blockquote>This article traces RPA back to the early 1970s, showing that it’s far from a new trend — it has been evolving for more than five decades.Anyway, let’s focus on summarizing its history:<ul><li>Computerized Automation (1970–1990)</li><li>Roots and Precursors (1990s → early 2000s)</li><li>Commercialization and the First Vendors (early-to-mid 2000s)</li><li>Enterprise Adoption and Maturity (2010s)</li><li>Shock, Scale, and the Pandemic (2020)</li><li>From Rule-Based Bots to Intelligent/Agentic Automation (late 2010s → 2020s)</li><li>Current State and Outlook</li></ul>For more details on each stage, you can refer to the sources I’ve shared below.<hr><h4>Popular RPA&nbsp;Tools</h4>When it comes to RPA, there’s no shortage of tools out there — each with its own use cases.Some of the names you’ll often hear include UiPath, Automation Anywhere, Blue Prism, and Microsoft Power Automate.Personally, exploring them feels a bit like walking into a candy store: each one promises to make repetitive work vanish, but in slightly different ways.The key is finding the tool that fits your workflow, team skills, and the type of tasks you want to automate.<hr><h4>What happened to&nbsp;RPA?</h4>After our brief introduction, I want to turn to the main focus of this article: what actually happened to RPA?I first heard about RPA in 2023, through a job announcement — one of the banks was looking for an RPA developer.Naturally, this caught my attention, and I decided to dig deeper.The role sounded genuinely interesting: as an RPA developer, you research which tasks can be automated, determine how to automate them, identify the necessary resources, and evaluate which tools are best suited for the job.It’s a mix of strategy, technology, and problem-solving, which immediately intrigued me.In recent years, automation has taken a new direction. Today, we hear a lot of buzzwords — AI, intelligent agents, smart bots, and more.Despite the different names, they all share the same fundamental goals as RPA:<ul><li>Cutting costs</li><li>Increasing workflow speed</li><li>Boosting productivity and revenue</li><li>Reducing human errors</li></ul>The tools and terminology may have evolved, but the core purpose remains the same.Perhaps I’m not the only one who finds the lines a bit blurred.If AI and RPA are essentially achieving the same goals, why do we give them different names?And why do we treat them as separate terms?<blockquote>At the end of the day, all of these technologies are implemented with a single aim: to increase efficiency and, ultimately, revenue.</blockquote>For a closer look, I’ll reference Wikipedia as an authoritative source:<blockquote>In traditional <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Workflow">workflow</a> <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Automation">automation</a> tools, a <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Software_developer">software developer</a> produces a list of actions to automate a task and interface to the <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Frontend_and_backend">back end</a> system using internal <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Application_programming_interfaces">application programming interfaces</a> (APIs) or dedicated <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Scripting_language">scripting language</a>.</blockquote>Even wikipedia confirms that similarities to other tools:<blockquote>RPA tools have strong technical similarities to <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Graphical_user_interface_testing">graphical user interface testing</a> tools. These tools also automate interactions with the <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Graphical_user_interface">GUI</a>, and often do so by <a target="_blank" rel="noopener" class="text-red-600 hover:text-red-800 underline markup--anchor markup--blockquote-anchor" href="https://en.wikipedia.org/wiki/Programming_by_demonstration">repeating a set of demonstration actions</a> performed by a user.</blockquote>So, why are we still treating them as separate terms?During my research, I noticed several emerging trends:<ul><li>New fields for automation — automation is expanding beyond traditional boundaries.</li><li>It’s no longer limited to rule-based systems — the scope has grown far beyond simple scripted tasks.</li><li>AI as a tool in RPA development — artificial intelligence is increasingly integrated to enhance automation capabilities.</li></ul>These trends show that while the terminology may differ, the underlying goal of improving efficiency and productivity remains the same.Today, we’re no longer limited to classic RPA tools when it comes to automating tasks and workflows.Alongside the big enterprise platforms, we now have a wide range of options — from free, open-source solutions like n8n, to paid, closed-source services like Zapier.This variety makes automation more accessible than ever, whether you’re a developer building custom flows or just someone looking to simplify everyday work.I’m also aware that some people might be rolling their eyes at this point, thinking: “What is he even talking about? AI, agents, reinforcement learning, automation, RPA — aren’t these all completely different concepts?What kind of article is this?”And honestly, that reaction makes sense.<blockquote>These are distinct terms in the technical world, each with its own definition and scope.</blockquote><blockquote>But from a practical perspective — especially for people just trying to get work done — they often blur together.</blockquote>I completely agree with and respect all of these perspectives — each concept has its own place and meaning.But at the same time, let’s pause and reflect: aren’t all of them, at their core, about the same thing — automation?At the end of the day, whether we call it RPA, AI, or something else, the common goal remains: making our processes faster, more efficient, and less dependent on repetitive human effort.So why do we keep creating endless boxes of terminology, debating definitions, and separating them as if they belong to different worlds?The answer, I believe, is simple: money.New terms create new markets, new job titles, and new ways to sell the same underlying idea.At last, what we truly want are technologies that deliver real value.And like every other technology, automation has not disappeared — it has transformed.In the first stage of automation, we were limited to programming rule-based systems that followed predefined, deterministic outputs: if the result is 1, do X; otherwise, do Y — and so on.It was straightforward, but also very rigid.Today, the picture is completely different.We now have tools that can handle almost any kind of output, and in many cases, they don’t even rely on strict predefined rules.Instead, they can adapt and solve problems dynamically within their environment, opening the door to far more flexible and intelligent automation.We automated chess as early as the 2000s with Deep Blue, and for a long time many believed that games like Go were beyond the reach of machines. Yet even that fortress eventually fell with AlphaGo.Fast forward to today, and we’re seeing automation reach an entirely new level — this time in software engineering itself, an industry worth more 50 billion USD.What once seemed unimaginable is now becoming reality.When we step back and look at this entire evolution from above, it feels like watching history unfold from the sky — one breakthrough after another, each expanding the horizon of what machines can do.At its core, this article is about automation.In my opinion, the evolution of automation can be seen like this:<ul><li>Rule-based systems (scripting languages, RPA)</li><li>Workflow automation (tools like Zapier, n8n)</li><li>Reinforcement learning (minimum human in the loop)</li><li>Agents in the loop (workflows enhanced by LLMs)</li></ul>But it’s important to note that this progression hasn’t been strictly chronological.These areas are developing asynchronously, often in parallel, influencing and overlapping with each other rather than following a neat, linear path.So, we are still witnessing the dominance of rule-based systems as a primary technological layer, even while other approaches — like Agentic AI and robotics AI — are still in their emerging stages.But this naturally leads to the question: what comes next?With the rapid evolution of AI, the lines between automation and standalone decision-making have become increasingly blurred.This means we should start approaching these terms in a way that goes beyond traditional academic definitions.Instead of separating them into rigid categories, we need a more fluid perspective — one that reflects how these technologies are actually converging in practice.<hr><h3>Let’s finish</h3>So, is RPA dead? Not really.What we are witnessing is not the disappearance of RPA, but its transformation into a broader landscape of automation where AI, agents, and intelligent workflows play alongside traditional bots.From the rigid, rule-based scripts of the past to today’s adaptive, AI-powered systems, automation has constantly reinvented itself.Each stage brought us closer to tools that can handle more complexity, with less human intervention, and deliver greater value.The terminology may keep changing — RPA, AI, agents, automation — but in practice, they are all part of the same continuum.What matters is not the label, but the outcome: technologies that free us from repetitive work, reduce errors, and help us focus on creativity, strategy, and innovation.In that sense, automation is very much alive.It hasn’t met its asteroid — it’s still evolving, reshaping industries, and redefining the way we work.The real challenge for us is not to debate the terms, but to learn how to harness this transformation in a way that creates meaningful, lasting value.<hr><h4>Sources</h4><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://www.grandviewresearch.com/industry-analysis/robotic-process-automation-rpa-market">Robotic Process Automation Market | Industry Report, 2030 The global robotic process automation market size was estimated at USD 3.79 billion in 2024 and is projected to reach…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://www.grandviewresearch.com">www.grandviewresearch.com</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://electroneek.com/rpa/history-of-rpa/">The History of Robotic Process Automation (RPA) | ElectroNeek RPA, as we have it now, has made a long way from UI testing and the enterprise sector. Discover the history behind RPA…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://electroneek.com">electroneek.com</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://www.metizsoft.com/blog/history-of-rpa">The History Of RPA (Robotic Process Automation) Explore the fascinating history of RPA, from automating simple tasks to becoming a revolutionary force in business…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://www.metizsoft.com">www.metizsoft.com</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://www.uipath.com/blog/rpa/the-evolution-of-rpa-past-present-and-future">The Evolution of Robotic Process Automation (RPA): Past, Present, and Future A look at how Robotic Process Automation (RPA) developed through the lens at its past, present, and future. See what…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://www.uipath.com">www.uipath.com</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://www.mindfieldsglobal.com/blog/evolution-of-rpa-into-ia">Embracing Automation: Turning Recession Challenges into Opportunities Discover how leveraging automation technologies during times of recession can unlock new opportunities, streamline…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://www.mindfieldsglobal.com">www.mindfieldsglobal.com</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://ramamtech.com/blog/the-history-of-robotic-process-automation">The History of Robotic Process Automation Discover the fascinating evolution of Robotic Process Automation (RPA) in our detailed blog. Learn how RPA has…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://ramamtech.com">ramamtech.com</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://www.nandan.info/history-of-robotic-process-automation-rpa/">The Remarkable History of Robotic Process Automation (RPA) When we started a few years back, Robotic Process Automation (RPA) was an emerging technology. We had to explain to…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://www.nandan.info">www.nandan.info</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline markup--anchor markup--mixtapeEmbed-anchor" href="https://www.auxiliobits.com/blog/the-evolution-of-rpa-past-present-and-future/">The evolution of RPA: Past, Present, and Future - auxiliobits RPA made a foray as early as 2000. RPA bots mimic humans and automate multiple activities and transactions…</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://www.auxiliobits.com">www.auxiliobits.com</a>

Robotic Process Automation

Do you ever feel like you’re two different people when you play&nbsp;chess?I kept asking myself this question while staring at nearly 11,000 games spread across my two accounts — IsmatS and Cassiny. It started as a simple curiosity: which account actually represents the “real” me? But the deeper I dug, the more I realized I was asking the wrong question entirely.Here’s what I found instead: we’re all multiple chess players, depending on when we sit down, what mood we’re in, and what we’re trying to prove to ourselves that day.<h3>The Short Version (For When You’re Between&nbsp;Games)</h3>Having two accounts taught me that the same brain can produce completely different chess depending on tiny choices — time controls, opening selections, even what hour you play. Win rates matter, sure, but the real story lives in the curves: how your rating moves over time, when you play your best games, and whether you’re the kind of player who experiments or sticks to what works.The most useful discoveries? Small experiments beat big revelations. Fifty games with better scheduling taught me more than any opening book ever did.<h3>What Caught Me Off&nbsp;Guard</h3>Time of day isn’t just preference — it’s performance. I always thought I was overthinking it when I felt sharper in the afternoons. Turns out the numbers don’t lie. Some hours really are “you” and others… aren’t.Consistency beats peaks. This was the hardest pill to swallow. My 52% account that rarely had dramatic swings actually climbed faster than my 58% account that would go on wild winning and losing streaks. Apparently, chess ratings reward reliability more than brilliance.Your opening choices reveal your personality. Looking at which openings I chose when felt like reading my own diary. Certain lines were comfort food — I’d reach for them when I was tired or stressed. Others were weekend experiments when I felt bold.<h3>What Still Puzzles&nbsp;Me</h3>The data can tell me what happened, but not always why. Did that rating drop in March happen because I was experimenting with new openings, or because work was crushing me? Was I deliberately using one account for testing and the other for serious play, or did it just evolve that way?And here’s the thing that keeps me up at night: I can see patterns, but I can’t always see the cause. Maybe that’s okay. Maybe the patterns are enough to work with.<h3>The Charts (Or: What the Numbers Actually Whispered)</h3>📊 Chart 1: Win Rates Overview This is where everyone looks first, and I get it — who doesn’t want to know their winning percentage? But here’s what surprised me: the account with the higher win rate wasn’t necessarily the one where I felt like I was playing better chess. Sometimes a high win rate just means you’re comfortable playing lower-rated opponents or you’ve found a time control that suits your natural rhythm. The real question isn’t “how often do I win?” but “when I win, does it feel earned or lucky?”<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*LoxGD1vZJeKOgs2ahteqnA.png">📈 Chart 2: Rating Evolution Rating curves are like emotional weather maps. Those slow, steady climbs? That’s usually when I was doing the boring work — studying endgames, sticking to a repertoire, showing up consistently. The dramatic drops hurt to look at, but they’re honest: life happened, I got distracted, or I tried to force improvement too fast. There’s something oddly comforting about seeing your chess journey laid out this way. Even the plateaus tell a story.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*KrHWnKOPaHyGq6OwFV98oA.png">♟️ Chart 3: Opening Analysis This chart made me laugh and cringe in equal measure. You can literally see my personality in the opening choices: the safe lines I clung to when I was rating-anxious, the wild experiments from weekends when I felt creative, the defenses I abandoned after one bad loss. Some openings were clearly working for me, but I kept avoiding them because they felt “too simple.” Note to self: simple isn’t shameful if it wins games.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*alvxENFaduWoXhhst8UJWg.png">⏱️ Chart 4: Time Control Performance Here’s where I learned I’m basically three different players depending on the clock. In blitz, I’m intuitive but sloppy. In rapid, I’m more solid but sometimes overthink simple positions. In longer games… well, let’s just say my attention span has room for improvement. The fascinating part is realizing that “getting better at chess” might actually mean “getting better at this specific time control.”<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*fGgtr_V0D9ufxskLYDlFNw.png">⚫⚪ Chart 5: Color Performance White vs. Black performance feels deeply personal, doesn’t it? My Black results were consistently weaker, and I spent months blaming it on “not having first-move advantage.” But the real issue was psychological — I approached Black games defensively before they even started. It’s amazing how much your mindset at move one affects move forty.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*hjeTVo5UVxZUfgK7taH_fw.png">🏁 Chart 6: Termination Analysis How your games end says a lot about who you are as a player. I resign too much in equal positions (working on this), but I also avoid timeout losses better than most. There's something humbling about seeing your chess habits reduced to categories: resignation, checkmate, time forfeit. It made me realize that half of chess improvement is just... not giving up too early.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*gjAvzUlZ__Zz9v7cEjBE2g.png">🕐 Chart 7: Time of Day Performance This chart changed how I schedule my life. I always knew I was “more of an afternoon person,” but seeing the numbers made it impossible to ignore. My evening games were disasters — not because I was tired (though I was), but because the competition was different. Late-night chess attracts a specific type of player, and apparently, I wasn’t one of them.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*8ovJ0mtVGgmH_bhOpYG60Q.png">📅 Chart 8: Monthly Activity and Trends Chess follows the calendar of your life in ways you don’t expect. December was always rough (holiday distractions), but so was August (vacation mode). There’s something liberating about accepting these patterns instead of fighting them. Now I plan light maintenance during my historically weak months and save intensive training for when I know I’ll be focused.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*oJtTZwlvlCqgWaw__oPtVg.png">💪 Chart 9: Opponent Strength Analysis This one stung. I was beating lower-rated players at a decent rate but struggling against equals. Classic case of getting comfortable in my rating range instead of pushing upward. The good news? My results against stronger players were better than expected, which suggests I might be sandbagging myself psychologically.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*gOSL-8TZfdj4AOastXWV5w.png">📏 Chart 10: Game Length Analysis Short games usually meant tactical disasters or opening disasters. Long games meant endgame disasters or concentration disasters. The sweet spot seemed to be somewhere in the middle — complex enough to be interesting, but not so long that my focus wandered. This chart basically told me where my chess stamina runs out.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*jtjewXvOuVdmIb_XBVH_og.png">📊 Chart 11: Performance Streaks I’m a streaky player, and this chart proved it. When I’m winning, I keep winning — confidence breeds better decisions. When I’m losing, I keep losing — frustration breeds worse ones. The scary part is how predictable the emotional spillover is. Winning three in a row almost guarantees I’ll be overconfident in game four.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*g1beDD6RHajwLj3F9vkPnA.png">📆 Chart 12: Weekday vs Weekend Performance Weekdays: structured, focused, consistent. Weekends: experimental, relaxed, volatile. Neither is inherently better, but understanding the difference helps me set appropriate expectations. If I want to climb rating, weekday chess is my friend. If I want to explore new ideas, weekends are perfect for that.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*SRbq23yQ5kBfmkOhmT2T5Q.png">🌸 Chart 13: Seasonal Performance Winter chess: serious, studied, indoor energy. Summer chess: distracted, casual, outdoor life calling. Spring and fall: the sweet spots where focus and energy align. I stopped feeling guilty about seasonal dips once I saw they were completely normal. Now I work with my natural rhythms instead of against them.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*0BCQU9-oX2dJ14mOBAwglg.png">🔥 Chart 14: Opening Performance Heatmap This visual is both beautiful and brutal — green squares where my openings work, red squares where they don’t. The pattern was clearer than I expected: I’m much better with familiar, principled systems than with sharp, theoretical lines. My homework became obvious: either study the red squares or stop playing them.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*I82bpSk_kHmEfRV3TClKrA.png">📈 Chart 15: Performance Volatility The moodiness metric. High volatility meant creative but inconsistent play — big wins followed by puzzling losses. Low volatility meant steady, predictable chess. Neither is wrong, but understanding your natural volatility helps you pick the right improvement strategy. Consistent players need to push boundaries; volatile players need to build foundations.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*gCJzwyrzGEQrbV1E8YsIwQ.png">⏰ Chart 16: Time Investment Analysis This hurt to look at. Hours spent on casual blitz versus hours spent studying openings versus hours analyzing games — the return on investment varied wildly. Some practice was clearly more valuable than others, but I’d been treating all chess time as equally useful. Spoiler alert: it’s not.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*U_DnQDgom4qYhz69tXdD-g.png">🎯 Chart 17: Comprehensive Dashboard The monthly health check. If you only look at one chart regularly, make it this one. It pulls together the major signals and helps you pick one or two things to focus on. I started using it like a chess physical exam — what’s working, what needs attention, what can wait until next month.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*2Hbi5fTxRFKdWP1bRcKO1w.png">🧮 Chart 18: Advanced Performance Metrics The nerd chart. Expected results versus actual results, statistical significance, confidence intervals. This is where the data gets honest about whether you’re actually improving or just getting lucky. Sometimes the answer is uncomfortable, but it’s always useful for planning what to work on next.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*HFv4m3XnZ_0pbHBrk16WhQ.png"><h3>Where This Leaves&nbsp;Me</h3>After swimming through all these numbers, here’s what I keep coming back to: small changes compound. The dramatic “I need to overhaul my entire chess game” moments never lasted. But playing during my best hours? That stuck. Cutting out my two worst openings? That worked.The data is fascinating, but the action is what matters. I’m not looking for perfect play — I’m looking for better patterns. And patterns, it turns out, are built one small decision at a time.If I had to pick one thing from this entire deep dive, it would be this: figure out when you play your best chess, then protect that time like it’s sacred. Everything else can wait.<h3>What I’m Still Curious&nbsp;About</h3>Do the patterns hold if I change my approach? If I forced myself to play only during “bad” hours, would they become good hours through repetition? How much of chess improvement is about getting better versus getting luckier with matchups?And the bigger question that started this whole journey: maybe asking “which account is the real me?” was backwards. Maybe the real question is “which version of me do I want to become?”That’s what I’m still figuring out, one game at a time.

The Tale of Two Chess Players

What happens when you try to become a founder while still thinking like a developer?That question sat quietly under everything I did for the last two years. It’s the lens through which I’ve been judging every late-night refactor, every marketing fumbling, every domain purchase and abandoned feature. This piece is the honest, chronological account of that journey — the first steps, the pragmatic choices (good and bad), the technical shortcuts that saved my time, the strategic mistakes that cost me momentum, and the lessons I still carry forward. Think of this as a long conversation with a friend who wants the unvarnished truth: not to convince you to join me, just to explain what it feels like to learn by doing.<hr><h3>Opening question (the one that kept me&nbsp;awake)</h3>Can a single developer — gifted at building but inexperienced at selling — start several products, learn quickly, and eventually discover a business that pays? My short answer now is: yes, but only if you learn to replace the “I can build it” faith with “people will pay for this” validation early and repeatedly.<hr><h3>The messy beginning: why I went from one idea to&nbsp;many</h3>I started with a simple, practical belief: reduce friction for users. At first that friction was about job search FOMO — candidates bouncing between platforms. The early idea was narrow and concrete: aggregate listings into a single place so people can discover opportunities without checking five different sites.That kernel of an idea evolved — because building felt like progress. Features accumulated: ATS-lite, match scoring, multi-post distribution, employer profiles. Then other products spun off: marketplaces, productivity tools, habit trackers, healthcare experiments, micro-utility sites, an interview simulator, and a blog. I launched more than ten websites across multiple domains. Many were experiments; few were money-making.Why did I do this? Because as a developer, shipping feels like achievement. A new repo, a clean deployment, a small user interaction — they’re immediate rewards. The business side offered slower, messier feedback: cold replies, pricing objections, and silence where I expected applause.<hr><h3>My technology choices and why they&nbsp;mattered</h3>I deliberately built a “zero-cost dev stack” so that technical constraints wouldn’t be an excuse:<ul><li>Framework: Next.js — allowed fast full-stack iteration, server-side rendering where needed, and a straightforward path from prototype to production.</li><li>Hosting: Vercel (hobby plan) — free deployments that minimized friction and encouraged continuous deployment.</li><li>Database: <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://Neon.tech">Neon.tech</a> (free tier) — lightweight Postgres for real data-backed prototypes.</li><li>Assisted coding: Claude Code / ChatGPT-style tools — reduced the time between idea and working feature by a lot.</li><li>Auxiliary: Resend API for transactional mail and contact forms on free tiers.</li></ul>This stack was liberating. I could launch, pivot, and test without worrying about monthly bills. It also fed a false security: low cost made it easy to build many things, and I did.<hr><h3>The portfolio: what I built (timeline and short takeaways)</h3>Below is an annotated inventory of the main projects and domains I launched, with the single most important lesson each taught me.<ul><li>BirJob — <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://careerhorizon.co">careerhorizon.co</a> · <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://careerhorizon.llc">careerhorizon.llc</a> · <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://eploy.io">eploy.io</a> · <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://jobry.me">jobry.me</a> · <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://birjob.com">birjob.com</a> — launched Jul 20, 2024 &nbsp;Built as an aggregator with ATS-lite features. Lesson: mixing fundamentally different value propositions (aggregation + ATS) creates buyer confusion and cannibalizes monetization.</li><li>TheyCan — <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://theycan.io">theycan.io</a> — launched Sep 5, 2024 &nbsp;A marketplace connecting startups with vetted engineering teams. Lesson: high-touch marketplaces need contracts, trust, and a sales function — engineering alone isn’t enough.</li><li>MyFrog — <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://myfrog.me">myfrog.me</a> — launched Nov 21, 2024 &nbsp;Lightweight task manager for freelancers. Lesson: low friction causes signups, but retention requires deliberate onboarding and habit-driven design.</li><li>Trackio / Trakio — <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://trakio.me">trakio.me</a> · <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://trackio.art">trackio.art</a> — Nov 24 &amp; Dec 9, 2024 &nbsp;Habit and productivity apps. Lesson: retention mechanics (streaks, nudges, analytics) are the product for habit apps — not feature lists.</li><li>iHealth — <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://ihealth.ink">ihealth.ink</a> — launched Apr 12, 2025 &nbsp;An AI-driven healthcare triage + remote monitoring prototype. Lesson: healthcare products bring regulatory and privacy overhead that can dwarf initial engineering work.</li><li>SwissKnife — <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://swissknife.site">swissknife.site</a> — launched Aug 17, 2025 &nbsp;Suite of micro-tools targeting SEO and organic traffic. Lesson: in content/utility plays, SEO and content engineering (not features) drive value.</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://Bir.Guru">Bir.Guru</a> — <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://bir.guru">bir.guru</a> — launched Aug 31, 2025 &nbsp;AI interview simulator. Lesson: scoring systems require defensible data pipelines; otherwise you can’t scale trust or monetization.</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://HRTech.blog">HRTech.blog</a> — launched Sep 18, 2025 &nbsp;Content hub for HR tech commentary. Lesson: content builds authority slowly but is a reliable long-term channel for certain products.</li></ul>I ran these as a mix of experiments and genuine attempts to find product-market fit. The domain list and the projects were evidence of motion — but motion isn’t the same as momentum.<hr><h3>The strategic mistakes I keep coming back&nbsp;to</h3><ol><li>Chasing features instead of clarity. Adding ATS features to an aggregator did not make the product more valuable — it made it more confusing. Narrow positioning is an undervalued product decision.</li><li>Building without monetization experiments. I shipped many production-ready products but didn’t put cheap paywalls or pricing tests early. I preferred perfect UX to imperfect revenue — but revenue tells you more than UX.</li><li>Not delegating sales/marketing early. My strength is building. My weakness is selling. I spent too long trying to become a salesperson by trial and error when hiring or partnering with a marketer/salesperson would have been cheaper than months of misdirected effort.</li><li>Domain and brand sprawl. Multiple domains diluted SEO value, split brand trust, and multiplied maintenance. Consolidation is an underrated growth lever.</li><li>Feature cannibalization by accident. If you’re scraping company listings and showing them for free, employers will see less reason to pay you to post unless you offer exclusive value (verification, analytics, direct messaging).</li></ol><hr><h3>Concrete examples from day-to-day reality</h3><ul><li>I remember debugging an email flow at 2 AM while a potential customer wrote “looks great — how much?” in the morning. I had no pricing page. I lost the sale because I was still optimizing the UI while the buyer wanted simple terms and a quick invoice.</li><li>On the other hand, because of the free dev stack, I could re-deploy a landing page in 15 minutes and test a headline. That speed taught me how small UX changes affect conversion.</li><li>I once paid for a small LinkedIn outreach campaign and learned in two weeks that certain verticals (local retail managers) responded better to direct messages than clicks on ads — a distribution lesson that I hadn’t learned from analytics alone.</li></ul><hr><h3>What surprised me the&nbsp;most</h3><ul><li>How fragile conversion is. A clear domain and a single call-to-action often converted more than a polished product page full of features.</li><li>AI reduced engineering friction far faster than I expected. Assisted coding let me maintain several products, but it did not replace customer discovery or negotiation skills.</li><li>Small pricing experiments reveal more about demand than months of feature work. Charging 9 AZN for a minimal, useful feature will tell you far more about product-market fit than a dozen new UX improvements.</li></ul><hr><h3>The metrics and measurements I learned to care about (not exhaustive, but prioritized)</h3>When product and business collide, these are the numbers that actually matter:<ul><li>Activation (for paid users): Did an employer post and receive applicants? Did a paid candidate open and engage with the curated matches?</li><li>Conversion (visitor → sign-up → paid): Tiny improvements here compound quickly.</li><li>Retention (D7, D30, D90): Are people coming back? For habit products, this is the product.</li><li>CAC and payback period: How long before the revenue from a customer covers acquisition cost?</li><li>LTV:CAC ratio: Are paid users worth the acquisition cost long-term?</li></ul>I tracked installs and pageviews for too long before I started instrumenting activation and revenue events. That was a costly delay.<hr><h3>Practical next steps I would recommend to anyone in my&nbsp;position</h3>This is what I wish I’d done earlier — a prescriptive, prioritized checklist:<ol><li>Choose one canonical brand for the product you commit to. Redirect the rest. Consolidate SEO and trust signals.</li><li>Ship one paid experiment within 14 days. Price can be small (test 9–30 AZN). Measure conversion and iterate weekly.</li><li>Hire or partner with a salesperson/marketer on commission. You need someone who can close early deals and give honest feedback on objections.</li><li>Instrument activation events from day one. Know what “first value” is and optimize for it.</li><li>Avoid internal cannibalization. If you scrape content, make paid listings clearly superior (verified, analytics, messaging).</li><li>Limit feature scope to one buyer persona. Don’t be everything to everyone — be the best at one thing for one audience.</li><li>Run weekly priced experiments. Make price a continuous variable you test, not a fixed assumption.</li></ol><hr><h3>What I still don’t know and what I’d explore&nbsp;next</h3><ul><li>Which product from my portfolio has the highest realistic willingness-to-pay when tested properly in the market? (I have hypotheses, but need priced experiments.)</li><li>What’s the minimum viable sales process to reliably close B2B deals in my region? (Cold outreach scripts, demo workflows, SDR playbook — I have fragments but not a repeatable system.)</li><li>How much inbound traffic consolidation (one canonical domain + prioritized content) will improve conversion vs the organic traffic I currently disperse across domains?</li><li>For my AI-based products (iHealth, <a target="_blank" rel="noopener noreferrer nofollow" class="text-red-600 hover:text-red-800 underline" href="http://Bir.Guru">Bir.Guru</a>), what data governance and defensibility hooks are required to convince enterprise buyers to pay? I can build the model, but the buyer needs data assurances.</li></ul><hr><h3>Final reflections — how the journey changed&nbsp;me</h3>I entered this journey thinking “build it and they will come.” That’s a comfortable, developer-centric fantasy. The truth I learned the hard way is more precise and less romantic: building is necessary but not sufficient. The skill set of a founder is wider than engineering — it includes product clarity, pricing intuition, sales empathy, and the discipline to run fast, cheap experiments that reveal whether money will actually change hands.Two practical changes in my mindset:<ul><li>I now treat revenue experiments like unit tests. They either pass or fail quickly. Failures are data, not shame.</li><li>I value one-channel focus (a single product + single domain) over multi-project heroism. Depth over breadth.</li></ul><hr><h3>If I had three months and one objective now, I’d do&nbsp;this</h3><ol><li>Pick one product from the portfolio that’s closest to a clear paid value (e.g., a single verified employer product, or a candidate-paid curated alerts feature).</li><li>Consolidate its domain and landing page. Build a checkout with one priced experiment (9–30 AZN).</li><li>Hire a commission salesperson for outreach and to close the first 10 paying customers; instrument every step to measure conversion and LTV.</li></ol>If this sounds like a pivot or a retreat, I don’t see it that way. It’s pruning: fewer branches, better fruit.<hr><h3>Parting question (what I’d still like to learn from&nbsp;you)</h3>If you were in my shoes, which of the things I built would you ask me to double-down on first? Which product sounds like it needs one more paid experiment before being shelved — and which one deserves the sales partner and a domain consolidation? Your perspective would be useful: I have the engineering muscle; I need the market sense.

My startup journey

I like challenges. Interviews are one of those challenges I enjoy: a 30-minute technical interview often teaches me more than five years at a corporate job. So I apply to a lot of roles. Most of the time I get past the first round and reach the second technical interview. Sometimes I get rejected at the first stage — often I think it’s because my salary expectations are higher than their range, or maybe the fit wasn’t right.Keeping track of all those applications and job boards became difficult. I developed FOMO — the feeling that I might miss a good opportunity. That feeling started the whole Birjob journey.The email scraper — useful but noisyMy first attempt was simple: an email notification system that scraped about 70 job boards and sent matching job results to users. The idea was to return only the jobs that matched each user’s keywords. A few colleagues tried it and liked the idea — but the execution was rough.Problems:<ul><li>No database. The system didn’t keep track of which jobs were new or old.</li><li>Lots of noise. Users got up to 200 job listings in one email. Imagine opening your inbox every morning and seeing 200 jobs — most of them repeats. You can’t tell what’s new or what you already saw.</li></ul>This was the first lesson: delivering everything is not the same as delivering useful things. I stopped that version.Everyday example: it was like subscribing to too many newsletters — at some point you stop opening any of them.From emails to a website — fast prototypes, messy choicesNext, I built a website. I’m not a perfectionist when launching — I prefer to ship and learn fast. I bought several domain names during this period (some names came from ChatGPT — don’t laugh):<ul><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://careerhorizon.co">careerhorizon.co</a> — bought: Jul 20, 2024</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://careerhorizon.llc">careerhorizon.llc</a> — bought: Jul 20, 2024</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://eploy.io">eploy.io</a> — bought: Aug 28, 2024</li></ul>I built the first working version while participating in a startup cohort. In under a month I had a live site — thanks to accelerated learning and a lot of late nights. My front-end skills were limited, so the first site was basic but functional.Tech stack (short and cheap):<ul><li>Frontend: HTML, CSS, JavaScript, Jinja2 templates</li><li>Backend: Django</li><li>Hosting: <a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://Render.com">Render.com</a> (easy setup, about $10/month back then)</li><li>Database: <a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://neon.tech">neon.tech</a> (Postgres, free 500 MB)</li><li>Scheduling / automation: GitHub Actions (not ideal for scraping schedules, but it worked)</li></ul>Because <a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://neon.tech">neon.tech</a> gave only 500 MB free, I used a “truncate-and-load” approach for each scrape: if the scrape succeeded I deleted old data and uploaded the new dump. This had pros and cons — it kept costs low and was simple to implement, but it erased historical data and made incremental updates harder. Practically, each scrape produced under ~50 MB of data, so I never hit a paid DB limit.GitHub Actions automated the scraping schedule. It’s not a perfect scheduler for scraping workloads, but it served while I was small and testing ideas.The founder trap — feature overloadLike many solo founders, I made the same mistake over and over: I tried to add too many features at once, without clear business validation.Features I built:<ul><li>Scraped job listing pages</li><li>A job posting page (so employers could post jobs)</li><li>ATS-like features: CV parsing, matching candidates to job descriptions</li><li>Candidate tracking and other advanced ATS functionality</li><li>Blog, user registration, in-house analytics, market trends</li></ul>These features were fun to build. But I didn’t check the business side first. If I was already scraping and showing jobs for free, why would employers pay me to post? Also, I didn’t have the budget to compete with the big, older local players.Lesson learned:<ul><li>Focus on one core problem and solve it well.</li><li>Validate whether customers will pay before building expensive features.</li></ul><h2>My latest tech stack</h2>I kept the stack small and practical so I could move fast with little money and limited frontend skills. This is what I’m using now and why:<ul><li>Claude Code (coding assistant): Because my Next.js experience is limited, I use Claude to generate most of the code. It writes pages, components, API routes, helpers — roughly 99% of the surface code. I still review, test, and tweak what Claude produces, but it saves huge time and helps me ship features quickly.</li><li>Next.js (fullstack): I use Next.js for everything — frontend pages, API routes, and server-side logic. It keeps the app tidy since one framework covers both client and server work.</li><li>Vercel (hosting &amp; deployment): The project is deployed on Vercel’s free Hobby plan. Deploys are automatic: push to the main branch and Vercel builds and publishes. It’s fast and frictionless for a one-person project.</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://neon.tech">neon.tech</a> (Postgres database): I keep the same free Neon Postgres instance and the same truncate-and-load approach for scraped data. Each scrape replaces the old dump with a fresh dump. It’s cheap and simple while the dataset is small.</li><li>GitHub Actions (scheduler for scrapers): I schedule scraping runs with GitHub Actions. I use cron-style schedules in Actions to run scrapers at set times, trigger scraper workflows on demand, and chain jobs (fetch → transform → load). GitHub Actions isn’t a perfect scheduler for heavy scraping, but it’s free, reliable enough for now, and keeps everything in one place (repo + CI).</li></ul>Why this works for me<ul><li>Ship fast: Claude + Next.js lets me build features quickly without deep Next expertise.</li><li>Low cost: Vercel + Neon free tiers keep monthly costs near zero during testing.</li><li>Simple operations: One repo, one deploy pipeline, one scheduler (GitHub Actions) — less to manage.</li></ul>Trade-offs / what I watch<ul><li>AI-generated code risk: Auto-generated code can hide bugs or non-idiomatic patterns. I review changes and run basic tests.</li><li>Scheduler limits: GitHub Actions has minutes/usage limits and isn’t made for high-frequency, heavy scraping — I watch run times and concurrency.</li><li>No history with truncate-and-load: The current DB flow is cheap but loses history and makes incremental updates harder.</li><li>Vendor limits: Free tiers mean watching build times, DB storage, and bandwidth so I don’t hit surprises.</li></ul>Small next steps I plan<ol><li>Add basic tests (unit + API smoke tests) to catch errors from AI-generated code.</li><li>Add simple CI checks (lint, typecheck) before Vercel deploys.</li><li>Monitor GitHub Actions usage and add retries/backoff to scraper jobs to avoid throttles.</li><li>Track DB size and scrape payloads; migrate from truncate-and-load to incremental updates when needed.</li><li>Start a small feature-flag or staging flow so I can safely test changes before they go live.</li></ol>Finding the niche — leaning into what workedThe next big shift was focus. I decided to concentrate on one niche: job aggregation for the Azerbaijani market. This had two advantages:<ul><li>Local competition was weak: most sites scraped only 2–3 sources and showed around 2,000 jobs.</li><li>My scraper returned 5,000+ jobs per cycle — a clear numeric advantage.</li></ul>At this stage I bought new domains and tightened the product focus:<ul><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://jobry.me">jobry.me</a> — bought: Dec 3, 2024 — first focused aggregator site</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://birjob.com">birjob.com</a> — bought: May 5, 2025 — final name for the single-purpose aggregator</li></ul>I removed the heavy ATS features and focused on collecting and showing jobs. I still made mistakes (I added blog pages, user registration, and other extras), but I iterated faster and cut features that didn’t help.What I removed after testing:<ul><li>Blog page (low immediate traffic and traction)</li><li>Email notification service (too noisy without identity or DB)</li><li>In-house user analytics (replaced by Google Analytics)</li><li>Market trends dashboard and heavy user tracing</li><li>Many small pages and features that added maintenance overhead but little user value</li></ul>After pruning, the product was simple: a job aggregator that surfaced many more listings than competitors.Technical trade-offs I madeTo move fast and keep costs low, I accepted several trade-offs:<ul><li>Truncate-and-load DB: cheap and simple, but loses history and makes updates less efficient.</li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://Render.com">Render.com</a> + GitHub Actions: low ops and cheap, but not the fastest or most resilient at scale.</li><li>Larger frontend bundles: because I’m not a frontend expert, pages can be slow. This is fine for an MVP but needs work for growth.</li></ul>Each choice got me to a working product quickly. They also left technical debt and scaling questions for later.What surprised me<ul><li>Having more jobs mattered more than a polished UI in the early days. Users saw value in breadth.</li><li>Email digests were often worse than no email. Users preferred using the site to find jobs rather than reading huge digests.</li><li>Competing with existing job boards is more about relationships and brand than just features. Employers trust long-standing platforms.</li></ul>What I still don’t know<ul><li>Monetization: Will employers pay for featured listings? Will recruiters pay for candidate leads? Can I sell API access or data? I have ideas, but no validated revenue yet.</li><li>Retention: Do users come back regularly because of broader listings, or do they visit once and never return? I need retention metrics to decide.</li><li>Product depth: Should Birjob stay a pure aggregator, or add a few light, paid recruiter tools without becoming a full ATS? I’m unsure where the line should be.</li><li>Scaling pains: Which part will fail first when traffic grows — DB, scraping, hosting, frontend? I have guesses but no real stress tests.</li></ul>Small experiments I’d run next (practical, low-cost)<ol><li>Monetization pilot: offer a featured listing package to 10–20 local employers and measure interest.</li><li>Retention tracking: start storing simple user/session data (cookies or light accounts) to measure return rates and funnels.</li><li>Smarter emails: move from massive digests to incremental emails like “3 new jobs since your last visit” so users aren’t overloaded.</li><li>Frontend cleanup: remove unused JS, lazy-load parts of the page, reduce bundle size — these are small wins with big UX returns.</li><li>Micro-ATS test: add one small recruiter feature (for example, bookmarking candidates) to see if recruiters will pay for tiny workflow gains.</li></ol>Final thoughts — this is not finishedRight now Birjob is simpler and clearer than at the start. The journey taught me to reduce noise, focus on one core value, and iterate in small steps. I don’t know yet how I’ll make money from the data or how to grow users cheaply — but I do know this won’t stop. I expect the process to continue: build, learn, remove, repeat.I keep asking myself: what is the smallest change that will move a real business metric — more weekly users, better employer conversions, or a little revenue? Which small bet will turn Birjob from a useful tool into a sustainable product?If you were sitting across from me now, I would ask: what would make you come back to a job site? Would you pay for:<ul><li>better filters and search,</li><li>curated or hand-picked roles,</li><li>or direct connections to employers?</li></ul>Your answer would help shape the next small experiment.<a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://www.birjob.com/">https://www.birjob.com/</a>

Birjob

<hr><h3></h3><h4></h4>What makes chess one of the rare competitive pursuits where experience, steady practice, and sporadic bursts of youthful creativity all coexist at the top — and what does that tell us about how we should train, compete, and design programs for elite players?That question has been sitting with me while I worked through the dataset and charts for this report on 1,200 elite FIDE players across 78 federations. Below I walk you through what I’ve learned so far, what still confuses me, what surprised me, and how I’d think about next steps if I had more time. I’ll keep the structure clear and the charts intact — every figure name and path is preserved so you can re-use or inspect them directly.<hr><h3>Executive reflection — quick&nbsp;preview</h3>We’re looking at a highly active, globally distributed elite: 1,200 players, 78 federations, 872 Grandmasters (72.7%), and nearly universal tournament engagement (97.8%). Ratings cluster tightly (mean ≈ 2,518) with a long elite tail (top = 2,839). On the surface that paints chess as both specialized and broadly inclusive: many reach elite status, and the very top still clearly stands apart.<hr><h3>What surprised me (and why it&nbsp;matters)</h3><ol><li>Age barely matters at the elite level. Correlation between age and rating is essentially negligible (−0.037). That surprised me because many sports show steep declines with age; chess appears to resist that pattern among elites. It suggests an extended peak-window where accumulated knowledge and pattern recognition compensate for modest cognitive slowing.</li><li>Almost everyone competes. 97.8% recent tournament activity (1,174/1,200) — elite chess is not a passive club of theoreticians; it’s an actively competing community. That’s important for organizers and federations: competitions are the lifeblood keeping skills sharp.</li><li>Cross-format skill transfer is strong. Correlations across classical/rapid/blitz (≈ 0.717–0.771) means training in one format benefits others. This undermines the simplistic “blitz is a separate skill” narrative at elite levels — fundamentals carry over.</li><li>Different national strategies succeed. India supplies the largest number of elite players (81), while the USA has the highest average rating (2,546). Both approaches produce excellence — one through breadth, the other through peak optimization.</li></ol><hr><h3>What I learned — guided tour through the findings (with preserved charts)</h3><blockquote>Note: every chart referenced below is included exactly as in the report; file names and paths are preserved for reproduction.</blockquote><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*dpzbkw9CILwiXCwpifCxqg.png"><h3>Rating distribution and elite&nbsp;tail</h3>&nbsp;Finding: Most elite players cluster near the mean rating (≈2,518) while a small elite tail (up to 2,839) marks world-championship caliber outliers. Practically, this suggests marginal improvements at the top are very hard and often separate the “top” from the “very top.”<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*uhQvUakDD0Rr54OgMQAOvQ.png"><h3>Age versus performance</h3>Finding: Age has minimal impact (correlation −0.037). What this means in practice: players in their 30s and 40s often match or exceed younger players — probably a mix of experience, opening knowledge, and more efficient thinking patterns.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*D0e5qIpUxp6iXXPCyRFOrg.png"><h3>Cross-format rating correlations</h3>Finding: Strong correlations (0.717–0.771) between classical, rapid, and blitz ratings. That’s a practical signal to coaches: build fundamentals (calculation, pattern recognition, opening understanding) and expect benefits across formats.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*Cd3Xxxz6aAnQO5VcW6qkCQ.png"><h3>Time-control performance differential</h3>Finding: On average, players score ~41 points higher in classical than blitz. Classical rewards deeper analysis; blitz rewards speed and intuition. A balanced training program ought to respect both demands.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*6gZDzXy4AYveBbvGN4kWCA.png"><h3>Federation participation leaders</h3>Finding: India leads participation with 81 elite players, followed by Russia (74) and the USA (69). This reflects chess’s globalization — development pipelines work differently across countries.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*AgWB8Ea82FFFBx7LIxjXTQ.png"><h3>Federation strength (average&nbsp;rating)</h3>Finding: USA has the highest average rating (2,546) despite fewer elite players than India. Different models: mass development vs. focused elite development.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*Qv9LGbjmM8UXSN7E8iU-Yg.png"><h3>Global geographic distribution</h3>Finding: Europe remains dominant (482 players, 40.2%), but Asia’s presence is substantial (341, 28.4%) — chess is becoming genuinely global.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*cA6nr6mFJ6dc6V60EPSxUg.png"><h3>Age-group patterns and peak&nbsp;age</h3>Finding: Most players are 20–30 (397 players, 33.1%). &nbsp;<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*FCmgTbL1fPI8Vx1-mWut4g.png"><h3>Rating by Age&nbsp;Bracket</h3>Finding: The 31–40 bracket enjoys the highest mean rating (2,526). That sweet spot feels like the intersection of experience and preserved cognitive resources.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*hQ4yWF7KRHk4vfKwZ62NwQ.png"><h3>Titles and rating stratification</h3>Finding: 72.7% of the dataset are Grandmasters (872). <img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*WJx8SHtjkr3vVJ9KDvHrhA.png"><h3>Rating by&nbsp;Title</h3>Finding: Grandmasters average 2,538, about 73 points above International Masters. Titles remain meaningful differentiators.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*ZyERdommEMIiFAO7nfSYFg.png"><h3>Top players (leaderboard snapshot)</h3>Finding: Magnus Carlsen leads at 2,839, followed by Nakamura, Erigaisi, Caruana, and Gukesh. The top few still stand noticeably apart, but the gap to the broader elite is not infinite.<img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*5K0VZCZZ1K5fRUikjHUX5Q.png"><h3>Tournament participation and its&nbsp;impact</h3>Finding: 97.8% active; average 4.7 recent tournaments per player. <img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*tOTRocXfyuc3k2C3faKnGw.png"><h3>Active vs&nbsp;Inactive</h3>Finding: Active players average 31 points higher than inactive players (2,519 vs 2,488). Active competition appears essential to maintain elite performance.<hr><h3>Where the data gets confusing or&nbsp;limited</h3><ol><li>Causal direction on activity and rating. Active players have higher ratings, but does activity raise rating, or do higher-rated players get invited more and therefore stay active? The correlation is clear; causation is not.</li><li>Selection bias in the dataset. We focused on 1,200 “elite” players — the conclusions apply to elites only. How representative are these findings for the broader chess-playing population or rising talents? Not very. If you want to generalize to national pipelines, you need a broader sample.</li><li>Time dynamics and trends. We have a snapshot of recent activity and ratings, but how these metrics evolve longitudinally (e.g., trajectories of players who become elite at 18 vs 28) needs longitudinal data. This report does not fully resolve that.</li><li>Federation-level confounders. Differences between federations (training infrastructure, selection bias, GDP, culture) are not controlled for. Saying “India has many elites” doesn’t explain whether this is due to systematic coaching, demographics, or selection thresholds.</li><li>Psychological and non-rating factors. Ratings and titles capture much, but not all: motivation, coaching relationships, sponsorship, and life events influence careers but are not present in the dataset.</li></ol><hr><h3>Everyday examples that make this feel&nbsp;real</h3><ul><li>The “age durability” I find believable if you think of chess like language fluency: a 40-year-old who has read thousands of books and practiced conversationally for decades can still out-debate a younger but less experienced interlocutor. Pattern recognition and accumulated knowledge compensate for small losses in processing speed.</li><li>Tournament activity advantage is like language immersion: if you stop speaking a language, conversational fluency drops. Similarly, if a player stops playing tournaments, their competitive edge dulls. The data’s ~31 point drop for inactive players mirrors this effect.</li><li>Cross-format transfer reminds me of driving different cars: if you’re excellent at driving standard vehicles (fundamentals), you’ll adapt quickly to sports cars or trucks. The fundamentals scale.</li></ul><hr><h3>Practical takeaways (for federations, players, and organizers)</h3>Federations<ul><li>Consider dual-track development: broad talent identification (India model) plus focused elite optimization (USA model). Both work; choose priority based on resources and goals.</li></ul>Players &amp; coaches<ul><li>Keep tournament rhythm. The activity-performance link is strong and consistent.</li><li>Train fundamentals to benefit multiple formats; format-specific sharpening matters but isn’t everything.</li></ul>Organizers<ul><li>Offer mixed-format events and age-inclusive programming: the dataset shows demand across ages and formats.</li></ul><hr><h3>What I don’t (yet) know and would explore&nbsp;next</h3><ol><li>Longitudinal career shapes. How do top players’ ratings evolve across decades? Who peaks early vs. late — and why? I’d want panel data across 10–20 years.</li><li>Interventions that cause jumps. Which training programs, coaching practices, or support mechanisms produce measurable rating improvements? This requires experimental or at least quasi-experimental designs.</li><li>Federation causal factors. Why does one federation produce breadth and another peak performers? I’d collect variables like public funding, coaching density, junior program structures, and socioeconomics.</li><li>Non-rating success metrics. Tournament wins, match-improvement under pressure, and longevity in selection teams are not fully captured by ratings. Can we integrate those for a fuller picture?</li><li>Psychological resilience and burnout. Given the intense tournament schedules, how does mental health interact with performance trajectories?</li></ol><hr><h3>Short, concrete summary (so you don’t lose the headline&nbsp;numbers)</h3><ul><li>Dataset: 1,200 elite FIDE players, 78 federations.</li><li>Grandmasters: 872 (72.7%).</li><li>Active tournament participation: 97.8% (1,174/1,200).</li><li>Mean rating ≈ 2,518; top = 2,839 (Magnus Carlsen).</li><li>Age-performance correlation: −0.037 (effectively negligible).</li><li>Cross-format correlations: 0.717–0.771.</li><li>Active players average +31 rating points vs inactive.</li><li>India leads participation (81 players); USA leads average rating (2,546).</li><li>Peak mean rating by age: 31–40 (2,526).</li></ul><hr><h3>Final honest thoughts — what I’d explore if I had your coffee and another afternoon</h3>I’d love to layer in longitudinal data and federation-level covariates next. With that I could start to say more confidently what causes elite emergence, what keeps a player at the top, and what programmatic choices actually move the needle. I’d also interview a subset of players across age groups to understand non-quantitative drivers (life balance, motivation, coaching relationships). That human texture would complement the numbers in a way that charts alone can’t.Data Source Note: The dataset analyzed in this article was scraped from the <a target="_blank" rel="noopener" class="text-blue-600 hover:text-blue-800 underline markup--anchor markup--p-anchor" href="https://lichess.org/fide?page=40">Lichess FIDE ratings list</a>, covering pages 1 through 40.

Elite Chess Players

<hr><h3></h3><h3>A detailed analysis of the Azerbaijan startup ecosystem based on data scraped from <a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://startup.az">startup.az</a>.</h3><h3>📊 Dataset&nbsp;Overview</h3><ul><li>Total Startups Analyzed: 139 unique startups</li><li>Business Segments: 18 different industry sectors</li><li>Data Coverage: Complete segment information for 100% of startups</li><li>Certification Rate: 82.7% of startups are officially certified</li><li>Digital Presence: 87.1% have websites, 100% have email contacts</li></ul><h3>📈 Key Insights &amp; Visualizations</h3><h3>1. Segment Distribution Analysis</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*yoRTnhJYAHA627RSCfyvUQ.png">Key Insights:<ul><li>Marketing dominates the ecosystem with 33 startups (23.7% of total)</li><li>Transportation is the second-largest sector with 19 startups (13.7%)</li><li>Finance &amp; E-commerce follows with 16 startups (11.5%)</li><li>Education sector shows strong presence with 14 startups (10.1%)</li><li>The ecosystem shows strong diversification across 18 different segments</li></ul>Business Implications:<ul><li>Marketing and advertising solutions are highly competitive in Azerbaijan</li><li>Transportation innovation is a key focus area, likely due to urban development needs</li><li>Digital transformation in finance and education sectors is actively pursued</li></ul><h3>2. Top Market&nbsp;Segments</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*vHG1-iQngKfWmRP9NA53WQ.png">Strategic Analysis:<ul><li>Top 4 segments account for 58.6% of all startups</li><li>Marketing technology gap: High number of startups suggests market opportunity</li><li>Transportation innovation likely driven by Baku’s urban mobility challenges</li><li>Financial inclusion focus aligns with Azerbaijan’s digital banking initiatives</li></ul><h3>3. Startup Operational Status</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*lS_oTbZWmgZYpZspLqHppw.png">Operational Health Insights:<ul><li>69.8% (97 startups) are actively operating — indicating a healthy ecosystem</li><li>30.2% are in sales/scaling phase — showing growth orientation</li><li>Low failure rate suggests either strong market validation or early-stage ecosystem</li><li>Most startups have moved beyond MVP to operational status</li></ul>Ecosystem Maturity:<ul><li>High operational rate indicates good business model validation</li><li>Sales focus suggests market-ready solutions</li><li>Sustainable growth trajectory evident</li></ul><h3>4. Investment Landscape</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*8iLh5kp4bOgt4B9FCgcP3A.png">Funding Ecosystem Analysis:<ul><li>79.9% (111 startups) are not actively seeking funding — indicating:</li><li>Self-sufficient business models</li><li>Bootstrap/organic growth preference</li><li>Possible lack of venture capital ecosystem</li><li>20.1% (28 startups) are seeking funding — showing growth ambitions</li><li>Conservative funding approach dominates the ecosystem</li></ul>Investment Implications:<ul><li>Large untapped market for venture capital</li><li>Founders prefer bootstrapping or angel investment</li><li>Opportunity for impact investors in education and social sectors</li></ul><h3>5. Official Certification Status</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*_pB1NUrfFJ3aCQwZRImZVA.png">Regulatory Compliance Insights:<ul><li>82.7% (115 startups) are officially certified — excellent compliance rate</li><li>17.3% lack certification — possible early-stage or stealth startups</li><li>Strong government support for startup registration</li><li>Regulatory clarity encourages formal business establishment</li></ul>Ecosystem Support:<ul><li>Government policies facilitate startup formation</li><li>Clear regulatory framework</li><li>High formalization rate indicates mature business environment</li></ul><h3>6. Digital Presence &amp; Accessibility</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*NDtYnOchjc770gYhd0bw7A.png">Digital Maturity Analysis:<ul><li>100% have email contacts — universal digital communication</li><li>87.1% have websites — strong online presence</li><li>69.8% have phone contacts — preference for direct communication</li><li>60.4% have all three contact methods — comprehensive accessibility</li><li>Only 4.3% lack website presence — indicating digital-first approach</li></ul>Market Readiness:<ul><li>High digital adoption suggests tech-savvy entrepreneur base</li><li>Strong online presence facilitates international market access</li><li>Comprehensive contact options improve customer acquisition</li></ul><h3>7. Segment vs Status Performance Matrix</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*oO3kr8ACwEXNPV2KfeN0Vw.png">Cross-Sector Performance Insights:<ul><li>Marketing startups show highest operational success (most are “İşləyir”)</li><li>Transportation sector has mixed performance — suggesting market validation challenges</li><li>Finance &amp; E-commerce shows strong operational rates</li><li>Education sector demonstrates stable operational performance</li></ul>Strategic Patterns:<ul><li>B2B-focused segments (Marketing, Finance) show higher operational success</li><li>B2C segments require more time for market validation</li><li>Technology-enabled services perform better than physical product startups</li></ul><h3>8. Investment Seeking by&nbsp;Segment</h3><img class="max-w-full h-auto rounded-lg" src="https://cdn-images-1.medium.com/max/1600/1*Fj-pxDWLYKj1pnL9xmjh5A.png">Sector-Specific Funding Analysis:<ul><li>Transportation startups most funding-hungry — capital-intensive business models</li><li>Marketing startups least likely to seek funding — asset-light business models</li><li>Education sector shows balanced approach — mix of funded and bootstrap models</li><li>Finance startups prefer self-funding — regulatory capital requirements</li></ul>Investment Strategy Insights:<ul><li>Transportation sector offers highest growth potential but requires significant capital</li><li>Marketing/advertising provides quick ROI opportunities</li><li>Education sector balances social impact with commercial viability</li></ul><h3>🎯 Strategic Recommendations</h3><h3>For Entrepreneurs</h3><ol><li>Marketing/AdTech space is saturated — consider differentiation or adjacent markets</li><li>Transportation innovation has high potential — urban mobility, logistics, delivery</li><li>Education technology shows growth opportunity — digital learning, skill development</li><li>Financial inclusion remains underexplored — payments, lending, insurance</li></ol><h3>For Investors</h3><ol><li>High-quality deal flow in transportation — capital-intensive but scalable</li><li>Marketing startups offer quick exits — lower capital requirements</li><li>Education sector provides impact investing opportunities</li><li>82.7% certified startups — reduced regulatory risks</li></ol><h3>For Policymakers</h3><ol><li>Venture capital ecosystem needs development — only 20% seeking funding</li><li>High certification rate indicates effective startup support</li><li>Digital infrastructure is strong — focus on advanced tech adoption</li><li>Cross-sector collaboration opportunities exist</li></ol><h3>📋 Methodology</h3><h3>Data Collection</h3><ul><li>Source: <a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://startup.az">startup.az</a> official registry</li><li>Scraping Method: Python-based web scraping with BeautifulSoup</li><li>Data Quality: 100% segment extraction success rate</li><li>Deduplication: Smart algorithm removes duplicate entries while preserving data quality</li></ul><h3>Analysis Framework</h3><ul><li>Descriptive Statistics: Distribution analysis across all key dimensions</li><li>Cross-tabulation: Segment vs status performance matrix</li><li>Categorical Analysis: Investment patterns, certification status, digital presence</li><li>Visualization: Professional charts using matplotlib and seaborn</li></ul><h3>🔍 Key Findings&nbsp;Summary</h3><ol><li>Ecosystem Maturity: 69.8% operational rate indicates healthy business validation</li><li>Market Concentration: Top 4 segments represent 58.6% of ecosystem</li><li>Funding Gap: Only 20% actively seeking investment — VC opportunity</li><li>Digital Readiness: 87% website presence shows strong digital adoption</li><li>Regulatory Support: 82.7% certification rate indicates government backing</li><li>Sector Opportunities: Transportation (capital-intensive), Education (impact), Marketing (competitive)</li></ol><h3>🚀 Future Research Directions</h3><ul><li>Temporal Analysis: Track startup lifecycle and graduation rates</li><li>Geographic Distribution: Analyze regional startup concentration</li><li>Founder Demographics: Age, education, previous experience analysis</li><li>Success Metrics: Revenue, employment, export potential assessment</li><li>Ecosystem Gaps: Identify underserved market segments</li></ul><hr>Analysis generated from <a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="http://startup.az">startup.az</a> data as of September 2025. For questions or collaboration opportunities, please refer to the dataset and methodology sections.

Azerbaijan Startup Ecosystem Analysis

Have you ever wondered what happens if you stop treating your attention like something endless and instead protect it like a scarce resource? I keep thinking about that. What if the point isn’t to quit screens forever but to learn how to use them without losing yourself in the process.Here’s what I’ve learned, what gets messy, what surprised me, and some small experiments that actually change things. I’ll be honest about what I don’t know and end with the questions I’d want to explore next.<h2>What I’ve learned so far</h2>Digital detox isn’t one thing. People mean different things when they say it. Sometimes it’s a weekend without a phone. Sometimes it’s turning off notifications for two hours in the evening. The useful way to think about it is on two axes: how long you’re away and how much you cut out. That helps pick tactics that actually match your goal.Goals matter more than the ritual. If your aim is better sleep, do something with evening screens and the bedroom. If your aim is deeper work, set blocks of time without social apps and close distracting tabs. If your aim is better conversation with family, put phones in another room at dinner. The goal changes the action.Small, repeatable habits beat dramatic stunts. A one-time fast feels great for a day but often doesn’t change behavior. Daily constraints — no social apps before noon, one evening a week phone-free — are the ones that tend to stick.Design your environment. Willpower is limited. Charging your phone in another room, turning off badges, or using an app timer reduces reliance on self-control. Architecture beats resolve.<h2>Where it gets confusing</h2>Not all screen time is the same. A video call with a friend can be nourishing. Doomscrolling the news feed at 2 a.m. is draining. Lumping everything together risks throwing out what’s actually useful.There’s a trade-off between connection and well-being. For many, social media and messaging are how long-distance friendships survive. Reducing digital contact can help mood and focus but can also increase loneliness depending on your social circle.Measuring outcomes is tricky. You can track screen minutes but the number doesn’t always tell you whether life improved. Less time doesn’t always mean higher quality attention. Sometimes shorter, more meaningful interactions are way better than fewer minutes overall.Relapse happens. People do a detox weekend and then binge. Sustainable change requires systems and social norms, not a single heroic effort.<h2>What surprised me</h2>Boredom is underrated. When you deliberately give yourself unstructured, low-stimulus time, creativity often shows up. The mind needs idle time to rearrange ideas.Social norms matter a lot. If everyone at the dinner table expects instant replies, your personal detox is hard to keep. If the household agrees on a phone-free dinner, it’s way easier.Tiny frictions work better than big promises. Putting your phone in a drawer makes impulse checking far less likely than trying to be “strong” about it.<h2>Everyday examples that actually work</h2>Micro-reset: No screens for 30 minutes after you wake and 30 minutes before bed. Less frantic mornings. Better wind-down at night.Work blocks: Two 90-minute focus windows with social apps blocked. You get deeper work done and finish tasks faster.Mealtime rule: Phone in another room during dinner and one weeknight. Conversations stretch longer. People notice the quiet.Weekend pause: Airplane mode for an afternoon walk and a book. The time feels fuller and less chopped up.These are experiments, not commandments. Try one for a week and see what shifts.<h2>Practical tactics people actually use</h2>Turn off non-essential notifications. Remove badges. Use focus modes or app timers during work hours. Make the bedroom a no-screen zone. Replace an automatic check with a small ritual — make tea before reading email. Schedule short boredom experiments — 10 minutes of nothing stimulating and see what comes up.<h2>What I don’t know</h2>There’s lots of individual variation. What works for a parent with small kids is different from what works for a remote engineer or a student. Personality, job demands, and family context change the cost and benefit.Long-term effects are unclear. We have plenty of short-term studies and anecdotes. Less is known about the cumulative impact of regular detox habits over years on relationships, career outcomes, or mental resilience.How algorithms react is still a question. Platforms are designed to pull you back in. If a large number of people take breaks, do the systems adapt to regain attention? I don’t have a confident answer.<h2>What I’d try next if I had time</h2>Personal A/B tests. Track sleep, focused hours, mood across different detox strategies and see what actually moves the needle for me.Household experiment. Phone-free dinners for a month and one shared check-in time. Observe how social expectations shift.Longer tracking. A few small, consistent metrics recorded over months to see if improvements last or roll back.Talk to other people. Parents, remote workers, students — hear their trade-offs and daily practices.<h2>Final thought and a question for you</h2>A digital detox is less about heroically escaping screens and more about curiosity. Try things. Observe. Make small rules that protect what you care about. Design your environment so the choices that lead to calm are easy.I’m still wondering how small habits compound over years. Do they change who we become, or just how we feel in the short run? I’d also like to know how to balance the real social value of digital tools with their cognitive cost.If you wanted to try a short experiment together — say three nights with a phone-free bedroom — what would you be willing to change, and what would you want to measure to see if it’s worth keeping?<h3>Maybe another sources?</h3><ol><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://en.wikipedia.org/wiki/Digital_detox">https://en.wikipedia.org/wiki/Digital_detox</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://www.webmd.com/balance/what-is-digital-detox">https://www.webmd.com/balance/what-is-digital-detox</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://toolkit.lifeline.org.au/articles/techniques/how-to-do-a-digital-detox">https://toolkit.lifeline.org.au/articles/techniques/how-to-do-a-digital-detox</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://www.verywellmind.com/why-and-how-to-do-a-digital-detox-4771321">https://www.verywellmind.com/why-and-how-to-do-a-digital-detox-4771321</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://www.brownhealth.org/be-well/what-digital-detox-and-do-you-need-one">https://www.brownhealth.org/be-well/what-digital-detox-and-do-you-need-one</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://www.newportinstitute.com/resources/mental-health/digital-detox/">https://www.newportinstitute.com/resources/mental-health/digital-detox/</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://www.bbc.com/travel/article/20250507-the-unstoppable-rise-of-digital-detox-retreats">https://www.bbc.com/travel/article/20250507-the-unstoppable-rise-of-digital-detox-retreats</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" class="text-blue-600 hover:text-blue-800 underline" href="https://www.nkchealth.org/blog/the-power-of-digital-detox">https://www.nkchealth.org/blog/the-power-of-digital-detox</a></li></ol>

Digital detox

That question has been rattling around my head lately when I look at job boards and org charts: two roles can share the same title but mean wildly different things depending on the company. So I wanted to sit with that confusion for a bit, sort it out, and tell you what I’ve learned — the parts that make intuitive sense, the places where titles and reality diverge, and the things that surprised me. Imagine we’re at a café with a paper napkin and a pen; here’s how I’d explain it.<hr><h2>Quick thesis (so you know where I’m heading)</h2><ul><li>Business Analysts (BAs) focus on problems in the business: requirements, processes, stakeholders, and making sure the organization’s decisions and workflows are aligned with goals. They may use data, but their primary instrument is domain knowledge and communication.</li><li>Data Analysts (DAs) focus on insights from data: pulling, cleaning, exploring, and visualizing data to answer questions or test hypotheses. They turn raw data into actionable findings.</li><li>Both roles overlap heavily; the distinction is more about orientation (business vs data) than a strict skill barrier.</li><li>Beyond those two, there’s a whole ecosystem — data engineers, data scientists, analytics engineers, BI developers, product analysts, ML engineers, analytics managers, etc. — each with a primary focus (pipelines, models, data transformation, dashboards, experiments, deployment, strategy).</li></ul><hr><h2>What I’ve learned so far — role by role (high level)</h2><h3>Business Analyst — the translator of needs into action</h3>Core goal: Understand and document what the business needs and ensure solutions (process changes, software, reports) actually solve those needs. Typical day: Meetings with stakeholders, mapping current processes, writing requirements, accepting user stories, verifying solutions. Skills &amp; tools: Requirements gathering, stakeholder management, process mapping, basic SQL and Excel, sometimes UML or business-case modeling, occasional BI tools. Output: Requirements documents, process diagrams, acceptance criteria, test cases, user stories, sometimes dashboards or KPIs. Where they sit: Often in product, operations, or business units; close to PM/ops.<h3>Data Analyst — the data-to-insight practitioner</h3>Core goal: Use data to answer concrete questions: what happened, why, and what might happen if we change X. Typical day: Querying databases, cleaning data, exploratory analysis, visualization, writing reports or dashboards, answering ad-hoc questions. Skills &amp; tools: SQL, Excel, Tableau/Power BI/Looker, Python/R for analysis, statistics basics, data visualization principles. Output: Dashboards, analyses, slide decks, ad-hoc reports, metrics definitions. Where they sit: Analytics teams, product, marketing, finance — anywhere decisions need numbers.<h3>Data Scientist — modelers and hypothesis testers</h3>Core goal: Build predictive or prescriptive models; test hypotheses with rigorous statistical/machine-learning techniques. Typical day: Feature engineering, experimenting with models, validating performance, communicating model behavior to stakeholders. Skills &amp; tools: Python/R, advanced statistics, machine learning frameworks (scikit-learn, TensorFlow, PyTorch), causal inference basics. Output: Models, model performance reports, prototypes, sometimes research papers. Where they sit: Central data science teams, product; often closer to engineering than BAs.<h3>Data Engineer — builders of data infrastructure</h3>Core goal: Design and maintain the pipelines and systems that deliver reliable data for others to use. Typical day: Building ETL/ELT pipelines, managing data warehouses/lakes, ensuring data reliability and performance. Skills &amp; tools: SQL, Python/Java/Scala, Airflow, dbt, cloud infra (AWS/GCP/Azure), Kafka. Output: Pipelines, data schemas, documentation, SLAs for data freshness/accuracy. Where they sit: Engineering or platform teams.<h3>Analytics Engineer — the pragmatic middle ground</h3>Core goal: Make raw data analysis-ready: transform, model, and document data in the warehouse so analysts can be productive. Typical day: Writing dbt models, testing data transformations, maintaining metric definitions, enabling reproducible analytics. Skills &amp; tools: SQL, dbt, git, data modeling, warehouse tech (Snowflake/BigQuery/Redshift). Output: Reusable transformations, documented datasets, consistent metrics. Where they sit: Often within analytics or data platform teams.<h3>BI Developer / BI Analyst — dashboard and reporting experts</h3>Core goal: Create and maintain dashboards and reporting systems that inform decision-makers. Typical day: Designing user-friendly dashboards, optimizing queries, scheduling reports, meeting with stakeholders to refine metrics. Skills &amp; tools: Looker/Tableau/Power BI, SQL, UX for dashboards, performance tuning. Output: Dashboards, scheduled reports, data extracts. Where they sit: Central analytics, finance, product.<h3>Product Analyst — metrics &amp; experimentation focused on product outcomes</h3>Core goal: Understand user behavior and product levers; run and interpret A/B tests; help prioritize product changes. Typical day: Funnel analysis, cohort analysis, experiment design and analysis, collaborating with PMs and engineers. Skills &amp; tools: SQL, statistical testing, experimentation platforms (Optimizely, internal tools), product analytics tools (Amplitude, Mixpanel). Output: Experiment reports, retention/engagement analyses, product recommendations. Where they sit: Product teams.<h3>ML Engineer — productionizing models</h3>Core goal: Take ML models and make them robust, reproducible, and scalable in production. Typical day: Building model-serving infrastructure, monitoring model drift, ensuring observability and latency requirements. Skills &amp; tools: Software engineering, Docker/Kubernetes, model-serving frameworks, MLOps tooling. Output: Production services, monitoring, CI/CD pipelines for models. Where they sit: Engineering teams or SRE/ML-platform teams.<h3>Analytics Manager / Head of Analytics — the strategy &amp; people layer</h3>Core goal: Translate business strategy into analytics priorities, manage teams, and ensure analytics impact. Typical day: Hiring, prioritizing projects, stakeholder management, setting standards for metrics/quality. Skills &amp; tools: Leadership, project prioritization, communication, some technical literacy. Output: Roadmaps, delivery frameworks, OKRs, team structure. Where they sit: Leadership, reporting to C-level or senior product/ops leaders.<hr><h2>Where it gets confusing (and why job titles feel slippery)</h2><ol><li>Small companies vs large enterprises: In a startup, a “data analyst” might build ETL pipelines, write models, and ship features — they wear multiple hats. In big companies, roles are specialized and narrow.</li><li>The same title, different expectations: A “Business Analyst” at a bank could mean strict compliance + process work; at a tech company it might be a product-facing analyst with SQL skills.</li><li>Overlap in tools and tasks: Both BAs and DAs can write SQL, build dashboards, and talk to stakeholders. Often title differences reflect what the role is accountable for rather than what software they use.</li><li>“Analyst” as an entry-level catch-all: Many orgs use “analyst” as the default for junior roles — so you need to read the job description, not just the title.</li><li>Emerging roles and composites: “Analytics Engineer” and “Product Analyst” are relatively new, and some companies conflate them with Data Engineer/Data Scientist roles.</li></ol><hr><h2>Examples from everyday life (to make this concrete)</h2><h3>Example 1 — Grocery chain launching a subscription box</h3><ul><li>Business Analyst: Interviews store managers to understand fulfillment constraints; maps the subscription process; writes acceptance criteria for the subscription feature; coordinates between marketing, operations, and IT.</li><li>Data Analyst: Looks at historical purchase data to estimate likely subscribers, churn, and per-box margin; builds a dashboard tracking sign-ups and churn.</li><li>Data Engineer: Ensures transaction data flows from POS systems into the warehouse daily so the DA can run queries.</li><li>Product Analyst: Designs and analyzes an A/B test for different subscription discounts.</li><li>Analytics Manager: Prioritizes which metrics to track for the C-suite and decides if the subscription project should move forward based on analytics capacity.</li></ul><h3>Example 2 — Streaming service wants to increase watch time</h3><ul><li>Data Analyst: Produces weekly cohort analyses showing which onboarding experiences correlate with longer engagement.</li><li>Data Scientist: Trains a recommender model to surface personalized content and quantifies expected watch-time lift.</li><li>ML Engineer: Deploys and monitors the real-time recommender service.</li><li>Business Analyst: Ensures content licensing constraints and UI changes meet legal/product requirements, and translates business needs into product requirements.</li></ul>These examples show how different roles intersect to deliver value.<hr><h2>What surprised me</h2><ul><li>Business Analysts often do more data work than you'd expect. In many organizations BAs run analyses, especially when a dedicated analytics team is missing. Communication skill + domain knowledge is powerful.</li><li>“Data Scientist” is a vague, aspirational title in many places. Some data scientists are researchers publishing papers; others are mostly feature engineers or heavy users of dashboards. The variance is huge.</li><li>The rise of analytics engineering (dbt, modular SQL) is changing workflows. Teams get faster when data transformations live near code and tests, which lets analysts focus on insight rather than fighting flaky datasets.</li><li>Tools don't define a role; culture does. Two companies using the same stack (say, BigQuery + Looker) can still structure their analytics very differently — who sits with product, who owns the metrics, who has final sign-off.</li></ul><hr><h2>How to read job postings and choose where to focus (practical advice)</h2><ul><li>Look at responsibilities, not title. If the posting lists “SQL, dashboards, stakeholder interviews,” it leans analyst/BA hybrid. If it lists “feature engineering, model deployment,” it’s DS/ML.</li><li>Check the team structure: Does the team report into Product, Engineering, or Finance? That gives clues about priorities and the role’s daily focus.</li><li>If you’re starting: learn SQL first. It’s the workhorse skill that unlocks many analytics careers. Next, learn at least one scripting language (Python/R) and a visualization tool. For infrastructure-focused roles, learn git and dbt or cloud basics.</li><li>Portfolio matters: show clear before/after stories — what question you answered, how you answered it, and what changed because of your work.</li></ul><hr><h2>Career paths and common transitions</h2><ul><li>Data Analyst → Product Analyst or Data Scientist (with stronger stats/ML skills)</li><li>Data Analyst → Analytics Engineer if you enjoy building robust transformations and testing.</li><li>Business Analyst → Product Manager if you enjoy defining product and owning roadmaps.</li><li>Data Engineer → Analytics Engineer if you like the intersection of engineering and analytics.</li><li>Data Scientist → ML Engineer if you prefer production and systems work over research.</li></ul>Many people move horizontally or pick a T-shaped path: broad business knowledge + one deep technical skill.<hr><h2>A concise comparison table (one-line summary)</h2><img class="max-w-full h-auto rounded-lg" src="https://i.postimg.cc/TP0nQ2Ft/analyst.jpg"><hr><h2>Honest gaps — what I don’t fully know (and it matters)</h2><ul><li>Exactly how titles will evolve with generative AI. Will analysts be expected to code less and ask better questions, or will automation increase the premium on engineering and causal understanding? I don’t have a confident answer yet.</li><li>Precise boundaries at every company. The split between BA and DA (or DS and DE) will always depend on organizational maturity. There’s no universal blueprint.</li><li>Salary and seniority mapping is noisy. Seniority labels (Senior, Staff, Principal) don’t correspond cleanly across companies, so compensation and responsibilities are best judged by specific job descriptions.</li></ul><hr><h2>If I had more time, here’s what I’d explore next</h2><ol><li>Map real job descriptions across 50 companies to quantify how often “data analyst” means dashboarding vs modeling vs engineering — to show concrete title-to-task probabilities.</li><li>Interview people in each role across different industries (finance, retail, SaaS) to capture the cultural differences in expectations.</li><li>Experiment with a mini-project that covers the whole pipeline: ingest sample transactions, create dbt models, build dashboards, run an A/B test design — to feel the handoffs between roles.</li><li>Study how tooling (dbt, Looker, BigQuery) reshapes team boundaries and whether analytics engineering reduces friction for data analysts.</li></ol><hr><h2>Final thoughts (the “coffee conclusion”)</h2>If you take one thing away from this chat: titles are signals, not definitions. Ask what the team cares about, how decisions are made, and what deliverables matter. A Business Analyst and a Data Analyst can both be called “analyst” and both be indispensable — they just bring different tools to bear. In some organizations the BA is the glue between users and product; in others, the DA is the glue between raw numbers and business intuition. And in the best teams, engineers, analysts, and product people form a feedback loop where each role amplifies the others.I’m still curious about how automation and better data tooling will reshape these roles in the next five years. Would you want me to turn one of the “next steps” into a concrete plan or a short reading list? I can sketch a 6–8 week learning path for someone deciding between BA and DA, or a checklist to evaluate a job posting if you want to apply this right away.