Latest blog posts

  • Transportation Revolution

    Humans relied on horses as the main mode of transport for about 5,000 years. Then, Ford’s Model T changed everything in less than a decade.

    I’m curious to see the visual transformations cities will undergo during the tech revolution unfolding right now.



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  • Social Equality VS Individual Freedom

    This is an eternal problem.
    The values of social equality and individual freedom contradict each other and do not coexist for long.
    It is a timeless issue for humanity—left and right, capitalism and socialism.
    Can there be a balance?



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  • My story of launching E-shop business with AI

    (in 2 months and 3 steps: Seed→Feed→Grow)

    In 2024, after six years in the corporate world, I left my secure job to start my own business. This time I took a fresh approach, using a lot of AI and automation to make things faster, easier and more fun. Here’s what I learned along the way! 👇

    So. My last day in Decathlon was emotional. I hugged my colleagues, signed the papers, and stepped into my first day without corporate obligations. It felt surreal. For the first time in years, no meetings, no messages, no reports, nothing…

    Honestly, at that time, I even had no idea what was next. For me, it was a ticket to a long ride into uncertainty.

    But it was impossible to ignore how quickly the world was changing. Even with my digital background, I felt my skills ageing. So I decided to dive into AI, automation, and no-code tools.

    After spending time learning and researching, one idea kept coming back to me: What if a single person, using AI and automation, could outpace big companies in the market?

    It was worth testing the idea in real life, so I rolled up my sleeves to see if it had potential or was just a fantasy.

    What is Seed.Feed.Grow.

    I chose to use the process taught in startup schools. With some adaptations to fit my needs, it became a 3-step process Seed.Feed.Grow.

    Here’s an algorithm:

    Step 1: Seed.
    Run Micro-Experiments.

    The goal at this early stage was to quickly and cheaply test a lot of ideas with real customers… But! Without making any sales (yes, it’s possible).

    Why? Because launching any sales takes energy, time, and money. But when you’re on your own, with limited resources and uncertain outcomes, micro-experiments are key. They cost next to nothing but offer first important market insights.

    I love online sales, so I researched and listed 10 product ideas that could potentially turn into a business.

    I knew most ideas would fail, so I tested them all and gathered real customer data.

    For each product idea, I created a simple web page with a buy button. However, clicking the button didn’t lead to a payment page. Instead, a message informed customers that the product was unavailable and they’d be notified later. I ran small online advertising campaigns to see if people would click the ‘buy’ button.

    At this moment, reality hit me in the face 🤜😘 . The idea that was my favourite and fueled my passion—selling frisbees—showed zero results. I was upset and had to let it go.

    But then, this data-driven approach highlighted an unexpected idea. It attracted significantly more traffic to the product page and more clicks on the buy button. For me, that was real market validation and a strong signal to move forward with building an MVP…

    AI & Tools I used:

    Step 2: Feed.
    Build a Minimum Viable Product (MVP)

    Alright, now that one idea is validated, it’s time to build an MVP—a simple business setup to start selling.

    On this stage I kept the effort minimal since the product could still fail.

    My minimum E-commerce business setup included:

    • A website
    • An online payment system
    • Localized product content
    • One supplier
    • One logistics partner
    • A small warehouse (at home)
    • Minimal legal & compliance: local laws research and simple sole proprietorship registration.
    • Basic customer service (AI chat & email)
    • A simple brand concept and communication
    • Minimal online advertising

    After two months of building, my rough website started making sales. 💸

    This isn’t the main point of the post, but here is the link to my e-shop Essetia.com so you can see what one person can build in eight weeks with AI.

    AI & Tools I used in the MVP stage:

    • Zapier for automatic data flow between apps and tools
    • A GPT bot that I trained on local Czech laws and documents to understand legal requirements (later confirmed with a legal specialist—never fully trust the robots).
    • Shopify to build the website
    • Claude AI to adapt the website code
    • Aya AI for content translation and localization
    • NotebookLM to review and negotiate contracts
    • Cursor and Replit AI to write code for logistics and warehouse management apps
    • Shopify Inbox for AI-powered chat
    • ComfyUI for product photo
    • ChatGPT, Perplexity, Gemini to build simple brand concept and communication
    • ElevenLabs and CapCut AI for creating marketing videos

    Step 3: Grow.
    Go All-in and invest.

    Right now, I’m in the MVP stage. But if the website data, unit economics, and customer feedback look good, I’ll go all in to capture the market. The risks of investments will be much lower thanks to the knowledge, data, and experience gained in the first two stages.

    Conclusions

    1. Business launch speed has increased exponentially compared to previous years.
      In the past, testing an e-commerce idea took six months with a team. This time, I did it alone in two months (including learning time). Next time, I could do it in one month—six times faster and at a much lower cost.
    2. The Seed.Feed.Grow approach proved incredibly useful for solopreneurship.
    3. Have I proven that one person can compete with big companies?
      I’ll put it this way: my belief in the idea has grown significantly. But there’s still so much I need to learn and accomplish on this journey. I will share more.

    Next steps

    The strategic reason I chose to do everything alone this time is to fully rethink each process before automating it with AI in future. If I don’t understand the details, I can’t explain them to AI agents, and I certainly can’t expect them to perform well for me in the next phase.

    And my next step is building a fully AI-native, agent-driven business.


    In any case, it’s an exciting and challenging problem for me to solve. I’ll have fun and learn a lot along the way. The new, profitable business model could be a fantastic bonus. 😏



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  • My next book – Feel Good Productivity

    After finishing Slow Productivity, my next book is Feel Good Productivity.
    Both reflect my desire to stay productive while enjoying the journey.



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  • When you spend too much time with a life coach 💀

    ICE BATH!



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  • An interesting look at Nike’s recent business struggles

    I found this story on LinkedIn here. The article was originally posted by MRKTNG.fi.

    I agree with two points:

    1. Consultants should guide the way, but not take control.
    2. Data is helpful, but knowing the business helps understand things that can’t be tracked, like why people choose certain sports shoes or their emotional connection to a brand.

    But I don’t agree with the article’s title. I don’t think consultants ruined the business. It looks more like a leadership problem.

    How Consultants Almost Destroyed Nike’s Brand

      On June 28, 2024, Nike faced the largest stock price collapse in its history. A single day wiped out $25 billion or 32% of its market value. Despite claims from trolling publications that the drop stemmed from “woke antics,” the true culprit was far less sensational: an overreliance on data-driven leadership.

      In business, certainty is often sought. The equation is simple: “If I invest X, I’ll achieve Y.” But Nike, a company whose brand is its lifeblood, doesn’t operate within this tidy formula.

      Creative branding thrives on associations and empathy—intangibles that are difficult to quantify yet generate immense value. At Nike, these intangibles are worth billions. But because branding lacks a neatly predefined ROI, consultants, who are eager to distill human experiences into numbers, often miss its true worth.


      The Consultants Take Over

      John Donahoe became CEO of the world’s second most valuable apparel brand in 2020. Before this role, Donahoe spent 23 years at Bain & Company, one of the world’s most prestigious consulting firms.

      His leadership, often described as data-driven, marked a dramatic shift for the company.

      According to Nike’s former marketing director, Massimo Giunco, the new leadership team initiated a significant restructuring based on three seemingly reasonable but ultimately disastrous decisions:

      1. “Streamlining the organization.” Consultants advised a restructuring that led to the departure of hundreds of experts. This mass exodus eroded tacit knowledge and slowed product innovation. They also dismantled Nike’s traditional product categories (running, basketball, soccer) in favor of a generic segmentation: men’s, women’s, and children’s. The idea was to drive sales with data rather than category-specific expertise.
      2. “Customer-centric transformation.” Nike adopted a Direct-to-Consumer (DTC) model, focusing marketing efforts on driving customers to Nike.com rather than relying on retailers. Long-standing partners like Macy’s and Foot Locker were dropped, creating shelf space for competitors. Initially, the changes appeared successful, particularly during the pandemic when e-commerce boomed. However, customers proved less loyal than expected. If Nike wasn’t available at the local shoe store, customers simply bought the next best thing. While DTC offered greater control and higher margins, it also reduced brand accessibility and exposed Nike to greater risks in shifting market conditions.
      3. “Data-driven digital marketing.” Replacing inspiring and memorable brand campaigns with tactical sales-driven messages weakened the emotional connection consumers had with the brand. The old strategy was to dominate every touchpoint; the new focus on e-commerce created opportunities for specialized competitors like Tracksmith to better address the needs of passionate athletes. Major rivals like Adidas had already learned from neglecting their brands. Nike could no longer rely on its branding to sell $200 sneakers. Instead, mounting inventory and relentless discounting eroded the brand’s value.

      Just Measure It

      The pursuit of short-term profits and cost optimization couldn’t compensate for the damage done to Nike’s brand.

      Product development faltered because data alone doesn’t create demand; innovation does. It’s about introducing consumers to something they didn’t even know they needed.

      Most importantly, the big, inspiring brand campaigns dwindled. It had been six years since the iconic Dream Crazy campaign. Nike had become cheap and stale.


      Back to Basics

      Brand visibility and salience in consumers’ minds are critical success factors.

      Donahoe lacked deep knowledge of sneaker culture or retail experience, leading to disaster. As a former management consultant, his best strategy was cost-cutting, which only worsened Nike’s problems.

      Nike has finally recognized its mistakes. Its new CEO, Elliot Hill, who started as an intern at Nike in 1988, took over in October. His deep understanding of the organization—evident in his now-viral LinkedIn résumé—has already made a difference.

      A return to Nike’s bold and vibrant roots was evident in its 2024 Paris Olympics campaign, “Winning Isn’t for Everyone / Am I a Bad Person.” But rebuilding brand equity takes time.


      Lessons for Marketers

      The Nike saga offers a clear takeaway: be cautious about over-relying on management consultants.

      Their processes may be analytical and data-driven, but they often fail to understand the importance of branding and marketing until it translates into sales.

      Just because something isn’t precisely measurable doesn’t mean it doesn’t exist. It could be the most significant driver of growth. Just look at the numbers.


      Erik Mashkilleyson

      This story was originally published in MRKTNG magazine January 14, 2025.

      The author is the Strategy & AI Director at Avidly, specializing in international branding, digital service design, AI and sustainable business.



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    1. Now build your own app in no time

      Personal software for everything is the new norm.



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    2. Customer Journey: Expectations vs Reality

      How marketers think buying works:

      1. See ad.
      2. Click ad.
      3. Buy product.

      How buying actually happens:

      1. See ad.
      2. Get distracted by a dog video.
      3. Forget the brand exists.
      4. Spot a social media post days later.
      5. Like the post.
      6. Keep scrolling.
      7. See another ad at work.
      8. Click and browse.
      9. Get distracted by emails.
      10. Forget the brand again…
      11. Notice favorite creator mentioning the brand.
      12. Click, browse, and maybe add to cart.
      13. Get distracted.
      14. Forget about it… again.
      15. Hear a friend mention it a week later.
      16. Dig through emails for a discount.
      17. Check Amazon for a better deal.
      18. Get distracted by random Amazon recommendations.
      19. Forget the brand exists (again).
      20. Late-night caffeine-fueled doom scrolling.
      21. See another ad.
      22. Click and browse.
      23. Get sleepy.
      24. Fall asleep.
      25. Personal drama is unfolding.
      26. Forget the brand.
      27. Clicks back to the cart.
      28. Finally buys…
        or… most of the time, ends up buying from a competitor or changing their mind.



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    3. Reminder: Take the time, avoid perfection

      This is from Slow Productivity, a book that has become both my compass and speedometer.

      Mastering the balance between giving myself enough time to create something cool without falling into the perfection trap is a skill I still need to learn and practice.



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    4. Don’t ever attack a nerd

      We will come back to school with trench coat and AK47 💀😂



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    5. Don’t feed them

      Remember that internet idiot you argued with last night?
      From his side, it might’ve looked like this



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    6. Reminder: Marketing

      A reminder to myself: Marketing is more than just sales.

      It is about the people you help.
      It is about solving real problems.
      It is about the stories you tell.
      It is about building strong connections.

      Does your customer remember you after paying?



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    7. Minimum Lovable Product

      The era of the Minimum Viable Product (MVP) is fading.

      Now, the focus shifts to the Minimum Lovable Product (MLP).

      For a long time, MVPs were the go-to strategy for startups—launch quickly, test, and refine. A smart approach, right?

      But AI is rewriting the rules. The MVP might already be irrelevant.

      Here’s why:

      MVPs pushed us to start building right away. Yet with tools like Perplexity and Cursor, anyone can research and create an app in no time.

      MVPs helped validate market needs. Yet platforms like Reddit and Semrush already reveal if demand exists.

      So, what’s the next step? The Minimum Lovable Product.

      MLPs focus on designing products people truly enjoy using.

      MLPs reveal if people care enough to share your product, turning them into advocates. And in today’s saturated markets, organic growth is crucial.

      MLPs also indicate whether users will keep coming back. Because if they don’t, they’ll move on. And retention is key.

      So, in a world where technology makes building easy, isn’t it time we focus on making products people actually love?

      difference between mvp and mlp


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    8. Reminder: Failures

      Reminder for myself. Learning never stops — but also, never stop trying and failing. Failure is like a tough but honest coach, delivering raw, unfiltered feedback about life (with a touch of pain). Every failure is a shortcut to deeper understanding.



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    9. Growth vs Profitability dilemma

      What do Amazon, Uber, Tesla, and Spotify have in common? They’re all high-tech companies that stayed unprofitable for decades, prioritizing rapid growth over immediate profit.

      But times are changing. A recent Y-Combinator podcast indirectly highlights this shift.

      Those speakers are deeply immersed in the startup ecosystem.
      And two points stood out to me:

      1. Growth Accelaration
        At 00:02:00, they discuss how the average growth rate for startups in their portfolio has dramatically accelerated since summer 2024. In the past, only the best-performing startups, like Airbnb, achieved a 10% weekly growth rate. Today, that rate is the market average. Some companies are even projecting $10–20 million in revenue in their very first operational year.
      1. Startup lifecycle
        At 00:12:02, they question whether the startups being built today will even survive in a world where AGI becomes a reality. Their honest answer? They’re not sure.

      Key takeaway: Go big now, stay lean, leverage artificial intelligence, and maximize profits ASAP. Don’t expect your company to last 10 years, even if you’re on the cutting edge of technology—your advantage will fade quickly.



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    10. Amazon’s income statement insights

      Amazon’s income statement is a masterclass in diversification. It’s not just an online store—it’s a tech company, a logistics powerhouse, a media giant, and even an advertising titan. Here are a few lessons entrepreneurs and businesses can take away:

      • Bet on multiple horses: From AWS to ads, Amazon’s growth strategy thrives on not putting all its eggs in one basket. They have 6 big baskets and plenty of small.
      • Customer obsession pays off: By investing 16% of total revenue in fulfillment, Amazon ensures top-tier customer satisfaction. This isn’t just an operational strategy—it’s a powerful marketing tool that keeps customers returning to Amazon.com.
      • Tech is the future: Spending $20B on technology isn’t just about today—it’s about leading tomorrow.


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    11. Money & Trust

      Money is a product of human imagination based on universal trust in the system.

      For over 5,000 years, humans have used some form of money. In the last 50 years, though, our trust in it has reached new heights. This shift began in 1971 when Nixon ended the Bretton Woods system, and the way we see money changed forever.

      These days, money isn’t backed by anything real (like gold or silver). All currencies in the world operate as fiat money, meaning their value is based on trust in governments and economies rather than tangible assets. And that’s understandable, especially since the total value of all mined gold today is $20 trillion. Honestly, that’s simply not enough to keep the modern economy moving at the pace it needs.

      Here’s an interesting fact: over $129 trillion circulates in the global economy today. But how much of that is physical? Just 10%. Only a small portion of the world’s money exists as paper bills and coins. The other 90% is digital—stored as numbers in bank databases. It’s fascinating when you think about it.

      (btw, the amount of money has grown 500% in 23 years 🤷)

      I find it insane how this system works. People who don’t trust each other daily trust money just because everyone else does. It’s a shared belief that keeps the system running.

      Well. It may not feel sustainable in the long run, but for now, it’s the best tool we have for cooperation and trust between strangers.. sometimes even enemies.



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    12. Devaluation of intelligence

      Cannot disagree



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    13. Future is agentic

      AI Agents will change how software works together very soon.

      No single software can handle all tasks. AI Agents will connect systems, gather information, and provide answers, just like a person might.

      Today, APIs let software work in predictable ways. AI Agents take this further by handling tasks that aren’t so straightforward. They’ll process requests, pull data from other systems, and return useful results.

      For example, an Agent in HubSpot could find customer details by pulling contracts from Dropbox or invoices from PayPal. Or, an Agent in Slack could help employees by accessing HR files in BambooHR and project plans in Trello.

      Are we ready to trust AI with decisions that shape our work?



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    14. Action produces information

      Pre-product-market fit rules:
      Keep doing stuff.
      If sharks stop swimming, they die.
      Action produces information.
      When facing a mountain in the fog, take three steps; the next three will reveal themselves.



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    15. My reflections on ‘Slow Productivity’ book


      I picked up this book because the idea of slow productivity had resonated with me for a while. In my fast-paced corporate job, people were constantly overwhelmed by emails, chats and meetings. Despite working hard, it often felt like projects didn’t progress quickly enough, causing burnout among talented people from time to time.

      I also noticed that people who focused on fewer tasks and worked at a steady pace achieved more. It seemed counterintuitive, but those who rushed less and did less performed better. I felt there had to be a better way to work, so I kept asking myself WHY and HOW questions.

      I think I found some answers in the book.

      (more…)


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    16. The cost of doing nothing

      This quote caught my attention, and I definitely agree with it. In reality, the cost of doing nothing is huge when looking at the long term. Investing in being wrong always has a return on investment – the experience that can be used in the future.



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    17. Heuristic and algorithmic decisions

      In business, decision-making often revolves around two primary approaches: heuristic and algorithmic. Both methods have distinct advantages and can be effective in different situations. Understanding when to use each approach can significantly impact your decision-making process.

      Heuristic Methods
      Heuristic methods rely on simple rules and past experiences to make quick decisions, which is particularly useful when time is limited or there’s an overload of information.

      • Pros:
        • Fast decision-making
        • Simple and easy to apply
        • Effective when dealing with limited information or tight deadlines
      • Cons:
        • Can lead to errors due to oversimplification
        • May not always result in the best outcome
        • Relies on past experiences, which might not always be applicable

      Algorithmic Approaches
      Algorithmic methods, on the other hand, follow a set of detailed rules to make decisions, often leveraging data analysis.

      • Pros:
        • More accurate and precise decisions
        • Consistent, based on data-driven insights
        • Effective for handling large amounts of data
      • Cons:
        • Can be complex and time-consuming to implement
        • May struggle to adapt to unexpected changes or new variables
        • Requires access to quality data to be effective

      Heuristic methods offer speed and simplicity, while algorithms provide accuracy and depth for more complex decisions. Both have their place in business.

      I favor an algorithmic approach, relying on data and facts over quick, emotional decisions. However, I’m working to incorporate more heuristic thinking, especially in startup mode where speed and flexibility are crucial. Finding the right balance between speed and accuracy is key, but I’m still figuring it out…



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    18. Back to coding after 30 years

      After a 30-year break, I returned to coding, and the experience brought back warm memories from my childhood…

      I was a 12-year-old in a family of Soviet engineers navigating the shift from a Soviet economy to a capitalist world. One day, my father took me to his research lab, showed me a computer, and taught me how to insert five-inch floppy disks and run files. The sound of launching DOS and Norton Commander still echoes in my mind.

      Exactly that sound

      That moment sparked my interest in programming, leading me to learn BASIC and Pascal. It was a challenge. I didn’t have a computer as it was too expensive for my family. So I spent evenings reading manuals and writing code with paper and pen. Twice a week, I could visit a kids’ club called Pioneer Palace 🫡 in Kyiv, where we used old, clunky computers for free. We had a deal with our teacher: finish blind typing training early, and we could do whatever we wanted afterward. While most kids played Pacman or Digger games, I was among the few nerds 🤓 writing code. Communicating with that metal machine in a special language was captivating. I’d create simple graphics and games, typing the code from my paper notes until the lesson ended, then transfer it back to my notepad to continue at home.

      Pioneer Palace computers looked like this

      I proudly showed my work to my parents during dinners. They looked at my papers with code, probably thinking I was a bit crazy, but they were just glad I wasn’t out on the street. Eventually, this led my father to the decision to save up some money and buy me my first IBM 486 — an old machine with a 128MB hard drive, and 1MB of RAM. It was a broken and frustrating second-hand computer, but it taught me a how to diagnose problems just by the mechanic sound of the hard drive’s heads, processor cooler and the screech of an 8-bit speaker🙂

      Few years later I lost interest in coding during my teenage years and shifted focus to friends and fun.

      Fast forward ⏩ 30 years, and here I am diving back into programming—this time, no-code programming. I simply speak to Cursor app and guidding the process with human language. It’s a completely new, magical experience, but the dopamine rush when all the errors are fixed and everything runs smoothly is just as satisfying as it was in childhood.

      Returning to coding after so many years reminded me that we may leave things behind, but sometimes they find their way back to us when we’re ready.

      IF enjoy_process = TRUE THEN
          PRINT "What a time to be alive!"
      END IF



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    19. Quotes about UI/UX

      “A user interface is like a joke. If you have to explain it, it’s not that good.”
      — Martin LeBlanc

      “We don’t know what is good User Interface, but we know how to measure.”
      — Marissa Mayer

      “Design is not just what it looks like and feels like. Design is how it works.”
      — Steve Jobs

      “Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.”
      — Antoine de Saint-Exupéry

      Customer-focused design requires balancing feedback with observation, data, and expertise to create solutions that exceed expectations.



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