[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-what-is-artificial-intelligence-explained-without-the-marketing":3,"$fXeC77ibo_YuG-ZnFG3hwyaNC83zFAKw-FvBNoL2JrIM":15},{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"bodyHtml":11,"metaTitle":12,"metaDescription":13,"date":14},"what-is-artificial-intelligence-explained-without-the-marketing","What artificial intelligence is (and what it is not)","The word AI now means everything, and therefore nothing. Here is what is actually behind it, and where technology ends and marketing begins.","Fundamentals","MightCore","7 min",null,"\u003Cp>Over the past three years the letters AI have been stuck onto so many products that they stopped meaning anything. Your spam filter is AI. A film recommendation is AI. A chatbot that writes poetry is AI too. If that feels confusing, you are not alone — and it is not your fault.\u003C\u002Fp>\n\n\u003Ch2>One sentence that covers it\u003C\u002Fh2>\n\u003Cp>Artificial intelligence is software that \u003Cem>learned\u003C\u002Fem> its behaviour from data instead of having it programmed in, rule by rule.\u003C\u002Fp>\n\u003Cp>That is the whole difference. And it is bigger than it sounds.\u003C\u002Fp>\n\u003Cp>A classical program is a recipe. A developer writes: if the total is over a thousand euros and the customer is new, ask for payment up front. When the rule changes, someone rewrites it. The program never does anything its author did not tell it to — which is simultaneously its greatest strength and its hardest limit.\u003C\u002Fp>\n\u003Cp>A machine learning model gets examples instead of rules. Tens of thousands of orders where we know which ones went unpaid. The model finds the patterns that separate them on its own. Nobody told it that the hour of the order matters, or whether the billing and delivery addresses match. It worked that out itself.\u003C\u002Fp>\n\n\u003Ch2>Why anyone bothers\u003C\u002Fh2>\n\u003Cp>Because some rules cannot be written down.\u003C\u002Fp>\n\u003Cp>Try defining exactly what makes a photo of a cat a photo of a cat. Not \"four legs and a tail\" — a dog has those. Not \"whiskers\" — often you cannot see them. A person recognises it in a tenth of a second and cannot explain how. That is precisely where classical programming hits a wall and learning from examples works.\u003C\u002Fp>\n\u003Cp>The same goes for speech, handwriting, the tone of an email, or guessing whether a customer is about to leave for a competitor. These are tasks where we have intuition but no rules.\u003C\u002Fp>\n\n\u003Ch2>What AI is not\u003C\u002Fh2>\n\u003Cp>It is not thinking. A model has no intentions, no opinions, no understanding of the world in the sense you have one. When a language model writes that it understands you, that is a statistically likely response to your sentence — not empathy.\u003C\u002Fp>\n\u003Cp>It is not knowledge either. A model does not remember facts the way an encyclopedia does. It remembers patterns. That is why it can fluently write something that sounds right and is nonsense. This is called hallucination, and it is not a malfunction — it follows directly from how the model works.\u003C\u002Fp>\n\u003Cp>And it is not magic. Behind every impressive output are the data it learned from. Where the data had nothing, the model fills the gap itself.\u003C\u002Fp>\n\n\u003Ch2>Where the line runs\u003C\u002Fh2>\n\u003Cp>This is probably the most useful thing to take away. AI is good at tasks where:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>plenty of examples exist to learn from,\u003C\u002Fli>\n\u003Cli>an occasional mistake is not a catastrophe,\u003C\u002Fli>\n\u003Cli>and a human can check the result.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>It is bad — or at least risky — anywhere you need to be right every time, where a mistake actually costs something, and where nobody reviews the output. Leave VAT calculation to ordinary code. It does not get things wrong, and when it does, you know exactly why.\u003C\u002Fp>\n\u003Cp>In practice it usually looks like this: AI does eighty percent of the work and a person does the rest. Less impressive than promises of full automation, but it works.\u003C\u002Fp>\n\n\u003Ch2>Why now\u003C\u002Fh2>\n\u003Cp>Neural networks are not a new idea; the foundations date to the 1960s. Three things changed at once: enough data (the internet), enough compute (graphics cards), and — since 2017 — an architecture called the transformer that learns far more efficiently than anything before it. That is what the language models everyone now knows are built on.\u003C\u002Fp>\n\u003Cp>So no, no miracle happened. Conditions that had been missing for decades simply arrived together and finally got an old idea moving.\u003C\u002Fp>\n\n\u003Ch2>The takeaway\u003C\u002Fh2>\n\u003Cp>Next time somebody offers you an \"AI solution\", ask two things: what data did it learn from, and what happens when it gets it wrong. If the first question gets no concrete answer and the second gets \"it won't\", you are looking at marketing, not technology.\u003C\u002Fp>","What is artificial intelligence — explained without the marketing","A plain explanation of what artificial intelligence is, how it differs from ordinary software, what it genuinely does well, and where its limits are.","2026-01-21T00:00:00.000Z",[16,22,28,34,40,47,54,59,63,69,75,80,85,90,95,100,105,110,115,121,122,127,132,137,142,147,152,157,162,167,172,177,182,187,191,196,201],{"slug":17,"title":18,"excerpt":19,"category":20,"author":8,"readingTime":9,"coverImage":10,"date":21},"how-to-talk-to-a-language-model-prompting-in-practice","How to talk to a model: prompting without incantations","Prompt engineering is not a list of magic phrases. It is the ability to say exactly what you want — which is harder than it sounds.","Guides","2026-07-08T00:00:00.000Z",{"slug":23,"title":24,"excerpt":25,"category":26,"author":8,"readingTime":9,"coverImage":10,"date":27},"how-to-choose-a-language-model-and-control-costs","Choosing a language model and keeping costs under control","Benchmarks tell you almost nothing useful. What actually decides a model choice, and where the costs nobody predicted come from.","Engineering","2026-06-30T00:00:00.000Z",{"slug":29,"title":30,"excerpt":31,"category":26,"author":8,"readingTime":32,"coverImage":10,"date":33},"programming-in-the-age-of-ai-what-actually-changed","Programming in the age of AI: what actually changed","No, it did not replace developers. But it changed where they spend their time — and not all of those changes are comfortable.","9 min","2026-06-17T00:00:00.000Z",{"slug":35,"title":36,"excerpt":37,"category":38,"author":8,"readingTime":9,"coverImage":10,"date":39},"ai-modelka-pre-vas-brand-sprievodca","An AI model for your brand: the complete guide","From brief through avatar creation to the first campaign — step by step.","AI UGC","2026-06-16T00:00:00.000Z",{"slug":41,"title":42,"excerpt":43,"category":44,"author":8,"readingTime":45,"coverImage":10,"date":46},"pripadova-studia-cleago","Case study: Cleago — a platform built on context","How we designed and built a solution for Cleago (www.cleago.sk) by first understanding the context and only then coding.","Case studies","5 min","2026-06-02T00:00:00.000Z",{"slug":48,"title":49,"excerpt":50,"category":51,"author":8,"readingTime":52,"coverImage":10,"date":53},"where-not-to-use-ai-and-why-nobody-tells-you","Where not to use AI (and why nobody tells you)","A company selling AI has little incentive to talk about its limits. Let us fix that — these are the places AI simply does not belong.","For business","6 min","2026-05-27T00:00:00.000Z",{"slug":55,"title":56,"excerpt":57,"category":44,"author":8,"readingTime":45,"coverImage":10,"date":58},"pripadova-studia-produktove-fotky","Case study: 80% less time spent creating product photos","A real example of deploying AI photos in an online store — from brief to results.","2026-05-19T00:00:00.000Z",{"slug":60,"title":61,"excerpt":62,"category":38,"author":8,"readingTime":45,"coverImage":10,"date":58},"ai-model-consistent-brand-across-campaigns","A consistent brand with an AI model across campaigns","An AI model can be a brand's steady face — if you handle consistency and transparency the right way. Here is how.",{"slug":64,"title":65,"excerpt":66,"category":51,"author":8,"readingTime":67,"coverImage":10,"date":68},"where-ai-actually-fits-in-a-company","Where AI actually fits in a company (and where it is just flashy)","Concrete applications across departments — what works today, what needs preparation, and what is still a demo rather than a tool.","8 min","2026-05-06T00:00:00.000Z",{"slug":70,"title":71,"excerpt":72,"category":73,"author":8,"readingTime":52,"coverImage":10,"date":74},"rest-vs-graphql-pre-eshopy","REST vs. GraphQL API for modern online stores","When to choose which approach and what the impact on performance and development is.","Development","2026-04-21T00:00:00.000Z",{"slug":76,"title":77,"excerpt":78,"category":51,"author":8,"readingTime":32,"coverImage":10,"date":79},"how-to-bring-ai-into-your-company-first-steps","How to bring AI into your company without wasting the money","Most AI projects do not fail on technology. They fail because nobody said what was supposed to get better. Where to start instead.","2026-04-15T00:00:00.000Z",{"slug":81,"title":82,"excerpt":83,"category":44,"author":8,"readingTime":52,"coverImage":10,"date":84},"case-study-monolith-to-modular-migration","Case study: from a monolith to a modular architecture with no downtime","An illustrative example of gradually modernising an older application — where every change was risky and maintenance expensive.","2026-04-14T00:00:00.000Z",{"slug":86,"title":87,"excerpt":88,"category":20,"author":8,"readingTime":67,"coverImage":10,"date":89},"ai-agents-and-tool-calling-when-it-makes-sense","AI agents and tool calling: when it makes sense and when it does not","An agent is a model allowed to act. That is interesting and dangerous in equal measure. How it works, and where to be careful.","2026-03-25T00:00:00.000Z",{"slug":91,"title":92,"excerpt":93,"category":20,"author":8,"readingTime":52,"coverImage":10,"date":94},"context-driven-development-context-gathering-in-practice","Context Driven Development in practice: how gathering context changes the outcome","The most expensive mistakes come from a misunderstood brief. Here is what the context gathering that prevents them looks like.","2026-03-17T00:00:00.000Z",{"slug":96,"title":97,"excerpt":98,"category":99,"author":8,"readingTime":52,"coverImage":10,"date":94},"uctovnictvo-novej-generacie","Next-generation accounting: a platform built on context","A vision of an intelligent layer on top of existing accounting tools.","Accounting",{"slug":101,"title":102,"excerpt":103,"category":20,"author":8,"readingTime":32,"coverImage":10,"date":104},"how-to-orchestrate-language-models-in-practice","Orchestrating language models: from one prompt to a system","One prompt is a demo. An application is something else. On splitting work, routing between models, and where to leave ordinary code alone.","2026-03-04T00:00:00.000Z",{"slug":106,"title":107,"excerpt":108,"category":99,"author":8,"readingTime":45,"coverImage":10,"date":109},"digitalising-accounting-e-invoicing","Digitalising accounting: e-invoicing and what it brings","Electronic invoicing and reporting are becoming the standard. What it means for businesses and how to prepare without panic.","2026-02-17T00:00:00.000Z",{"slug":111,"title":112,"excerpt":113,"category":7,"author":8,"readingTime":67,"coverImage":10,"date":114},"what-is-an-llm-large-language-model-explained","What an LLM is: large language models without the mystique","How does a program that predicts the next word end up writing working code? A look at what actually happens inside a language model.","2026-02-11T00:00:00.000Z",{"slug":116,"title":117,"excerpt":118,"category":119,"author":8,"readingTime":45,"coverImage":10,"date":120},"ako-ai-setri-naklady-na-video","How AI cuts the cost of video content production","Concrete numbers and a workflow for creating AI videos for online stores.","Marketing","2026-02-10T00:00:00.000Z",{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":123,"title":124,"excerpt":125,"category":73,"author":8,"readingTime":52,"coverImage":10,"date":126},"vector-databases-and-embeddings","Vector databases and embeddings: how machines grasp meaning","Semantic search sits behind many AI features. Here is what embeddings are and why modern data work rests on them.","2026-01-20T00:00:00.000Z",{"slug":128,"title":129,"excerpt":130,"category":73,"author":8,"readingTime":52,"coverImage":10,"date":131},"trendy-v-ai-vyvoji-2026","Trends in AI development for 2026","What awaits companies in the area of AI agents, automation and infrastructure.","2026-01-14T00:00:00.000Z",{"slug":133,"title":134,"excerpt":135,"category":44,"author":8,"readingTime":45,"coverImage":10,"date":136},"case-study-ai-product-photography-cosmetics","Case study: AI product photography for a cosmetics e-shop","An illustrative example of how AI content replaced repeated photoshoots and brought a consistent visual identity across seasons.","2025-12-09T00:00:00.000Z",{"slug":138,"title":139,"excerpt":140,"category":73,"author":8,"readingTime":52,"coverImage":10,"date":141},"integrating-ai-into-existing-systems","How to integrate AI into existing systems without a rewrite","You do not have to throw away working software to use AI. Here is an approach that adds value step by step, with low risk.","2025-11-18T00:00:00.000Z",{"slug":143,"title":144,"excerpt":145,"category":20,"author":8,"readingTime":9,"coverImage":10,"date":146},"gdpr-a-ai-obsah","GDPR and AI content: what to watch out for","The legal minimum for companies working with AI content and personal data.","2025-11-11T00:00:00.000Z",{"slug":148,"title":149,"excerpt":150,"category":38,"author":8,"readingTime":52,"coverImage":10,"date":151},"virtualne-ai-modelky","Virtual AI models: the future of advertising or a passing trend?","The possibilities, limits and ethics of virtual influencers for brands.","2025-10-20T00:00:00.000Z",{"slug":153,"title":154,"excerpt":155,"category":38,"author":8,"readingTime":45,"coverImage":10,"date":156},"ai-ugc-in-performance-marketing","AI UGC in performance marketing: what works and what applies","How to use AI content in Meta and TikTok campaigns, why creative testing matters, and what AI-labelling rules apply.","2025-10-14T00:00:00.000Z",{"slug":158,"title":159,"excerpt":160,"category":73,"author":8,"readingTime":52,"coverImage":10,"date":161},"llm-hallucinations-how-to-limit-them","LLM hallucinations and how to limit them in practice","Why AI sometimes states nonsense with confidence, and the techniques we use to keep output trustworthy.","2025-09-16T00:00:00.000Z",{"slug":163,"title":164,"excerpt":165,"category":38,"author":8,"readingTime":45,"coverImage":10,"date":166},"co-je-ai-ugc","What AI UGC is and why the whole world is talking about it","An introduction to AI-generated UGC and its impact on advertising and customer trust.","2025-09-15T00:00:00.000Z",{"slug":168,"title":169,"excerpt":170,"category":44,"author":8,"readingTime":52,"coverImage":10,"date":171},"case-study-b2b-eshop-faster-delivery","Case study: a B2B e-shop ready in weeks, not months","An illustrative example of how context gathering and AI execution shortened a wholesale e-shop build — without cutting quality.","2025-08-19T00:00:00.000Z",{"slug":173,"title":174,"excerpt":175,"category":119,"author":8,"readingTime":45,"coverImage":10,"date":176},"ai-v-marketingu-od-experimentu-k-vysledkom","AI in marketing: from experiment to real results","How to move from \"playing with AI\" to a measurable return on investment.","2025-08-06T00:00:00.000Z",{"slug":178,"title":179,"excerpt":180,"category":99,"author":8,"readingTime":45,"coverImage":10,"date":181},"ai-invoice-processing-in-accounting","AI invoice processing: from scan to posting","Intelligent document processing cuts the routine of retyping invoices. How it works and where AI has its limits.","2025-07-15T00:00:00.000Z",{"slug":183,"title":184,"excerpt":185,"category":20,"author":8,"readingTime":52,"coverImage":10,"date":186},"rag-why-context-decides-ai-quality","RAG: why context decides the quality of AI output","Retrieval-Augmented Generation connects a language model to your own data. Here is how it works and when to use it.","2025-06-18T00:00:00.000Z",{"slug":188,"title":189,"excerpt":190,"category":73,"author":8,"readingTime":52,"coverImage":10,"date":186},"shopsys-vs-vlastne-riesenie","ShopSys vs. a custom build: when a framework pays off","A decision framework for e-shop owners facing the choice of platform.",{"slug":192,"title":193,"excerpt":194,"category":119,"author":8,"readingTime":45,"coverImage":10,"date":195},"ako-ai-meni-ecommerce-na-slovensku","How AI is changing e-commerce","Practical examples of AI in product content, search and personalisation for online stores.","2025-05-21T00:00:00.000Z",{"slug":197,"title":198,"excerpt":199,"category":73,"author":8,"readingTime":9,"coverImage":10,"date":200},"context-driven-development-novy-pristup","Context Driven Development: a new approach to building software","An explanation of the CDD methodology from gathering context to deployment — step by step.","2025-04-09T00:00:00.000Z",{"slug":202,"title":203,"excerpt":204,"category":73,"author":8,"readingTime":52,"coverImage":10,"date":205},"koniec-ery-predrazeneho-vyvoja","Why the era of overpriced software development is over","How AI and a context-driven approach are changing the economics of building software — and why paying for inflated hours no longer makes sense.","2025-03-12T00:00:00.000Z"]