[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-programming-in-the-age-of-ai-what-actually-changed":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},"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.","Engineering","MightCore","9 min",null,"\u003Cp>The debate about AI and programming has two camps and both are wrong. One says developers will be replaced within a year. The other says it is a toy that writes broken code. The reality is duller and more interesting at once.\u003C\u002Fp>\n\n\u003Ch2>What genuinely changed\u003C\u002Fh2>\n\u003Cp>Not the ability to write code. That was always the easy part of the job.\u003C\u002Fp>\n\u003Cp>The ratio changed. A developer used to spend a large slice of the day on things they knew how to do but still had to type out: writing the test that mirrors the one next to it, generating CRUD, converting data from one shape to another, looking up syntax in the docs. Routine that needed no thought, only time.\u003C\u002Fp>\n\u003Cp>That layer got dramatically cheaper. Which is good news, because it was the tedious part.\u003C\u002Fp>\n\u003Cp>What did not change at all: deciding what to build. Understanding why it should be this way and not that. Working out why the system falls over once a week at 3am. Telling a client that what they want is a bad idea.\u003C\u002Fp>\n\n\u003Ch2>The centre of gravity moved from writing to reading\u003C\u002Fh2>\n\u003Cp>This is the most significant change, and the least discussed.\u003C\u002Fp>\n\u003Cp>When a model gives you eighty lines in ten seconds, your work is not done — it just started. You have to read that code and decide whether it does what you think it does. And that is harder than writing it, because writing builds understanding as a side effect. Reading requires you to build it deliberately.\u003C\u002Fp>\n\u003Cp>Model-written code also has one insidious property: it looks good. Well-named variables, comments, consistent style. Exactly the signals we have used for years to judge quality at a glance. Except here they guarantee nothing.\u003C\u002Fp>\n\u003Cp>We once reviewed a function that looked textbook and quietly ignored one edge case. It would have passed review, had anyone merely skimmed it.\u003C\u002Fp>\n\n\u003Ch2>Where it genuinely helps\u003C\u002Fh2>\n\u003Cp>From what we see in our own work:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Tests.\u003C\u002Fstrong> The single biggest win. Writing twenty input variants is work nobody wants to do, which is why it does not get done. A model handles it and you concentrate on whether it tests the right things.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Unfamiliar code.\u003C\u002Fstrong> \"Explain what this class does\" on a ten-year-old undocumented project saves hours.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>A first version.\u003C\u002Fstrong> Not the final one. But an empty file is a worse starting point than something you can rewrite.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>An unfamiliar language or framework.\u003C\u002Fstrong> When you know what you want and only lack the local idiom.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Tedious conversions.\u003C\u002Fstrong> JSON to types, SQL to a migration, one format into another.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\u003Ch2>Where it hurts more than it helps\u003C\u002Fh2>\n\u003Cp>Architecture. A model proposes what it saw most often — the average of the internet. In architecture, average is usually the wrong answer, because your constraints are not average.\u003C\u002Fp>\n\u003Cp>Debugging unusual failures. Race conditions, memory leaks, \"works on my machine\". The model suggests the most likely causes, which is precisely what you already tried.\u003C\u002Fp>\n\u003Cp>And anything depending on context the model does not have. It does not know you cannot change that table because accounting reads from it. It does not know this endpoint is called by a customer you have a contract with. This is the core of our whole approach — a model is exactly as good as the context it was handed.\u003C\u002Fp>\n\n\u003Ch2>Juniors, and the uncomfortable question\u003C\u002Fh2>\n\u003Cp>There is no tiptoeing around it: the routine work juniors learned on for years is now automated. That creates a real problem nobody has properly solved.\u003C\u002Fp>\n\u003Cp>There is another side, though. A junior with a model reaches a working result far faster and can learn on bigger things sooner. The risk is learning to \u003Cem>accept\u003C\u002Fem> code rather than understand it.\u003C\u002Fp>\n\u003Cp>The difference between those two paths is one question they have to ask: \"why did it write it this way?\" Ask it and you learn faster than any generation before. Skip it and you learn nothing, and will not notice.\u003C\u002Fp>\n\n\u003Ch2>What this does to quality\u003C\u002Fh2>\n\u003Cp>When writing code is cheap, code multiplies. More code means more places for a bug and more to maintain. Generation speed is not free — you pay later.\u003C\u002Fp>\n\u003Cp>Which is why tests, static analysis and code review matter more, not less. They are the only gates that work identically whether a human or a model wrote the code. And unlike our eyes, they cannot be fooled by nice formatting.\u003C\u002Fp>\n\n\u003Ch2>The summary\u003C\u002Fh2>\n\u003Cp>Programming did not shift toward whoever types fastest. It shifted toward whoever knows more precisely what they want, and more reliably notices when they got something else.\u003C\u002Fp>\n\u003Cp>Which, incidentally, is exactly what was hard about this job before. It just can no longer be hidden behind being busy typing.\u003C\u002Fp>","Programming and AI — what actually changed","How AI changed a developer's work: where it genuinely helps, where it hurts, what it means for juniors, and why reading code suddenly matters more than writing it.","2026-06-17T00:00:00.000Z",[16,23,28,29,35,42,49,54,58,64,70,75,80,85,90,95,100,105,111,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,191,196,201],{"slug":17,"title":18,"excerpt":19,"category":20,"author":8,"readingTime":21,"coverImage":10,"date":22},"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","7 min","2026-07-08T00:00:00.000Z",{"slug":24,"title":25,"excerpt":26,"category":7,"author":8,"readingTime":21,"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.","2026-06-30T00:00:00.000Z",{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":30,"title":31,"excerpt":32,"category":33,"author":8,"readingTime":21,"coverImage":10,"date":34},"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":36,"title":37,"excerpt":38,"category":39,"author":8,"readingTime":40,"coverImage":10,"date":41},"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":43,"title":44,"excerpt":45,"category":46,"author":8,"readingTime":47,"coverImage":10,"date":48},"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":50,"title":51,"excerpt":52,"category":39,"author":8,"readingTime":40,"coverImage":10,"date":53},"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":55,"title":56,"excerpt":57,"category":33,"author":8,"readingTime":40,"coverImage":10,"date":53},"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":59,"title":60,"excerpt":61,"category":46,"author":8,"readingTime":62,"coverImage":10,"date":63},"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":65,"title":66,"excerpt":67,"category":68,"author":8,"readingTime":47,"coverImage":10,"date":69},"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":71,"title":72,"excerpt":73,"category":46,"author":8,"readingTime":9,"coverImage":10,"date":74},"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":76,"title":77,"excerpt":78,"category":39,"author":8,"readingTime":47,"coverImage":10,"date":79},"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":81,"title":82,"excerpt":83,"category":20,"author":8,"readingTime":62,"coverImage":10,"date":84},"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":86,"title":87,"excerpt":88,"category":20,"author":8,"readingTime":47,"coverImage":10,"date":89},"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":91,"title":92,"excerpt":93,"category":94,"author":8,"readingTime":47,"coverImage":10,"date":89},"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":96,"title":97,"excerpt":98,"category":20,"author":8,"readingTime":9,"coverImage":10,"date":99},"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":101,"title":102,"excerpt":103,"category":94,"author":8,"readingTime":40,"coverImage":10,"date":104},"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":106,"title":107,"excerpt":108,"category":109,"author":8,"readingTime":62,"coverImage":10,"date":110},"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.","Fundamentals","2026-02-11T00:00:00.000Z",{"slug":112,"title":113,"excerpt":114,"category":115,"author":8,"readingTime":40,"coverImage":10,"date":116},"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":118,"title":119,"excerpt":120,"category":109,"author":8,"readingTime":21,"coverImage":10,"date":121},"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.","2026-01-21T00:00:00.000Z",{"slug":123,"title":124,"excerpt":125,"category":68,"author":8,"readingTime":47,"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":68,"author":8,"readingTime":47,"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":39,"author":8,"readingTime":40,"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":68,"author":8,"readingTime":47,"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":21,"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":33,"author":8,"readingTime":47,"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":33,"author":8,"readingTime":40,"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":68,"author":8,"readingTime":47,"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":33,"author":8,"readingTime":40,"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":39,"author":8,"readingTime":47,"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":115,"author":8,"readingTime":40,"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":94,"author":8,"readingTime":40,"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":47,"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":68,"author":8,"readingTime":47,"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":115,"author":8,"readingTime":40,"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":68,"author":8,"readingTime":21,"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":68,"author":8,"readingTime":47,"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"]