Why Replacing Developers with AI is Going Horribly Wrong

    This video discusses the failure of AI to replace human developers as predicted in 2023. Instead of increasing productivity and saving costs, AI has led to a technical debt crisis and a surge in "slop code" (0:06).

Key points from the video include:

• Failed Promises of AI (0:06-1:14): Despite predictions that AI would replace 80% of software developers by 2025, it has instead led to massive layoffs and a significant increase in technical debt. Most AI integrations have failed to deliver measurable returns, with 95% of generative AI pilots showing zero net impact on the bottom line.
• "Vibe Coding" and its Consequences (1:57-3:12): This trend, where developers use natural language to generate code with AI, results in simpler, repetitive, and less robust code. While it can speed up junior developers on basic tasks, it makes the final product less maintainable, leading to a "slop layer" of unfixable code.
• Security Vulnerabilities and Reduced Productivity (3:22-4:10): AI-generated code often contains significant security vulnerabilities. Seasoned engineers report being slower when using AI tools, spending an average of 11 hours a week correcting "hallucinations" (syntactically correct but logically flawed code).
• Impact on the Tech Workforce (4:11-6:11): The belief that AI could handle junior-level tasks led to a nearly 50% plummet in entry-level hiring between 2023 and 2025, creating a "junior death spiral." This also allows companies to suppress wages, as the market is flooded with displaced developers, using AI productivity as leverage in negotiations.
• Exposed "AI Washing" and Catastrophic Errors (6:12-7:25): The collapse of Builder AI exposed a scheme where human engineers performed tasks marketed as AI-driven. The video also highlights instances of AI making catastrophic errors, such as wiping production drives, demonstrating AI's lack of accountability.

In conclusion, the video argues that AI did not replace developers but rather exposed the delusion that software development is an easy, automated task (7:30). The companies succeeding are those reinvesting in human architects, recognizing that "free AI code" is the most expensive debt (7:46).

FENIKS podkast - Petar Veličković - Google Deep Mind / Univerzitet Kembridž

Torvalds Speaks: Impact of Artificial Intelligence on Programming

Large Language Models explained briefly

Here’s a summary and explanation of the key ideas from the video “Large Language Models explained briefly” (by Grant Sanderson / 3Blue1Brown) — along with why they matter and how to think about them:

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🎯 What is a Large Language Model (LLM)?

• At its core, an LLM is a function (a mathematical model) that given some text, tries to predict what word comes next. 3blue1brown.com+1

• Instead of just picking a single “most likely” word, the model assigns a probability distribution over all possible next words. YTScribe+1

• When you interact with a chatbot built on an LLM, you’re basically feeding it your prompt, it predicts words one after another to generate a response. 3blue1brown.com

Why this matters: It gives us a simple way to think about how the “intelligence” of such a model arises — by mastering the statistical patterns of language, to the extent that it can produce fluent, coherent responses.

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🧠 How does the model learn?

1. Pre-training

   • The model is fed massive amounts of text (e.g., from the internet) and learns to predict the next word given the previous context. 3blue1brown.com+1

   • This is done by starting with many “parameters” (weights) initialised randomly, and then using a training algorithm (e.g., back-propagation) to adjust these parameters so that the model’s predictions align with the actual next word in many examples. 3blue1brown.com

   • Because the amounts of data and parameters are enormous (hundreds of billions of weights, trillions of tokens) the scale is huge. The video emphasizes how incredible the computational cost is. YTScribe+1

2. Fine-tuning / Reinforcement Learning with Human Feedback (RLHF)

   • After pre-training, if you want the model to behave like a helpful assistant (rather than just predict next‐word in arbitrary text), you train it further. Workers provide feedback, flag bad outputs, reward good ones. The model is adjusted accordingly. 3blue1brown.com+1

Why this matters: Understanding those two phases explains why LLMs can generate sensible responses—and also why they sometimes go off (because their training objective is “next‐word prediction”, not “always be correct” or “always be helpful”).

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🔍 The architecture: Transformers & Attention

• The video explains that modern LLMs use a model type called a transformer, which is capable of processing much of the input text at once, instead of strictly one word after another. 3blue1brown.com

• Key concept: Attention — each word/token has a numeric embedding, and by using attention, tokens can “look at” other tokens in the context to refine their meaning (for example distinguishing “bank” meaning “river bank” vs “financial bank” depending on context). 3blue1brown.com

• After many layers of alternating attention + feed-forward operations, you arrive at a final representation which is used to predict the next word. 3blue1brown.com

Why this matters: It provides the mechanism by which the model handles context (what came before) and generates fluent, context-aware text. Without attention, handling long contexts or nuanced meaning becomes much harder.

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✅ Putting it all together: Why LLMs seem “smart”

• Because the model has seen so much text, it’s learned a lot about how language is usually used — grammar, patterns of meaning, typical word sequences, associations, even some reasoning.

• When you ask: “Explain how a car engine works,” the model doesn’t have a built-in “understanding” like a human expert, but it has internalised hugely many examples of explanations, so it can construct a plausible answer by predicting next words in a coherent way.

• The video emphasises: even though the model is just doing “next‐word prediction”, the output looks like dialogue, reasoning, or explanation. Because it’s good at predicting—the patterns it learned embed a lot of structure of language and knowledge. 3blue1brown.com

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⚠️ Limitations & important cautions

• Just because the output looks smart doesn’t mean the model knows things in the human sense. It can hallucinate (make up plausible-looking but incorrect things) because the next‐word objective doesn’t guarantee truth.

• The underlying architecture is complex and parameter heavy, and the “why” for a specific model output is hard to interpret (emergent behaviour). The video notes that you often can’t trace exactly why the model picked one word out of many. 3blue1brown.com+1

• Bias, training-data issues, cost, computation, energy: all major concerns with scaling LLMs. While the video may not go deeply into all ethical issues, the broader literature (see Wikipedia) shows these are real. Wikipedia

PREDRAG PETROVIC

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🧭 Why watch/understand this video

• It gives a clear, visual, intuitive explanation of what LLMs are and how they work — especially useful if you’re not deep in AI.

• It demystifies the “magic” of things like ChatGPT or other chatbots: yes, there’s a lot of engineering, but at base it’s “predict next word given context”, scaled up massively.

• It helps ground expectations: when you use an LLM, you understand what its strength is (fluent generation from pattern-learning) and what its weaknesses are (lack of guaranteed truth, reasoning limitations, interpretability).

UX IZAZOVI I AI

https://www.xn--pea-wqa.com/

Kako veštačka inteligencija menja korisničko iskustvo (UX) — i zašto je to važno i za biznis u Srbiji

Uvod

Korisnici više ne prihvataju „dovoljno dobro“ iskustvo. Očekuju da sve bude personalizovano, brzo i intuitivno. Istovremeno, tehnologije poput veštačke inteligencije (AI) menjaju pravila igre u UX dizajnu — iskustvo više nije samo vizuelno, već i pametno.

U ovom tekstu istražujemo kako se UX i AI dopunjuju, koje konkretne promene donose digitalnim projektima, i zašto je ovo važno i za domaće biznise.

Zašto UX postaje strateška prednost

Dobar UX znači zadovoljnog korisnika, a zadovoljan korisnik znači rast. Prema istraživanjima, kompanije koje ulažu u UX u proseku imaju do 35% veći ROI u odnosu na konkurenciju.

UX nije samo „lep dizajn“ – to je način na koji korisnik postiže svoj cilj bez frustracije. Loše iskustvo vodi do napuštanja stranice, gubitka poverenja i slabije prodaje.

Kako AI menja UX

1. Personalizacija u realnom vremenu

AI analizira ponašanje korisnika, prepoznaje obrasce i automatski prilagođava sadržaj — od preporuka proizvoda do načina prikaza interfejsa.

2. Automatizacija dizajna i testiranja

AI alati mogu samostalno testirati UX elemente i predlagati poboljšanja. Analizom toplotnih mapa, AI otkriva gde korisnici najviše klikću — i automatski optimizuje raspored elemenata.

3. Predviđanje ponašanja korisnika

AI modeli mogu prepoznati kada korisnik verovatno odustaje od kupovine — i u tom trenutku aktivirati poruku, popust ili predlog.

4. Dostupnost i inkluzivnost

AI pomaže u stvaranju pristupačnijeg UX-a — od automatskih opisa slika za osobe sa oštećenjem vida, do glasovnih interfejsa i chatbot-asistenata.

Primene u praksi (i u Srbiji)

• Automatska analiza korisničkog toka — prepoznaj gde korisnik odustaje.
• Pametni chat-botovi za bolju korisničku podršku.
• UX-generatori koji pomažu u prototipovanju i predlozima dizajna.
• AI testiranje — simulacija stotina korisnika pre lansiranja.

Izazovi i etička pitanja

• Privatnost podataka: korisnici moraju znati kako se njihovi podaci koriste.
• Transparentnost: UX ne sme biti manipulativan – korisnici moraju razumeti kako AI donosi odluke.
• Ljudski faktor: AI može pomoći, ali UX dizajner ostaje ključan da bi iskustvo bilo empatično i smisleno.

Perspektiva za Srbiju i region

Lokalni korisnici imaju specifična očekivanja — jezik, brzinu, i navike korišćenja mobilnih uređaja. Zbog toga UX+AI kombinacija mora biti lokalizovana – dizajniran interfejs mora razumeti srpskog korisnika.

Prednost: Kada lokalni tim primeni globalne AI-UX principe, dobija ogromnu konkurentsku prednost – jer kombinuje lokalno razumevanje i tehnološku inovaciju.

Saveti za dizajnere i biznise

1. Uradite mini UX audit – otkrijte gde korisnici najčešće napuštaju proces.
2. Isprobajte AI alate (npr. Uizard, Framer AI, Figma plugins).
3. Testirajte personalizaciju – promenite poruke i raspored na osnovu ponašanja korisnika.
4. Merite rezultate – dužina boravka, CTR, konverzije.
5. Uključite korisnike u proces – AI je pomoć, ali povratne informacije su nezamenjive.

Zaključak

UX i AI zajedno čine moćan tandem. Veštačka inteligencija može automatizovati analize, predlagati rešenja i personalizovati sadržaj, ali ljudska kreativnost i empatija ostaju srce svakog dobrog korisničkog iskustva.

U Srbiji se upravo sada otvara prostor da se ovi principi primene na lokalne projekte — bilo da ste dizajner, developer ili vlasnik biznisa.

💡 Ako želiš da unaprediš UX svog projekta kroz AI, napiši mi — možemo zajedno osmisliti prilagođenu strategiju.

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