vladzely.zip

Не прошло и недели — американское правительство ограничило доступ для не-американцев(!) к новой модели Клода 5.0. Тут детали от анпропика: https://www.anthropic.com/news/fable-mythos-access Ссылка на проект европа2031 в посте выше.

Интересный фикшнл проект о рисках в будущем, связанных с неспособностью Европы угнаться за AI. https://europe2031.ai/

https://intenseminimalism.com/2026/words-are-cheap-use-fewer/

Output isn’t design Design keeps being misunderstood in our industry. New tools keep promising to generate interfaces faster, move words to product instantly, or collapse design directly into code. The assumption behind them is clear: that design is the act of producing. That is the misunderstanding. The hard part of design is rarely generating the form. It is understanding the problem well enough to know what and how something should exist at all. There is use and place for these tools, but tools are not the design process. Christopher Alexander came closer than anyone to naming this clearly. In Notes on the Synthesis of Form, he describes design as the search for a good fit between a form and its context. Context, in his sense, is not a background condition. It is the full set of forces that make a problem what it is: human needs, technical constraints, conflicting requirements, habits, edge cases, and relationships that are easy to miss until you spend time with them. Bad design appears where those forces remain unresolved. Good design appears where those misfits have been worked through carefully. That distinction matters even more now because so how AI encourages you to work. They generate plausible outputs quickly, but they do not necessarily help you understand the underlying problem. In practice, they often do the opposite. They generate outputs, instead first trying shape the problem or the form to the real conditions of the problem. You can already see the result in products that look polished, ambitious, and impressive at first glance, but begin to unravel the moment you actually use them. They feel brittle, poorly integrated, and full of decisions that were never fully worked through. The form is there. The fit is not. That is also why I still prefer designing visually over prompting. Working visually keeps me close to the problem and is slow enough gives me time to think while I work. Moving things around, testing relationships, and refining structure is not separate from the thinking. It is part of how clarity emerges. There is something cathartic about that process, in the same way writing can be. Writing helps clarify thought because the act itself forces you to organize it. Asking AI to write for you can produce text, but it usually does not rearrange your thinking. Design works the same way for me. The value is not only in the output. It is in the gradual understanding that comes through doing the work. AI can still be useful. It can help prototype, explore, and surprise you. But that is different from design. Design still requires judgment, conversation, tension, and time. The risk is mistaking generated form for solved problems. The core design is still about understanding, not output. Karri Saarinen April 17, 2026

"Design isn't waterfall, or the double diamond, or "iterate and test", or coding or comping or any of the dozens of processes ... Design is at its core the *intention* to improve material conditions under conditions of uncertainty based on understanding as much as you can about the context. Sometimes this is a competitive advantage, and sometimes it's not. (This is where so much "UX" talk goes wrong.)" Erika Hall src

Researchers at UW Allen School and Stanford just ran the largest study ever on AI creative diversity. 70+ AI models were given the same open-ended questions. They all gave the same answers. They asked over 70 different LLMs the exact same open-ended questions. "Write a poem about time." "Suggest startup ideas." "Give me life advice." Questions where there is no single right answer. Questions where 10 different humans would give you 10 completely different responses. Instead, 70+ models from every major AI company converged on almost identical outputs. Different architectures. Different training data. Different companies. Same ideas. Same structures. Same metaphors. They named this phenomenon the "Artificial Hivemind." And the paper won the NeurIPS 2025 Best Paper Award, which is the highest recognition in AI research, handed to a small number of papers out of thousands of submissions. This is not a blog post or a hot take. This is award-winning, peer-reviewed science confirming something massive is broken. The team built a dataset called Infinity-Chat with 26,000 real-world, open-ended queries and over 31,000 human preference annotations. Not toy benchmarks. Not math problems. Real questions people actually ask chatbots every single day, organized into 6 categories and 17 subcategories covering creative writing, brainstorming, speculative scenarios, and more. They ran all of these across 70+ open and closed-source models and measured the diversity of what came back. Two findings hit hard. First, intra-model repetition. Ask the same model the same open-ended question five times and you get almost the same answer five times. The "creativity" you think you're getting is the same output wearing a slightly different outfit. You ask ChatGPT, Claude, or Gemini to write you a poem about time and you keep getting the same river metaphor, the same hourglass imagery, the same reflection on mortality. Over and over. The model isn't thinking. It's defaulting to whatever scored highest during alignment training. Second, and this is the one that should really alarm you, inter-model homogeneity. Ask GPT, Claude, Gemini, DeepSeek, Qwen, Llama, and dozens of other models the same creative question, and they all converge on strikingly similar responses. These are models built by completely different companies with different architectures and different training pipelines. They should be producing wildly different outputs. They're not. 70+ models all thinking inside the same invisible box, producing the same safe, consensus-approved content that blends together into one indistinguishable voice. So why is this happening? The researchers point directly at RLHF and current alignment techniques. The process we use to make AI "helpful and harmless" is also making it generic and boring. When every model gets trained to optimize for human preference scores, and those preference datasets converge on a narrow definition of what "good" looks like, every model learns to produce the same safe, agreeable output. The weird answers get penalized. The original takes get shaved off. The genuinely creative responses get killed during training because they didn't match what the average annotator rated highly. And it gets even worse. The study found that reward models and LLM-as-judge systems are actively miscalibrated when evaluating diverse outputs. When a response is genuinely different from the mainstream but still high quality, these automated systems rate it LOWER. The very tools we built to evaluate AI quality are punishing originality and rewarding sameness. Think about what this means if you use AI for brainstorming, content creation, business strategy, or literally any task where you need multiple perspectives. You're getting the illusion of diversity, not the real thing. … Src

Интересный анализ орг структур и дизайн лидершипа/инфлюенса в тек компаниях. https://www.blixtdunder.com/design-leadership2026/

Многое SaaS компании жёстко страглят с превращением в AI компании. Intercom — одно из тех компаний, которая «шмагла». Тут их СЕО рассказывает: как. Линк

А вот и продолжение: Яр Рассадин о том как первый атриубтируемый бессознателельно параметр формы, развивающий метафору, является пропорция. Что она самой представляет, как параметр axb уже нам что то говорит.

“Design leadership, at its highest level, is not about managing complexity. It’s about deciding where complexity belongs, and where it doesn’t.” Src