I’m often asked which AI training project cost millions of dollars and two years of my life. People wonder: why is it so expensive? My usual answer is that it’s not particularly expensive—especially considering we don’t own our own hardware yet. Training AI has always been about massive data centers; that’s just the reality of the field. When you’re not immersed in it, the sheer scale can be hard to visualize. ...

This series of posts will be my way of documenting the journey of creating one of our team’s most ambitious products: the intelligent assistant, A.V.E.L.I.N. To give you some context, my development team and I are currently beta-testing the project within our Mozgii Ecosystem AI platform. Our primary focus is on A.V.E.L.I.N.—an intelligent personal assistant in Telegram built to handle both basic and complex tasks involving AI-powered search, processing, and analysis of information. ...

In traditional companies, everything is built on a clear hierarchy: decisions are made at the top and executed at the bottom. This approach might work in a stable environment, but for an IT startup, especially in AI, it stifles growth. IT companies need adhocracy: a management model where competence and results are valued more than titles. It’s about flexibility over bureaucracy and speed over approvals. The value of an idea is judged by its effectiveness, not by the position of its author. ...

From my experience working with neural networks, it’s become obvious how two people can interact with the same model and get radically different outcomes. This isn’t a minor variation—it signals a fundamental departure from the search engine paradigm. We have entered a new era of interacting with artificial intelligence. AI doesn’t just aggregate data; it selects and synthesizes relevant information in response to specific requests. This changes the very nature of how we engage with information. Where the key skill was once finding data in search engines, it is now the ability to correctly formulate requests to an AI. ...

I’m often asked why international AI models, like those from OpenAI, consistently outperform Russian counterparts such as GigaChat. To understand the gap, we need to look beyond the code and analyze the foundational, structural challenges. Here are the key factors limiting Russia’s position in the global AI race. 1. The Compute Bottleneck Effective AI development at scale depends on raw computational power. Since 2022, access to essential high-performance NVIDIA chips (like the A100 and H100) has been severed. Training a model on the scale of GPT-4 requires a cluster of over 10,000 GPUs—a resource capacity that simply doesn’t exist in Russia. For context, Sber’s most powerful supercomputer, Christofari Neo, operates at around 12 petaflops, making it 50 to 100 times less powerful than the world’s leading AI research centers. ...

Messengers have long evolved past being simple tools for exchanging messages. Today, they are the space where our work, personal lives, news, and friends converge. The logical next step is to integrate an AI assistant into this environment—one that helps structure communication and interacts with you in a familiar interface, just like any other contact. A majority of the solutions on the market are bots running on simplified or free versions of GPT. They can generate answers, but often fail to provide high-quality, deep information. ...

Users see a chat window. Sometimes voice, sometimes images. But behind this familiar interface lie radically different architectures and capabilities. Here are five key parameters that distinguish the top AI models in 2025: 1. Memory (Context Window) This defines how much information a model can retain within a single conversation. GPT-4o: 128k tokens (~300 pages of text) Claude 3 Opus & Gemini 2.5 Pro: Up to 1 million tokens (~2,000 pages) DeepSeek-VL Mini: ~8k tokens (~20 pages) More memory enables greater context and reduces hallucinations, but it also demands more powerful hardware. ...

OpenAI has released Codex CLI, an open-source AI agent for developers. This marks a quiet but significant victory for the open-source community. The tool allows developers to use natural language directly in the terminal—the agent interprets the request, then writes, executes, and tests the code. Most importantly, this entire process runs locally, without sending data to the cloud. With this release, the industry moves one step closer to a system that can independently understand, build, and deploy solutions. It underscores a critical point: the future isn’t just about choosing the right model, but about engineering the right architecture that connects thought → action. ...

Yesterday, OpenAI opened access to the GPT-4.1 API. It’s a refined version of their flagship model—faster and architecturally closer to the concept of ‘agents.’ In parallel, the company officially announced it is winding down GPT-4.5, its most resource-intensive model, due to its excessive complexity and support challenges. With GPT-4.5, it seems they hit an architectural dead end. We are at a point where models appear and disappear rapidly. They are becoming what they should be: tools, not landmark events. We have a growing catalog of specialized AIs: some calculate, others write code, plan tasks, or generate video. But the average user should not be expected to know and choose between every AI in existence. That paradigm defies the logic of good user experience. ...

Your Thought Process, Packaged Deep Research isn’t just another AI feature; it’s a fundamental shift toward an agent-based architecture. In this model, the LLM stops being a simple chatbot and becomes a co-author—an agent that independently searches, filters, validates, and structures information. What does this change? If you’re designing a business, a startup, or a product, you don’t have time to personally read 200 sources. Now, an AI agent does it for you. This frees you up to do the high-value work: to think, not just to search. ...