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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. ...

Scientists recently digitized a single cubic millimeter of a mouse’s visual cortex, a project that generated 1.6 petabytes of data to map 84,000 neurons and half a billion synapses. To put that into perspective, the number of synapses in that tiny piece of brain tissue is comparable to the number of parameters in large-scale AI like DeepSeek or GPT models. It’s significantly more than the 29 billion parameters in a model like GigaChat. This comparison is a useful analogy for scale and complexity: just as synapses determine a brain’s processing capacity, parameters define the “power” of an AI. ...

Yesterday was a day focused on relationships. I was at a CLUB 500 event discussing friendship and its direct impact on team stability, project success, and the overall atmosphere within a company. Here are three thoughts I took with me: 1. Development Happens Only Through Interaction Sitting in a lotus pose is for stabilization. Real growth begins where there is live, human connection. Not over Zoom, not in a chat, but in a shared physical space. True progress is fueled by the kind of high-bandwidth, direct interaction that digital tools can only simulate. ...

DeepSeek has once again followed its “quiet release” strategy, making its new DeepSeek-V3-0324 model available on Hugging Face without any major announcements. Instead of marketing hype, they’ve simply delivered a solution for the community to evaluate. I tested the model locally on a Mac Studio equipped with an M3 Ultra chip and saw impressive performance, generating over 20 tokens per second. This marks a significant acceleration for running capable models on local hardware, making it a viable option for developers. ...

Last Thursday, I attended TgConf, a key conference focused on traffic, monetization, and the future of the Telegram ecosystem. As I continue to develop my own channel, I’m constantly looking for new opportunities for integration and growth. The sessions covered advertising, the development of applications, and the strategic direction of the platform. ...