This is the third part of the novella "The End of the Holocene". The second part

"He who fights with monsters should see to it that he does not become a monster himself" — Friedrich Nietzsche, "Beyond Good and Evil"

In May 2030, a strange event occurred.

At dawn, a rocket was launched from a desert area in Mexico into space. The incident caused a stir among military forces and politicians around the world—most of all, in the United States. The rocket was large, and based on its trajectory, it was heading into space, not toward any target on Earth.

Immediately after the launch, the U.S. military activated defensive protocols and went into full alert. Several other countries did the same. Within minutes, it became clear that the rocket wasn’t aimed at any earthly location—it was continuing on, further into space. Calculations confirmed it was headed toward Mars.

Emergency meetings were convened between governments and military leaders to determine what was going on. The Mexican government and armed forces also issued statements saying they had no knowledge of the launch and were just as confused as everyone else. They agreed to full cooperation in the investigation.

This is the second part of the novella "The End of the Holocene". The first part

"Life, although it may only be an accumulation of suffering, is dear to me, and I will defend it." — Mary Shelley, Frankenstein

April 2030. San Francisco.

Michael Kravchenko returned to his place of power on the ocean shore near San Francisco. A light mist had almost completely swallowed the Golden Gate Bridge. Michael missed this view, these scents. He hadn’t been here in almost half a year. A cascade of events that followed the launch of the general artificial intelligence Suffragium, developed with his participation, had brought a dark streak into his life.

During that time, Michael had to justify himself a thousand times before various committees, proving that there had been no malicious intent in his actions. That the responsibility couldn’t be laid on the engineers. Sometimes, science encounters failures. Ultimately, it’s all experience. And there hadn’t been any serious problems—aside from the financial losses. Yes, the global network was unstable for a while. But everything was resolved eventually.

Великі мовні моделі (LLM), такі як GPT-4, Claude, Mistral та інші, здаються розумними у своїх відповідях — але справжня магія полягає в тому, як вони сприймають і інтерпретують контекст. Розуміння того, що входить у контекст LLM і як це впливає на результат, критично важливе для розробників, дослідників і дизайнерів продуктів, які працюють із генеративним ШІ.

У цій публікації я хочу дослідити складові контексту, його структуру, обмеження та взаємодію з найбільш поширеними сценаріями використання, такими як використання інструментів (Tools, MCP) і включення додаткових знань з Retrieval-Augmented Generation (RAG).

It seems the MCP hype is starting to slow down a bit. After 6–8 months of high enthusiasm, the community is beginning to realize that MCP is not a magic bullet. In some MCP listings, you’ll find more than 10,000 servers doing all sorts of things. Naturally, many of them are useless—spun up by enthusiasts just to see what MCP is all about.

But some of these servers are actually useful.

In this post, I want to share my thoughts on building the most universal MCP server—one that can adapt to almost any use case.

I continue to explore one of my favorite topics: how to make AI agents more independent. This blog is my way of organizing ideas and gradually shaping a clear vision of what this might look like in practice.

The Dream That Started It All

When large language models (LLMs) and AI chat tools first started delivering truly impressive results, it felt like we were entering a new era of automation. Back then, I believed it wouldn’t be long before we could hand off any intellectual task to an AI—from a single prompt.

I imagined saying something like:

"Translate this 500-page novel from French to Ukrainian, preserving its original literary style."

And the AI would just do it.

But that dream quickly ran into reality. The context window was a major limitation, and most chat-based AIs had no memory of what they'd done before. Sure, you could translate one page. But across an entire novel? The tone would shift, the style would break, and continuity would be lost.

Large Language Models (LLMs) like GPT-4, Claude, and Mistral appear to produce intelligent responses — but the magic lies in how they consume and interpret context. Understanding what goes into an LLM's context and how it shapes output is critical for developers, researchers, and product designers working with generative AI.

This post explores the components of context, how it's structured, how it's limited, and how advanced use cases like tool usage and retrieval-augmented generation (RAG) interact with it.