*This is the second part of the novella "The End of the Holocene". The [first part](/blog/post/turning-on-2)*
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*"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 artifici
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LLM, контекст, ШІ, машинне навчання
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 Retrie
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MCP, server, AI, machine learning
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.
## Quick Recap: What Is MCP?
MCP stands for **Model Context Protocol**
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AI, agents, planning, autonomy, MCP
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 nove
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LLM, context, AI, machine learning
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.
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## What Is Context in an LLM?
"Context" refers to the entire inpu
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transport, MCP, servers, programming
I would like to expose one more benefit of the Model Context Protocol (MCP) — the ability to easily change the transport protocol. There are three different transport protocols available now, and each has its own benefits and drawbacks.
However, if an MCP server is implemented properly using a good SDK, then switching to another transport protocol is easy.
## Quick Recap: What is MCP?
* **Model Context Protocol (MCP)** is a new standard for integrating external tools with AI chat applications. For example, you can add Google Search as an MCP server to Claude Desktop, allowing the
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