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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In recent months, the Model Context Protocol (MCP) has gained a lot of traction as a powerful foundation for building AI assistants. While many developers are familiar with its core request-response flow, there's one feature that I believe remains underappreciated: the ability of MCP servers to send **notifications to clients**.
Let’s quickly recap the typical flow used by most MCP-based assistants:
* A user sends a prompt to the assistant.
* The assistant attaches a list of available tools and forwards the prompt to the LLM.
* The LLM generates a response, possibly requesting the
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Recently, I introduced the idea of using MCP (Model Context Protocol) to implement memory for AI chats and assistants. The core concept is to separate the assistant's memory from its core logic, turning it into a dedicated MCP server.
If you're unfamiliar with this approach, I suggest reading my earlier article: [Benefits of Using MCP to Implement AI Chat Memory](/blog/post/benefits-of-using-mcp-to-implement-ai-chat-memory/).
## What Do I Mean by “AI Chat”?
In this context, an "AI Chat" refers to an AI assistant that uses a chat interface, with an LLM (Large Language Model) as it
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I'm excited to introduce a new package for Go developers: [**CleverChatty**](https://github.com/Gelembjuk/cleverchatty).
**CleverChatty** implements the core functionality of an AI chat system. It encapsulates the essential business logic required for building AI-powered assistants or chatbots — all while remaining independent of any specific user interface (UI).
In short, **CleverChatty** is a fully working AI chat backend — just without a graphical UI. It supports many popular LLM providers, including OpenAI, Claude, Ollama, and others. It also integrates with external tools us
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