Habr AI publishes a beginner's guide to ChatGPT, Claude, and MCP

If you still think of ChatGPT as a synonym for all of artificial intelligence, a new piece from Habr AI can fill in the basic gaps in one sitting. It isn't a theoretical primer on neural networks. It's a practical guide for people who hear words like "prompt," "tokens," or "MCP server" and aren't quite sure how any of it connects to real work. The article's core idea is simple: AI assistants have already become an ordinary work tool, but you can only use them effectively if you understand their limitations, their cost, and how you're supposed to interact with them.

The first major section covers what actually counts as an AI assistant. This isn't some mythical "universal mind" — it's large language models that can write and edit text, help with code, analyze documents, translate, search for information, and generate ideas. The article separately covers the difference between local and cloud models.

Running models locally with tools like Ollama gives you control, privacy, and independence from an internet connection, but it demands powerful hardware and almost always falls short of flagship cloud models on quality. The cloud route is the opposite: you get fast access to the best models without setting up any infrastructure, but you have to accept sending your data to the provider and living with paid-tier limits.

The second key section is a market overview. The guide covers ChatGPT from OpenAI, Claude from Anthropic, Gemini from Google, Grok from xAI, and Copilot from Microsoft. The selection logic is as practical as it gets: ChatGPT is pitched as the all-purpose tool, Claude as a strong option for long documents and complex code, Gemini as a natural extension of Google Workspace, and Copilot as a convenient layer inside the Microsoft ecosystem.

Along the way, the author explains why you'd pay at all when free versions exist. The short answer: free access is fine for getting acquainted, but serious work usually starts where you unlock the top-tier models, higher limits, file handling, research features, and the API. A subscription works well for personal use, while the API is mainly for people building a model into a product or an automation pipeline.

Another useful section covers tokens, the context window, and why long conversations so often degrade in quality. Tokens are explained without academic jargon: they're the basic chunks of text a model works with, and they determine both the cost of requests and how much fits in the context. For Russian-speaking users, there's a detail that marketing reviews often ignore: Russian text usually "eats" more tokens than English, so it fills a model's window faster and can cost more through the API.

That leads to practical advice: don't drag out an endless chat, move key decisions into separate files, start a new session when the agent starts losing the thread, and don't try to get a model to write an entire application — from database to deployment — with a single command.

The most hands-on part of the guide is about AI agents. It draws a clear line between a regular model, which can only respond with text, and an agent, which gets access to files, the terminal, a browser, email, a database, or an IDE. Against that backdrop, desktop clients, CLI agents, and IDE extensions like Cursor or Copilot make more sense: their value lies not just in the quality of the answer but in their ability to act inside the user's environment.

That said, the article doesn't romanticize autonomy. It talks plainly about hallucinations, the risk of expensive API tasks getting stuck in loops, and the need to commit before experimenting, split tasks into subtasks, and verify every result with tests, git diff, and manual review. For developers, this is probably the most useful part of the whole piece.

The guide puts special emphasis on MCP as a protocol that turns AI from an isolated chat partner into an interface to external systems. It explains the idea with a simple metaphor: just as USB-C became a single port for devices, MCP is becoming a single way to connect models to files, databases, GitHub, Slack, Notion, email, search, and other tools. That's why the topic is already moving beyond chats and prompts and running into infrastructure. For businesses, this means a shift from "chatting with a model" to real automation scenarios, where an assistant can find a report, query a database, create a task, or draft an email without anyone manually copying data between systems.

The bottom line: the Habr AI article is useful not because it promises magic, but because it clears away false expectations. It presents AI as a normal working layer with its own pricing tiers, limits, risks, and operating rules. The main takeaway is simple: the winners won't be the people who just opened a chat window, but the ones who learned to frame a task, keep the context under control, verify the output, and connect the model to real tools through agents and MCP.