Habr AI releases guide to ChatGPT, Claude, and mcp for newcomers

If you still perceive ChatGPT as a synonym for all artificial intelligence, the new Habr AI piece can close the basic gaps in one pass. This is not a theoretical primer on neural networks, but a practical guide for those who hear words like “prompt,” “tokens,” or “mcp server” and do not really understand how all of this connects to real work. The main idea of the article is simple: AI assistants have already become a standard work tool, but you can use them effectively only if you understand their limitations, cost, and interaction format.

The first major section is devoted to what should count as an AI assistant in the first place. This is not about some mythical “universal mind,” but about large language models that can write and edit texts, help with code, analyze documents, translate, search for information, and generate ideas. The difference between local and cloud models is also explained separately.

Running locally through solutions like Ollama gives you control, privacy, and independence from the internet, but it requires powerful hardware and almost always falls short of flagship cloud models in quality. The cloud scenario, by contrast, gives fast access to the best models without infrastructure setup, but forces you to accept sending data to the provider and paid limits. The second important section is a market overview.

The piece mentions ChatGPT by OpenAI, Claude by Anthropic, Gemini by Google, Grok by xAI, and Copilot by Microsoft. The selection logic is presented in a highly practical way: ChatGPT is framed as a universal 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. At the same time, the author explains why you should pay at all if free versions exist.

The short answer is that free access is suitable for getting acquainted, but real work usually starts where you get access to higher-end models, larger limits, file handling, research features, and API. A subscription is convenient for personal use, while API is needed first of all by those who embed a model into a product or automation. Another useful section is devoted to tokens, the context window, and why the quality of a long dialogue often degrades quickly.

Tokens are explained here without academic language: they are the basic pieces of text that the model operates on, and they are exactly what determine request cost and context capacity. For Russian-speaking users, one detail matters that is often ignored in marketing overviews: Russian text usually “consumes” more tokens than English, which means it fills the model window faster and can cost more through API. From this follow practical recommendations: do not drag on an endless chat, move key decisions into separate files, start a new session when the agent begins to lose the thread, and do not try to make the model write an entire application from the database to deployment with a single command.

The most practical part of the piece is about AI agents. It draws a clear distinction between a regular model that can only answer in text and an agent that gets access to files, terminal, browser, email, database, or IDE. Against that background, desktop clients, CLI agents, and IDE extensions like Cursor or Copilot become easier to understand: the value is not only in answer quality, but also in the ability to act inside the user’s environment.

At the same time, the article does not romanticize autonomy. It speaks directly about hallucinations, about the risk of expensive API tasks looping, about the need to make commits before experiments, split tasks into subtasks, and verify every result with tests, git diff, and manual review. For developers, this is probably the most useful fragment in the entire text.

A separate emphasis is placed on mcp as a protocol that turns AI from an isolated interlocutor into an interface to external systems. The idea is explained through a simple metaphor: if USB-C became a single port for devices, then mcp is becoming a single way to connect models to files, databases, GitHub, Slack, Notion, email, search, and other tools. That is why the topic is already moving beyond chats and prompts and starting to run into infrastructure.

For business, this means a transition from “chat with a model” to real automation scenarios, where the assistant can find a report, go to a database, create a task, or prepare an email without manually copying data between systems. In the final analysis, the Habr AI article is useful not because it promises magic, but because it removes false expectations. It shows AI as a normal working layer with its own pricing, limits, risks, and operating rules.

The main conclusion is simple: the winners will not be those who simply opened a chat, but those who learned to formulate a task, keep context under control, verify the result, and connect the model to real tools through agents and mcp.