Structure and Types of File Systems in Linux

Large language models (LLMs) are gaining popularity today. They are capable of generating not only text but also many other types of content: code, images, video, and audio. Major companies, having large resources, train their models on text data collected by humanity throughout its history. Naturally, the international IT giant Google is no exception: it not only created its own model, Gemini, but also integrated it into its ecosystem of services. This article will discuss the large language model Gemini, its features, and capabilities. Overview of Gemini Gemini is a family of multimodal large language models (LLMs), launched by Google DeepMind in December 2023. Before that, the company used other models: PaLM and LaMDA. As of today, Gemini is one of the most powerful and flexible LLM neural networks, capable of conducting complex dialogues, planning multitasking scenarios, and working with any type of data, from text to video. Capabilities of Gemini The Gemini model not only generates content but also provides many additional functions and broad capabilities for working with different types of content: Multimodality. Through interaction with auxiliary models (Imagen and Veo), Gemini can work with different types of content: text, code, documents, images, audio, and video. Large context window. On paid plans, Gemini can analyze up to 1 million tokens in a single session. This is approximately one hour of video or 30,000 pages of text. AI agents. With some built-in functions, Gemini can autonomously perform chains of actions to search for information in external sources: third-party sites or documents in Google Drive. Integration with services. On paid subscription plans, Gemini integrates with services from the Google ecosystem: Gmail, Docs, Search, and many others. Special API. With the API provided by the Google Cloud platform, Gemini can be integrated into applications developed by third parties. With this set of features, Gemini can be used without limitations. It serves as a universal platform both for end users who need content generation or specific information, and for developers who want to integrate a powerful multimodal AI into their applications. How to Use Gemini AI As part of the Google ecosystem, the Gemini model has many touchpoints with the user. It is available in several places: from search results in the browser to office applications on mobile devices. So technically, you can access Google Gemini AI through various interfaces; all of them are merely “windows” into the central core. Google Search Results You can see Gemini at work in Google search results: the system supplements the list of found sites with additional reference information generated by Gemini. However, this doesn’t always happen. In Google, this feature is called Generative AI Snippet. Gemini analyzes the query, gathers information, and displays a short answer below the search box. Often, such a snippet turns out to be very useful. It provides a brief summary of the topic of interest. Thus, Google search results allow you to obtain information on a certain subject without going to websites. Web Application The most common and professional tool for interacting with Gemini is a dedicated website with a chatbot designed for direct dialogues with the model. This is where all the main Gemini features are available. With such dialogues, you can communicate, create text, write code, and generate images and videos. The Gemini web application has an interface typical of most LLM services: in the center is the chat with the model, at the bottom is a text input field with an option to attach files, and on the left is a list of started dialogues. The interaction algorithm with the model is simple. The user enters a query, and the model generates a response within a few seconds. The type of response can be anything: a story, recipe, poem, reference, code, image, or video. Yes, Gemini can generate images and videos using other models developed by Google: Imagen. A diffusion model for generating photorealistic images from text descriptions (text-to-image), notable for its high level of detail and realism. Veo. An advanced model for generating cinematic videos from text descriptions (text-to-video) or other images (image-to-video), notable for its high level of coherence and dynamics. Thanks to such integration, you can enter text prompts for generating images and videos directly inside the chatbot. Quick and convenient! The web version contains a wide range of tools for professional content generation and information gathering: Deep Research. A specialized mode for conducting in-depth, multi-step research using information from publicly available internet sources. With intelligent agents, Gemini autonomously searches, reads, analyzes, and synthesizes information from hundreds or even thousands of sources, ultimately producing a full report on the topic of interest. Unlike regular search, which provides short answers and links, Deep Research mode generates detailed reports by analyzing and summarizing information. However, one should understand that such deep analysis takes time, on average, from 5 to 15 minutes. Canvas. An interactive workspace that allows users to create, edit, and refine documents, code, and other materials in real time. Essentially, it is a kind of virtual “whiteboard” for more dynamic interaction with the language model. Thus, Canvas is focused on interactive creation, editing, and real-time content collaboration, while Deep Research is aimed at collecting and synthesizing information to provide comprehensive reports.   Deep Research Canvas Purpose In-depth data collection/analysis Interactive creation and editing of content Result Detailed reports Edited documents Mode Autonomous Active Execution time Several minutes Instant Task type Research, reviews, analytics, summaries Writing, coding, prototyping Users can attach various files to their messages, from documents to images. Along with a text prompt, Gemini can analyze media files, describing their content. Thus, the user can create multimodal queries consisting of both text and media simultaneously. This approach increases the accuracy of responses and creates a wider communication channel between humans and AI. In other words, the browser version is the main way to use Gemini. It is also worth briefly discussing how to register for Gemini and what is required for this. In most LLM services, authorization is required. Gemini is no exception. To launch the chatbot, you must sign in with a Google account. The registration process is standard. You need to provide your first and last name, phone number, and desired nickname. After this, you can use not only Gemini but also the rest of the Google ecosystem applications. Mobile App for Android and iOS You can download the official Gemini mobile app from Google Play or App Store. Functionality-wise, it is not very different from the web version available in a browser, but it has deeper features for user interaction and smartphone integration. Moreover, on many Android devices, the app comes pre-installed. Essentially, it is a mobile client that expands cross-platform access to the Gemini language model. The main differences lie in optimization for specific platforms: Content management. On the browser version accessed from a computer, it is much more convenient to work with text, code, tables, graphs, diagrams, images, and video. Conversely, the mobile app interface, designed for touch and gesture interaction, simplifies use on smartphones and tablets, but does not offer the same efficiency as a keyboard and mouse. Voice input and interaction. The mobile app has more advanced voice input and live interaction features (Gemini Live), allowing you to communicate with the model in real time, using the camera to show objects, the microphone for direct conversation, and screen capture to share images. The browser version lacks this functionality. Device-specific features. The Gemini mobile app integrates closely with smartphone functions (clock, alarm, calendar, documents) for more personalized interaction. The browser version exists in a kind of vacuum and knows almost nothing about the user’s computer. Apart from accessing other websites, it has no “window” into the outside world. In rare cases, it can extract data from other Google services such as Gmail and Google Docs. Multitasking convenience. On a large computer screen, it is easier to work with multiple windows, copy and paste information, which enables more efficient interaction with Gemini. On the other hand, the portability of the mobile app makes it possible to use the model “on the go,” simplifying quick queries during travel. Nevertheless, Google regularly releases updates, and Gemini’s functionality is constantly evolving. Therefore, the differences between the web version and the mobile app change over time. Gemini Assistant On many smartphones running the Android operating system, the Gemini model is gradually replacing the classic Google Assistant. That is, when you long-press the central button or say the phrase “Hey Google,” Gemini launches. It accepts the same voice commands but generates more accurate responses with expanded explanations and consolidated information from different apps. This may also include functions for managing messages, photos, alarms, timers, smart home devices, and much more. Some smartphone manufacturers specifically add a quick-access Gemini button directly to the lock screen, allowing you to instantly continue a conversation or ask a question without unlocking the phone. Thus, Gemini is gradually bringing together multiple functions, transforming into a unified smart control center for the phone. And most likely, this trend will only continue. Chrome Browser In new versions of Google’s Chrome browser, the Gemini neural network is built in by default and is available via an icon in the toolbar or by pressing a hotkey. This way, on any page, you can run queries to analyze text, create a summary, or provide brief explanations of the content of the open site. And let’s not forget third-party extensions that allow Gemini to be integrated into the browser, expanding its basic functionality. Google Ecosystem Services On paid plans, Gemini is available in many Google Workflow services. It adds interactivity to working with documents and content: Gmail. Helps draft and edit emails based on bullet points or existing text. Docs. Generates article drafts and edits text and sentence style. Slides. Instantly creates multiple versions of illustrations and graphics based on a description of the required visuals. Drive. Summarizes document contents, extracts key metrics, and generates information cards directly in the service interface. This is only a small list of apps in the Google ecosystem where you can use Gemini. The main point of integrating the model into services is to automate routine tasks and reduce the burden on the user. Plugins and Extensions for Third-Party Applications For third-party applications, separate plugins are available for integration with Gemini. The most common are extensions for IDE editors, messengers, and CRM systems. For example, there is the official Gemini Code Assist extension, which embeds Gemini into integrated development environments such as Visual Studio Code and JetBrains IDEs. It provides autocomplete, code generation and transformation, as well as a built-in chat and links to source documentation. There are also unofficial plugins for CRM systems like Salesforce and HubSpot, as well as for messengers like Slack and Teams. In these, Gemini helps generate ad copy and support responses, as well as automates workflows through the API. Versions and Pricing Plans for Gemini First, Google offers both free and paid plans for personal use: Free. A basic plan with limited functionality. Suitable for most standard tasks. Free of charge. Access to basic models, Gemini Flash and Gemini Pro. The first is optimized for fast and simple tasks, the second offers more advanced features but with limitations. Limited context window size up to 32,000 tokens (equivalent to about 50 pages of text). No integration with Google Workspace apps (Gmail, Docs, and others). No video generation functions. Data may be used to improve models (this can be disabled in settings, but it is enabled by default). Limited usage quotas for more advanced models and functions. Advanced. An enhanced plan with extended functionality. Suitable for complex tasks requiring deep data analysis. Pricing starts at $20/month. Access to advanced and experimental models without restrictions. Increased context window size up to 1 million tokens (equivalent to about 1,500 pages of text or 30,000 lines of code). Deep integration with Google Workspace apps. Image and video generation functions. Data is not used to improve models. Expanded voice interaction capabilities via Gemini Live, including the ability to show objects through the camera. Priority access to future AI features and updates. Second, there are extended plans for commercial (business) and non-commercial (educational) organizations, offering additional collaboration and management features: Business. Provides extended functionality of the Advanced plan with additional tools for team use. Designed for small and medium businesses. Pricing starts at $24/month. Enterprise. Provides extended functionality of the Business plan with additional tools for AI meeting summaries, improved audio and video quality, data privacy, and security protection. It also has higher limits and increased priority access. Designed for large international companies with high security and scalability requirements. Pricing starts at $36/month. Education. Provides full access to Gemini’s generative capabilities for educational institutions, including many additional features tailored to the learning environment. Custom pricing. Gemini API for Developers Specifically for developers engaged in machine learning and building services based on large language models, Google provides a full API for interacting with Gemini without a graphical user interface. Moreover, Google has separate cloud platforms for more efficient development and testing of applications built with the Gemini API: Google AI Studio. A lightweight and accessible platform designed for developers, students, and researchers who want to quickly experiment with generative models, particularly the Gemini family from Google. The tool is focused on working with large language models (LLMs): it allows you to quickly create and test prompts, adjust model parameters, and get generated content. The platform offers an intuitive interface without requiring deep immersion into machine learning infrastructure. Simply put, it’s a full-fledged sandbox for a quick start in the AI industry. Vertex AI. A comprehensive artificial intelligence and machine learning platform in Google Cloud, designed to simplify the development, deployment, and scaling of models. It combines various tools and services into a unified, consistent workflow. Essentially, it is a unified set of APIs for the entire AI lifecycle, from data preparation to training, evaluation, deployment, and monitoring of models. In short, it is a complete specialized ecosystem. Gemini Gems. A set of features in Google Gemini designed to automate repetitive tasks and fine-tune model behavior. It allows you to create mini-models tailored for specific, narrow tasks: creating recipes, writing code, generating ideas, translating text, assisting with learning, and much more. In addition to manual configuration, there are many ready-made templates. Naturally, Google provides the API as a separate channel for interacting with Gemini. With its help, developers can integrate text generation, code writing, image processing, audio, and video capabilities directly into their applications. Access to the API is possible through the Google Cloud computing platform. Working with Gemini without a graphical user interface is a separate topic beyond the scope of this article. You can find more detailed information about the Gemini API in the official Google Cloud documentation. Nevertheless, it can be said with certainty that working with the Gemini API is no different from working with the API of any other service. For example, here is a simple Python code that performs several text generation requests: from google import genai # client initialization client = genai.Client(api_key="AUTHORIZATION_TOKEN") # one-time text generation response = client.models.generate_content( model="gemini-2.0-flash", contents="Explain in simple words how generative AI works", ) print(response.text) # step-by-step text generation for chunk in client.models.stream_generate_content( model="gemini-2.0-pro", contents="Write a poem about spring", ): print(chunk.text, end="", flush=True) At the same time, Google provides numerous reference materials to help you master cloud-based AI generation: Documentation. Official reference for all possible capabilities and functions of the Gemini API. GitHub Examples. Numerous examples of using the Gemini API in Go, JavaScript, Python, and Java. GitHub Cookbook. Practical materials explaining how to use the Gemini API with ready-made script examples. Thus, Gemini offers developers special conditions and tools for integrating the model into the logic of other applications. This is not surprising, since Google has one of the largest cloud infrastructures in the world. Conclusion The Gemini model stands out favorably from many other LLM neural networks, supporting working with multimodal data: text, code, images, and video. Google, with its rich ecosystem, seeks to integrate Gemini into all its services, adding flexibility to the classic user experience.

Choosing an operating system is not just a matter of taste. In 2025, Windows and Linux are diverging more than ever: one offers a predictable experience with minimal configuration, the other—full flexibility, but depending on your knowledge and involvement. Windows has a familiar interface, built-in drivers, support for QuickDocs and Photoshop, but you’ll pay for it: with money and with restrictions. Linux offers a system you can fully customize for yourself and use for free, but not everything works “out of the box.” In this article, we compare Linux and Windows on key points: installation complexity, hardware support, running programs and games, long-term performance, privacy and data collection, choice depending on tasks. Installation and What You Get After Windows and Linux install in a similar way: download the image, write it to a USB drive, and start the installation. But then the differences begin. Windows 11 already requires an internet connection and a Microsoft account from the start. Without this, the installation won’t continue. In addition, older computers face another restriction: the system requires a TPM chip and will stop installation if it’s missing. This is a special security module on the motherboard that ensures system integrity checks and is required to install Windows 11. After installation, the user doesn’t get a “clean” desktop, but a preloaded set of apps—App Store, widgets, Copilot, Xbox, OneDrive—some of which cannot be removed with standard tools. The system can also automatically update or download data in the background. Linux (for example, Ubuntu or Linux Mint) works differently. Hardware requirements are usually lower and less strict, allowing installation even on relatively old computers. An account is not required, and an internet connection is often unnecessary for basic installation, although some distributions may offer to download updates during setup. The installation interface is also simple: choose language, time zone, and target disk. After startup, the user gets a minimally configured system: desktop, browser, file manager. Everything else can be installed later at your choice and convenience. The system does not take action without the user’s request. And an important point: Windows requires a paid license. Without it, you face personalization restrictions and a watermark on the screen. Most Linux distributions are free and have no such restrictions, although some professional or enterprise versions may require a paid subscription or license. Hardware Compatibility: Sound, Wi-Fi, Video This is one of the main questions before switching to Linux: will everything work? The answer, in most cases, is yes, but with nuances. With Windows, it’s simple: install, and almost everything works. If something doesn’t, go to the manufacturer’s website, download and install the driver. Manufacturers officially support Windows, which is convenient. In Linux, most devices usually work right after installation. Wi-Fi, sound, display, USB drives, mouse, keyboard, and even Bluetooth—all this will most likely work without extra configuration, especially if the computer isn’t brand new or built with rare components. But sometimes there are issues: for example, on some laptops, the Wi-Fi module may not be detected automatically; in that case, you need to install a driver via terminal. For NVIDIA video cards, it’s recommended to manually install the proprietary driver for full support and performance. Fingerprint scanners on new laptops often lack official support, and enthusiasts write drivers for them. With printers and scanners, results vary: some work immediately, others require manual fixes. The advantage of Linux is that it’s not tied to new hardware. If you have an old laptop or PC that Windows 11 refuses to run on, Linux will most likely give it a second life. It doesn’t require TPM, doesn’t complain about a “too old processor,” and doesn’t force BIOS updates. The downside is that not everything always works right away. Sometimes you need to read a short guide. Sometimes, type a command in the terminal. If you’re not afraid to spend 15 minutes fixing something, it’ll work out. Programs and Games This is probably the main fear when switching to Linux: Can I still do what I used to do? The answer depends on what you use. Everyday Tasks For everyday tasks, such as the internet, email, video, music, and documents, Linux poses almost no problems. Most distributions include everything you need: Firefox and Chrome, Telegram and WhatsApp, Zoom and OBS, VLC and Spotify, and office suites like LibreOffice. These apps work quickly and install through built-in stores. Professional Tasks Things get trickier with professional software. Programs like Photoshop, AutoCAD, QuickBooks, or CorelDRAW are made for Windows and don’t install directly on Linux. There are workarounds—emulators like Wine or alternatives (e.g., GIMP instead of Photoshop)—but they don’t always offer the same capabilities. If you work with QuickBooks or file tax reports through services like TurboTax, it often makes sense to use a second machine or a virtual Windows machine. Games A few years ago, Linux was nearly unusable for gaming. Today, the situation is much better. Steam works great, and Valve actively develops Proton technology, which allows many games to run without extra setup. In ProtonDB’s compatibility database, many popular games are rated “Gold” or “Platinum.” Titles like Baldur’s Gate 3, Cyberpunk 2077, Elden Ring, The Witcher 3 run successfully. However, there’s an important limitation: games using incompatible anti-cheat systems (e.g., Easy Anti-Cheat) may not run on Linux. Fortnite and Valorant are the most well-known examples. Also, some launchers like Battle.net or Rockstar Games Launcher may work unstably or require extra setup. In short: for everyday work, Linux fits without major restrictions. But if you depend heavily on specific Windows software or often play multiplayer games with anti-cheat, check compatibility beforehand or consider dual-boot. Long-Term Performance Many Windows users notice performance degradation over time. This happens due to the accumulation of temporary files, updates, hidden services, and background processes. Some things install automatically, some remain after uninstalled programs. Constantly running services like antivirus, OneDrive, or Cortana also increases resource load, especially on older or weaker devices. Linux has similar mechanisms—temporary files, background processes, and updates exist in any OS. But they are fewer, and management is more transparent and predictable. The system doesn’t launch extra services without user request and doesn’t install new components without explicit consent. This helps maintain stable performance even after long-term use. Important: Linux has no mandatory built-in antivirus, no ads, and no intrusive services. Users can always check which processes consume resources and disable them if needed. The system stays transparent and manageable, without hidden background tasks that can’t be disabled through standard tools. Directly compared, Windows needs more regular optimization and maintenance to keep performance, while Linux tends to stay stable longer with minimal user effort. Privacy and Security Windows knows a lot about you. Even without extra configuration, the system by default sends telemetry data on how the device is used, which apps are launched, how often certain features are accessed. This is “to improve quality,” but it cannot be fully disabled with standard tools. Additionally, Windows requires Microsoft account binding, integrates with OneDrive, and shows personalized ads in the Start menu. Updates may download and install automatically without explicit user consent. Linux usually offers a more transparent approach to data collection. Many distributions don’t collect telemetry by default and don’t require an account. Some systems, like Ubuntu-based ones, may enable anonymous stats collection, but usually this can be disabled during installation. In most popular distributions, users themselves decide what data to send and when, and updates only install with explicit request. Security is also important. Because of Windows’ popularity, there’s a significant amount of malware. If users are careless or fail to update the system, the infection risk increases. Built-in antivirus helps, but doesn’t eliminate the threat. Linux has a different security architecture. Installing programs requires superuser rights and explicit approval. Malware is much less widespread, and vulnerabilities are usually patched quickly. Installing from official repositories is like downloading from a verified app store, reducing infection risk. In summary, Windows is integrated into Microsoft’s ecosystem and collects detailed usage data by default for analytics and personalization. Linux usually gives users more control over privacy and updates. Flexibility and Customization Windows allows customization within limits: change wallpaper and theme, move the taskbar, add icons, enable dark mode. But removing preinstalled components, disabling background services, or deeper interface changes often requires third-party apps or registry edits. Some elements can’t be changed at all with standard tools, like the Start menu, OneDrive integration, or built-in widgets. The result is an environment designed and tightly controlled by the developer. Linux follows another philosophy. You can change literally everything. Users can choose between different desktop environments—from minimalist XFCE to visually rich KDE. You can even configure the system to boot without a graphical interface, keeping only essential components. Want a macOS-style desktop? No problem. In Linux, you can customize not just appearance, but behavior: how the network works, how drives connect, which services run, which commands are available. Example: if you don’t want a file manager to auto-open when plugging in a USB drive, find the relevant setting or disable automount. In a minimal installation, you can simply skip unnecessary packages and services, leaving only what you need. In conclusion, Windows offers a ready-made solution with limited customization, while Linux lets you build a fully individual environment tailored to your tasks and preferences. Learning and Migration Switching from Windows to Linux is like moving to another country. The basics are familiar: desktop, icons, folders, browser. But many things work differently. Programs aren’t installed via .exe files, but through a package manager, like an integrated app store. Settings may be scattered across different menus. Sometimes solving issues requires opening a terminal and entering a command, which may feel unusual at first. But it’s not that scary. Most popular distributions have simple interfaces and large communities. Almost any question is already discussed in detail and documented. Just search for it and you’ll find step-by-step instructions. On Telegram chats and forums, you can get help and advice from experienced users. Linux doesn’t demand deep admin knowledge from beginners. You can use it like a normal user: install a browser, office apps, media players, and work without complex settings. But if you want, you can learn more: scripting, system administration through terminal, automating tasks. This flexibility is like having a toolbox: you don’t need to use everything at once, but it’s good that they’re available. In summary, Linux takes a bit more time to get used to, but in return offers more possibilities. Learning is not hard—especially if you have some patience. Windows vs Linux: Quick Summary by User Type For internet, movies, and documents: Both systems work. Linux provides all the necessary functions, doesn’t need a license fee, and usually runs faster on old hardware. Windows is more familiar, especially if you just want everything to work without tweaks. For gamers: Depends on games. Many titles, including Baldur’s Gate 3, The Witcher, Elden Ring, Cyberpunk 2077, run great on Linux. However, for games with strict/incompatible anti-cheat systems, such as Valorant, Fortnite, and GTA Online, choose Windows or consider setting it up as a secondary OS. For office and communication: Linux fully covers documents, video calls (Zoom), browser, email. Often more convenient due to fewer ads and notifications. For specialized software: If you use QuickBooks, AutoCAD, Photoshop, or submit reports via services like TurboTax, Windows is preferable. Such programs don’t always work properly in Linux, even with emulators. For developers: Linux offers a very comfortable environment: tools like Git, SSH, Docker, Python, and web servers work out of the box. For those who want stability without forced background services/updates: Linux is worth considering as your main system. Which Linux Distributions to Try If you’ve never used Linux, start with a simple distribution. Here are good options: Linux Mint: One of the most beginner-friendly options. An interface similar to Windows 7, everything works “out of the box,” and a convenient app store is available. Ubuntu: The most well-known distro. Good balance of simplicity and modern solutions. Fits both beginners and developers. Zorin OS: Great for those switching from Windows. The interface is very similar, and there’s a Lite version for weak computers. Fedora: A modern distribution with great support for new hardware and advanced technologies Popular among advanced users. Arch Linux or EndeavourOS: For those who want full control and aren’t afraid to learn. Everything is built manually, but in return, you fully understand the system. You can try any of them in Live mode. Boot from a USB drive and test without installing. This helps you see if Linux suits you and which flavor feels right. What’s Next: The Future of Windows and Linux By 2025, it’s clear that Microsoft focuses on cloud integration, AI technologies, and creating a unified device ecosystem. Windows is gradually turning from a traditional OS into a service with subscriptions, regular updates, and features like Copilot. All this pushes users deeper into Microsoft’s ecosystem. At the same time, the system increasingly depends on online functions and accounts—without them, full use is difficult. Linux is evolving differently, focusing on autonomy, minimalism, and privacy. Many users choose lightweight, control-oriented distros, including non-systemd options. The community doesn’t aim to create a universal “desktop for everyone”. Instead, it supports variety: from ready-to-use systems like Ubuntu and Linux Mint to highly customizable solutions like Arch Linux or NixOS. This lets users choose what best fits their needs: simplicity or full control. In the end, the choice between Windows and Linux depends on user priorities. Those who value a familiar interface, ready-to-use apps, and Microsoft service integration will prefer Windows. Those who want control, transparency, and flexible customization may consider Linux as a full alternative.

In recent years, the development of large language models (LLMs) has become one of the key areas in the field of artificial intelligence. From the first experiments with recurrent and convolutional networks, researchers gradually moved to attention-based architectures—the Transformer, proposed in 2017 by Google’s team. This breakthrough paved the way for scaling models capable of processing enormous volumes of textual data and generating coherent, meaningful answers to a wide variety of questions. Against the backdrop of Western dominance, the work of Chinese research groups is attracting more and more attention. The country is investing significant resources into developing its own AI platforms, seeking technological independence and a competitive advantage in the global market. One of the latest embodiments of these efforts is the DeepSeek neural network, which combines both the proven achievements of the Transformer architecture and its own innovative optimization methods. In this article, we will look at how to use DeepSeek for content generation, information retrieval, and problem solving, as well as compare its characteristics with Western and domestic counterparts. What is DeepSeek AI and How It Works DeepSeek is a large language model (LLM) developed and launched by the Chinese hedge fund High-Flyer in January 2025. At its core lies the transformer architecture, distinguished by a special attention mechanism that allows not only analyzing fragments of information in a text but also considering their interconnections. In addition to the transformer foundation, DeepSeek employs several innovations that may be difficult for a non-technical person to grasp, but we can explain them simply: Multi-Head Latent Attention (MLA). Instead of storing complete “maps” of word relationships, the model keeps simplified “sketches”—compact latent vectors. When the model needs details, it quickly “fills in” the necessary parts, as if printing out a fragment of a library plan on demand rather than carrying around the entire heavy blueprint. This greatly saves memory and speeds up processing, while retaining the ability to account for all important word relationships. Mixture-of-Experts (MoE). Instead of a single universal “expert,” the model has a team of virtual specialists, each strong in its own field: linguistics, mathematics, programming, and many others. A special “router” evaluates the incoming task and engages only those experts best suited for solving it. Thanks to this, the model combines enormous computational power with efficient resource usage, activating only the necessary part of the “team” for each request. Thus, DeepSeek combines time-tested transformer blocks with the innovative MLA and MoE mechanisms, ensuring high performance while relatively conserving resources. Key Capabilities of DeepSeek: From Code to Conversations The DeepSeek neural network can generate and process various types of content, from text and images to code and documents: Dialogues. Builds natural human-like conversations with awareness of previous context. Supports many tones of communication, from formal to informal. Manages long-session memory up to 128,000 tokens of context. Exploring specific topics. Instantly responds to queries across a wide range of fields: science, history, culture. Collects information from external sources to provide more accurate data. Creative writing and content generation. Generates ideas and assists in writing articles, stories, scripts, slogans, marketing texts, narratives, poems, and other types of textual content. Code generation and understanding. Performs any code-related tasks in the most popular programming languages: writing, autocompletion, refactoring, optimization, inspection, and vulnerability detection. Moreover, the model can generate unit tests and function documentation. Essentially, DeepSeek can do everything a human programmer can.Supported languages include: C, C++, C#, Rust, Go, D, Objective-C, JavaScript, TypeScript, HTML, CSS, XML, PHP, Ruby, Python, Perl, Lua, Bash/Shell/Zsh, PowerShell, Java, Kotlin, Swift, Dart, Haskell, OCaml, F#, Erlang, Elixir, Scala, Clojure, Lisp/Scheme, SQL, JSON, Markdown, and many more. Document and website analysis. Summarizes the contents of documents, condenses information from external sites, extracts key ideas from large texts. Translation from foreign languages. Translates text into dozens of languages while preserving original terminology and style. In short, anything that can be done with textual data, DeepSeek can do. The only limits are the imagination of the user. DeepSeek Chatbot: Three Key Modes The DeepSeek chatbot offers three core modes, each optimized for different types of tasks and depth of processing: Normal. Fast and lightweight answers to common questions. Has a limited context window but provides relatively high-quality responses with minimal delay. Suitable for direct factual queries: definitions, short explanations, notes. DeepThink. In-depth analytical research with complex reasoning. Has an expanded context window but requires much more time to generate responses. Performs multi-step processing, breaking tasks into sub-tasks. Uses a “chain of thought” method, forming intermediate conclusions for the final answer. Suitable for logic-heavy queries: solving math problems, writing essays, detailed analysis of scientific articles, comprehensive strategic planning. Search. Thorough analysis of external sources to provide up-to-date information. Automatically connects to the internet to search for current data, news, statistics. Uses specialized APIs and search engines, verifies sources, processes results, cross-checks facts, filters out irrelevant information. Suitable for finding fresh data and fact-checking. Comparative Table of Modes Mode Response Speed Context Size Depth of Analysis External Sources Normal high limited low no DeepThink low maximum high no Search medium variable medium yes Thus, if you just need a quick answer, use Normal mode. For deep reasoning and detailed justifications, choose DeepThink. To obtain the latest verified data from external sources, use Search. How to Use DeepSeek: Interface, Access, and Launch Although DeepSeek AI does not exist within a vast ecosystem (like Google’s Gemini), the neural network offers several ways to interact with it. Option 1. Remote Application In the simplest case, there are three ways to interact with the model hosted on DeepSeek’s remote servers: Desktop browser app Android mobile app iOS mobile app All options provide dialogue with the model through a chatbot. In every case, the user interface includes a dialogue window, a message input field, file attachment buttons, and a panel with active sessions. To access the model, you must either register with DeepSeek using an email address or log in through a Google account. After that, a familiar chatbot page opens, where you can converse with the model and manage active sessions, just like with other LLMs such as ChatGPT, Gemini, Claude, etc. Option 2. Local Application A more advanced way is to install DeepSeek on a local machine. This is possible thanks to its open-source code, unlike many other LLM services. DeepSeek can run on Windows, macOS, and Linux. Minimum requirements: 8 GB of RAM and 10 GB of free disk space, plus Python 3.8 or higher. When running locally, there are several interaction methods: Method 1. Web interface.  A graphical UI that allows querying, viewing logs, connecting external storage, monitoring metrics, analyzing performance, and more. The local interface differs from the public one by offering advanced model management tools. It is primarily intended for internal use by individual users or companies and contains parameters that only specialists would understand. Method 2. Console terminal. Method 3. REST API. A full REST interface for sending HTTP requests to the locally installed model. Example with curl: curl -X GET 'http://localhost:8080/api/search?index=my_index&query=search' \   -H "Authorization: Bearer UNIQUE_TOKEN" This universal method does not depend on the client type, whether a console terminal or a complex C++ program. Method 4. Python script. DeepSeek provides a wrapper fully compatible with the OpenAI API, allowing use of the standard OpenAI client with only a URL change. Example: from openai import OpenAI client = OpenAI(api_key="UNIQUE_TOKEN", base_url="http://localhost:8080") response = client.chat.completions.create( model="deepseek-chat", messages=[ {"role": "system", "content": "You are a helpful assistant, DeepSeek."}, {"role": "user", "content": "Hello!"}, ], stream=False ) print(response.choices[0].message.content) Method 5. JavaScript script. Similarly, you can interact with DeepSeek using the OpenAI client in JavaScript. Example (Node.js): import OpenAI from "openai"; const openai = new OpenAI({ baseURL: 'http://localhost:8080', apiKey: 'UNIQUE_TOKEN' }); async function main() { const completion = await openai.chat.completions.create({ messages: [{ role: "system", content: "You are a helpful assistant." }], model: "deepseek-chat", }); console.log(completion.choices[0].message.content); } main(); Notably, it is precisely the open-source nature that made DeepSeek popular and competitive in the LLM market. However, the local version is intended for advanced users with deep ML knowledge and specific tasks requiring local deployment. Detailed information on local installation is available in the official DeepSeek GitHub repository and the HuggingFace page. Specialized DeepSeek Models In addition to the core model, several specialized versions exist: DeepSeek Coder. For working with code (analysis and editing) in multiple programming languages. Available on GitHub. DeepSeek Math. For solving and explaining complex mathematical problems, performing symbolic computations, and constructing formal proofs. Available on GitHub. DeepSeek Prover. For automated theorem proving. Available on HuggingFace. DeepSeek VL. A multimodal model for analyzing and generating both text and images. Available on GitHub. DeepSeek Pricing Plans The DeepSeek service provides completely free access to its core models (DeepSeek-V and DeepSeek-R) through the website and mobile app. At present, there are no limits on the number of queries in the free version. The only paid feature in DeepSeek is the API, intended for application developers. In other words, if someone wants to integrate DeepSeek into their own app, they must pay for API usage, which processes the requests. Payment in DeepSeek follows a pay-as-you-go model with no monthly subscriptions. This means that the user only pays for the actual API usage, measured in tokens. There are no minimum payments. The user simply tops up their balance and spends it as queries are made. The balance does not expire over time. You can find more details on API pricing in the official DeepSeek documentation.   DeepSeek-V DeepSeek-R 1 million tokens (input) $0.27 $0.55 1 million tokens (output) $1.10 $2.19 To control expenses, manage API tokens, and view usage statistics, DeepSeek has DeepSeek Platform. It also provides links to documentation and reference materials that describe the basics of using the model, integrating with external applications, and pricing specifics. Prompts for DeepSeek: How to Give Commands and Get Results Although prompts for DeepSeek can vary, there are several general principles to follow when writing them. Clarity and Specificity It’s important to clearly describe both the details of the request and the desired format of the answer. Avoid vague wording, and provide context if needed. For example, you can specify the target audience and the approximate output format: I’m preparing a school report on history. I need a list of the 5 most important discoveries of the early 20th century, with a short explanation of each in the format of a headline plus a few paragraphs of text. For such queries, you can use Search mode. In this case, DeepSeek will reinforce the response with information from external sources and perform better fact-checking. In some cases, you can describe the format of the response in more detail: I need a list of the 15 most important discoveries of the early 20th century in the form of a table with the following columns: Name of the discovery (column name: “Name”) Authors of the discovery (column name: “Authors”) Date of the discovery (column name: “Date”) Short description of the discovery (column name: “Description”) Hyperlinks to supporting publications (column name: “Sources”, data in the format [1], [2], [3], ... with clickable links, but no more than 5 sources) The table rows must be sorted by date in descending order. The more detail you provide, the better. When writing prompts for DeepSeek, it’s worth taking time to carefully consider what you need and in what format. You can also use text descriptions to set filters: date ranges, geography, language of sources, readability level, and many other parameters. For example: I need a table of the 15 most important discoveries of the early 20th century that were made in the UK between 1910 and 1980. The table rows must be sorted by date in descending order, and the columns should be: Name (column: “Name”) Authors (column: “Authors”) Date (column: “Date”) As you can see, filtering in DeepSeek is done through natural language text rather than the sliders or filters familiar from internet catalogs or UGC platforms. Clear Formalization In addition to detailed text descriptions, you can formalize requests with a structured format, including special symbols: [Task]: Create a table of the 10 most important discoveries of the early 20th century.   [Constraints]:   - Territory: United Kingdom   - Period: 1910–1980   [Structure]:   - Columns: number, name, author, date (day, month, year)   [Context]: For history students specializing in British history.   This creates a clear request structure: Task. What needs to be done. Context. Where to search and for whom. Constraints. What to include or exclude. You can, of course, customize the structure depending on the task. Advanced Techniques LLM-based neural networks are extremely flexible. They support more complex dialogue patterns and information-processing methods. To get more relevant answers, you can use advanced prompting techniques, often mirroring real human dialogue. Option 1. Role-based prompts Explicitly asking the model to take on a role with specific qualities can add depth and define the style of the answer. Imagine you are an expert in English history with more than 30 years of experience studying the nuances of the UK’s scientific context. In your opinion, what 10 discoveries in the UK can be considered the most important of the 20th century? Please provide a brief description of each, just a couple of words. This style of prompt works best with DeepThink mode, which helps the model immerse itself more deeply in the role and context. Option 2. Query chains In most cases, obtaining a comprehensive response requires multiple queries—initial exploratory prompts followed by more specific ones. For example: First, a clarifying question: What sources exist on scientific discoveries in the UK during the 20th century? Then, the main request: Based on these sources, prepare a concise description of 5 scientific discoveries. Format: title + a couple of explanatory paragraphs. The best results often come from combining DeepThink and Search modes. DeepSeek will both gather external information and process it in depth to synthesize a thorough answer. DeepSeek vs. Other AI Models: Comparison and Conclusions Unique Features of DeepSeek Free access. The two main models (one for simpler tasks, one for complex tasks) are available completely free of charge. Only the developer API is paid, and the pricing is usage-based, not subscription-based. No limits. All models are not only free but also unlimited, i.e., users can generate as much content as they want. While generation speed may not be the fastest, unlimited free use outweighs most drawbacks. Open source. Industry experts, AI enthusiasts, and ordinary users can access DeepSeek’s source code on GitHub and HuggingFace. Global availability. The DeepSeek website is accessible in most countries. Comparison with Other LLM Services Platform Generation Speed Free Access Pricing Model Content Types Developer Country Launch Year DeepSeek High Full Pay-as-you-go Text High-Flyer China 2025 ChatGPT High Limited Subscription Text, images OpenAI USA 2022 Gemini High Limited Subscription Text, images, video Google USA 2023 Claude Medium Limited Subscription Text Anthropic USA 2023 Grok Medium Limited Subscription Text, images xAI USA 2023 Meta AI Medium Limited Subscription / Usage Text, images Meta (banned in RF) USA 2023 Qwen Medium Full Pay-as-you-go Text Alibaba China 2024 Mistral High Limited Subscription Text Mistral AI France 2023 Reka High Full Pay-as-you-go Text Reka AI USA 2024 ChatGLM Medium Limited Pay-as-you-go Text Zhipu AI China 2023 Conclusion On one hand, DeepSeek is a fully free service, available without volume or geographic restrictions. On the other hand, it is a powerful and fast model, on par with many industry leaders. The real standout, however, is its open-source code. Anyone can download it from the official repository and run it locally. These features distinguish DeepSeek from competitors, making it not only attractive for content generation but also highly appealing for third-party developers seeking integration into their own applications. That’s why when ChatGPT or Gemini fall short, it’s worth trying DeepSeek. It just might find the right answers faster and more accurately.