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Top AI Coding Tools in 2025: Best Models for Developers & Programmers

Top AI Coding Tools in 2025: Best Models for Developers & Programmers
Hostman Team
Technical writer
Infrastructure

In the early days of computing, programmers wrote code entirely on their own, from scratch and solo. Hardcore mode! The only help they had was paper reference books describing the syntax of specific languages.

Today, things are very different. In addition to countless electronic manuals, guides, articles, videos, and forums, we now have neural networks, arguably one of the most groundbreaking technologies of the early 21st century.

Trained on massive datasets, these AI models have become the primary source of coding assistance.

The advantages are obvious. AI coding tools speed up the development process by taking on much of the routine work involved in writing code. This allows developers to focus on architecture and logic instead of syntax errors and inefficient constructs.

Some tools generate code from scratch, and others analyze and complete already-written code.

However, in recent years, so many AI-powered projects have emerged that it can be difficult for the average person to figure out which AI is actually the best for programming.

There are both specialized and general-purpose models. Some only generate specific types of data (like code), while others handle all kinds (text, code, images). Some are free, others paid.

To determine which AI is the best for programming (and why), we first need to create a list of the top coding AIs, and then analyze the pros and cons of each one.

1. GitHub Copilot

Copilot is arguably the best AI coding assistant, developed by GitHub in collaboration with OpenAI. It’s positioned as an AI co-programmer trained on millions of open-source GitHub repositories.

Features

Developed by the largest cloud-based code hosting platform, Copilot leads the list of neural networks for programming, offering a wide range of capabilities:

  • Code Generation: Produces ready-to-use code snippets in all major languages based on text descriptions: scripts, functions, classes, even entire files. While the AI sometimes generates imperfect results, this can be resolved by making the user’s request more specific.

  • Code Translation: Converts code written in one programming language into logically equivalent code in another. This feature alone puts Copilot ahead of many other coding AIs, as not all models can do this effectively.

  • Code Autocompletion: Suggests autocompletion based on the overall context of the codebase.

  • Refactoring: Enhances code structure, optimizes algorithms, and fixes errors. It can also suggest alternative, more efficient solutions that a developer might not have initially considered.

  • Editor Integration: Integrates via plugins into popular text editors and IDEs like Visual Studio Code, Neovim, JetBrains IDEs, and others.

These features help automate routine coding tasks.

A lesser-known use case of Copilot is learning programming languages. The AI can generate simple code snippets that demonstrate the syntax and mechanics of a specific language.

Interestingly, this teaching method aligns with Stephen Krashen’s Comprehensible Input Hypothesis, which states that language acquisition is driven by understandable input, i.e., the material that the learner can interpret with explanation.

Similarly, Copilot can be used as an interactive reference, potentially replacing resources like Stack Overflow.

Languages Supported

Copilot supports all major programming languages: C, C++, C#, Go, Java, JavaScript, Kotlin, PHP, Python, Ruby, Rust, Scala, Swift, and TypeScript.

It can also generate code using popular frameworks and libraries like React, Angular, Vue.js, Node.js, Django, Flask, and Ruby on Rails.

Pricing Plans

Naturally, GitHub offers only a limited set of Copilot features for free. The free version also has monthly limits on code generations.

The full version is available through subscriptions for individuals, teams, and enterprises. Pricing starts at $4/month, with a 30-day free trial. In return, users get a powerful tool for faster coding.

Despite requiring a subscription, many developers consider Copilot the best AI coding assistant, especially when compared to general-purpose models like ChatGPT, which aren't primarily designed for code generation.

2. Tabnine

Tabnine is an AI that generates code snippets not based on explicit prompts but on the development context formed by the programmer’s current work.

Features

Unlike Copilot, Tabnine primarily focuses on code autocompletion. However, it also offers several distinctive features:

  • Offline Mode: The Enterprise version of Tabnine can run entirely offline, generating code without internet access. This improves data privacy, as code is processed locally and not sent to the cloud; however, it does require more system resources.

  • Personalized Generation: Tabnine learns from a specific developer’s codebase, mimicking their unique style and preferences. This results in personalized suggestions that feel as if the code were written by the developers themselves, in contrast to Copilot, which was trained on public GitHub repositories.

  • IDE Integration: Since Tabnine is not a standalone application but a smart autocompletion engine, it integrates with virtually all major IDEs through plugins, including VS Code, IntelliJ, Visual Studio, Eclipse, Android Studio, AppCode, CLion, GoLand, Neovim, PhpStorm, PyCharm, Rider, RubyMine, WebStorm.

  • Interactive AI Chat: Tabnine also offers a built-in chat interface for personalized communication with the AI. Users can ask questions related to the code in their current editor tab.

All in all, Tabnine is geared more toward typing speed and efficiency rather than generating large chunks of code from scratch. Think of it as classic autocompletion but supercharged with AI.

Languages Supported

Like Copilot, Tabnine supports autocompletion for all major programming languages: Python, JavaScript, TypeScript, Java, C/C++, C#, Go, Ruby, Swift, PHP, Rust, Kotlin, Perl, Dart, Scala.

Pricing Plans

Tabnine is available under two subscription plans:

  • Dev – from $9/month for individual developers.
  • Enterprise – from $39/month for teams and companies.

The Enterprise plan offers advanced customization options, enhanced security, and on-premise deployment for maximum privacy.

3. ChatGPT

ChatGPT is a generative AI developed by OpenAI, based on the GPT (Generative Pre-trained Transformer) architecture.

Unlike Copilot and Tabnine, ChatGPT can generate not just code but also various forms of text content. That makes it a general-purpose neural network—a versatile tool for generating any data based on a user's prompt.

Some might argue that ChatGPT is more text-oriented than code-focused. However, it remains one of the best free AI for coding, as its basic version is available to everyone without a paid subscription.

Features

ChatGPT operates through a chat interface, where prompts are entered as natural language messages.

That’s why its name consists of Chat and GPT:

  • Chat: its ability to hold conversations, answer questions, and maintain real-time dialogue.
  • GPT: short for Generative Pre-trained Transformer:
    • Generative: creates original text rather than copying answers,
    • Pre-trained: trained on vast data corpora,
    • Transformer: refers to the model’s architecture, which uses attention mechanisms to understand and generate contextually accurate responses.

In short, ChatGPT is a conversational AI capable of tackling almost any language-based task — including code generation.

Here’s what it can do:

  • Conversational Interaction: The AI responds as if you were chatting with another person. You can ask it to use a certain tone, generate text, tables, code, and even simple images. This makes ChatGPT a virtual assistant, coworker, or advisor.

  • Free Code Generation: The base model is completely free to use. More advanced versions offer improved performance but require a subscription.

  • Multi-Format Output: It can create more than just code. You can generate any language-based content based on a clear prompt and adapt it to the ongoing context of the conversation.

For example, you could write this fun prompt:

“Give me an example of Python code with a helicopter and a car class. Each should have a fuel variable initialized to 100. Then create objects of each class and have the helicopter hook the car with a cable.”

ChatGPT would generate something like this:

class Helicopter:
    def __init__(self):
        self.fuel = 100
        self.hooked_car = None

    def hook_car(self, car):
        self.hooked_car = car
        print("The helicopter has hooked the car with a cable.")

class Car:
    def __init__(self):
        self.fuel = 100

helicopter = Helicopter()
car = Car()
helicopter.hook_car(car)

You can check this code in any online Python interpreter and get the expected output:

The helicopter has hooked the car with a cable.

So, if you're working late at night and wondering which neural network is best for hands-off code generation, ChatGPT is worth considering. After all, OpenAI is a global leader in machine learning.

At the very least, ChatGPT is the best conversational AI for code creation, capable of generating not only code but also full documents, tables, and even basic images.

Languages Supported

Since it was trained on a vast linguistic dataset, ChatGPT can generate code in nearly any language and not just general-purpose ones.

It supports all major programming languages, including Python, JavaScript, TypeScript, Java, C, C++, C#, Go, PHP, Swift, Kotlin, Ruby, Rust, Haskell, Lisp, Elixir, Erlang, and F#.

It also understands domain-specific languages: HTML, CSS, SASS/SCSS, SQL, GraphQL, Shell, PowerShell, Lua, Perl, YAML, and JSON.

Listing them all would be pointless, as ChatGPT can understand and generate code or text in virtually any format. That's its defining strength.

Pricing Plans

OpenAI offers four subscription tiers for ChatGPT, each expanding the capabilities of the last:

  • Free – All basic features. No cost.
  • Plus – Enhanced performance and access to newer models with better contextual understanding and faster responses. Starts at $20/month.
  • Pro – Full access with no usage limits. Starts at $200/month.
  • Team – Adds collaborative tools, custom roles, and enhanced security for data sharing and storage. Team data is excluded from AI training — ensuring full confidentiality. Starts at $25/month.

Paid plans provide higher accuracy, better performance, and more stability. Still, the free version offers nearly identical functionality — the difference lies in the fine details.

4. Claude

Claude is another natural language processing AI developed by Anthropic. According to its creators, Claude is a safer, more ethical, and more predictable alternative to ChatGPT.

Features

Overall, Claude's capabilities are similar to ChatGPT’s, with a few notable distinctions:

  • Image and Document Analysis: Claude can interpret the contents of images and documents in detail, recognizing real-world objects, diagrams, graphs, numbers, and text. ChatGPT is also capable of this, but only in its paid version. Claude offers it natively.

  • Massive Context Window: Claude supports up to 200,000 tokens, which allows it to analyze large volumes of data. By comparison, ChatGPT maxes out at around 128,000 tokens. One token is roughly 5 characters of English text.

  • High Ethical Standards: Thanks to built-in ethical constraints, Claude is less likely to generate inappropriate content, making its responses more conservative. While this may not matter to some users, from a broader perspective, output filtering is a key trait that separates the best AI coding tools from the rest, especially as AI tools become mainstream.

In short, Claude offers high factual accuracy, which is crucial for generating reliable code based on user instructions.

Languages Supported

According to Anthropic, Claude performs best when generating Python code. However, it also supports other popular languages: JavaScript, Java, C++, Go, PHP, Ruby, C#, Swift, TypeScript, Kotlin, and Rust.

Of course, the full list of supported languages isn’t publicly available, as the model was trained on diverse datasets. Practical testing is the best way to determine support.

Pricing Plans

Claude offers several pricing tiers:

  • Free – Standard chat access via browser or mobile app (iOS/Android). No cost.
  • Pro – Enables structured chats, document analysis, and access to additional Claude models and features. Starts at $18/month.
  • Team – Adds collaboration features for group work. Starts at $25/month.
  • Enterprise – Provides deeper control over generation processes, user role management, and enhanced data privacy. Custom pricing.

Despite Claude being one of the top free AI for coding, it can’t be considered a full competitor to ChatGPT.

Here’s why:

  • Smaller Knowledge Base: ChatGPT was trained on more data, producing more accurate and diverse responses.
  • Limited Availability: Claude is not as widely accessible as ChatGPT and is available in fewer countries.
  • Few Integrations: ChatGPT is integrated into many products (e.g., Office, Azure), while Claude is not.
  • Slower Development: ChatGPT evolves rapidly, releasing updates and features faster than Claude.

Still, Claude is worth trying for anyone who regularly uses AI in programming or text generation tasks.

5. Snyk Code

Snyk Code is an AI-powered static analysis tool for detecting vulnerabilities and errors, part of the broader Snyk ecosystem.

Features

Trained on a database of known vulnerabilities (updated regularly), Snyk Code focuses on secure development:

  • Vulnerability Detection: Performs real-time code analysis during development and commits to catch threats before they reach production.

  • Development Tool Integration: Works with GitHub, GitLab, Bitbucket, and Azure Repos, and is compatible with popular IDEs: VS Code, IntelliJ IDEA, PyCharm, WebStorm, Eclipse.

  • Contextual Fix Recommendations: For every issue found, it provides an explanation and sample fixes, helping developers patch their code quickly and securely.

In essence, Snyk Code is best used after you have written the code as an added security layer before deployment.

Languages Supported

Snyk Code supports major programming languages only: Apex, C, C++, Go, Groovy, Java, Kotlin, JavaScript, .NET, PHP, Python, Ruby, Scala, Swift, Objective-C, TypeScript, VB.NET.

Pricing Plans

Snyk Code is free for individual use, but teams and companies can choose from the following:

  • Free – Basic analysis with a limit of up to 200 scans per month.
  • Team – Adds support for private repos, CI/CD integration, and advanced security features. Starts at $25/month.
  • Enterprise – Includes local deployment, advanced analytics, and enterprise-level controls. Custom pricing.

While Snyk Code doesn’t generate code, its powerful analysis tools and free tier perfectly justify its inclusion in any list of the best free AI tools for coding.

6. Documatic

Documatic is an AI that automatically generates documentation and enables codebase exploration. It analyzes the project, extracts key information, and structures it for easy reference.

Features

Documatic is designed for codebase analysis; all other functionality stems from this core:

  • Automatic Documentation Generation: Produces detailed code explanations, reducing the need for manual comments.

  • Code Search and Navigation: Responds to developer queries with relevant code snippets and context.

  • Project Structure Visualization: Displays project components (dependencies, microservices, repos) as interactive graph nodes, useful for understanding complex architectures.

  • Code Explanation: Clarifies algorithms and logic, making unfamiliar projects easier to understand.

Documatic is passive: it doesn’t generate code, only analyzes and documents it.

Languages Supported

It supports modern interpreted and compiled languages: Python, Java, JavaScript, TypeScript, Go, C#, PHP.

Pricing Plans

Documatic keeps things simple with just two tiers:

  • Free – Full basic functionality for solo developers, including auto-documentation. No cost.
  • Team / Enterprise – A combined tier for corporate use, offering unlimited analysis, integrations, reporting, and collaboration tools. Custom pricing.

While it’s easy to chase the best AI coding tools, it’s crucial to remember: the developer matters more than the AI. Skills, logic, creativity, and experience outweigh any neural network’s output.

You should only upgrade to premium tools when free features no longer meet your needs.

7. Mintlify

Mintlify is a comprehensive online platform for automating code documentation with AI.

Unlike Documatic, Mintlify offers cloud hosting with visually styled, user-accessible documentation sites.

For instance, a developer or team building a JavaScript library can generate full documentation from a GitHub repo, resulting in a live, multi-page site with API references. These pages are editable using a WYSIWYG editor.

Fun fact: Anthropic uses Mintlify to power the documentation for Claude.

Features

Mintlify connects the project’s codebase to a public-facing documentation site, offering:

  • Automated Documentation Generation: Generates detailed documentation (including API references) directly from your codebase.

  • Version Control Integration: Syncs with GitHub and GitLab, ensuring documentation updates automatically when the code changes, which makes it perfect for CI/CD pipelines.

  • Documentation Site Hosting: Creates a stylish, SEO-optimized site with editable sections.

  • Analytics & Feedback: Provides user analytics and supports direct feedback collection to improve documentation quality.

While powerful, Mintlify has a learning curve as its feature-rich interface takes time to master.

Languages Supported

Supports 12 modern languages: Python, JavaScript, TypeScript, C, C++, PHP, Java, C#, Ruby, Rust, Dart, Go.

Pricing Plans

Mintlify offers four plans:

  • Hobby – Free plan for individuals with full standard functionality.
  • Pro – Advanced configuration and analytics. Starts at $150/month.
  • Growth – Allows full customization, branding removal, and other perks. Starts at $550/month.
  • Enterprise – Full configuration control and dedicated support. Custom pricing.

Where other AI coding tools show their intelligence directly, Mintlify’s AI works silently in the background.

At first glance, it may seem like a manual documentation editor; however, over time, it reveals itself as an automation powerhouse, seamlessly connecting code to documentation.

8. Codeium

Codeium is an AI-powered coding assistant that consists of several products built on artificial intelligence:

  • Windsurf Editor – an integrated development environment (IDE) with built-in AI.
  • Forge – an AI assistant for code analysis and review.

In addition to these, there’s a browser-based chat called Live, as well as numerous IDE extensions – Codeium Extensions.

E7261d0c 08e8 4178 9ec8 Bb509066df1d.png

The Codeium Windsurf Editor integrated development environment, with the code editor on the left and the AI chat on the right. Source: codioailab.com

Features

Codeium offers a wide range of features that assist during coding and code editing:

  • Code Autocompletion: Provides intelligent suggestions as you type.

  • Chat Assistant: A built-in AI chat can explain code snippets in detail, offer refactoring suggestions (passively while you write), and answer programming questions directly within the development environment. It can also advise on build commands and configuration.

  • Intelligent Search: Ensures quick access to classes, methods, functions, and code fragments, streamlining navigation in large codebases.

Essentially, Codeium aims to provide a comprehensive suite of tools for virtually all coding scenarios – all powered by AI.

Languages Supported

Supports all popular programming languages, including: Python, JavaScript, TypeScript, Go, Java, C#, PHP, Ruby, Kotlin, Swift.

Pricing Plans

Codeium offers several pricing plans for both individual developers and entire teams:

  • Free – All standard features. Free of charge.
  • Pro – Expanded context and deeper AI understanding, faster autocompletion, and other advanced features. Starting at $15/month.
  • Pro Ultimate – Even more useful tools and priority support. Starting at $60/month.
  • Teams – Collaboration and analytics tools for teams. Starting at $35/month.
  • Teams Ultimate – Enhanced AI model access. Starting at $90/month.
  • Enterprise SaaS – Custom pricing upon request.

9. Gemini

Gemini is a versatile AI developed by Google. Despite being relatively new, it rounds out our list of the top AI coding assistants in 2025. Unsurprisingly, it’s a direct competitor to both ChatGPT and Claude. 

Features

It’s important to recognize that Google is a major player (arguably a monopolist) in the software market. With vast cloud infrastructure, massive data resources, and many popular services (plus its own OS, Android), Gemini offers a broad array of capabilities for working with both text and visual data:

  • Text Generation, Analysis, and Translation.

  • Image Generation and Analysis: Generates images from text prompts and can also analyze images and describe their contents.

  • Code Generation and Analysis: Generates code snippets in any language and format. Also understands and analyzes code, providing suggestions for improvement. Google also offers the Gemini Code Assist extension for popular IDEs.

  • Integration with Google Services: Integrated with many Google apps and Android tools.

  • Fast Response Generation: Provides answers faster than ChatGPT and generally operates at a higher speed.

  • Large Context Window: Can handle up to 1 million tokens.

Notably, the advanced capabilities of Gemini’s language model are available through a special AI Studio for developers. This environment allows not only text-based interaction but also screen sharing for more detailed feedback.

AI Studio is designed for app developers who want to test Gemini integration with their products.

Languages Supported

Gemini supports the following major programming languages: Python, Java, C++, JavaScript, Go, TypeScript, C#, Ruby, PHP, Swift, Kotlin, Rust, SQL, HTML, CSS, Bash, Perl, Lua, R, Dart, Scala, Julia, Fortran.

Pricing Plans

Google offers a fairly straightforward pricing structure for Gemini:

  • Free – Standard model access.
  • Advanced – Enhanced performance, accuracy, and multimodal capabilities. Starting at $22/month.

Thus, just like ChatGPT, Gemini is another great free AI for programming, particularly when it comes to working with general-purpose data. The ability to generate not only code but also supporting text is an important asset in development.

Conclusion

So, what is the best AI for coding? That’s for each user to decide. Some may be satisfied with intelligent autocompletion, while others may require the generation of large code fragments across multiple languages – complete with detailed explanations.

Model

Type

Features

Pricing

Copilot

Specialized

Code generation, autocompletion

Subscription

Tabnine

Specialized

Autocompletion

Subscription

ChatGPT

General

Generation, analysis

Free, subscription

Claude

General

Generation, analysis

Free, subscription

Snyk Code

Specialized

Analysis

Free, subscription

Documatic

Specialized

Documentation

Free, subscription

Mintlify

Specialized

Documentation, hosting

Free, subscription

Codeium

Specialized

Generation, analysis

Free, subscription

Gemini

General

Generation, analysis

Free, subscription

Ultimately, the most important factor is not the tool itself, but the developer using it. Skills, experience, logic, critical thinking, and creativity all outweigh the capabilities of any neural network.

So, switching to paid versions of AI products – whether they’re code generators or analyzers – only makes sense when the free version clearly falls short for your needs.

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The RDP client receives the graphical interface of the server's OS, which is controlled using input devices. The graphical interface may be transmitted as a full graphical copy or as graphical primitives (rectangles, circles, text, etc.) to save bandwidth. By default, RDP uses port 3389, but this can be changed if necessary. A typical use case is managing a Windows remote desktop from a Linux system. From anywhere in the world, you can connect to it via the internet and work without worrying about the performance of the RDP client. Originally, RDP was introduced in Windows NT 4.0. It comes preinstalled in all modern versions of Windows. However, implementing a Linux remote desktop solution requires special software. RDP Security Two methods are used to ensure the security of an RDP session: internal and external. Standard RDP Security: This is an internal security subsystem. The server generates RSA keys and a public key certificate. When connecting, the RDP client receives these. If confirmed, authentication takes place. Enhanced RDP Security: This uses external tools to secure the session, such as TLS encryption. Advantages of RDP RDP is network-friendly: it can work over NAT, TCP, or UDP, supports port forwarding, and is resilient to connection drops. Requires only 300–500 Kbps bandwidth. A powerful server can run demanding apps even on weak RDP clients. Supports Linux RDP connections to Windows. Disadvantages of RDP Applications sensitive to latency, like games or video streaming, may not perform well. Requires a stable server. File and document transfer between the client and server may be complicated due to internet speed limitations. Configuring an RDP Server on Windows The most common RDP use case is connecting to a Windows server from another system, such as a Linux client. To enable remote access, the target system must be configured correctly. The setup is fairly simple and works "out of the box" on most modern Windows editions.  Enable remote desktop access via the Remote Access tab in System Properties. Select the users who can connect (by default, only administrators). Check firewall settings. Some profiles like “Public” or “Private” may block RDP by default. If the server is not in a domain, RDP might not work until you allow it manually via Windows Firewall → Allowed Apps. If behind a router, you might need to configure port forwarding via the router’s web interface (typically under Port Forwarding). Recall that RDP uses TCP port 3389 by default. Best RDP Clients for Linux Remmina Website: remmina.org Remmina is a remote desktop client with a graphical interface, written in GTK+ and licensed under GPL. In addition to RDP, it supports VNC, NX, XDMCP, SPICE, X2Go, and SSH. One of its key features is extensibility via plugins. By default, RDP is not available until you install the freerdp plugin. After installing the plugin, restart Remmina, and RDP will appear in the menu. To connect: Add a new connection. Fill in connection settings (you only need the remote machine's username and IP). Customize further if needed (bandwidth, background, hotkeys, themes, etc.). Save the connection — now you can connect with two clicks from the main menu. If you need to run Remmina on Windows, a guide is available on the official website. FreeRDP Website: freerdp.com FreeRDP is a fork of the now-unsupported rdesktop project and is actively maintained under the Apache license. FreeRDP is a terminal-based client. It is configured and launched entirely via the command line. Its command structure is similar to rdesktop, for example: xfreerdp -u USERNAME -p PASSWORD -g WIDTHxHEIGHT IP This command connects to the server at the given IP using the specified credentials and screen resolution. KRDC Website: krdc KRDC (KDE Remote Desktop Client) is the official remote desktop client for KDE that supports RDP and VNC protocols. It offers a clean and straightforward interface consistent with KDE's Plasma desktop environment. KRDC is ideal for users of KDE-based distributions like Kubuntu, openSUSE KDE, and Fedora KDE Spin. It integrates well with KDE's network tools and provides essential features such as full-screen mode, session bookmarking, and network browsing via Zeroconf/Bonjour. KRDC is actively maintained by the KDE community and is available through most Linux package managers. GNOME Connections Website: gnome-connections Vinagre was the former GNOME desktop's default remote desktop client. GNOME Connections, a modernized remote desktop tool for GNOME environments, has since replaced it. GNOME Connections supports RDP and VNC, providing a simple and user-friendly interface that matches the GNOME design language. It focuses on ease of use rather than configurability, making it ideal for non-technical users or quick access needs. Features: Bookmarking for quick reconnections Simple RDP session management Seamless integration into GNOME Shell Connections is maintained as part of the official GNOME project and is available in most distribution repositories. Apache Guacamole Website: guacamole.apache.org This is the simplest yet most complex remote desktop software for Linux. Simple because it works directly in a browser — no additional programs or services are needed. Complex because it requires one-time server installation and configuration. Apache Guacamole is a client gateway for remote connections that works over HTML5. It supports Telnet, SSH, VNC, and RDP — all accessible via a web interface. Although the documentation is extensive, many ready-made scripts exist online to simplify basic setup. To install: wget https://git.io/fxZq5 -O guac-install.sh chmod +x guac-install.sh ./guac-install.sh After installation, the script will provide a connection address and password. To connect to a Windows server via RDP: Open the Admin Panel, go to Settings → Connections, and create a new connection. Enter the username and IP address of the target machine — that's all you need. The connection will now appear on the main page, ready for use. Conclusion RDP is a convenient tool for connecting to a remote machine running Windows or a Linux system with a GUI. The server requires minimal setup — just a few settings and firewall adjustments — and the variety of client programs offers something for everyone.
09 June 2025 · 6 min to read
Infrastructure

Docker Container Storage and Registries: How to Store, Manage, and Secure Your Images

Docker containerization offers many benefits, one of which is image layering, enabling fast container generation. However, containers have limitations — for instance, persistent data needs careful planning, as all data within a container is lost when it's destroyed. In this article, we’ll look at how to solve this issue using Docker’s native solution called Docker Volumes, which allows the creation of persistent Docker container storage. What Happens to Data Written Inside a Container To begin, let’s open a shell inside a container using the following command: docker run -it --rm busybox Now let’s try writing some data to the container: echo "Hostman" > /tmp/data cat /tmp/data Hostman We can see that the data is written, but where exactly? If you're familiar with Docker, you might know that images are structured like onions — layers stacked on top of each other, with the final layer finalizing the image. Each layer can only be written once and becomes read-only afterward. When a container is created, Docker adds another layer for handling write operations. Since container lifespans are limited, all data disappears once the container is gone. This can be a serious problem if the container holds valuable information. To solve this, Docker provides a solution called Docker Volumes. Let’s look at what it is and how it works. Docker Volumes Docker Volumes provide developers with persistent storage for containers. This tool decouples data from the container’s lifecycle, allowing access to container data at any time. As a result, data written inside containers remains available even after the container is destroyed, and it can be reused by other containers. This is a useful solution for sharing data between Docker containers and also enables new containers to connect to the existing storage. How Docker Volumes Work A directory is created on the server and then mounted into one or more containers. This directory is independent because it is not included in the Docker image layer structure, which allows it to bypass the read-only restriction of the image layers for containers that include such a directory. To create a volume, use the following command: docker volume create Now, let’s check its location using: docker volume inspect volume_name The volume name usually consists of a long alphanumeric string. In response, Docker will display information such as the time the volume was created and other metadata, including the Mountpoint. This line shows the path to the volume. To view the data stored in the volume, simply open the specified directory. There are also other ways to create a Docker Volume. For example, the -v option can be added directly during container startup, allowing you to create a volume on the fly: docker run -it --rm -v newdata:/data busybox Let’s break down what’s happening here: The -v argument follows a specific syntax, indicated by the colon right after the volume name (in this case, we chose a very creative name, newdata). After the colon, the mount path inside the container is specified. Now, you can write data to this path, for example: echo "Cloud" > /data/cloud Data written this way can easily be found at the mount path. As seen in the example above, the volume name is not arbitrary — it matches the name we provided using -v. However, Docker Volumes also allow for randomly generated names, which are always unique to each host. If you’re assigning names manually, make sure they are also unique. Now, run the command: docker volume ls If the volume appears in the list, it means any number of other containers can use it. To test this, you can run: docker run -it --rm -v newdata:/data busybox Then write something to the volume. Next, start another container using the exact same command and you’ll see that the data is still there and accessible — meaning it can be reused. Docker Volumes in Practice Now let’s take a look at how Docker Volumes can be used in practice. Suppose we're developing an application to collect specific types of data — let’s say football statistics. We gather this data and plan to use it later for analysis — for example, to assess players’ transfer market values or for betting predictions. Let’s call our application FootballStats. Preserving Data After Container Removal Obviously, if we don’t use Docker Volumes, all the collected statistics will simply be lost as soon as the container that stored them is destroyed. Therefore, we need to store the data in volumes so it can be reused later. To do this, we use the familiar -v option:  -v footballstats:/dir/footballstats This will allow us to store match statistics in the /dir/footballstats directory, on top of all container layers. Sharing Data Suppose the FootballStats container has already gathered a certain amount of data, and now it's time to analyze it. For instance, we might want to find out how a particular team performed in the latest national championship or how a specific player did — goals, assists, cards, etc. To do this, we can mount our volume into a new container, which we’ll call FootballStats-Analytics. The key advantage of this setup is that the new container can read the data without interfering with the original FootballStats container’s ongoing data collection. At the same time, analysis of the incoming data can be performed using defined parameters and algorithms. This information can be stored anywhere, either in the existing volume or a new one, if needed. Other Types of Mounts In addition to standard volumes, Docker Volumes also supports other types of mounts designed to solve specialized tasks: Bind Mount Bind mounts are used to attach an existing path on the host to a container. This is useful for including configuration files, datasets, or static assets from websites. To specify directories for mounting into the container, use the --mount option with the syntax <host path>:<container path>. Tmpfs Mount Tmpfs mounts serve the opposite purpose of regular Docker Volumes — they do not persist data after the container is destroyed. This can be useful for developers who perform extensive logging. In such cases, continuously writing temporary data to disk can significantly degrade system performance. The --tmpfs option creates temporary in-memory directories, avoiding constant access to the file system. Drivers Docker Volume Drivers are a powerful tool that enable flexible volume management. They allow you to specify various storage options, the most important being the storage location — which can be local or remote, even outside the physical or virtual infrastructure of the provider. This ensures that data can survive not only the destruction of the container but even the shutdown of the host itself. Conclusion So, we’ve learned how to create and manage storage using Docker Volumes. For more information on how to modify container storage in Docker, refer to the platform’s official documentation. 
09 June 2025 · 6 min to read

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