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What is Code Review and When Is It Needed?

What is Code Review and When Is It Needed?
Hostman Team
Technical writer
Infrastructure

You can write code. You can edit existing code. You can even rewrite it from scratch. There’s a lot you can do with code. But what’s the point if the code lives in its own echo chamber? If the same person writes, views, and edits it, many critical errors can drift from one version to another unnoticed without external evaluation. Code locked within the confines of a single text editor is highly likely to stagnate, accumulating inefficient constructs and architectural decisions, even if written by an experienced developer.

This is why every developer should understand what code review is, how it’s done, and what tools are needed. Presenting your code properly to others, gathering feedback, and making changes wisely is important. Only this way can code remain “fresh” and efficient, and applications based on it — secure and high-performing.

Code review is the process of examining code by one or more developers to identify errors, improve quality, and increase readability.

Types of Code Review

1. Formal Review

A formal review is a strict code-checking process with clearly defined stages. It’s used in critical projects where errors can have serious consequences — for example, in finance or healthcare applications. The analysis covers not just the code but also the architecture, performance, and security. Reviewers often include not just developers but also testers and analysts.

For example, a company developing a banking app might follow these steps:

  • Development: A developer completes a new authentication module and submits a pull request via GitHub.
  • Analysis: A review group (2 senior developers + 1 security specialist) is notified and checks the code for logic, readability, and security (e.g., resistance to SQL injection and XSS attacks).
  • Discussion: Reviewers meet the developer over Zoom and give feedback.
  • Documentation: All notes are posted in GitHub comments and tracked in Jira. For instance, some RESTful requests may be flagged as vulnerable with a recommendation to use parameterized queries.
  • Fixes: The developer updates the code and the pull request; the cycle repeats until approval.
  • Approval: Once reviewers are satisfied, the code is merged into the main branch.

2. Informal Review

Informal code review is less strict and more flexible, usually involving:

  • Quick code discussions in chat or meetings
  • Showing code to a colleague in person
  • Asking an expert a technical question

This kind of review happens often in day-to-day work and is characterized by spontaneity, lack of documentation, informal reviewer choice, and shallow checks.

In simpler terms, it’s more like seeking advice than a formal third-party audit. It's a form of knowledge sharing.

Types include:

  • Over-the-Shoulder Review: One developer shows their code to another in real time (via screen share, chat message, or simply turning the monitor).
  • Ad-hoc Review: A developer sends code to a colleague asking them to check it when convenient, e.g., I wrote this handler, but there’s an error. Can you take a look?
  • Unstructured Team Review: Code is discussed at a team meeting, casually and collaboratively, often with knowledge sharing.

Feedback is given as recommendations, not mandates. Developers can ignore or reject suggestions.

Although informal reviews are less reliable than formal ones, they’re quicker and easier, and often complement formal reviews.

Examples of integration:

  • Preliminary Checks: Before a pull request, a dev shows code to a colleague to discuss and fix issues.
  • Informal Discussion During Formal Review: Reviewers may chat to resolve issues more efficiently.
  • Quick Fixes: Developers make changes right after oral feedback instead of long comment exchanges.

3. Pair Programming

Pair programming is when two developers work together on one machine: one writes code, and the other reviews it in real-time.

It’s literally simultaneous coding and reviewing, which helps catch bugs early.

Roles:

  • Driver: Writes code, focused on syntax and implementation.
  • Navigator: Reviews logic, looks for bugs, suggests improvements, and thinks ahead.

Roles can be switched regularly to keep both engaged.

Variants:

  • Strong Style: Navigator makes decisions, and the driver just types. It works well if one of the developers is more experienced.
  • Loose Pairing: Both share decision-making, swapping roles as needed.

Though rare, pair programming has advantages:

  • Instant Feedback: Bugs are fixed immediately.
  • In-depth Review: The second dev is deeply involved in writing the code.
  • On-the-job Learning: Juniors learn directly from experienced peers.

It’s more of a collaborative development method than a strict review.

4. Automated Review

Automated code review uses tools that analyze code for errors, style, and vulnerabilities without human intervention.

These tools are triggered automatically (e.g., after compilation, commit, or pull request).

They analyze, run tests (e.g., unit tests), and generate reports. Some tools can even auto-merge code if it passes checks.

Automated code review is part of DevOps and is common in CI/CD pipelines before deploying to production.

Types:

  • Static Analysis: Checks code without executing it — syntax errors, bad patterns, etc.
  • Dynamic Analysis: Runs code to detect memory leaks, threading issues, and runtime errors.

However, for now, tools can't catch business logic or architectural issues. As AI evolves, tools will likely become better at "understanding" code.

When is Code Review Needed?

Ideally, you should conduct code reviews both in small and large-scale projects.

The only exceptions might be personal side-projects (pet projects), although even these can benefit from outside input.

Automated testing has become standard, from JavaScript websites to C++ libraries.

Still, code review can be skipped for:

  • Trivial changes (e.g., formatting, UI text updates)
  • Peripheral code (e.g., throwaway scripts, config files)
  • Auto-generated code — unless manually modified

In short, review the code only if it plays a critical or central role in the app and a human wrote it.

Main Stages of Conducting Code Review

Regardless of whether a review is formal, informal, or automated, there are several common stages.

Preparation for Review

Whether the written code is a new component for a production application or a modification of an existing method in a personal project, the developer is usually motivated to have it reviewed, either by fellow developers or by using automated testing tools.

Accordingly, the developer has goals for the review and a rough plan for how it should be conducted, at least in broad terms.

It’s important to understand who will participate in the review and whether they have the necessary competencies and authority. In the case of automated testing, it’s crucial to choose the right tools.

Otherwise, the goals of the review may not be achieved, and critical bugs might remain in the code.

Time constraints also matter: when all reviewers and testing tools will be ready to analyze the code, and how long it will take. It’s best to coordinate this in advance.

Before starting the actual review, it can also be helpful to self-review—go over the code yourself and try to spot any flaws. There might be problems that can be fixed immediately.

Once the developer is ready for the review, they notify the reviewers via chat, pull request, or just verbally.

Code Analysis and Error Detection

Reviewers study the code over a period of time. During this process, they prepare feedback in various formats: suggested fixes in an IDE, chat comments, verbal feedback, or testing reports.

The format of the feedback depends on the tools used by the development team, which vary from project to project.

Discussion of Edits and Recommendations

Reviewers and the developer conduct a detailed discussion of the reviewed codebase.

The goal is to improve the code while maintaining a productive dialogue. For instance, the developer might justify certain controversial decisions and avoid making some changes. Reviewers might also suggest non-obvious improvements that the developer hadn't considered.

Documentation and Task Preparation

All identified issues should be clearly documented and marked. Based on this, a list of tasks for corrections is prepared. Kanban boards or task managers are often used for this, e.g., Jira, Trello, and GitHub Issues.

Again, the documentation format depends on the tools used by the team.

Even a solo developer working on a personal project might write tasks down in a physical notebook—or, of course, in a digital one. Though keeping tasks in your head is also possible, it’s not recommended.

Nowadays, explicit tracking is better than implicit assumptions. Relying on memory and intuition can lead to mistakes.

Applying Fixes and Final Approval

Once the list of corrections is compiled, the developer can begin making changes. They often also leave responses to comments.

Bringing code to an acceptable state may take several review rounds. The process is repeated until both reviewers and the developer are satisfied.

It’s crucial to ensure the code is fully functional and meets the team’s quality standards.

After that, the final version of the code is merged into the main branch—assuming a version control system is being used.

Tools for Code Review

In most cases, code review is done using software tools. Broadly speaking, they fall into several categories:

  • Version control systems: Most cloud platforms using version control systems (typically Git) offer built-in review tools for viewing, editing, and commenting on code snippets.
  • Collaboration tools: Development teams often use not just messengers but also task managers or Kanban boards. These help with discussing code, assigning tasks, and sharing knowledge.
  • Automated analyzers: Each programming language has tools for static code analysis to catch syntax issues, enforce style rules, and identify potential vulnerabilities.
  • Automated tests: Once statically checked, the code is run through automated tests, usually via language-specific unit testing libraries.

This article only covers the most basic tools that have become standard regardless of domain or programming language.

GitHub / GitLab / Bitbucket

GitHub, GitLab, and Bitbucket are cloud-based platforms for collaborative code hosting based on Git.

Each offers tools for convenient code review. On GitHub and Bitbucket, this is called a Pull Request, while on GitLab it’s a Merge Request.

Process:

  1. The developer creates a Pull/Merge Request documenting code changes, reviewer comments, and commit history.
  2. Reviewers leave inline comments and general feedback.
  3. After discussion, reviewers either approve the changes or request revisions.

Each platform also provides CI/CD tools for running automated tests:

  • GitHub Actions
  • GitLab CI/CD
  • Bitbucket Pipelines

These platforms are considered the main tools for code reviews. The choice depends on team preferences. The toolas are generally similar but differ in details.

Crucible

Atlassian Crucible is a specialized tool dedicated solely to code review. It supports various version control systems: Git, SVN, Mercurial, Perforce.

Crucible suits teams needing a more formalized review process, with detailed reports and customizable settings. It integrates tightly with Jira for project management.

Unlike GitHub/GitLab/Bitbucket, Crucible is a self-hosted solution. It runs on company servers or private clouds.

Pros and cons:

Platform

Deployment

Managed by

Maintenance Complexity

GitHub / GitLab / Bitbucket

Cloud

Developer

Low

Atlassian Crucible

On-premise

End user/admin

High

Crucible demands more setup but allows organizations to enforce internal security and data policies.

Other Tools

Each programming language has its own specialized tools for runtime and static code analysis:

  • C/C++: Valgrind for memory debugging
  • Java: JProfiler, YourKit for profiling; Checkstyle, PMD for syntax checking
  • Python: PyInstrument for performance; Pylint, Flake8 for quality analysis

These tools often integrate into CI/CD pipelines run by systems like GitHub Actions, GitLab CI, CircleCI, Jenkins.

Thus, formal code review tools are best used within a unified CI/CD pipeline to automatically test and build code into a final product.

Best Practices and Tips for Code Review

1. Make atomic changes

Smaller changes are easier and faster to review. It’s better to submit multiple focused reviews than one large, unfocused one.

This aligns with the “Single Responsibility Principle” in SOLID. Each review should target a specific function so reviewers can focus deeply on one area.

2. Automate everything you can

Automation reduces human error. Static analyzers, linters, and unit tests catch issues faster and more reliably.

Automation also lowers developers’ cognitive load and allows them to focus on more complex coding tasks.

3. Review code, not the developer

Code reviews are about the code, not the person writing it. Criticism should target the work, not the author. Maintain professionalism and use constructive language.

A good review motivates and strengthens teamwork. A bad one causes stress and conflict.

4. Focus on architecture and logic

Beautiful code can still have flawed logic. Poor architecture makes maintenance and scaling difficult.

Pay attention to structure—an elegant algorithm means little in a badly designed system.

5. Use checklists for code reviews

Checklists help guide your review and ensure consistency. A basic checklist might include:

  • Is the code readable?
  • Is it maintainable?
  • Is there duplication?
  • Is it covered by tests?
  • Does it align with architectural principles?

You can create custom code review checklists for specific projects or teams.

6. Discuss complex changes in person

Sometimes it’s better to talk in person (or via call) than exchange messages—especially when dealing with broad architectural concerns.

For specific code lines, written comments might be more effective due to the ability to reference exact snippets.

7. Code should be self-explanatory

Good code speaks for itself. The simpler it is, the fewer bugs it tends to have.

When preparing code for review, remember that other developers will read it. The clarity of the code affects the quality of the review.

Put yourself in the reviewers’ shoes and ensure your decisions are easy to understand.

Conclusion

Code review is a set of practices to ensure code quality through analysis and subsequent revisions. It starts with syntax and architecture checks and ends with performance and security testing.

Reviews can be manual, automated, or both. Typically, new code undergoes automated tests first, then manual review—or the reverse.

If everything is in order, the code goes into production. If not, changes are requested, code is updated, and the process is repeated until the desired quality is achieved.

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Infrastructure

Top 11 AI Video Generation Tools: Review and Feature Comparison

After OpenAI's successful debut in 2022, AI tools rapidly entered everyday life.  When we talk about text generation, ChatGPT comes to mind. When it comes to image generation, we think of Midjourney. Then there are Gemini, DALL-E, Claude, Stable Diffusion, and many other leading models in the field. But what comes to mind when it comes to video generation? Or at least, what should come to mind? That’s exactly what we’ll discuss in this article. 1. Kling Kling is a Chinese AI video generation tool developed by Kuaishou in 2024. It is one of the best video generation AI tools on the market, ideal for marketers, bloggers, and large teams who need to produce high-quality videos quickly. Kling's standout feature is its balanced blend of cinematic aesthetics and flexible settings—you can get hyper-realistic or stylized clips. The model processes both text prompts and static images, turning them into dynamic, high-quality videos—up to 10 seconds long, FullHD resolution (1080p), and 30 FPS. Naturally, the best features are available only on paid plans. The service supports complex camera behavior for expressive angles: panning, tilting, and zooming. You can also set keyframes (start and end) to generate video in between them. There's also an "extension" function to prolong an already generated video up to 3 minutes. Additionally, the model supports lip-syncing—synchronizing mouth movement with speech. The interface is intuitive, though slightly overloaded. It’s easy to get the hang of but can occasionally be confusing.   Free Plan Paid Plans (from $3/month) Resolution up to 720p up to 1080p Duration up to 5 sec up to 10 sec Generations up to 6 per day from 18 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no up to 3 minutes Extra Features no yes Note: On the free plan, Kling allows about 10x more generations per month than the paid plan. However, those videos are shorter and lower quality. The free quota is added on top of the paid quota. 2. Hailuo AI Hailuo AI is a Chinese AI video generator developed by MiniMax in 2024. It offers a simple and flexible toolkit for creating content on the go, from marketing clips to social media stories. In just minutes, it can turn a text or static image into a high-quality, albeit short, video, significantly cutting down the time and resources needed for traditional video production. Hailuo AI focuses on quickly generating short videos (up to 6 seconds at 25 FPS) based on text descriptions or static images. The resolution maxes out at 720p. While these limitations are acceptable for fast marketing tasks, they can be a dealbreaker for serious projects. You can combine text and image inputs for more control over the video story. In addition to full camera control (angle, zoom, pan), Hailuo AI reduces random motion noise and maintains character appearance across scenes. The interface is both simple and flexible, allowing cinematic effects without a steep learning curve. It also offers an API for integration into external apps. Ideal for quick short-form videos like animated teasers and promo clips. For longer, more complex videos, you’ll need something else.   Free Plan Paid Plans (from $14/month) Resolution up to 720p up to 720p Duration up to 6 sec up to 6 sec Generations up to 90/month from 130/month Faster Generation no yes Watermarks yes no Upscaling no no Extension no up to 2 minutes Extra Features no yes Note: There’s also progressive pricing based on generation volume. From $1 for 70 credits, enough for a couple of generations. 3. Fliki Fliki is an American AI video generator created by Fliki in 2021. It’s an all-in-one platform combining various AI modules for generating presentations, audio, and video. Fliki specializes in automatically turning any text format (article, script, website URL, PDF/PPT) into a video with realistic voiceovers (2,000+ voices, 100+ dialects) and animated avatars (70+ characters). You can even clone your voice and dub videos in 80+ languages. Fliki also gives access to millions of stock images, video clips, stickers, and music for rapid video creation. Unlike services that render each frame from scratch, Fliki assembles clips, slideshows, presets, and transitions into a cohesive video. Final length can be up to 30 minutes. Runs in-browser with no downloads needed. Just enter your text, select a voice, add media, and you’ll get a professional video with voiceover and subtitles in minutes. Its broad feature set in a simple package makes it suitable for small teams and large enterprises alike. Paired with classic editing tools, Fliki’s potential is immense.   Free Plan Paid Plans (from $28/month) Resolution up to 720p up to 1080p Duration up to 5 min (8 sec scenes) up to 30 min (8 sec scenes) Generations up to 5 min/month from 180 min/month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Paid plans also unlock thousands of voices and dialects, millions of premium images, videos, sounds, and access to Fliki’s API. 4. Dream Machine Dream Machine is an American AI video generator created by Luma AI in 2024. It specializes in generating short videos from text prompts or static images, making it easy to produce dynamic clips with natural movement and cinematic composition—no editing expertise needed. Users can describe or show what they want, and Dream Machine generates fluid, natural videos. Default output is 5–10 seconds at 1080p and 24 FPS. You can adjust aspect ratio, animation style, motion intensity, and transition smoothness. Dream Machine supports keyframe-based generation (start and end image), has an intuitive minimalist interface, and offers an API for integration. It’s not suitable for long, complex videos. But for fast marketing and ad content, it’s a top pick.   Free Plan Paid Plans (from $9/month) Resolution up to 720p up to 1080p Duration up to 10 sec up to 10 sec Generations up to 30/month from 120/month Faster Generation no yes Watermarks yes no Upscaling no up to 4K Extension no up to 30 sec Extra Features no yes 5. Runway Runway is an American AI video platform developed by Runway AI in 2018. It's a full-fledged cloud platform for generating and storing high-quality cinematic media. Runway is both powerful and easy to use. It excels at quickly creating short clips, experimenting with visual styles, and automating parts of the creative process. It can generate videos with outstanding photorealism and character motion consistency. It's one of the most advanced commercial tools for video generation. You can create clips from text or images, restyle existing footage, or edit content. By default, videos are 720p, 24 FPS, and 5 or 10 seconds long. However, you can upscale to 4K and extend to 40 seconds. Runway offers several models: Gen-2, Gen-3 Alpha, Gen-3 Alpha Turbo, Gen-4. The latest (Gen-4) allows for deep control over generation: aspect ratio, camera behavior, style prompts, and more.   Free Plan Paid Plans (from $9/month) Resolution up to 720p up to 720p (4K upscale) Duration 5 or 10 sec 5 or 10 sec Generations up to 5/month from 25/month Faster Generation no yes Watermarks yes no Upscaling no up to 4K Extension no up to 20 sec Extra Features no yes Note: Paid plans include up to 100 GB of cloud storage, while free users get only 5 GB. 6. PixVerse PixVerse is a Chinese AI video generation model developed by AISphere in 2023. Thanks to a wide range of tools, PixVerse can transform text descriptions, images, and video clips into short but vivid videos — from anime and comics to 3D animation and hyperrealism. PixVerse wraps numerous generation parameters in an extremely user-friendly interface: source photos and videos, aspect ratio, camera movement, styling, transitions, sound effects, voiceover, and more. The output videos are 5 to 8 seconds long, with resolutions up to 1080p at 20 frames per second. Naturally, videos can be upscaled and extended. You can also upload an already finished video and additionally stylize it using the neural network — add visual effects, voiceover, or extend the duration. As expected in such a powerful service, an API is also available—any external app can perform automatic video generation. On the PixVerse homepage, you’ll find numerous examples of generated videos along with their original prompts. Anyone can use them as a base for their own projects or simply see the model’s capabilities in action.   Free Plan Paid Plans (from $10/month) Resolution up to 540p up to 720p Duration 5 or 8 seconds 5 or 8 seconds Generations up to 20 per month from 40 per month Faster Generation no yes Watermarks yes no Upscaling up to 4K up to 4K Extension no no Extra Features no yes 7. Genmo Genmo is another AI model for video, launched in 2022. In essence, Genmo is the simplest possible service for turning text descriptions into short video clips with minimal configuration options. As simple as you can imagine—which is both good and bad. On one hand, Genmo’s entry barrier is extremely low—even someone with no experience can create a video. On the other hand, the service is hardly suitable for complex projects due to the lack of control over generation. The neural network is based on the open-source Mochi model and has many limitations: it only uses text descriptions, and video resolution is capped at 480p with a fixed duration of 5 seconds at 30 fps. Although generated videos contain visual artifacts (flickering or shifting geometry and colors) that reveal the use of AI, they still look coherent and interesting — good enough for visualizing ideas and concepts. The user interface is extremely minimalistic—a prompt input field on the homepage followed by the best generations from the past day with their corresponding prompts. It's important to understand that AI models that don't use images or video as input require more specificity in prompts—clear descriptions of visuals, environments, and details.   Free Plan Paid Plans (from $10/month) Resolution up to 480p up to 480p Duration 5 seconds 5 seconds Generations up to 30 per month from 80 per month Faster Generation up to 2 per day from 8 per day Watermarks yes no Upscaling no no Extension no up to 12 seconds Extra Features no yes 8. Sora Sora is a neural network created by OpenAI in 2024. Based on detailed text descriptions, Sora can generate images and videos with the highest level of detail. It’s a model whose output can easily be mistaken for real photos or videos. It’s significant that Sora was developed by OpenAI, a global leader in generative AI and the company behind ChatGPT and DALL·E. Sora’s interface follows the design system used across OpenAI products—sleek black theme and minimal elements. A small sidebar is on the left, a grid of popular user-generated content in the center, and a prompt field with configuration options at the bottom. Sora-generated videos have photo-realistic detail, whether hyperrealistic or animated, almost nothing gives away the AI origin. The quality and imagination in the visuals are astounding. The videos can be up to 20 seconds long, 1080p resolution, and 30 fps—significantly more than most competitors. Sora unifies all video configuration into the prompt itself—the real power of the model lies in the quality of your description. The better the prompt, the better the result. Thus, generating video with Sora becomes a constant game of tweaking prompts, words, and phrasing. Sora can definitely be considered one of the most advanced AI models for generating images and video.   Free Plan Paid Plans (from $20/month) Resolution – up to 1080p Duration – up to 20 seconds Generations – from 50 per month Faster Generation – yes Watermarks – no Upscaling – no Extension – no Extra Features – yes The free plan in Sora does not allow video generation at all—only image generation, limited to 3 per day. 9. Pika Pika is another AI-powered video creation service, launched in 2023. The platform is easy to use and designed for everyday users who are not experts in video editing or neural networks. Its primary use case is modifying existing video footage: adding transitions, virtual characters, changing a person’s appearance, and more. Still, Pika can also generate videos from scratch. Pika’s features are standard for AI video services: generation from text, from images, or between two frames (start and end). Maximum resolution is 1080p. Frame rate is 24 fps. Video duration is up to 10 seconds. Styles can vary—from cartoony to cinematic. In short, Pika is a simple and convenient tool for quickly creating videos from text or images without powerful hardware. It’s especially useful for prototyping, social media, marketing, and advertising.   Free Plan Paid Plans (from $10/month) Resolution up to 1080p up to 1080p Duration up to 10 seconds up to 10 seconds Generations up to 16 per month from 70 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Pika’s free plan has generation limits—you can create videos, but in small quantities. The standard paid plan increases your generation limits and unlocks newer model versions, but does not remove watermarks. The professional plan removes all limitations, provides access to advanced tools, speeds up generation, and removes watermarks from final videos. 10. Veo Veo is a video generation model developed in 2024 by DeepMind, a Google-owned company. There are several ways to access the model: Via Google Labs tools — VideoFX or VertexAI Through Google AI Studio Veo can be considered a full-fledged tool for creating high-quality, hyperrealistic clips indistinguishable from real footage. Of course, it also supports animation. Veo generates videos at 720p resolution, 24 fps, and up to 8 seconds long. In private developer previews, 1080p resolution and 4K upscaling are available—but not yet public. It accepts both text prompts and still images as input. For the latter, the neural network preserves the original composition and color palette. Most importantly, Veo supports various cinematic effects: time-lapse, panorama, slow-mo, and many more—with flexible parameter control. Veo ensures excellent consistency, stability, and smooth motion. Every video generated includes a SynthID digital watermark, invisible to the human eye or ear—a tool developed by Google to help detect AI-generated media. Thus, any image, video, or audio can be scanned using SynthID to verify AI generation. Veo also pays attention to small details—hair movement, fabric fluttering, atmospheric behavior, and more. As they say, the devil is in the details.   Free Plan Paid Plans Resolution up to 720p up to 720p Duration up to 8 seconds up to 8 seconds Generations up to 30 per month from 50 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Like most Google cloud services, Veo uses pay-as-you-go pricing—$0.50 per second or $30 per minute of generated video. So, a standard 10-second clip will cost $5—cheap for professionals, pricey for casual users. 11. Vidu Vidu is a Chinese AI model developed in 2024 by ShengShu AI in collaboration with Tsinghua University.  Vidu generates smooth, dynamic, and cohesive video clips, both realistic and animated. It can also add AI-generated audio tracks to videos. Vidu can accurately simulate the physical world, creating videos with developed characters, seamless transitions, and logical event chronology. The platform offers three main tools: generation from text, from images, and from videos. Additional tools include an AI voiceover generator and a collection of templates. Maximum video resolution is 1080p. Max duration is 8 seconds. Frame rate is up to 24 fps. The model is based on a "Universal Vision Transformer" (U-ViT) architecture, which processes text, image, and video inputs simultaneously to create coherent video sequences. This ensures object consistency throughout the video. For professionals and studios, Vidu is a powerful tool with great potential; for beginners, it’s an easy gateway into generative video.   Free Plan Paid Plans (from $8/month) Resolution up to 1080p up to 1080p Duration up to 8 seconds up to 8 seconds Generations up to 40 per month unlimited Faster Generation no yes Watermarks yes no Upscaling no no Extension no up to 16 seconds Extra Features no yes Which AI to choose? The vast majority of AI video generation services have similar video parameters: resolution from 720p to 1080p, durations of 5 to 10 seconds, and frame rates around 24 fps. Almost all can generate video based on text prompts, images, or video inputs. Differences in output results are usually minor—video styles and presence of visual artifacts revealing the AI.  The choice largely depends on your input and goals: text descriptions, images, or existing video. Some AI models offer higher detail than others. Always check the sample videos shown on service homepages. And keep in mind: video is a much more complex data format than text. Unlike LLMs, completely free AI video generation tools don’t exist as training the models and powering generation requires significant resources. That said, most services offer a low-tier paid plan that removes major limitations. Name Max Duration Max Resolution Max FPS Starting Price Kling 10 seconds 1080p 30 fps $3/month Hailuo AI 6 seconds 720p 25 fps $14/month Fliki 30 minutes 1080p 30 fps $28/month Dream Machine 10 seconds 1080p 24 fps $9/month Runway 10 seconds 720p 24 fps $15/month PixVerse 8 seconds 1080p 20 fps $10/month Genmo 5 seconds 480p 30 fps $10/month Sora 20 seconds 1080p 30 fps $20/month Pika 10 seconds 1080p 24 fps $10/month Veo 8 seconds 720p 24 fps $0.50/sec Vidu 8 seconds 1080p 24 fps $8/month
08 August 2025 · 15 min to read
Infrastructure

How Perplexity AI Works

In today's article, we will take a detailed look at the Perplexity AI neural network: we'll explore how it works, how to use it, how it differs from its main competitor ChatGPT, and what opportunities it offers for everyday use. What is Perplexity AI?  Perplexity AI is an artificial intelligence-based platform that combines the functionality of a chatbot and a search engine. The service's architecture is based on the use of large language models (LLMs). When developing Perplexity AI, the creators aimed to provide an alternative to traditional search engines that could help users find accurate and meaningful answers to complex and ambiguous questions. What Does Perplexity AI Do?  As previously mentioned, Perplexity is built on large language models. The supported models include Sonar, Claude 3.5 Sonnet, GPT-4.1, Gemini 1.5 Pro, Grok 3 Beta, and o1-mini. With access to multiple models, the neural network can generate accurate and comprehensive answers to user queries in real time. A key feature of Perplexity is its ability to analyze user queries while simultaneously gathering information from the internet in real time and generating responses with a list of all sources used. You can view sources not only for the entire generated text but also for individual sentences or even specific words. The Perplexity workflow includes: Query analysis: once the user submits a prompt (text request), the neural network analyzes its context and content using built-in language models. Data search: information is retrieved from the internet. The search includes not only articles and text-based data but also videos, social media posts, and user comments. Priority is given to authoritative sources. Response generation: the collected and processed information is compiled into a single response with citations and source links. Perplexity uses different data models to ensure the response is as accurate and reliable as possible. Additional functionality (if needed): in Copilot and Deep Research modes, the system refines queries further to deliver more accurate and relevant answers. Step-by-Step Guide: How to Use Perplexity AI  Let's explore how to use the neural network in practice. We'll start with the interface and its basic functions, then move on to using prompts to evaluate the results. Go to the official website of Perplexity AI. You will see the home page. By default, the interface will be in English. To view available interface languages or switch them, click on the language at the bottom of the page. The left-hand panel includes the following elements: New Thread button (plus icon) – allows you to start a new conversation or query. In Perplexity, a Thread is a separate message chain that is not connected to previous queries. Useful for asking about new topics. Home button – takes you back to the home page at any time. Discover – lets you view and customize a news blog with trending topics. Users can choose their interests and get fresh, relevant content. Spaces – used for creating and organizing workspaces to group conversations and uploaded files by topics or projects. The query interface includes: Search mode – the default mode where the AI analyzes the query and generates an answer in real time. Research mode – used for deep analysis and information gathering. It offers a more in-depth report with comprehensive source analysis. This mode takes a bit more time. Model selection – lets you choose one of eight supported AI models. In the free plan, only Auto mode is available, where Perplexity selects the best model based on the query. Source selection – you can choose from Web (all sources), Academic (scientific sources only), or Social (social media and informal sources). File attachments – Perplexity supports uploading files with your query. For example, you can upload a file with Python code to find errors. Supported formats include text files, PDFs, and images (JPEG, PNG). You can upload files from local devices, Google Drive, or Dropbox. Dictation mode – allows you to create queries via voice input. Submission is still manual. Voice mode – enables full voice interaction. You can dictate your query and receive voice responses. Unlike Dictation, Voice mode supports hands-free interaction. Using Text Prompts  Let's test how Perplexity AI handles user prompts.  We'll start with text-based queries and create several different prompts. The first one will test how the neural network handles a complex scientific topic. First prompt: I'm writing a scientific paper. Write a text on 'Differential Equations.' The text should cover basic first-order differential equations and partial differential equations. The style should be academic. As shown in the screenshot, the AI began by explaining what differential equations are. Then, following the prompt structure, it provided a breakdown of first-order and partial differential equations, complete with equations. Perplexity provides a list of sources used, which are shown in the Sources tab.  If the query includes a practical task (e.g., solving a math problem, writing a program), the AI uses technical sources and lists them in the Tasks section. The text is accompanied by numbered source links. Clicking a number opens the relevant page. On the right, a context menu appears, breaking down the highlighted text and showing each part's source.  You can reuse the AI's response to create a new query. Select a paragraph, sentence, or word, and click Add to follow-up. The selected fragment will be added to the new prompt field. Second prompt: What is a passive source? Give real-world examples and advice for beginners. This prompt tests how the AI provides practical advice.  As per the prompt, the AI also generated a block of beginner tips. As shown in the screenshots, Perplexity provided detailed examples and actionable advice, completing the task effectively. Using Files in Queries Next, we'll test file handling. We create a text file with Python code containing an intentional error (printed instead of print): print("\nNumbers from 1 to 5:") for i in range(1, 6):   printed(i, end=" ") We save the file as .txt (other extensions like .py or .js aren't supported due to security policies). Now we ask the AI to find and fix the error.  Image Search  Perplexity AI can both generate and search for images online using text prompts. Let’s search for an image online.  Prompt: Find an image of rainy London. There should be a telephone booth in the foreground and Big Ben in the background. As shown in the screenshot, the AI found a bunch of relevant images. To view more results, go to the Images tab. Comparing Perplexity AI vs ChatGPT  Perplexity AI's main competitor is ChatGPT. Below is a comparison table of their key features: Feature Perplexity AI ChatGPT Primary Purpose General-purpose tool for various tasks. Suitable for text creation, math problems, academic and educational content. Same as Perplexity: versatile use including text generation, coding, etc. Built-in Modes Search, Research Search, Reason, Deep Research Free Access Yes, but limited: auto model selection only; max 3 file uploads/day Yes, with limits: restricted use of GPT-4o, o4-mini, and deep research mode Paid Plans One plan: Pro at $20/month Four plans: Plus ($20/mo), Pro ($200/mo), Team ($25/mo billed annually), Enterprise (custom pricing) Mobile App Yes (iOS and Android) Yes (iOS and Android) Desktop App Yes (Windows and macOS) Yes (Windows and macOS) Hidden Features of Perplexity AI  Although it may appear similar to competitors, Perplexity has unique features that enhance the user experience: Financial Data Analysis: built-in tools for viewing stock quotes and financial reports, with data from Financial Modeling Prep. YouTube Video Summaries: the AI can summarize videos, regardless of language. Focus Mode: restricts search to academic papers or specific websites for faster, more targeted results. Advantages  Key strengths of Perplexity AI include: Real-time data sourcing for up-to-date answers. Convenient source tracking and citation. File upload support in queries. Built-in financial data analysis tools. Two work modes: Search and Research. The Research mode provides deeper, more detailed answers. Integrated voice assistant for prompts and conversations. Image generation and image search features. Built-in YouTube video summarization. Disadvantages  Like any neural network, Perplexity AI has its drawbacks: Free plan limitations. Prompt-dependent accuracy: for complex scientific/technical topics, even with many sources, it can sometimes give inaccurate responses. Conclusion  In this review, we examined Perplexity AI—a powerful tool built on large language models. It is well-suited for a wide range of tasks and stands out due to its advanced source-handling features and personalized approach.
07 August 2025 · 8 min to read
Infrastructure

How to Host an ARK Server: Detailed Guide

Have you ever wondered why ARK: Survival Evolved is so popular among the masses? The question is simple - the world is massive, the challenges are tough, and teaming up with friends brings it all to life. But what if you're tired of lag, trolls, or losing progress on shared public servers? This is why you need to host your own ARK dedicated server. In this tutorial, you'll learn how to host an ARK server on your own machine or through a cloud provider like Hostman. We'll walk through everything you need—from setup to launch. Ark: Survival Evolved servers list Key Takeaways A dedicated ARK server gives you full control over settings, players, mods, and performance. You can host your own ARK server on Windows or Linux using SteamCMD. ARK servers require solid hardware—at least 16 GB RAM and a fast CPU are recommended. Hosting through cloud providers like Hostman can simplify setup and improve uptime. With the right configuration, your ARK server can run smoothly, even with custom maps and mods. What Is an ARK Dedicated Server? An ARK dedicated server is a private environment where your game world runs independently of the default multiplayer options. Instead of relying on Studio Wildcard’s shared servers, you run your own—from your PC or a remote cloud instance. That means you can control the experience all by yourself, only limited by your own morals. You can host PvE or PvP sessions, manage mods, control who joins, and set your own rules. For groups that want a consistent, customizable experience, it’s the best way to play. Benefits of Playing ARK: Survival Evolved Online While ARK is a good game for solo leveling experience, the game truly shines in multiplayer. Building a base, taming dinosaurs, and exploring tropical maps becomes even more rewarding with others. Here’s why: Shared workload: More fun when you play with your friends. Faster progression: Gather resources, craft tools, and level up faster in cooperation with friends. Social connection: It's more fun to build, battle, and explore. PvP opportunities: Challenge others in combat or form alliances. Knowledge sharing: Let the new players learn quickly from experienced survivors. Multiplayer isn't just about more people—it's about better gameplay, deeper strategy, and shared stories that keep you coming back. Why Do You Need an ARK Dedicated Server? It’s not always useful to rely on public servers since they can lead to a traumatizing experience in ARK: Survival Evolved. A lot of external factors can ruin your game session, among them—lags, cheaters, trolls, server wipes. If you choose to run your own ARK server you become more worry-free of these problems if you have a desire to play peacefully. You can set your own rules, choose who gets access, and configure performance settings based on your needs. Want to tame dinos faster? Adjust the multipliers. Prefer PvE over PvP? You decide. Plus, your game world will stay active even when you’re not logged in. Hosting your own server (especially with Hostman’s VPS Server) helps you to stop relying on your own (or someone elses) hardware, which is often the case in peer-hosted multiplayer games. It can be frustrating when the host logs out or their machine crashes, meaning that session ends for everyone. A dedicated setup avoids this entirely. An example of how to setup your own rule on Ark dedicated server How To Setup an ARK Game Server: Detailed Guide Setting up an ARK dedicated server is pretty simple. Let’s show you some useful steps. Step 1: Prepare Your Server Before anything else, make sure your system meets the recommended ark server requirements: CPU: Quad-core, 3.5 GHz or higher RAM: At least 16 GB (more if using mods) Storage: 50 GB SSD minimum Upload Bandwidth: 20 Mbps+ You can use Windows or Linux, but many prefer Linux for its stability and lower resource consumption. Make sure your OS is 64-bit and kept up to date. Step 2: Install SteamCMD SteamCMD is the command-line tool used to install game server files. To install SteamCMD on Linux: sudo apt update && sudo apt install steamcmd On Windows: Download SteamCMD from the official site. Extract it to C:\steamcmd. Run steamcmd.exe. Step 3: Install ARK Server Files Create a folder for the ARK server installation: mkdir ark-server && cd ark-server Launch SteamCMD and download the ARK server using the anonymous login: steamcmd +login anonymous +force_install_dir ./ark-server +app_update 376030 validate +quit Use 2430930 if you want to install ARK: Survival Ascended instead of the classic version. Step 4: Configure the Server Once installed, navigate to: cd ark-server/ShooterGame/Saved/Config/LinuxServer/ Or on Windows: C:\ark-server\ShooterGame\Saved\Config\WindowsServer\ Create and edit these two files: GameUserSettings.ini Game.ini Basic settings to add: [ServerSettings] ServerAdminPassword=YourAdminPass ServerPassword=OptionalPlayerPass SessionName=HostmanARKServer Add any gameplay tweaks here as needed (e.g., dino taming speed, resource multipliers). Step 5: Open Required Ports Make sure your firewall and hosting provider allow traffic through these ports: UDP 7777 – Game port UDP 27015 – Query port UDP 27020 – RCON port (optional) On Linux: sudo ufw allow 7777/udp sudo ufw allow 27015/udp sudo ufw allow 27020/udp Step 6: Start the ARK Server You can launch the server using a startup script.  For Linux: ./ShooterGame/Binaries/Linux/ShooterGameServer TheIsland?SessionName=HostmanARKServer?ServerPassword=MyPass?ServerAdminPassword=MyAdminPass?listen -server -log For Windows, create a batch file like this: start ShooterGameServer.exe TheIsland?SessionName=HostmanARKServer?ServerPassword=MyPass?ServerAdminPassword=MyAdminPass?QueryPort=27015?Port=7777?listen Double-click the .bat file to start your server. Step 7: Join Your ARK Server To connect: Launch ARK: Survival Evolved Click Join ARK Set filter to Unofficial Search your server name or IP address If your server doesn’t appear, go to Steam → View → Servers → Favorites and manually add: your.server.ip:27015 Now you’re ready to enter your world! Conclusion Running your own ark dedicated server gives you full control over your ARK: Survival Evolved experience. This is way better if you want to dedicate your precious time to your beloved game, where you want to build a small and warm community of players. If you're just getting started, consider launching your ARK server on a reliable cloud provider. A VPS from Hostman, for example, allows you to scale resources as your community grows, while enjoying a simplified deployment process and responsive support. FAQ What are the minimum ARK server requirements? For smooth gameplay, your server should have at least a quad-core CPU (3.5 GHz or higher), 16 GB of RAM, and an SSD with 50 GB or more of free space. A stable internet connection with at least 20 Mbps upload speed is also essential. Can I host an ARK server on my own PC? Yes, you can self-host an ARK server from your own machine, but this works best for small groups and limited sessions.  How many players can join my ARK dedicated server? The player limit depends on your hardware and internet bandwidth. With proper specs (16 GB RAM and above), you can comfortably support 10–30 players. Is hosting an ARK server free? You can host it for free on your own hardware, but you’ll cover electricity, bandwidth, and maintenance costs.
31 July 2025 · 6 min to read

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