Author Archives: demensdeum

Pixel Perfect: myth or reality in the era of declarativeness?

In the world of interfaces development, there is a common concept – “Pixel Perfect in the Lodge” . It implies the most accurate reproduction of the design machine to the smallest pixel. For a long time it was a gold standard, especially in the era of a classic web design. However, with the arrival of the declarative mile and the rapid growth of the variety of devices, the principle of “Pixel Perfect” is becoming more ephemeral. Let’s try to figure out why.

Imperial Wysiwyg vs. Declarative code: What is the difference?

Traditionally, many interfaces, especially desktop, were created using imperative approaches or Wysiwyg (What You See is What You Get) of editors. In such tools, the designer or developer directly manipulates with elements, placing them on canvas with an accuracy to the pixel. It is similar to working with a graphic editor – you see how your element looks, and you can definitely position it. In this case, the achievement of “Pixel Perfect” was a very real goal.

However, modern development is increasingly based on declarative miles . This means that you do not tell the computer to “put this button here”, but describe what you want to get. For example, instead of indicating the specific coordinates of the element, you describe its properties: “This button should be red, have 16px indentations from all sides and be in the center of the container.” Freimvorki like React, Vue, Swiftui or Jetpack Compose just use this principle.

Why “Pixel Perfect” does not work with a declarative mile for many devices

Imagine that you create an application that should look equally good on the iPhone 15 Pro Max, Samsung Galaxy Fold, iPad Pro and a 4K resolution. Each of these devices has different screen resolution, pixel density, parties and physical sizes.

When you use the declarative approach, the system itself decides how to display your described interface on a particular device, taking into account all its parameters. You set the rules and dependencies, not harsh coordinates.

* Adaptability and responsiveness: The main goal of the declarative miles is to create adaptive and responsive interfaces . This means that your interface should automatically adapt to the size and orientation of the screen without breaking and maintaining readability. If we sought to “Pixel Perfect” on each device, we would have to create countless options for the same interface, which will completely level the advantages of the declarative approach.
* Pixel density (DPI/PPI): The devices have different pixel density. The same element with the size of 100 “virtual” pixels on a device with high density will look much smaller than on a low -density device, if you do not take into account the scaling. Declarative frameworks are abstracted by physical pixels, working with logical units.
* Dynamic content: Content in modern applications is often dynamic – its volume and structure may vary. If we tattered hard to the pixels, any change in text or image would lead to the “collapse” of the layout.
* Various platforms: In addition to the variety of devices, there are different operating systems (iOS, Android, Web, Desktop). Each platform has its own design, standard controls and fonts. An attempt to make an absolutely identical, Pixel Perfect interface on all platforms would lead to an unnatural type and poor user experience.

The old approaches did not go away, but evolved

It is important to understand that the approach to interfaces is not a binary choice between “imperative” and “declarative”. Historically, for each platform there were its own tools and approaches to the creation of interfaces.

* Native interface files: for iOS it were XIB/Storyboards, for Android-XML marking files. These files are a Pixel-PERFECT WYSIWYG layout, which is then displayed in the radio as in the editor. This approach has not disappeared anywhere, it continues to develop, integrating with modern declarative frames. For example, Swiftui in Apple and Jetpack Compose in Android set off on the path of a purely declarative code, but at the same time retained the opportunity to use a classic layout.
* hybrid solutions: Often in real projects, a combination of approaches is used. For example, the basic structure of the application can be implemented declaratively, and for specific, requiring accurate positioning of elements, lower -level, imperative methods can be used or native components developed taking into account the specifics of the platform.

from monolith to adaptability: how the evolution of devices formed a declarative mile

The world of digital interfaces has undergone tremendous changes over the past decades. From stationary computers with fixed permits, we came to the era of exponential growth of the variety of user devices . Today, our applications should work equally well on:

* smartphones of all form factors and screen sizes.
* tablets with their unique orientation modes and a separated screen.
* laptops and desktops with various permits of monitors.
* TVs and media centers , controlled remotely. It is noteworthy that even for TVs, the remarks of which can be simple as Apple TV Remote with a minimum of buttons, or vice versa, overloaded with many functions, modern requirements for interfaces are such that the code should not require specific adaptation for these input features. The interface should work “as if by itself”, without an additional description of what “how” to interact with a specific remote control.
* smart watches and wearable devices with minimalistic screens.
* Virtual reality helmets (VR) , requiring a completely new approach to a spatial interface.
* Augmented reality devices (AR) , applying information on the real world.
* automobile information and entertainment systems .
* And even household appliances : from refrigerators with sensory screens and washing machines with interactive displays to smart ovens and systems of the Smart House.

Each of these devices has its own unique features: physical dimensions, parties ratio, pixel density, input methods (touch screen, mouse, controllers, gestures, vocal commands) and, importantly, the subtleties of the user environment . For example, a VR shlesh requires deep immersion, and a smartphone-fast and intuitive work on the go, while the refrigerator interface should be as simple and large for quick navigation.

Classic approach: The burden of supporting individual interfaces

In the era of the dominance of desktops and the first mobile devices, the usual business was the creation and support of of individual interface files or even a completely separate interface code for each platform .

* Development under iOS often required the use of Storyboards or XIB files in XCode, writing code on Objective-C or SWIFT.
* For Android the XML marking files and the code on Java or Kotlin were created.
* Web interfaces turned on HTML/CSS/JavaScript.
* For C ++ applications on various desktop platforms, their specific frameworks and tools were used:
* In Windows these were MFC (Microsoft Foundation Classes), Win32 API with manual drawing elements or using resource files for dialog windows and control elements.
* Cocoa (Objective-C/Swift) or the old Carbon API for direct control of the graphic interface were used in macos .
* In linux/unix-like systems , libraries like GTK+ or QT were often used, which provided their set of widgets and mechanisms for creating interfaces, often via XML-like marking files (for example, .ui files in Qt Designer) or direct software creation of elements.

This approach ensured maximum control over each platform, allowing you to take into account all its specific features and native elements. However, he had a huge drawback: duplication of efforts and tremendous costs for support . The slightest change in design or functionality required the introduction of a right to several, in fact, independent code bases. This turned into a real nightmare for developer teams, slowing down the output of new functions and increasing the likelihood of errors.

declarative miles: a single language for diversity

It was in response to this rapid complication that the declarative miles appeared as the dominant paradigm. Framws like react, vue, swiftui, jetpack compose and others are not just a new way of writing code, but a fundamental shift in thinking.

The main idea of the declarative approach : Instead of saying the system “how” to draw every element (imperative), we describe “what“ we want to see (declarative). We set the properties and condition of the interface, and the framework decides how to best display it on a particular device.

This became possible thanks to the following key advantages:

1. Abstraction from the details of the platform: declarative fraimvorki are specially designed to forget about low -level details of each platform. The developer describes the components and their relationships at a higher level of abstraction, using a single, transferred code.
2. Automatic adaptation and responsiveness: Freimvorki take responsibility for automatic scaling, changing the layout and adaptation of elements to different sizes of screens, pixel density and input methods. This is achieved through the use of flexible layout systems, such as Flexbox or Grid, and concepts similar to “logical pixels” or “DP”.
3. consistency of user experience: Despite the external differences, the declarative approach allows you to maintain a single logic of behavior and interaction throughout the family of devices. This simplifies the testing process and provides more predictable user experience.
4. Acceleration of development and cost reduction: with the same code capable of working on many platforms, significantly is reduced by the time and cost of development and support . Teams can focus on functionality and design, and not on repeated rewriting the same interface.
5. readiness for the future: the ability to abstract from the specifics of current devices makes the declarative code more more resistant to the emergence of new types of devices and form factors . Freimvorki can be updated to support new technologies, and your already written code will receive this support is relatively seamless.

Conclusion

The declarative mile is not just a fashion trend, but the necessary evolutionary step caused by the rapid development of user devices, including the sphere of the Internet of things (IoT) and smart household appliances. It allows developers and designers to create complex, adaptive and uniform interfaces, without drowning in endless specific implementations for each platform. The transition from imperative control over each pixel to the declarative description of the desired state is a recognition that in the world of the future interfaces should be flexible, transferred and intuitive regardless of which screen they are displayed.

Programmers, designers and users need to learn how to live in this new world. The extra details of the Pixel Perfect, designed to a particular device or resolution, lead to unnecessary time costs for development and support. Moreover, such harsh layouts may simply not work on devices with non-standard interfaces, such as limited input TVs, VR and AR shifts, as well as other devices of the future, which we still do not even know about today. Flexibility and adaptability – these are the keys to the creation of successful interfaces in the modern world.

Why do programmers do nothing even with neural networks

Today, neural networks are used everywhere. Programmers use them to generate code, explain other solutions, automate routine tasks, and even create entire applications from scratch. It would seem that this should lead to an increase in efficiency, reducing errors and acceleration of development. But reality is much more prosaic: many still do not succeed. The neural networks do not solve key problems – they only illuminate the depth of ignorance.

full dependence on LLM instead of understanding

The main reason is that many developers are completely relying on LLM, ignoring the need for a deep understanding of the tools with which they work. Instead of studying documentation – a chat request. Instead of analyzing the reasons for the error – copying the decision. Instead of architectural solutions – the generation of components according to the description. All this can work at a superficial level, but as soon as a non -standard task arises, integration with a real project or the need for fine tuning is required, everything is crumbling.

Lack of context and outdated practices

The neural networks generate the code generalized. They do not take into account the specifics of your platform, version of libraries, environmental restrictions or architectural solutions of the project. What is generated often looks plausible, but has nothing to do with the real, supported code. Even simple recommendations may not work if they belong to the outdated version of the framework or use approaches that have long been recognized as ineffective or unsafe. Models do not understand the context – they rely on statistics. This means that errors and antipattterns, popular in open code, will be reproduced again and again.

redundancy, inefficiency and lack of profiling

The code generated AI is often redundant. It includes unnecessary dependencies, duplicates logic, adds abstractions unnecessarily. It turns out an ineffective, heavy structure that is difficult to support. This is especially acute in mobile development, where the size of the gang, response time and energy consumption are critical.

The neural network does not conduct profiling, does not take into account the restrictions of the CPU and GPU, does not care about the leaks of memory. It does not analyze how effective the code is in practice. Optimization is still handmade, requiring analysis and examination. Without it, the application becomes slow, unstable and resource -intensive, even if they look “right” from the point of view of structure.

Vulnerability and a threat to security

Do not forget about safety. There are already known cases when projects partially or fully created using LLM were successfully hacked. The reasons are typical: the use of unsafe functions, lack of verification of input data, errors in the logic of authorization, leakage through external dependencies. The neural network can generate a vulnerable code simply because it was found in open repositories. Without the participation of security specialists and a full -fledged revision, such errors easily become input points for attacks.

The law is pareto and the essence of the flaws

Pareto law works clearly with neural networks: 80% of the result is achieved due to 20% of effort. The model can generate a large amount of code, create the basis of the project, spread the structure, arrange types, connect modules. However, all this can be outdated, incompatible with current versions of libraries or frameworks, and require significant manual revision. Automation here works rather as a draft that needs to be checked, processed and adapted to specific realities of the project.

Caution optimism

Nevertheless, the future looks encouraging. Constant updating of training datasets, integration with current documentation, automated architecture checks, compliance with design and security patterns – all this can radically change the rules of the game. Perhaps in a few years we can really write the code faster, safer and more efficiently, relying on LLM as a real technical co -author. But for now – alas – a lot has to be checked, rewritten and modified manually.

Neural networks are a powerful tool. But in order for him to work for you, and not against you, you need a base, critical thinking and willingness to take control at any time.

Gingerita Prototype Windows

I present to your attention fork Kate text editor called Gingerita. Why Fork, why, what is the goal? I want to add the functionality that I need in my work, so as not to wait for the correction, adding features from the Kate team, or the adoption of my corrections to the Main branch.
At the moment, a prototype version for Windows is currently available, almost vanilla version of Kate with minimal changes. For Gingerita, I have developed two plugs – an image of the images directly from the editor and the built -in browser, for debugging my web projects or for interacting with AI with assistants such as ChatGPT.

The version for Windows can be tested by the link below:
https://github.com/demensdeum/Gingerita/releases/tag/prototype

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