Why Choose Group Hikes

Hiking with a group is not only safer but also more enjoyable. Shared experiences, conversations on the trail, rest stops, and group photos in nature bring people closer together, even if they barely knew each other before. Studies show that group activity in fresh air reduces stress and improves mood. Additionally, logistics become easier—responsibilities like carrying a first-aid kit or snacks can be shared—and there’s always someone to support you if you feel tired or if something unexpected happens.
For residents of Portugal, such hikes are becoming part of a cultural tradition. Many families go on Sunday trips to nature parks, while friends or coworkers often choose short suburban routes that can be completed in a day. Around cities like Lisbon, Porto, Cascais, and Setúbal, there are dozens of well-marked day trails for every fitness level.

Where to Find Day Hike Trails

One of Portugal’s main charms is its proximity to nature. Even if you live in central Lisbon, you can reach the trails of Sintra or the coastal area of Arrábida Natural Park within just 30–40 minutes. In the northern part of the country, the Peneda-Gerês National Park is popular—you can explore it in sections with short weekend hikes.
Platforms like AllTrails or Wikiloc can help you select a route by length, difficulty, elevation gain, and other hikers’ reviews. There are also Portuguese local websites and communities with recommendations, trail descriptions, and even organized hiking groups.
If you’re based near Porto, consider the eco-trail in Paços de Ferreira or a walk through the Sousa River valley. For those living further south, routes in Alentejo and along the Costa Vicentina are always an option.

What to Bring on a Weekend Hike

Although these hikes don’t require complicated preparation, some thought should still go into planning. Comfortable shoes are key. Even on flat ground, good-soled sneakers will prevent injury and fatigue. A light backpack with water (1–2 liters), snacks, a hat, and a raincoat is always a good idea, especially in spring or autumn when the weather can shift.
Don’t forget sunscreen—especially in Portugal, where even April sun can be intense. A small first-aid kit with plasters, antiseptic, and insect repellent is also worth taking. That way, you’re ready for any minor incidents on the trail.
If this is your first time, choose a trail under 10 kilometers with minimal elevation gain. That way, you’ll enjoy the hike without overexerting yourself.

How to Organize a Group Hike

There are many ways to gather a group: invite friends, colleagues, family, or join an organized group. In many Portuguese cities, hiking clubs like Clube de Montanhismo da Arrábida or Lisbon Hikers regularly plan outings of various difficulty levels.
If you’re organizing a hike yourself, create a checklist for participants: meeting time, what to bring, route, duration, and contact details. It’s ideal if at least one person in the group is familiar with the route or knows how to use GPS. And don’t forget the group photo at the end—it’ll be a great memory.

Why Weekend Hikes Are Great for Portugal’s Residents

Portugal combines diverse nature with short travel distances. In one day, you can walk a forest trail, reach the ocean, or climb to a viewpoint over vineyards. That makes the country almost ideal for fans of short nature escapes.
You don’t need much planning: pick a free day, select a route in advance, grab some company—and your mini-holiday is ready. These walks help you see familiar places differently and discover new ones just an hour away from home.
When extended vacations aren’t always possible, and the need to disconnect is growing, weekend hikes are a lifesaver. They cost nothing, take little time, but bring a powerful dose of energy and joy. The key is to start—and soon these mini-adventures can become your favorite part of the weekend.

The post Weekend Hikes: Exploring Nature in Groups Near the City appeared first on Foaas.

Robots in Factories: No Longer Science Fiction

Whereas industrial robots were once primarily associated with the automotive sector, today they are increasingly found in the food, pharmaceutical, textile, and even woodworking industries. Modern robots are not just mechanical arms repeating the same movement. They are equipped with sensors, machine learning algorithms, and connected to the Industrial Internet of Things (IIoT), enabling them to adapt to changing conditions and interact with other machines.
Hungary serves as a prime example, with major industrial zones in Győr, Kecskemét, and Miskolc actively adopting robotic systems, especially in the automotive and electronics sectors. This not only reduces costs but also allows companies to remain competitive in the global market.

Automation and Jobs: Rivals or Allies?

One of the most pressing questions surrounding the rise of robotization is its impact on the labor market. At first glance, automation seems to threaten jobs: why hire dozens of employees when one robot can do the same work around the clock without errors? However, the reality is far more nuanced.
In fact, automation doesn’t so much eliminate jobs as it transforms them. Routine and physically demanding tasks are indeed taken over by machines. But in their place arises a demand for skilled labor — engineers, CNC operators, programmers, specialists in maintaining and adapting robotic systems. For example, factories like Audi and Bosch in Hungary are significantly expanding their teams of IT specialists and technical engineers.

Education and Retraining: The Key to Adaptation

To stay relevant amid these changes, it is critically important to develop educational programs that meet new market demands. Hungarian universities and technical schools are increasingly including modules on industrial automation, robotic programming, data analysis, and digital modeling. Initiatives like the Digital Hungary program aim to accelerate digitalization and train future professionals for smart factories and automated production.
Companies are also taking part in this educational process. Large enterprises are setting up their own training centers in collaboration with the government and academic institutions. This allows the workforce to adapt more quickly to new realities, and the economy to maintain momentum.

Technologies Shaping the Future

Artificial Intelligence and Machine Learning

Automation is increasingly based not on rigid algorithms but on self-learning systems. Artificial intelligence helps analyze production processes, identify bottlenecks, and predict equipment failures before they occur. This reduces maintenance costs and increases equipment reliability.

Cognitive Robots

Unlike traditional industrial robots, cognitive robots can make decisions based on their surroundings. They are already being used on assembly lines where quick adaptation to diverse parts or non-standard situations is required.

Collaborative Robots (Cobots)

One direction of development is cobots — robots that work side by side with humans. Their main task is not to replace operators but to enhance their efficiency. In Hungary, such solutions are increasingly being adopted by small and medium-sized enterprises where a full transition to automated lines is not yet economically feasible.

Hungary’s Approach: Challenges and Opportunities

Despite the clear advantages, automation implementation in Hungary faces several challenges. These include a shortage of skilled technical personnel, limited investment capacity among small businesses, and the need to modernize existing infrastructure. Nonetheless, the Hungarian government is gradually increasing its support for digitalization and industrial automation through financing programs and tax incentives for companies investing in high-tech equipment.
Moreover, European programs such as Horizon Europe offer additional funding channels for Hungarian companies looking to integrate modern technological solutions. Coupled with growing private sector interest, this could drive development not only for individual companies but also for the entire industrial ecosystem of the country.

Conclusion: A Transformation to Be Ready For

Automation and robotization are not a threat but an evolution. They require flexibility, a willingness to learn, and investment — both from businesses and the state. Hungary’s industry has already set out on the path of digital transformation, and the speed and foresight with which the next steps are taken will determine the country’s competitiveness on the global stage. The key is to view robots not as competitors but as tools that can enhance human potential and free up resources for more creative and strategic tasks.

The post Robotization and Automation: The Future of Labor in Industry appeared first on Foaas.

ChatGPT and Copilot: How It Works in Practice

GitHub Copilot, built on OpenAI’s Codex model, has become a real breakthrough in code automation. It integrates into familiar development environments (such as Visual Studio Code) and suggests code snippets, entire functions, and even architectural solutions based on the lines being written. A developer starts writing a comment or part of a structure — Copilot picks it up and offers a logical continuation, often reflecting best practices.
ChatGPT works a bit differently. It doesn’t embed into the editor but is capable of interacting with the programmer in dialogue format. The user describes a task in natural language, and the model generates code, offers architectural solutions, and explains algorithms. ChatGPT is useful both at the design stage and during debugging or refactoring. It’s especially valuable for beginner developers or those dealing with unfamiliar technologies.
For instance, a web app developer working on an entertainment platform can ask ChatGPT to generate a Python backend with an authorization system. Or they may ask how to handle cryptocurrency transactions correctly — and receive not only a code example but also an explanation of the security risks involved. This approach reduces training time, lowers the chance of errors, and allows the developer to focus on the product logic rather than syntax.

The Impact of AI on the Developer’s Role

With the arrival of intelligent assistants, the profession of programmer has begun to shift. If previously the developer was associated solely with technical implementation — “writing code” — now their role increasingly involves setting tasks, testing hypotheses, and evaluating AI-suggested solutions.
Of course, machine intelligence cannot yet fully replace a human. It handles routine tasks well and can suggest elegant solutions, but still requires review, especially in non-standard situations. However, it is already clear that a developer proficient in AI tools becomes significantly more productive.
Moreover, the use of such tools is making development more accessible. For example, specialists in Portugal who lack formal training but want to enter the field — whether it’s for web apps or interactive systems for digital entertainment — now have the opportunity to create functional projects with minimal learning costs.

Business Potential: Accelerating Time to Market

For startups and companies, this means not only saving time and money but also gaining agility. The ability to quickly test an idea, build an MVP with minimal investment, and assess market response is a key factor in a competitive environment. This is especially relevant in high-stakes markets like mobile gaming, online betting, and real-money casino platforms, where time-to-market and implementation quality can make all the difference.
Using ChatGPT or Copilot not only speeds up the coding process but shifts the entire work model: from the traditional “order – develop – test” to a more flexible and interactive “task – co-creation – result” approach. This is especially important for remote teams, where communication is limited, and high autonomy is required from each team member.

Challenges and Limitations

Despite their enormous potential, AI technologies in software development are not without flaws. First, they are trained on massive datasets, including public GitHub repositories. This can lead to the generation of code that contains vulnerabilities or inefficient solutions — particularly if the developer blindly relies on the suggested fragments.
Second, there are still open questions about responsibility and authorship. Who is accountable for a bug in code generated by Copilot? Who is the true author of a unique solution if it was proposed by AI but adapted by the programmer? These questions still require regulatory clarity, especially in EU countries like Portugal, where increasing attention is paid to copyright protection and the ethics of using AI.

What’s Next?

AI has already become part of daily reality for thousands of developers. ChatGPT and Copilot don’t replace humans, but they significantly enhance their capabilities. Rather than fearing the “end of the profession,” developers are adapting — becoming curators and architects, where the key task is to ask the right questions and assess the solutions received.
In the coming years, AI tools are expected to integrate even more deeply into IDEs, DevOps systems, and QA processes. We may soon see platforms where entire codebases are generated through dialogue with AI — from architecture to final build. And for businesses operating in fast-moving digital niches — whether fintech, eCommerce, or entertainment portals with gamified elements — this will mean even greater speed, lower costs, and higher competitiveness.

Conclusion

Artificial intelligence in programming is no longer science fiction — it’s the new normal. ChatGPT and Copilot are changing the rules of the game, helping create code faster, more efficiently, and at lower cost. Their impact is felt not only in traditional IT but also in adjacent sectors of digital entertainment. It is important not just to use these tools but to understand how to build new approaches to digital product creation with their help.

The post Artificial Intelligence in Software Development: How ChatGPT and Copilot Are Changing the Coding Approach appeared first on Foaas.

Choosing the Right Place and Time

Before starting preparations, it’s important to evaluate which area in your yard would be suitable for screenings. It’s best to choose a spot protected from bright streetlights and strong winds. A flat surface like a wall, fence, or stretched fabric is ideal. If there are trees or bushes nearby, they can add a cozy ambiance and a natural frame to the setting.
As for timing, a warm, dry evening is best — starting at dusk when it’s dark enough for a clear picture but still warm enough to be comfortable.

Equipment: What You Need for an Outdoor Film Screening

Projector — the Heart of the Cinema

The key element is, of course, the projector. It’s best to choose models with at least 2000 lumens of brightness so the image remains sharp even with residual lighting. A resolution of at least Full HD is preferable, especially if you’re watching modern films or animated features.
Mini projectors are also an option — they’re compact, easily connect to smartphones, and often support wireless transmission. However, be aware that they may have limited brightness.

Screen: Wall, Sheet, or Special Fabric

If you have a smooth, light-colored wall, you’re already halfway there. Otherwise, you can use a stretched white sheet or purchase an affordable folding screen. It’s important that the surface is smooth and wrinkle-free — this affects the image quality. A good option is a roll-up projection screen that can be stored indoors when not in use.

Sound Is Just as Important as Picture

Using a projector’s built-in speakers isn’t ideal for outdoor settings. Instead, use external portable speakers or Bluetooth audio systems. If you’re inviting guests, it’s best to opt for a powerful speaker that can evenly distribute sound throughout the space.

Connecting and Accessing Movies

Most modern projectors support HDMI, USB, and wireless connections. You can connect a laptop, smartphone, tablet, or media player. Movies can be downloaded in advance or streamed from online platforms if there’s stable Wi-Fi in the viewing area.
Services like Netflix, HBO Max, VOYO, and Apple TV+ are especially popular in Slovakia. If you want to watch movies in Slovak or with subtitles, be sure to check these options in advance.

Comfort and Atmosphere: Details Matter

Creating a truly pleasant atmosphere can be done with a few simple touches. Set up soft chairs, add pillows, blankets, or even inflatable mattresses. Use string lights, lantern candles, or LED lamps to create cozy lighting that doesn’t interfere with the viewing experience.
Don’t forget insect repellent candles or sprays — warm Slovak evenings can be just as attractive to bugs as to movie lovers.
If you have a barbecue or grill, you can combine the movie night with an outdoor dinner. And to complete the cinema vibe, set up a snack table with popcorn, chips, lemonade, and other drinks.

Event Format: Just Family or Neighborhood Festival?

Depending on the scale, you can organize an intimate evening for your family or invite neighbors and friends. In the latter case, it’s wise to give advance notice to avoid misunderstandings. You can even print small “tickets” and display a film schedule — this will add a special charm to the event.
If you live in a private house in a small Slovak town or village, neighbors might want to join in. Such an evening can be a great opportunity for socializing and strengthening neighborhood ties.

Safety Tips

Don’t forget about electrical safety: if you’re using extension cords, make sure they’re high quality and avoid leaving equipment out in the rain or dew. After the show, it’s best to store the gear in a dry place.
It’s also important to keep the volume at a reasonable level so as not to disturb others. In some cases, it may be best to finish the screening before midnight, especially on weekdays.

When to Consider Making It a Tradition

If your first experience goes well, why not turn it into a tradition? You can organize theme nights — horror on Fridays, cartoons on Sundays, or retro films under the stars. Some enthusiasts even create posters, hold votes for the next movie, and post pictures on social media.
Kids especially remember such events for a long time — it’s magical for them. And for adults, it’s a rare chance to enjoy that special moment of watching a film surrounded by nature, quiet, and loved ones.

Conclusion

An at-home outdoor cinema isn’t just about gear and setup. It’s about giving yourself and your loved ones moments that can’t be replicated in a standard theater. In Slovakia, with its calm surroundings and cozy yards, this format fits perfectly. All it takes is a bit of preparation, imagination, and a good mood. Let every summer evening feel like a scene from a feel-good movie.

The post How to Set Up an Outdoor Cinema in Your Backyard appeared first on Foaas.

The Code Writing Revolution: From Autocomplete to Code Generation

Until recently, the main assistants for programmers were IDEs and suggestion plugins. Today, they are being replaced by full-fledged “cognitive coding”, where AI doesn’t just complete a line — it generates entire functional blocks, including documentation and unit tests.
GitHub Copilot, built on OpenAI’s Codex, became the first widely adopted tool that offers real-time code suggestions based on context. A user writes a comment — Copilot offers a ready-made implementation. This technology saves hours, especially in routine work, and allows teams to focus on architecture and business logic.
ChatGPT, in turn, extends capabilities beyond the IDE. It can explain unclear code, assist with debugging, adapt old fragments to new platforms, or even act as a coach, offering best practices. In Hungarian IT companies, especially in agile teams, this has become a popular form of internal support that doesn’t require involving an external consultant.

How AI Is Changing the Development Approach

A new trend is generating software code based on natural language task descriptions. AI, when given a request like “create a REST API for user management with authorization”, can not only suggest the code but also structure the project, propose a data model, connect a database, and generate routes.
This brings development closer to the so-called “no-code” future — with an important distinction: control still remains with the engineer. This is particularly crucial in sectors with strict security and compliance requirements, such as financial technologies or online gaming.
For example, platforms offering online casino with real money must comply not only with national regulations but also with international security protocols. In this context, automatic code generation requires manual review, and AI acts more like a co-author than an executor.

The Role of Copilot and ChatGPT: Differences and Intersections

Although both tools are built on OpenAI technologies, their behavior and use cases differ.
Copilot is deeply integrated into the code editor. It instantly responds to the developer’s actions, offering short and context-aware snippets. This makes it convenient for writing new functions, especially in a familiar environment.
ChatGPT is effective as a universal assistant working “in dialogue.” It helps not only to generate code but also to analyze bugs, suggest architectural solutions, and compare frameworks. ChatGPT is especially popular among beginner developers in Budapest and other Hungarian cities, where it is used as an alternative to educational courses and reference materials.
Interestingly, many professionals today use both tools in parallel. One — for productive work in the editor, the other — for clarifying concepts and exploring approaches. This combination is becoming standard among full-stack developers and DevOps engineers.

Perspectives: Generating Entire Applications

One of the most ambitious trends is the generation of complete applications. We’re not just talking about code but the creation of entire systems: with front-end, back-end, databases, CI/CD, and even Docker configurations. AI-powered solutions are already available that can generate a web application and automatically publish it on a hosting platform based on a simple description.
However, these solutions are still far from perfect. Deep refinement is often necessary, especially if the project requires non-standard solutions or deals with sensitive data. Nevertheless, the future is clear: AI is increasingly involved in the technical implementation of ideas, allowing developers to focus on high-level tasks.
This is precisely where AI assistants can transform the approach in entire industries. Take digital platforms for online casino, for example, where it is crucial to quickly adapt to changing jurisdictional rules, currencies, and payment methods. Thanks to AI-driven development, the release of new features can be accelerated — from adding new providers to creating a localized interface for Hungarian users with support for their language and currency.

A Future with AI: Learning, Control, Ethics

Despite the obvious advantages, widespread AI implementation in development comes with new challenges. One is the potential skill loss among newcomers. If a developer relies on Copilot or ChatGPT from the very beginning, they may not learn the fundamental principles of programming.
Another issue is quality control. Generated code may contain vulnerabilities, logical errors, or fail to meet company standards. That’s why it’s crucial to train teams not just to use AI but to critically assess its output.
And finally, ethics. Who is responsible for errors in AI-generated code? How should such fragments be documented? Where is the line between original work and machine generation?
The answers to these questions are still forming, but one thing is clear: artificial intelligence has already become part of the programming culture. It is changing the way we work, the structure of teams, and in some cases — the professions themselves.

Conclusion

AI tools like GitHub Copilot and ChatGPT don’t just speed up development — they are shaping a new way of thinking. In an environment of growing demand for fast and high-quality software, especially in sectors sensitive to security and time-to-market, such as online gaming and financial services, they become indispensable allies. Hungarian developers are already using these technologies to solve real-world problems — and increasingly, the question isn’t “should we use it?” but “how do we use it best?”

The post AI in Software Development: Copilot, ChatGPT, and the Future of Code Generation appeared first on Foaas.

A New Role for the Developer: From Coder to Architect

There was a time when programmers would spend hours manually writing every line of code. Today, many tasks — from generating template fragments to automated testing — are delegated to intelligent algorithms. AI assists in code creation, debugging, error prediction, and even architectural decision-making. This does not mean that humans are pushed aside — rather, their role is evolving. The developer is no longer just an executor but becomes the architect of the system: defining logic, structure, and component interaction, while leaving the routine to the machine.
For Slovakia, where tech hubs are rapidly developing in Bratislava and Košice, this transformation opens up new opportunities. Educational programs increasingly include modules on machine learning and artificial intelligence, preparing professionals who can work in tandem with smart algorithms.

How AI Works in Programming

AI in programming is based on several key technologies: machine learning, natural language processing, and neural networks. In practice, this means that an algorithm can analyze large volumes of code, identify recurring structures, and suggest optimized solutions. Tools like GitHub Copilot or Amazon CodeWhisperer already help speed up development by offering code snippets in real-time based on the task’s context.
These technologies are especially useful in environments where development speed is critical — for example, in the field of online entertainment. In this highly competitive sector, where updates are frequent, AI becomes an indispensable assistant. Online platforms, including best slovenske online casino, increasingly use intelligent algorithms to adapt interfaces, personalize user experience, and instantly generate game scenarios. Although these systems are not always visible to the end user, they are the very mechanisms that ensure fluid and dynamic interaction in real time.

Code Generation: From Idea to Implementation

One of the most impressive uses of AI in programming is generating code based on descriptions. It’s enough to describe a task in natural language — for instance, “create a registration form with email and password validation” — and the algorithm will suggest ready-to-use code. This significantly accelerates the process and makes development more accessible to beginners.
However, it’s important to remember that AI is still not capable of understanding the context and goals of a project as deeply as a human. That’s why it remains a helper, not a full replacement for a developer. Limitations must also be acknowledged: most code generators tend to repeat standard solutions, which may be undesirable when creating unique products.

Advantages and Risks of Implementing AI

The main advantages of using AI in programming include speed, a reduction in routine tasks, and improved code reliability. AI performs well in vulnerability checks, automated testing, and detecting non-obvious logical errors. This allows for shorter development cycles and faster time to market.
However, there are also risks. One is the dependency on technologies that most developers do not fully understand. Not every specialist is able to dissect how exactly a neural network arrived at a particular decision. This creates verification issues, especially in critical systems.
There’s also the ethical aspect: who bears responsibility if an error made by AI leads to failure or damage? This question remains open even among international regulators, let alone local IT communities in countries like Slovakia.

Impact on the Labor Market

AI is also changing the labor market. New specializations are emerging — AI solution developers, big data analysts, machine learning engineers. At the same time, the demand for “pure” programmers may decline, especially in low-complexity tasks. For professionals in Slovakia, this is a signal to reassess career priorities and adapt to new demands.
The good news is that AI doesn’t close doors — on the contrary, it opens them. By mastering how to work with AI tools, a programmer can become more efficient and in-demand. This is also relevant for those working remotely or freelancing: mastering AI tools is a competitive advantage, especially in projects with limited time and resources.

The Future: A Symbiosis of Human and Machine

Looking ahead, one thing is clear: artificial intelligence and programming will evolve side by side. We already see how AI helps build programs, optimize databases, and analyze user behavior. In the near future, AI will be capable of acting not just as a helper but as a full-fledged co-author. It will offer architectural solutions, consider business goals, and even adapt to the style of a specific developer.
Nevertheless, the key element will remain the human. Intuition, creativity, the ability to think outside the box — these are qualities that cannot yet be digitally replicated. Therefore, symbiosis, not competition, will become the main trend — and it is this approach that will allow Slovakia to continue moving forward in digital transformation, while preserving its strong engineering traditions.

Conclusion

Artificial intelligence is already influencing how digital products are created. Programming, as a craft, is experiencing a second birth — becoming faster, more precise, and more adaptive. For Slovaks, this is a chance to be among those who not only use technology but shape its future. From online games to serious corporate solutions — wherever code exists, AI has already made its mark. The only thing that matters now is not to fear change and to learn how to use intelligence — both artificial and human — wisely.

The post Artificial Intelligence in Programming appeared first on Foaas.

What is HATEOAS?

HATEOAS is a principle of REST architecture that states that interaction with a server should be done through hypermedia that provides information about the possible actions and states of a resource. This means that the API not only returns data, but also provides links to related resources and possible actions that the client can take.
HATEOAS working principles:

Resource Navigation: Clients can navigate between related resources by following the links provided. This reduces the need to know specific URLs and simplifies interaction.

Dynamic discovery: Clients can discover available actions and transitions based on information provided by the server, rather than from a fixed set of static URLs.

Application State: The server manages the state of the application by providing the client with the necessary links to perform further actions.

How does HATEOAS work?

In the context of HATEOAS, each resource representation that a client receives from a server includes links to related resources and actions. For example, if a client requests information about a user, the server may return not only user data, but also links to related resources such as the user’s order list, the user’s profile, and so on.

An example of HATEOAS in action:

Resource Request: A client sends a request for data about a resource, such as a user.

Server response: The server returns data about the user and includes links to other related resources. For example, the response may include a link to a list of the user’s orders and an option to update the user’s data.

Navigation: The client uses these links to perform additional actions, such as viewing orders or updating user information.

Benefits of HATEOAS

Simplified interaction: Customers can interact with the API by following the links provided, without needing to know specific URLs or the API structure. This simplifies interaction and reduces the risk of querying errors.

Flexibility and extensibility: When new resources are added or the API structure changes, the server can simply update the links without breaking the existing interface for clients.

Self-describing APIs: The API becomes more self-descriptive as links and available actions are provided in responses, making the API easier to understand and use.

HATEOAS implementation

Implementing HATEOAS in a RESTful API may involve the following steps:

Resource Design: Determine which resources and actions should be accessible via hypermedia. Think about which links and actions will be useful to clients.

Implement links: When creating a resource view, include links to related resources and possible actions in the response. This can be implemented through special fields or structures in the JSON/XML response.

Update and maintain: When changing the API or adding new features, update the links and actions in the response so that clients can continue to interact with the updated resources.

The post Using HATEOAS in a RESTful API appeared first on Foaas.

Pagination in REST API

Pagination is the process of dividing a large set of data into separate pages, each containing a certain number of items. This method is necessary to prevent sending huge amounts of data to the client in a single request, which can slow down the system and create excessive load on the server.

Why pagination is important:

Optimize performance: The server can efficiently process requests and reduce response time by sending only a portion of the data instead of the entire collection.
Improved user experience: Users receive data faster, making the application more responsive.
Resource Management: Pagination reduces the load on server infrastructure by avoiding unnecessary memory and CPU usage.

How pagination works:

Typically, clients request data in chunks, passing parameters such as page number and number of elements on the page in the requests. For example, a user may request “page 5” with “10 elements on each page”. The server responds by providing data for that page and sometimes metadata (such as the total number of elements or pages) so that the client can continue to navigate.

Key aspects of pagination:

“limit” and ‘offset’ parameters: These are the most common pagination parameters. “Limit” specifies how many elements to return, and ‘offset’ specifies which element to start sampling from.

Pagination: In addition to data, the server can return links to previous and next pages to facilitate navigation.

Data filtering in the REST API

Filtering is the ability to limit data output based on certain criteria. This mechanism allows users to retrieve only the information they need, reducing the amount of data transferred and speeding up query processing.

Why filtering is needed:

Server load reduction: Filtering helps reduce the amount of data the server has to process and transmit.

User-friendliness: Users can quickly find the information they need by applying filters for specific fields or parameters.

How filtering works:

Filtering is usually done through query parameters, where users can specify criteria for data selection. For example, you can filter records by date, category, status, and other fields. For example, a query can return only active orders or users from a specific city.

Types of filtering:

By exact match: For example, a query can return records that have the “status” field equal to “active”.

By value range: This can be useful for dates or numeric data. For example, you can query for orders created in a specific time period.

By multiple criteria: Users can combine multiple filters to narrow down the selection more precisely. For example, query products of a specific category and price range.

Sorting data in the REST API

Sorting is a mechanism that allows you to organize data by certain criteria, such as alphabetically, by creation date, or by descending price. Sorting is especially important when the user needs not just to retrieve data, but to see it in a specific order.

Benefits of sorting:

User-friendly: Users can see data in the order that is most important to them. For example, this could be sorting items by price or by date added.

Interface Improvement: Sorting makes data more organized and easier to understand, especially if the data set is large.

How sorting works:

Typically, sorting in REST APIs is performed via a query parameter, where the user specifies by which field and in what order to sort the data (ascending or descending). For example, a user can request a list of users sorted alphabetically by name.

The post Pagination, filtering and sorting of data in REST API appeared first on Foaas.

What is authentication and authorization?

Authentication is the process of confirming a user’s authenticity, that is, verifying their identity. For example, when a user enters a username and password, the system must verify that this data is correct and belongs to the real user.

Authorization is the process of granting or restricting access to certain resources or actions based on a user’s rights. For example, a regular user may only have access to view their data, while an administrator may have access to all system data.

OAuth 2.0: the foundation for secure authentication

OAuth 2.0 is a standard protocol for authentication that provides access tokens to work with resources on behalf of a user. This protocol is widely used in web applications to allow third-party applications to securely interact with resources without passing logins and passwords.

How OAuth 2.0 works:

OAuth 2.0 uses a model where different roles are separated:

• A Client is an application that requests access to resources.
• Resource Server is the server that stores the user’s resources (such as APIs).
• Authorization Server – A server that validates the user and issues an access token.
• User (Resource Owner) – The resource owner who grants access authorization.

The main stages of OAuth 2.0:

Authorization Request: The client (e.g., mobile app) redirects the user to an authorization server where the user enters their login credentials (e.g., username and password).

Granting permissions: The user agrees to allow the app access to their resources (e.g., allow the app to access their email).

Obtaining an authorization code: After successful authentication, the authorization server sends the client a code that will be used to obtain a token.

Obtaining an access token: The client sends the code to the authorization server and receives an Access Token, which is used to work with the API.

Accessing Resources: The client uses the token to access the user’s resources on the resource server.

Types of tokens in OAuth 2.0:

Access Token: An access token used to make requests on behalf of the user. This token usually has a limited validity period.

Refresh Token: A token that allows a new Access Token to be obtained without re-authenticating the user. It is used for a long-term session.

Benefits of OAuth 2.0:

Security: OAuth 2.0 allows applications to work with users’ resources without asking them for logins and passwords.
Flexibility: Supports different types of clients (web applications, mobile applications, servers).
Multi-level authorization: Ability to grant access to only certain parts of resources.

Disadvantages of OAuth 2.0:

Configuration complexity: OAuth 2.0 requires configuring multiple components (clients, servers, tokens), which increases the complexity of implementation.
Token security: If an access token is compromised, an attacker can access user data.

JWT: A standard for data transfer

JSON Web Token (JWT) is a compact and secure format for transferring data between two parties in the form of tokens. JWT is often used to transfer user information after user authentication.
How JWT works:

JWT consists of three parts separated by dots:

Header: Contains information about the type of token and the encryption algorithm used (e.g., HMAC SHA256).

Payload: Contains encoded data (e.g., user ID, access rights, and token expiration date).

Signature: This is a digital signature to ensure that the data has not been altered. The signature is created using the secret key and algorithm specified in the header.

The post Authentication and authorization in RESTful API: OAuth 2.0 and JWT appeared first on Foaas.

In addition, the development of REST was more focused on creating new APIs. GraphQL developers, on the other hand, focused on API performance and flexibility.

Following are some other differences.

Client-side query
The following describes how a REST request works.

HTTP verbs that define the action
A URL that defines the resource where the HTTP command should be executed
Parameters and values to analyze if you want to create or modify an object in an existing server resource
For example, you use a GET request to retrieve read-only data from a resource, a POST to add a new resource record, or a PUT to update a resource.

In contrast, GraphQL queries use the following:

• A query to retrieve read-only data
• Data modification function
• Subscribe to receive event-based or streaming data updates
• The data format describes how the server should return the data, including objects and fields that match the server-side schema. You can also enter new data. Internally, GraphQL sends each client request as an HTTP POST request.

Server-side schema

GraphQL uses a server-side schema to define data and data services, which is different from the REST API.

The schema, written in the GraphQL schema definition language, contains the following information:

Object types and fields belonging to each object
Server-side resolver functions that define the operation for each field
The schema clearly defines the types that describe all the data available in the system and how clients can access or modify that data.

On the other hand, server-side schema is not required for REST APIs. But you can define it additionally for efficient API design, documentation, and client development.

Version control
As APIs evolve, their data structures and operations may change. Clients unaware of these changes can crash their systems or make unknown errors.

To address this issue, REST APIs often use a version detection function in the URL, such as https://example.com/api/v1/person/12341. However, version control is not mandatory and can cause errors.

For GraphQL, backwards compatibility of the API is required. Thus, deleted fields return an error message and fields with an outdated tag return a warning.

Error handling
GraphQL is a strictly typed API architecture, so it requires a detailed description of the data, its structure, and data operations in the schema. Because of the level of schema detail, the system can automatically detect query errors and provide useful error messages.

REST APIs are loosely typed and you must build error handling into the appropriate code. For example, if a PUT request analyzes a numeric value as text rather than an integer, the system does not automatically identify the error.

The post Key differences: GraphQL and REST appeared first on Foaas.