Online search is undergoing one of its most significant transformations since the birth of search engines.

For many years, the model was relatively simple:

Query ⇨ Search results page ⇨ Click ⇨ Website

Today, this journey can no longer be taken for granted.

Google, Bing, and emerging AI-powered search platforms are increasingly able to understand complex questions, perform multiple searches, compare sources, and generate answers directly for users.

Google has introduced and progressively expanded AI Overviews and AI Mode. Microsoft is integrating search with Bing and Copilot, while systems such as ChatGPT Search are further transforming the concept of a search engine by combining web search with AI-generated answers.

The result is a significant shift in user behaviour: for many informational queries, users no longer need to visit multiple websites to build an answer themselves.

Search engines are therefore evolving from lists of documents into systems for retrieving, understanding, and synthesising information.

This does not mean that SEO is dead.

It means that its purpose is expanding.

We should no longer ask only:

“How can I rank this page?”

but also:

“How can I make this page a reliable source that a search engine or AI system can find, understand, retrieve, and cite?”

From SEO to AEO and GEO

In recent years, terms such as SEO, AEO, and GEO have become increasingly common.

SEO: Search Engine Optimization

Optimising websites and content to gain visibility in traditional search engines.

AEO: Answer Engine Optimization

Optimising content so that it can directly contribute to the answer provided to the user.

GEO: Generative Engine Optimization

Optimising a website’s presence within AI-powered and generative search experiences.

However, these should not be considered three completely separate disciplines.

SEO remains the foundation.

Google has clarified that AI Overviews and AI Mode continue to rely on its core Search ranking and quality systems and that no special technical requirements are needed to appear in generative search features.

Microsoft is also increasingly using the term GEO to describe how content participates in AI experiences and is selected as a source for generated answers.

We can therefore represent this evolution as:

SEO ⇨ Discovery

AEO ⇨ Answer

GEO ⇨ Citation & Generative Visibility

SEO enables content to be discovered.

AEO and GEO extend this concept towards making content easier to understand, retrieve, and use within generative answers.

Types of Queries and the Risk of Traffic Loss

The impact of generative search is not the same for every type of query.

Informational Queries: HIGH RISK

These are queries where the user is primarily looking for an explanation.

For example:

• What is the OAuth2 protocol?
• What does canonical URL mean?
• How does a CDN work?
• What is the difference between SEO and SEM?

For these queries, a generative answer can often provide a sufficiently complete explanation immediately.

The consequence is an increase in zero-click searches: searches that end without the user visiting an external website.

Comparative and Research Queries: MEDIUM/HIGH RISK

AI platforms are particularly effective at comparisons.

A question such as:

“Which CMS is better suited to an international healthcare portal: Drupal or WordPress?”

may require the system to compare:

• security;
• scalability;
• multi-country management;
• editorial workflows;
• SSO;
• privacy;
• integrations;
• costs;
• maintenance.

Google AI Mode and other generative systems can perform multiple searches to build this comparison directly.

Clicks remain important when users want to verify a source, explore a technical aspect in greater depth, read about real-world experience, or analyse more detailed data.

Transactional Queries: LOWER RISK

When users need to complete an action, websites continue to play a fundamental role.

For example:

• purchasing a product;
• booking a hotel;
• requesting a quote;
• signing up for a service;
• downloading software;
• contacting a company.

However, this area is evolving as well.

AI Agents are progressively transforming artificial intelligence from a system that simply provides answers into a technology capable of interacting with services and applications.

The evolution could therefore be represented as:

Search ⇨ Answer ⇨ Conversation ⇨ Action

How Generative Search Works

Not every search engine uses the same architecture, and we do not know every detail of how their systems work.

However, we can identify a general model.

1. Understanding Intent

The system interprets the request and attempts to understand:

• meaning;
• context;
• entities;
• intent;
• relationships between concepts.

As a result, an exact match between the keyword entered by the user and the text on a page is becoming less important.

2. Query Fan-Out

One of the most interesting features of generative search is query fan-out.

Google has officially confirmed that AI Overviews and AI Mode can perform multiple related searches across different subtopics and data sources.

A complex query can therefore be broken down into several secondary searches.

For example:

“What is the best CMS platform for a European HCP portal?”

could generate searches related to:

healthcare CMS

Drupal security

WordPress healthcare

GDPR CMS

multi-country CMS

HCP authentication

SSO healthcare portal

Content therefore does not necessarily need to match the exact wording of the user’s original query to become a relevant source.

Retrieval, Grounding, and RAG

One of the fundamental concepts behind AI-powered search is grounding.

A Large Language Model contains knowledge acquired during training, but when answering questions that require current information, it can use external sources.

One approach used for this is Retrieval-Augmented Generation (RAG).

In simplified terms:

Query
⇩
Retrieval
⇩
Source Selection
⇩
Information Extraction
⇩
Answer Generation
⇩
Citations / Links

Google explicitly confirms the use of RAG and grounding techniques within its generative search features.

Microsoft also considers grounding a central component of the AI-powered web: web sources allow generative systems to connect their answers to up-to-date and verifiable information.

For SEO and content professionals, this means something important:

before content can be used in an AI-generated answer, it needs to be discoverable, understandable, and considered sufficiently relevant.

This is precisely where traditional SEO continues to play a fundamental role.

Google, Bing, and the New Search Landscape

This transformation is not limited to Google.

Google

Google primarily operates through:

• traditional Search;
• AI Overviews;
• AI Mode.

AI Overviews synthesise information directly within the SERP when Google determines that doing so adds value to the search experience.

AI Mode extends this model further by enabling more complex searches, comparisons, and conversational follow-up questions.

Both can use query fan-out to discover sources that may differ from those surfaced by a single traditional search query.

Bing and Microsoft Copilot

Microsoft is following a similar direction through Bing and Copilot.

One particularly interesting development is the introduction of AI Performance in Bing Webmaster Tools.

The report allows publishers to analyse metrics such as:

• total citations;
• cited pages;
• grounding queries;
• citation trends over time.

Microsoft is therefore beginning to distinguish more explicitly between traditional SEO visibility and visibility within AI-generated answers.

This may provide an early indication of how SEO analytics tools will evolve over the coming years.

ChatGPT Search and Emerging AI Search Engines

Alongside traditional search engines, we are seeing the growth of systems where search is integrated directly into an AI assistant.

ChatGPT Search, for example, can search the web and use external pages as sources for its answers.

For publishers, this introduces another dimension of organic visibility.

There is no longer only:

Google ranking

but rather an ecosystem consisting of:

Search visibility

AI visibility

Citation visibility

Brand visibility

A website can therefore generate value even when its content is used as a source without necessarily generating an immediate click.

From Keywords to Topics, Intent, Entities, and Context

Keywords are not disappearing.

Their strategic role, however, is changing.

The traditional approach:

One keyword ⇨ One page

is becoming less effective when applied too rigidly.

A modern approach should instead consider:

Keyword ⇨ Topic ⇨ Intent ⇨ Entity ⇨ Context

Google has also clarified that there is no need to create dozens of pages simply to cover every possible long-tail variation.

Modern search systems can understand synonyms, concepts, and entities even when there is no literal match with the user’s query.

Building comprehensive semantic coverage of a topic is therefore becoming increasingly important.

How to Write Content for AI-Powered Search

There is no magic formula for getting cited.

Nor is there an ideal paragraph length that guarantees inclusion in an AI-generated answer.

The objective should be to make content easy to understand for both people and information retrieval systems.

A good structure can include:

• descriptive headings;
• clear paragraphs;
• direct answers when appropriate;
• tables for comparing data;
• lists when they improve readability;
• precise definitions;
• examples;
• verifiable sources;
• connections between related concepts.

The old idea of an Atomic Answer that must always be 40-60 words long should therefore not be considered an SEO rule.

The answer simply needs to be clear, complete, and proportionate to the question.

The Real Competitive Advantage: Non-Commodity Content

Artificial intelligence makes it extremely easy to produce generic content.

And that is precisely why generic content is losing value.

Google uses the concept of non-commodity content to describe content that adds something beyond information that is already widely available.

This may include:

• first-hand experience;
• tests;
• benchmarks;
• proprietary data;
• original research;
• case studies;
• screenshots;
• real-world examples;
• well-supported expert opinions;
• internally developed methodologies.

An article that simply repeats:

“10 benefits of cloud computing”

can easily be summarised by an AI system.

An article presenting:

“How we reduced response times by 38% by migrating this architecture to Azure”

contains information that the model is unlikely to find elsewhere.

This distinction will become increasingly important.

E-E-A-T in AI-Powered Search

Experience, Expertise, Authoritativeness, and Trustworthiness continue to represent important concepts for assessing content quality.

However, they should not be interpreted as a single “AI ranking factor”.

The fundamental principle remains demonstrating that the content is supported by genuine experience, expertise, and verifiable information.

Experience

Demonstrate real-world experience through:

• tests;
• real cases;
• screenshots;
• original photos;
• problems encountered;
• results achieved.

Expertise

Demonstrate expertise through:

• technical insights;
• appropriate terminology;
• sources;
• data;
• accurate explanations;
• clear author profiles.

Authoritativeness

Build authority through:

• citations;
• backlinks;
• mentions;
• publications;
• industry reputation.

Trustworthiness

Strengthen trust through:

• verifiable sources;
• identifiable authors;
• editorial transparency;
• publication and update dates;
• clear company information;
• website security.

Structured Data: Important, but Without the Myths

Schema.org and structured data remain important SEO tools.

They help search engines understand certain types of information and may enable specific rich results.

However, they should not be considered a requirement for being used in AI-generated answers.

Google has clarified that there is no special Schema.org markup for AI Overviews or AI Mode.

Structured data should therefore be implemented when it accurately represents the content on the page and provides genuine value to Search.

For example:

• Article;
• Product;
• Organization;
• Person;
• Event;
• BreadcrumbList;
• LocalBusiness.

The principle remains simple:

Use structured markup when it adds meaning; do not create artificial markup simply to “convince the AI”.

Images, Video, and Multimodal Content

Search is becoming increasingly multimodal.

Answers can include:

• text;
• images;
• video;
• products;
• maps;
• structured information;
• interactive elements.

Google explicitly recommends complementing textual content with high-quality images and videos where appropriate.

It is therefore important to consider:

• image quality;
• alt text;
• file names;
• surrounding text;
• captions;
• video SEO;
• image/video sitemaps where appropriate;
• consistency between visual and textual content.

An original diagram or technical screenshot can provide significantly greater informational value than a generic stock image.

Topic Clusters and Internal Linking

Generative search further reinforces the importance of topical authority.

A single isolated article is unlikely to demonstrate the same depth as a connected content ecosystem.

An effective structure might look like this:

Pillar Page

Deep Dive A

Deep Dive B

Case Study

FAQ

Tutorial

Comparison

Internal linking helps search engines understand:

• relationships between content;
• hierarchy;
• context;
• topical depth.

It also improves crawling and content discovery.

Technical SEO: What Does Not Change

Artificial intelligence does not eliminate the need for a technically solid website.

The fundamentals remain essential:

• crawlability;
• indexability;
• robots.txt;
• XML sitemaps;
• canonical tags;
• correct redirects;
• HTTP status codes;
• performance;
• mobile usability;
• accessibility;
• JavaScript rendering;
• internal linking;
• understandable HTML;
• important content available in textual form.

If a search engine cannot properly retrieve content, it is unlikely to be able to use that content as a source.

The New SEO KPIs

One of the biggest mistakes will be continuing to measure success exclusively through organic traffic.

Traditional KPIs remain important:

• rankings;
• impressions;
• CTR;
• clicks;
• organic sessions;
• conversions.

However, they increasingly need to be complemented by new metrics:

• citations in AI-generated answers;
• cited pages;
• branded searches;
• brand mentions;
• direct traffic;
• referrals from AI platforms;
• engagement;
• conversion rate;
• visibility for strategically relevant queries.

Bing has already started this transition through AI Performance in Bing Webmaster Tools, providing data on citations and grounding queries from Copilot, Bing AI, and selected partner experiences.

Google, on the other hand, currently includes AI Overviews and AI Mode within the overall Web search traffic reported in Search Console.

Measuring AI visibility is therefore still a rapidly evolving area.

SEO Is Not Disappearing: Its Objective Is Changing

For years, the primary SEO objective was:

Achieve a ranking
⇩
Generate a click
⇩
Bring the user to the website

Today, the journey can increasingly become:

Be discovered
⇩
Be considered relevant
⇩
Be retrieved as a source
⇩
Be cited
⇩
Build trust
⇩
Generate a visit or an action

This fundamentally changes how we should assess the value of content.

Content can contribute to brand visibility even before a click occurs.

Conclusion

SEO is not dead.

On the contrary, it remains part of the infrastructure on which much of the new AI-powered search ecosystem is being built.

What is changing is the context.

We are no longer optimising solely for a results page made up of ten blue links.

We are optimising for an ecosystem where several different experiences coexist:

Search Engine

Answer Engine

Generative Engine

AI Assistant

AI Agent

Google AI Overviews, AI Mode, Bing, Microsoft Copilot, and emerging AI platforms are progressively transforming how information is discovered and used.

The future of SEO is therefore not about finding tricks to “please LLMs”.

It is about building technically accessible websites and creating content that has something genuinely valuable to offer:

experience, data, expertise, clarity, authority, and original information.

In the past, the main objective was to get the click.

Today, the objective is also to become a source for the answer.

Tomorrow, it may be about becoming both a source and a service that AI agents can interact with directly.

Primary Sources:
Google Search Central – AI features and your website,Google – Generative AI optimization guide,Bing Webmaster – AI Performance

L'articolo How SEO Is Changing with the Advent of AI Overview, AI Mode, and Generative Search proviene da Giovanni Anfuso.

Over the past few years, we have witnessed an incredible evolution of Artificial Intelligence.

First came chatbots, then AI-powered coding assistants such as GitHub Copilot, and today we have entered the era of AI Agents-systems capable not only of answering questions but also of interacting with external tools, databases, Git repositories, CRMs, and enterprise applications.

In this rapidly evolving landscape, a new standard is emerging that is likely to become increasingly important: Model Context Protocol (MCP).

Although it is still relatively unknown today, it will likely become one of the fundamental concepts every software developer should understand.

What is Model Context Protocol?

Model Context Protocol, commonly referred to as MCP, is an open protocol designed to enable AI models to communicate with applications, services, and data sources through a standardized interface.

Simply put, it provides a common language between AI models and the tools we use every day.

Let’s look at a simple example.

Imagine asking an AI assistant:

“Show me all the orders placed today by customer Rossi.”

Without MCP, the assistant would need to understand the ERP’s specific APIs, authentication methods, response formats, and custom integration logic.

With MCP, all of this is standardized.

The AI doesn’t need to know how every individual application works internally. Instead, it communicates through a shared protocol that any compatible service can expose.

Why was MCP created?

Until recently, every integration with a Large Language Model required custom development.

Every application exposed different APIs.

Every authentication method was unique.

Every response followed its own format.

The result?

Developers spent a considerable amount of time building integrations that were difficult to reuse.

Model Context Protocol was created to solve exactly this problem.

Just as HTTP standardized communication between web browsers and web servers, MCP aims to standardize communication between AI models and software applications.

How does it work?

The overall architecture is remarkably simple.

On one side, there is the AI model.

On the other, one or more MCP servers expose tools, data, and capabilities.

When a user submits a request, the AI model can:

• query databases;
• search documentation;
• interact with a CRM;
• browse Git repositories;
• read local files;
• call REST APIs;
• retrieve enterprise data;
• execute authorized operations.

The model doesn’t guess how to interact with external systems. It knows which tools are available and uses them through a standardized protocol.

This makes integrations significantly more reliable and predictable than custom-built solutions.

A real-world example

Imagine a company that uses:

• a CRM;
• an ERP;
• a customer database;
• a document management system;
• Git repositories;
• a ticketing platform.

Today, enabling an AI assistant to work with all these systems would require numerous custom integrations.

With MCP, each system can expose its capabilities as standardized tools.

The AI assistant can then:

• retrieve business data;
• compare information across systems;
• generate reports;
• create documentation;
• assist developers and support teams.

All without reinventing the integration logic every time.

Why developers should pay attention

Many people believe AI will reduce the need for software developers.

Personally, I think the reality is different.

What will change is the nature of our work.

We’ll spend less time writing repetitive code and more time designing architectures, defining workflows, integrating systems, and building tools that AI can safely interact with.

In this context, understanding MCP could become a real competitive advantage.

MCP and programming languages

One of MCP’s biggest strengths is that it is language-agnostic.

MCP servers can be implemented using virtually any modern programming language, including:

• PHP;
• C# and .NET;
• Java;
• JavaScript and Node.js;
• Python;
• Go;
• Rust.

This means existing applications can become AI-ready without requiring a complete rewrite.

Possible use cases

The possibilities are virtually endless.

Using technologies I’m most familiar with as examples, a Drupal website could expose capabilities that allow an AI assistant to:

• create content;
• search articles;
• edit pages;
• query Views;
• retrieve analytics.

An ASP.NET application could expose tools to:

• query databases;
• manage users;
• generate reports;
• retrieve documents.

A PHP-based ERP or business application could allow AI to:

• verify customer orders;
• check inventory availability;
• prepare quotations;
• retrieve invoices.

All through the same open standard.

The future of AI Agents

More and more companies are investing in AI Agents, and the difference compared to traditional chatbots is significant.

A chatbot answers questions.

An AI Agent gets work done.

It can use tools, make decisions within predefined limits, retrieve information, and collaborate with multiple business systems.

To do this effectively, however, it needs a standardized way to access external resources.

That is exactly where MCP comes in.

Is it worth learning today?

In my opinion, absolutely.

We’re at a stage that feels very similar to where REST APIs were fifteen years ago.

Back then, they seemed like a niche technology used by only a handful of developers.

Today, REST APIs are at the heart of virtually every modern application.

I believe Model Context Protocol could follow a similar path.

It won’t replace traditional APIs, but it will become an additional layer that makes it much easier for AI models to interact with the software we use every day.

Final thoughts

Artificial Intelligence isn’t just changing the way we write code.

It’s changing the way software systems communicate with one another.

I believe Model Context Protocol represents one of the first serious attempts to establish a shared standard between AI models and software applications.

For this reason, I believe that learning MCP today means preparing for a transformation that has already begun.

Just as cloud computing, Docker, Kubernetes, and REST APIs reshaped software engineering over the past decade, developers who start experimenting with MCP today will likely have a significant advantage when this technology becomes part of everyday software development.

Personally, I believe that in the coming years we’ll hear less about simple chatbots and far more about intelligent applications designed from the ground up to collaborate with AI through open standards such as Model Context Protocol.

L'articolo MCP: The Technology Few Are Talking About That Could Change the Way Developers Work proviene da Giovanni Anfuso.

What Is PrestaShop

PrestaShop is an open-source eCommerce platform built in PHP, based on an MVC architecture and using a MySQL database. It was launched in 2007 to provide a flexible and customizable solution for building online stores.

Unlike SaaS platforms, PrestaShop is self-hosted, which means it requires:

• web hosting
• manual installation
• ongoing technical management

This makes it particularly suitable for developers and technical teams.

Key Features

PrestaShop offers a complete ecosystem for eCommerce:

Technical Core

• Language: PHP
• Database: MySQL
• Architecture: MVC (evolving toward Symfony in newer versions)
• REST APIs available
• Override and hook system for advanced customizations

eCommerce Features

• Product catalog management (variants, combinations)
• Multi-language and multi-currency support
• Shipping and carrier management
• Payment integrations
• Advanced back office for orders and customers

Ecosystem

• Official marketplace with hundreds of modules
• Customizable templates (Smarty/Twig)
• Active community

Modules and Templates

Modules (Plugins)

Modules extend functionality, including:

• payments (Stripe, PayPal)
• marketing (SEO, email automation)
• logistics (shipping, tracking)
• ERP/CRM integrations

Technical note:
Poorly developed modules can significantly impact performance and security.

Templates (Themes)

PrestaShop uses:

• Smarty (legacy)
• Twig (modern versions)

Templates control:

• frontend layout
• UI/UX
• page rendering

Customization approaches:

• overrides
• child themes
• hooks

Strengths

1. Open Source and Highly Customizable

Full access to the codebase makes it ideal for complex custom projects.

2. No License Cost

The software itself is free (but the overall project is not).

3. Moderate Scalability

Suitable for:

• small eCommerce stores
• mid-sized projects

4. Full Control

Hosting, security, and performance are fully manageable by developers.

Weaknesses

1. Technical Complexity

Not plug-and-play:

• requires PHP knowledge
• debugging can be challenging

2. Hidden Costs

• premium modules
• maintenance
• custom development

3. Performance Issues

With many modules, it can become:

• slow
• harder to optimize

4. Risky Updates

Major updates can:

• break compatibility
• require refactoring

Security

PrestaShop can be secure—but not by default.

Security depends on:

• regular updates
• module quality
• server configuration (HTTPS, firewall, etc.)

Best practices:

• avoid unverified modules
• use optimized hosting
• implement WAF/CDN
• enable automated backups

Average Costs for a PrestaShop eCommerce

Although the software is free, real costs include:

Basic Setup

• Hosting: €100–€500/year
• Domain: €10–€20/year

Development

• Template: €50–€300
• Custom development: €500–€5000+

Modules

• €30–€300 per module

Maintenance

• €50–€300/month (depending on complexity)

Estimated total cost:

• Small project: €500 – €1500
• Medium: €1500 – €5000+
• Advanced: €5000 – €20,000+

When to Choose PrestaShop

Choose it if:

• you are a developer (junior with support or senior)
• you want full control over the code
• you need custom functionality
• you are building standard B2C eCommerce projects

When NOT to Choose PrestaShop

Avoid it if:

• you want a fast, no-maintenance solution
• you lack technical skills
• you need extreme enterprise-level scalability

Differences with Other eCommerce Platforms

PrestaShop vs WooCommerce

• WooCommerce: WordPress plugin → easier to use
• PrestaShop: dedicated platform → more structured

WooCommerce wins in simplicity, PrestaShop in control

PrestaShop vs Shopify

• Shopify: SaaS → no technical management
• PrestaShop: self-hosted → full flexibility

Shopify wins in speed, PrestaShop in customization

PrestaShop vs Magento

• Magento: enterprise-level
• PrestaShop: mid-level

Magento for large enterprises, PrestaShop for SMEs

Alternatives to PrestaShop

1. WooCommerce

• Ideal for WordPress-based sites
• Easy to use
• Large ecosystem

2. Shopify

• SaaS
• Perfect for non-technical users

3. Magento (Adobe Commerce)

• Enterprise-level
• Highly scalable but complex

4. OpenCart

• Similar to PrestaShop but lighter

5. Shopware

• Modern alternative, widely used in Europe

Conclusion

PrestaShop is a strong balance between:

• flexibility
• cost
• technical complexity

It’s a solid choice for developers who want to build customized eCommerce solutions without reaching the enterprise-level complexity of Magento.

However, choosing the right platform should always depend on:

• budget
• technical skills
• business goals

L'articolo PrestaShop: What It Is, Features, Costs, and When to Choose It proviene da Giovanni Anfuso.

In today’s digital landscape, choosing a reliable, secure and flexible cloud solution is essential. pCloud stands out thanks to its ease of use, advanced features and flexible pricing models.

With millions of users worldwide, pCloud is one of the most popular platforms for storing, sharing and protecting files online.

What is pCloud and How Does It Work

pCloud is a cloud storage platform that allows you to store files, photos, videos and documents in one place, accessible from any device.

You can get started for free with up to 10 GB of storage, without needing a credit card.

Main features include:

• Secure file storage
• Access from web, desktop and mobile
• Automatic synchronization across devices
• File sharing via links
• Automatic backup of photos and data

Advanced Features of pCloud

pCloud is more than just storage – it’s a complete file management platform.

File Management and Sharing

You can easily share even large files via links, with options such as:

• Password protection
• Customizable links
• Download statistics

You can also receive files from others, even if they don’t have a pCloud account.

pCloud Drive: Cloud Storage Without Using Local Space

With pCloud Drive, you can use the cloud as a virtual disk on your computer.

This allows you to:

• Access files without storing them locally
• Save disk space
• Work directly on cloud-based files

Sync and Backup

All files are automatically synchronized across your devices.

Additionally:

• Automatic backup for photos and videos
• File versioning to restore previous versions
• File recovery options

Security and Privacy

Security is one of pCloud’s strongest points.

• AES 256-bit encryption
• Secure TLS/SSL data transfer
• Multiple file copies stored on secure servers
• Two-factor authentication
• Client-side encryption with pCloud Crypto

This ensures maximum protection for both personal and professional data.

WordPress Integration

pCloud is especially useful for WordPress users.

Website backup

You can store full backups of your website safely in the cloud.

Media management

Images and videos can be stored on pCloud, reducing your hosting load.

Data protection

Keeping off-site backups helps prevent data loss from attacks or technical issues.

Creating Web Pages with pCloud

A lesser-known but powerful feature is the ability to share web content online.

With pCloud you can:

• Upload HTML files
• Create simple landing pages
• Share content via direct links

This is ideal for:

• Online portfolios
• Mini websites
• Promotional landing pages

pCloud Plans: Maximum Flexibility

One of pCloud’s biggest strengths is its flexible pricing.

Available options

• Monthly plans
• Annual plans
• One-time payment solutions (For Life)

Storage options

• Up to 10 GB free
• Premium plans up to multiple TB

This flexibility allows you to choose between short-term convenience or long-term investment.

Why Choose pCloud

pCloud is ideal for:

• Bloggers and content creators
• Freelancers
• Businesses
• Photographers
• Private and Families
• Anyone looking for secure and simple file management

Key advantages:

• Easy to use
• High-level security
• Powerful file sharing
• Multi-device access
• Excellent value for money

Get Started with pCloud

If you’re looking for a complete, secure and flexible cloud storage solution, pCloud is one of the best options available.

Discover all features and create your free account:

Choosing pCloud means having full control over your data with a modern platform designed to simplify your digital life.

L'articolo pCloud: Secure and Complete Cloud Storage proviene da Giovanni Anfuso.

Some time ago I felt the need to start a new personal project using .NET. It had been a while since I had worked with this technology and I wanted to refresh my knowledge and explore the new features of the framework.

The problem was simple: I had no idea what to build.

For a few days I kept thinking about possible ideas but nothing really inspired me. Then something unexpected happened.

A Childhood Memory That Sparked the Idea

About three years ago, while reorganizing some old childhood games, I started digging through small surprise boxes from Mulino Bianco snacks.

Among those forgotten objects, I found a small horse figurine and a dice that I used when I was a kid to simulate races on a small improvised track with other toy horses.

At that moment, an idea clicked.

I decided to combine my passion for programming with my love for horse racing, and build a horse racing management game.

That’s how Horse Racing was born.

The Development Stack: .NET 8, C# and JavaScript

The game was developed using the following technologies:

• .NET 8
• C#
• JavaScript
• SQL Server as the data source

At the time, AI tools were not as widespread as they are today, so the entire development process was done the old-fashioned way: designing systems, experimenting with algorithms and gradually building the gameplay.

And honestly, it was a lot of fun.

The First Game Features

I started with the core elements typical of any management simulation game.

Buying Your First Horse

The game begins with purchasing your first horse, which represents the starting point for building your own racing stable.

From there, everything grows: training, race preparation and improving performance.

Race Preparation

Before each race, the player can:

• prepare the horse
• analyze the competitors
• place bets on potential winners
• choose the best jockey

All of this adds tension and strategy before the race begins.

The Race Simulation

When the starter signals the beginning of the race, the action starts.

I always imagine the scene as if it were a real race:

• horses galloping at full speed
• jockeys fighting for position
• dust rising from the track
• sudden overtakes
• side camera views
• exciting photo finishes

Winning races rewards players with virtual credits, which can then be used to improve stables and buy better horses.

It’s important to note that there are no real money bets or winnings. All credits are purely virtual, just like in a classic management simulation game.

Designing an Algorithm for Unpredictable Races

One of the most interesting challenges was designing an algorithm capable of making races unpredictable.

I wanted to avoid races that always ended the same way and create a system that felt more realistic and dynamic.

To achieve this, several variables influence the race outcome, such as:

• horse skill level
• jockey ranking
• race conditions
• random unexpected events

This ensures that each race can produce different results, even with the same competitors.

Features Added Over Time

While developing the game, I kept adding new mechanics to make it deeper and more engaging.

Stable Management

Players can:

• buy multiple horses
• sell horses
• increase horse skills and market value
• train horses to improve performance

Competitions and Championships

The game includes different types of races:

• single races
• league championships
• knockout tournaments

Statistics and Records

To add depth and longevity, the game tracks:

• race record times
• individual horse statistics
• performance trends over time

Virtual Betting System

Before each race, players can place bets on potential winners, always using virtual credits.

Additional Game Features

As development progressed, the project grew with many additional features:

• race zoom during gameplay
• full race replay system
• fast simulation for less important races
• time progression system
• scheduled race events
• customizable jockey uniforms
• jockey rankings that influence race performance
• injuries during races

Step by step, the project evolved into a complete horse racing management game.

What Is Still Missing

Despite all the work done so far, the game is not completely finished yet.

For example, 3D animations are still missing. Unfortunately, I haven’t learned how to create them yet.

Maybe AI will help me with that someday.

Future Ideas for Horse Racing

I’m already thinking about several new features that could make the game even more interesting:

• online multiplayer mode
• horse breeding mechanics
• foal and stallion development
• improved race simulation
• additional management features

At the moment Horse Racing is not yet publicly available online, but the goal is to finish it and release it to players.

A Project Built for Passion

This project started simply because I wanted to:

• return to developing with .NET
• build something fun
• transform a childhood memory into a real game

And honestly, I had a great time building it.

Now I can’t wait to finish the game and publish it, so I can receive feedback, ideas and suggestions from players and developers.

The journey of Horse Racing is not over yet.
In many ways, it has only just begun.

L'articolo Horse Racing: The Management Game I Built with .NET proviene da Giovanni Anfuso.

What Orchard Core Really Is

When talking about CMS platforms in the .NET ecosystem, people often think of tools designed primarily to build websites. Orchard Core, however, was created with a different idea in mind: not just to manage content, but to serve as a foundation for building web software.

Orchard Core is an open-source project built on ASP.NET Core and represents a complete modern rewrite of the original Orchard CMS. The key difference is that it is not just a CMS: it is first and foremost a modular multi-tenant framework on top of which the content management system is built.

In practice, it can be used in two ways:

• as a traditional CMS
• as an application platform for building software products

This completely changes how it should be interpreted: it is not a direct competitor to classic website-focused CMS platforms, but a tool designed for projects meant to grow over time.

A CMS Designed as a Platform

The philosophy behind Orchard Core is simple: start minimal and add only what is needed.

Every feature is an activatable module. The result is a system that never becomes inherently heavy, because the application includes only the components actually in use.

This approach leads to two important consequences:

The first is technical: the codebase remains clean, maintainable, and upgradable even after years.

The second is architectural: the CMS becomes the foundation for complex applications, not just editorial websites.

Not surprisingly, the framework is also designed to build modular SaaS software, where multiple teams can develop different parts of the same product and reuse them across projects.

Multi-Tenant: The Feature That Changes Everything

The real core of Orchard Core is its native multi-tenancy.

With a single installation, it is possible to manage multiple completely separate sites: databases, users, themes, and configurations remain isolated. This is not simulated multisite functionality, but truly independent environments sharing only the engine.

This makes it particularly suitable for:

• SaaS platforms
• multi-brand corporate portals
• white-label solutions
• agencies managing many clients

In cloud environments, it is possible to host thousands of sites on a single infrastructure while maintaining full data isolation.

Performance and Modern Architecture

One of the main goals of the rewrite was speed.
The CMS was designed to leverage the ASP.NET Core pipeline and a data management approach different from traditional CMS platforms.

Although it uses relational databases, it adopts a document abstraction layer (YesSql) that allows content to be treated as documents, improving performance in typical CMS operations.

The result behaves much more like a web application than a traditional dynamic website.

Headless, Decoupled, or Traditional

One of Orchard Core’s most interesting qualities is architectural freedom.
It can be used in different ways without changing platforms:

• full website with themes and templates
• content backend for custom frontends
• headless CMS via GraphQL or REST APIs

In other words, it adapts to the project instead of forcing the project to adapt to the CMS.

Security

From a security perspective, Orchard Core inherits ASP.NET Core protections and adds highly granular permission management.

Each role can have specific access to content, administrative sections, and features. External authentication, approval workflows, and tenant isolation are supported.

This structure makes it suitable for enterprise and public-sector environments, where access management is often the critical factor.

Scalability and Cloud

Orchard Core was designed with scalability in mind from the very beginning, not added later.

It runs smoothly in Docker containers, cloud environments, and distributed architectures. Native multi-tenancy makes it easy to transform into the engine behind a software product.

It is therefore more accurate to compare it to an application platform with an integrated CMS rather than a simple editorial tool.

Scalability is one of the main reasons to choose Orchard Core:

• native multi-tenancy
• Docker container deployment
• cloud-ready architecture
• hosting thousands of sites in parallel
• per-client data isolation

Database and Flexible Data Management

It supports:

• SQL Server
• MySQL / MariaDB
• PostgreSQL
• SQLite

It uses a document-oriented abstraction layer (YesSql) on top of relational databases — an approach that improves performance in modern CMS environments.

In practice, it is designed more for platforms than for individual websites.

When It Makes Sense to Choose It

Orchard Core becomes particularly interesting when a project has these characteristics:

• it needs to grow over time
• it includes separate clients or environments
• it is intended to become a commercial product
• it requires integration with other systems
• it uses an enterprise .NET stack

SaaS and Platforms

• software delivered as a service
• customized customer portals
• white-label applications

Enterprise and Intranet

• document management
• approval workflows
• corporate portals

Headless CMS

• content backend for mobile apps
• React/Vue/Angular frontends
• custom ecommerce solutions

Multi-site

• brand networks
• publishing groups
• franchises

On the other hand, it is often oversized for small informational websites or simple blogs, where its real strengths would remain underutilized.

Community and Ecosystem

The community is not massive but highly technical and development-oriented.
The project is active, regularly updated, and maintained by experienced ASP.NET developers.

The documentation is extensive and geared more toward application developers than purely editorial users — another indication of its true nature.

Final Personal Considerations

Orchard Core is not an open-source CMS that can be evaluated at a glance: it appears to be a content management tool, but in reality it is an application platform that includes a CMS.

It is designed for complex web software, multi-client portals, and SaaS solutions. In these scenarios, it offers enormous advantages in terms of maintenance, scalability, and project organization.

If your goal is simply to publish content quickly, simpler solutions exist.
If instead you are building a system meant to last for years, it is likely exactly the kind of foundation worth starting from.

L'articolo Orchard Core: the Modular and Multi-Tenant .NET CMS for Scalable Applications proviene da Giovanni Anfuso.

Introduction: Umbraco in the Modern CMS Landscape

In recent years, the CMS ecosystem has seen the emergence of solutions focused on performance, flexibility, and the ability to integrate with external services. Umbraco is one of them: an open-source Content Management System based on Microsoft .NET, designed to offer maximum extensibility, full content control, and an intuitive interface for both editors and developers.

What Is Umbraco CMS

Umbraco is a CMS built on the ASP.NET Core platform that enables the creation of websites, corporate portals, and digital applications with a high degree of customization. Unlike more “closed” CMS platforms, it relies on modern architectures and supports both traditional usage and headless approaches for multi-channel content delivery.

Key Features

Intuitive and Easy-to-Use Interface
Umbraco provides a simple yet powerful management panel: even non-technical users can create and update content easily thanks to visual tools and a drag-and-drop editor.

Maximum Flexibility and Customization
Its open structure allows every element of a website to be modeled: custom templates, tailored document types, and specific extensions for any project requirement.

Extensibility and API Integrations
Thanks to its API-first architecture, Umbraco integrates easily with CRMs, marketing tools, e-commerce platforms, and external services such as Google Analytics, HubSpot, or automation tools.

SEO and Performance
It supports advanced SEO practices: optimized URLs, metadata control (title, description, canonical), XML sitemaps, and robots management without the need for external plugins. Additionally, technologies such as caching and CDN compatibility improve page loading times.

Security and Access Control
Umbraco implements robust security measures, including regular updates, TLS/SSL support, multi-factor authentication, and granular user role management.

Multilingual and Multi-Site Management
Native support for multilingual and multi-site content makes it ideal for international projects or organizations managing multiple brands or domains from a single installation.

Why Choose Umbraco

Umbraco is appreciated for its balance between simplicity and advanced capabilities. It offers a solid framework for developers and an accessible environment for content editors, enabling projects ranging from corporate websites and informational portals to complex digital platforms.

Additionally, an active developer community and a rich package ecosystem allow the CMS to be continuously extended and updated while maintaining high performance and up-to-date security.

When to Use It

Umbraco is ideal if you want:

• full control over code and architecture
• custom integrations
• long-term scalability
• support for enterprise or multilingual projects

It is not generally the simplest “plug-and-play” solution for those looking for a zero-configuration CMS, but it excels when project customization and flexibility are the top priorities.

L'articolo Umbraco: the .NET CMS, Features, Advantages, and Use Cases proviene da Giovanni Anfuso.

What Magento Is and Why It Matters

Magento is an open-source eCommerce platform based on PHP and MySQL, designed for building robust and scalable online stores. Launched in 2008, it gained a significant share of the global market thanks to its flexibility and the ecosystem of extensions and themes available.

Over time, Magento has become synonymous with an enterprise-level solution for professional eCommerce, capable of meeting the needs of both large brands and companies managing complex product catalogs.

Magento’s Strengths

1. Scalability and Flexibility
Magento is extremely scalable: it can manage catalogs with thousands of products, multiple stores (multi-store), currencies, and languages from a single installation.

2. SEO Optimization and Marketing Tools
The platform includes native features for SEO optimization (friendly URLs, meta tag management, XML sitemaps) and advanced tools for promotions, coupons, and marketing campaigns.

3. Ecosystem of Extensions and Integrations
Magento can be extended with thousands of modules and extensions to add functionality such as integrations with CRM, ERP systems, payment gateways, external PIM platforms, and shipping services.

4. Internationalization
Native support for multilingual and multi-currency environments, with advanced tax rule management and local regulation handling, making it ideal for selling in global markets.

Limitations and Challenges

Complexity and Learning Curve
Magento requires advanced technical skills. Installation, customization, and maintenance often require specialized developers, increasing both time and costs.

Management Costs
Compared to SaaS solutions or simpler CMS platforms (such as Shopify or WooCommerce), Magento can be more expensive to maintain, especially regarding high-performance hosting and dedicated technical resources.

Security and Updates
Managing security patches and updates requires constant attention. Ignoring them can expose the website to serious vulnerabilities.

The Evolution of Magento and Adobe Commerce

Magento is no longer just an isolated open-source project: today it is part of the Adobe Commerce ecosystem, a complete eCommerce platform with advanced tools for marketing, customer experience, and cloud scalability.

This evolution has introduced benefits such as:

• improved integration with Adobe tools for data and automation
• advanced personalization and AI capabilities
• enhanced scalability and performance

However, many developers and merchants point out that complexity remains a critical factor, especially for mid-sized businesses seeking leaner solutions compared to Magento.

Magento in Today’s Landscape

In recent years, Magento has maintained its position in the enterprise market, but the competitive landscape has evolved rapidly. Solutions like Shopify or WooCommerce are often preferred by those looking for simplicity and lower management costs, while Magento continues to excel in complex and highly customized use cases.

Conclusions

Magento remains one of the most powerful and flexible eCommerce platforms available, suitable for projects that require:

• high scalability
• complex product catalogs
• deep customization
• integration with complex enterprise systems

However, its limitations in terms of complexity and cost require a careful evaluation, especially when compared with lighter or SaaS-based alternatives.

L'articolo Magento: Analysis of the eCommerce Platform Between Flexibility, Limitations, and Evolution proviene da Giovanni Anfuso.

Introduction

The integration between Jira by Atlassian and Figma allows development and design teams to work more collaboratively, bringing designs directly into the development workflow. With this connection, you can view live previews of Figma files inside Jira issues, sync updates in real time, and reduce the time spent switching between different tools.

In this in-depth guide, we’ll explore how to install the integration, connect files and prototypes, view design status, and automate workflows between Jira and the Figma design tool.

What Is the Jira–Figma Integration

The integration combines the Figma for Jira app with specific features that allow you to:

• Link Figma design files and prototypes directly to Jira issues.
• View design previews without opening Figma separately.
• Synchronize design status in real time with development status in Jira.

This integration is designed for Agile teams working with sprints, backlogs, and issue tracking, helping improve communication between designers and developers.

How to Install “Figma for Jira”

To use the integration, the first step is installing the Figma for Jira app in your Jira instance:

• In your Jira account, open the sidebar menu and go to Apps → Explore more apps.
• Search for Figma for Jira in the available apps list.
• Click Get it now and follow the installation process.

Once installed, a Designs section will appear inside Jira issues, where you can add your Figma files.

How to Link Figma Files and Prototypes to Jira Issues

After installing the app:

• Open the Jira issue where you want to add a design.
• If the Designs section is not visible, click Add design.
• Paste the URL of the Figma file or prototype into the indicated field.
• Click Link to connect the design to the issue.

Once linked, you can view and interact with the design directly in Jira by expanding the preview or opening the full file in a new tab.

Jira Plugin in Figma for Developers

In addition to the basic integration, you can install the official Jira plugin in Figma to:

• Import and link Jira issues to pages or layers within a Figma file.
• View issue details without leaving Figma.
• Create new issues directly while working on the design.

This feature is especially useful for developers and designers who want to maintain full context between design work and Jira tasks.

Viewing Design Status in Jira

Once a Figma file is connected, Jira displays the current design status:

• Design updated: the file has been modified after being linked.
• Ready for dev: indicates the design is ready for the development phase.

These details are automatically updated through webhooks connected to the Figma file, with no additional configuration required.

Useful Automations for Development Teams

With Jira, you can create automations based on linked designs to:

• Automatically notify developers about updates.
• Change issue status based on design modifications.
• Add labels or tags when a design is linked or updated.

These automations improve productivity and reduce repetitive manual tasks.

Removing the Integration

If needed, you can:

• Remove individual linked files or prototypes from an issue.
• Completely uninstall the Figma for Jira app from your Jira account, removing all associated links and integrations.

Conclusion

The integration between Jira and Figma is a powerful solution for improving collaboration between designers and developers. It centralizes information, reduces communication errors, and keeps everyone updated in real time within the Agile workflow.

For teams working with sprints and complex design processes, implementing this integration can transform the UX-to-development handoff into a smooth and efficient workflow.

Follow here all the Release Notes.

L'articolo Jira and Figma Integration: Complete Guide for Developers and Agile Teams proviene da Giovanni Anfuso.

Vibe Coding: When Development Meets Artificial Intelligence

In recent years, web development has been undergoing a radical transformation. The widespread adoption of Artificial Intelligence tools has changed the way developers write code, design architectures, and solve problems.

In this context, Vibe Coding has emerged: an approach to development where the focus shifts from manual implementation to guiding AI, while maintaining creative and technical control over the project.

But what exactly is Vibe Coding, and why is it becoming increasingly popular among developers?

What Is Vibe Coding

Vibe Coding is a programming approach based on:

• continuous interaction with AI systems
• intent- and goal-driven development
• rapid iteration and experimentation

The developer does not necessarily write every line of code from scratch but instead:

• describes the problem
• evaluates AI-generated solutions
• refines, corrects, and optimizes

Code becomes the result of a human–machine dialogue, rather than simple typing.

The Main AI Systems Used in Vibe Coding

Vibe Coding would not exist without a new generation of intelligent tools. Among the most relevant are:

GitHub Copilot
Probably the most widely used tool in everyday coding.
It suggests code in real time directly inside the editor, based on file and project context.

Ideal for:

• speeding up development
• reducing boilerplate code
• exploring alternative solutions

Main limitation: it does not always understand the project’s overall architecture.

ChatGPT
Commonly used for:

• complex logic design
• refactoring
• code explanation
• conceptual debugging

It is one of the most powerful tools in Vibe Coding because it enables reasoning about code, not just generating it.

Gemini
Highly effective for:

• understanding large contexts
• analyzing complex workflows
• integrating with cloud ecosystems

Often used for technical brainstorming and architectural evaluation.

Other Relevant Tools

• Codeium
• Tabnine
• Cursor
• Sourcegraph Cody

All contribute to an ecosystem where developers focus more on the why rather than the how.

How to Use Vibe Coding Effectively

Vibe Coding truly works only when applied with a structured approach. A healthy workflow includes:

• Clearly defining the problem
• Using AI to generate an initial solution
• Critically analyzing the produced code
• Adapting it to real project requirements
• Testing and validation

AI accelerates development, but it does not replace technical thinking.

How the Web Development Approach Has Changed

Before AI:

• linear development
• focus on syntax
• longer implementation times

With Vibe Coding:

• greater focus on architecture
• more time spent on strategic decisions
• extremely fast prototyping
• continuous experimentation

Developers increasingly become solution orchestrators, not just executors.

Seniority: Why It Still Matters (Maybe Even More)

A frequently underestimated aspect is that Vibe Coding amplifies skill differences.

An experienced developer:

• recognizes incorrect patterns
• identifies security issues
• evaluates performance and scalability impacts
• understands when AI produces fragile code

A less experienced developer risks:

• blindly trusting outputs
• accumulating technical debt
• losing understanding of the codebase

AI does not eliminate seniority — it makes it even more essential.

What to Watch Out for with Vibe Coding

Using Vibe Coding without awareness can create serious problems:

• insecure code
• inefficient solutions
• over-engineering
• dependency on AI
• loss of fundamental skills

The biggest risk is not AI itself, but losing control over the code.

Conclusion

Vibe Coding represents one of the most significant evolutions in modern web development. Tools like ChatGPT, GitHub Copilot, and Gemini are redefining how developers work.

However, one principle remains unchanged:

AI is an extremely powerful tool, not a substitute for expertise.

Those who learn to use it with critical thinking and solid technical foundations will gain a significant competitive advantage.

L'articolo Vibe Coding and Artificial Intelligence: How Web Development Is Evolving proviene da Giovanni Anfuso.