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.

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.

Drupal is one of the longest-standing and most versatile CMS platforms on the market, known for its flexibility and modularity. Over the years, it has evolved from a platform for complex websites to a tool capable of supporting enterprise projects, community portals, and advanced digital platforms. This article examines Drupal’s strengths and challenges, its evolution over time, and future prospects, particularly with the integration of Artificial Intelligence technologies.

Strengths of Drupal

Drupal stands out for several key features that make it a robust CMS appreciated by developers:

Modularity and flexibility: Thanks to its modular architecture, functionalities can be extended without compromising the core.
Advanced security: Regular updates and an active community ensure protection even for high-risk sites.
Complex content management: Supports custom content types, taxonomies, and advanced views.
Scalability: Ideal for enterprise websites, government portals, and high-traffic platforms.
Community and ecosystem: Thousands of modules and themes, alongside a global developer community.

Areas for Improvement

Despite its strengths, Drupal presents some challenges for developers and organizations:

Steep learning curve: The CMS complexity can be daunting for those coming from WordPress or more “out-of-the-box” solutions.
Frontend management not straightforward: Even with Twig, design and frontend integration require advanced skills.
Performance on small sites: Without caching and optimization, simple sites can perform slower than lighter solutions.
Module updates and compatibility: Some third-party modules require manual updates, occasionally causing conflicts.

Historical Evolution of Drupal

Drupal has undergone significant evolutionary steps:

Versions 6–7: Consolidation of content management and first developer APIs.
Version 8: Introduction of Symfony, REST API, greater adherence to modern standards.
Versions 9, 10, and 11: Improvements in security, modern PHP compatibility, integration with headless tools, and support for advanced editors like CKEditor.

This evolution has allowed Drupal to adapt to enterprise requirements and the growing demand for headless and omnichannel architectures.

Drupal and the Rise of AI

Artificial Intelligence is transforming the web, and Drupal is embracing these trends:

Content intelligence: Modules for automatic content suggestions, smart tagging, and text generation.
Advanced personalization: AI to suggest relevant content to different users in real-time.
Workflow automation: From SEO optimization to media management, AI can reduce editors’ workload.
Integration with external tools: APIs for NLP, data analysis, and predictive systems.

Adopting AI in Drupal represents an opportunity to simplify development, enhance UX, and deliver personalized content, while maintaining the platform’s security and flexibility.

Conclusion

Drupal remains a reference platform for developers and organizations seeking a powerful, scalable, and secure CMS. Its strengths, including modularity, security, and advanced content management, keep it competitive, while challenges such as the learning curve and performance can be mitigated through best practices and modern tools.

With AI integration, Drupal is poised to become not just a CMS for complex content, but an intelligent platform capable of adapting dynamically and predictively to the needs of users and development teams.

L'articolo Drupal Ecosystem: Strengths, Challenges, and Prospects with the Rise of AI proviene da Giovanni Anfuso.

Figma: what it is and why it has become a standard in digital design

Figma is a web-based UI/UX design tool used by designers, developers, and product teams to collaboratively create digital interfaces. Unlike traditional software, Figma works directly in the browser (with optional desktop apps) and allows multiple people to work simultaneously on the same file in real time.

Thanks to its simplicity, flexibility, and power, Figma has become a standard in product design, web design, and application development, adopted by both startups and large enterprises.

How Figma works

Figma is built on a cloud-first architecture. All files are saved online and accessible from anywhere, without the need for local versions or manual transfers.

Its main features include:

• Advanced vector editor for interface creation
• Real-time collaboration (similar to Google Docs)
• Reusable components and design systems
• Interactive prototyping without code
• Comments and feedback directly on the design
• Integrations and plugins to extend functionality

Technically, every user action is synchronized instantly with Figma’s backend, ensuring real-time updates for all collaborators.

Key advantages of Figma

Figma’s tremendous success comes from several key advantages:

1. Real-time collaboration
Multiple users can edit the same file simultaneously, eliminating version conflicts.

2. Accessibility
Being browser-based, Figma is accessible from any operating system without complex installations.

3. Performance and reliability
Even with complex files and large teams, Figma maintains high performance.

4. Scalable design systems
Components ensure visual consistency and speed up workflows.

5. Evolving ecosystem
Plugins, APIs, and integrations make Figma extremely extensible.

The scalability challenge: from a few users to over 10 million

With such rapid growth, Figma faced one of the toughest challenges for any SaaS platform: infrastructure scalability.

Initially, the team had to scale a monolithic PostgreSQL database designed for far fewer users than current volumes.

In a recent technical thread, Figma shared the journey from a single database to a horizontally sharded architecture capable of supporting over 10 million active users.

Phase 1: immediate relief with vertical scaling

The first phase addressed the most urgent issues:

• Vertical scaling of PostgreSQL
• Introduction of read replicas to reduce read load
• Connection pooling via PG Bouncer

These solutions provided a quick improvement but were insufficient for the long term.

Phase 2: stability with logical partitioning

In the medium term, Figma adopted logical partitioning, separating table groups into dedicated databases.

Notable innovations included:

• PostgreSQL views to simulate sharding
• Query testing on sharded structures before physical separation

During migration, the team optimized logical replication, temporarily removing indexes to speed up data transfer, drastically reducing migration time.

Phase 3: horizontal sharding and distributed architecture

The final phase achieved true scalability:

• Introduction of horizontal sharding
• Creation of a custom query router
• Development of a DB proxy in Golang for intelligent query routing across shards

Deployment occurred in two stages, using PG Bouncer to ensure safe transitions without downtime, allowing gradual and controlled traffic routing.

Why these optimizations matter

These optimizations are not just relevant for Figma—they are a key case study for anyone building high-growth SaaS applications.

They demonstrate how:

• PostgreSQL can scale to extremely high levels
• Incremental solutions are often more effective than full rewrites
• The combination of architectural choices and custom tooling can make a huge difference

Conclusion

Figma is not just a design tool but an excellent example of a well-engineered tech product, both in terms of user experience and backend architecture.

Its evolution, from a simple collaborative tool to a global platform serving millions of users, shows how innovation, performance, and scalability can coexist when technical decisions are guided by a clear, progressive vision.

For professionals in design, development, or software architecture, Figma remains an essential reference point.

L'articolo Figma: the design tool that keeps growing thanks to technological innovation proviene da Giovanni Anfuso.

What is Gemini 3 Flash

An AI Model Built for Speed
Gemini 3 Flash is an advanced AI model that combines “frontier-level” reasoning capabilities with extremely low latency. Google’s goal is clear: provide intelligent, contextual responses almost instantly, making the user experience smoother and more natural.

“Gemini 3 Flash: frontier intelligence built for speed”
— Google, official blog

Differences Compared to Previous Models

Compared to Gemini 2.5 Flash, the new version offers:

• Faster response times
• Improved reasoning abilities
• Lower usage costs
• Greater efficiency for high-traffic applications

Key Features of Gemini 3 Flash

Advanced Multimodal Intelligence
Gemini 3 Flash can understand and generate content from:

• Text
• Images
• Audio
• Video

This makes it ideal for virtual assistants, content analysis tools, and interactive AI applications.

Reasoning and Contextual Understanding
The model maintains strong problem-solving, planning, and complex analysis capabilities, proving effective in areas such as:

• Programming
• Decision support
• Complex information processing

Availability of Gemini 3 Flash

Integration in the Gemini App
Google has made Gemini 3 Flash the default model in the Gemini app, improving everyday user experience with faster and more accurate responses.

Gemini 3 Flash on Google Search
The new model is also integrated into AI Mode on Google Search, providing smarter, deeper, and more contextual results for complex queries.

Developer Tools
Gemini 3 Flash is available through:

• Gemini API
• Google AI Studio
• Vertex AI
• Android Studio
• Gemini CLI

This broad availability allows developers to create scalable and high-performance AI applications.

Benefits for Users and Enterprises

For End Users

• Faster responses
• Higher accuracy
• Improved conversational experience

For Developers and Businesses

• Lower costs
• High performance
• Easy integration
• Support for real-time use cases

Conclusion

With Gemini 3 Flash, Google takes a decisive step toward faster, more accessible, and efficient AI. The new model demonstrates how cognitive power and speed can be combined, making AI an increasingly central tool in daily digital life and in the development of innovative solutions.

Source: Google Blog

L'articolo Google Unveils Gemini 3 Flash: Ultra-Fast AI with Advanced Intelligence proviene da Giovanni Anfuso.