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Neto Camarano

What is Generative AI? Everything you need to know

Estimated reading time: 6 minutes

Generative AI is a cutting-edge branch of artificial intelligence that produces a variety of content types, including text, images, audio, and synthetic data. Its recent surge in popularity stems from user-friendly interfaces that allow the creation of high-quality content — text, graphics, and videos — in mere seconds.

Evolution of generative AI

However, the technology is not entirely new. It dates back to the 1960s with the development of the first chatbots. The real breakthrough came in 2014 with the introduction of Generative Adversarial Networks (GANs). A machine learning algorithm capable of creating convincingly realistic images, videos and audio.

Two significant developments brought generative AI into the mainstream: transformers and the language models they enabled. Transformers revolutionized machine learning by allowing researchers to train large models without having to pre-label all the data.

This innovation has led to more insightful responses from AI systems, capable of analyzing not just words but also complex data such as code, proteins, and even DNA.

Large language models (LLMs), boasting billions or trillions of parameters, have ushered in a new era of generative AI. These models can create engaging text, create photorealistic images, and even generate entertainment content.

Multimodal AI now enables the simultaneous generation of text, image, and video content. This innovation therefore powers tools like DALL-E, which can produce images from text descriptions or generate captions from images.

How does it work?

Generative AI works by responding to a prompt, whether it’s text, an image, a video, or even musical notes. The AI uses various algorithms to produce new content based on that input, such as essays, realistic fakes, or problem-solving solutions.

Thus, in its early stages, generative AI required developers to send data through APIs or use best tools specialized. Today, the user experience has improved dramatically, allowing users to enter requests in plain language and receive personalized responses based on style, tone, and other preferences.

Generative AI models

Generative AI models combine multiple algorithms to represent and process different types of content. For example, to generate text, natural language processing techniques convert raw characters into sentences and actions, represented as vectors.

Similarly, images are broken down into visual elements and processed as vectors. However, it is essential to note that these models may encode biases, inaccuracies, or harmful content from the data they were trained on.

Once the data is represented, neural networks like GANs or variational autoencoders (VAEs) generate new content. These models can then create realistic human faces, synthetic data to train AI systems, or even realistic representations of specific individuals.

Popular Generative AI Tools

Generative AI applications have gained widespread recognition, including:

DALL-E: A multimodal AI model that links text descriptions to visuals, allowing users to generate images from written prompts.
ChatGPT: Launched in November 2022 and built on GPT-3.5, this chatbot simulates natural conversations and allows for interactive feedback. GPT-4, released in March 2023, has further improved its capabilities.
Gemini: Developed by Google, Gemini uses transformative AI for language and content generation. While its initial launch faced challenges, its most recent iterations have improved efficiency and visual responses.

Use cases for generative AI

Generative AI can be applied in several fields, including:

Creation of chatbots for customer service.
Generating deepfakes for entertainment or potentially harmful purposes.
Improve language dubbing in films and educational content.
Writing emails, resumes, or essays.
Design photorealistic art or new products.
Optimize chip design and suggest new drug compounds.
Compose music in specific styles.

Benefits

Generative AI offers significant advantages, such as:

Automate content creation processes.
Simplify email responses and technical queries.
Generate realistic representations of people and summarize complex information into coherent narratives.
Simplify the creation of content in specific styles and tones.

Limitations

While generative AI holds great promise, it also brings challenges:

It does not always provide sources for content, making verification difficult.
It can reflect biases and prejudices present in your training data.
Content that appears realistic can obscure inaccuracies.
Tuning AI models for specific scenarios can be complex.

Generative vs. Generative AI Traditional AI

Generative AI focuses on creating new content and solutions based on user prompts. It relies on neural networks like transformers, GANs, and VAEs.

In contrast, traditional AI typically follows predefined rules to process data, making it better suited for tasks that involve structured outputs.

The Future of Generative AI

The rapid rise of best AI tools Generative AI technologies like ChatGPT, DALL-E, and Midjourney have led to increased demand for AI training and applications across industries. As generative AI technology matures, it will likely integrate more seamlessly into existing workflows, enhancing everything from grammar checkers to design tools.

However, as generative AI becomes more prevalent, the need for tools that track content provenance and improve trustworthiness will become critical. Thus, the long-term impact of generative AI is still evolving, but its potential to transform industries, automate tasks, and redefine human expertise is immense.

Want to learn more about the best AI tools without having to code? Be sure to check out our NoCodeIA Training!

September 26, 2024
4 min

Matheus Castelo

Customer Service with NoCode AI Agents

Estimated reading time: 7 minutes

Have you ever felt frustrated trying to resolve a problem with a product you purchased or a service you contracted? If so, you’re not alone. After all, customer service and support are undoubtedly one of the biggest challenges for companies in all segments today.

However, what if there was a solution that could serve customers 24/7, with fast responses based on personalized information from the company itself? In this post, we will explore how you can create a virtual assistant with AI agents using NoCode technologies.

The Era of NoCode AI Agents

You AI agents are not new, but their application in customer service processes has proven to be revolutionary. Unlike traditional chatbots, which follow a predefined flow, AI agents use generative artificial intelligence and natural language processing (NLP).

This means that when interacting with an AI agent, the customer feels like they are talking to a real person, capable of understanding nuances and responding in a more elaborate way.

Benefits of AI Agents vs. Traditional Chatbots in Customer Service

Humanized responses: AI agents use advanced models, such as GPT, to generate responses that appear to have been written by a person, not a machine.
Flexibility: While traditional chatbots follow rigid scripts, AI agents can be trained with specific data, such as PDFs, spreadsheets, and websites, to answer questions based on the context provided.
Short-term and long-term memory: AI agents can “remember” previous interactions, offering a more personalized and contextualized experience for the user.
Advanced functions: In addition to answering questions, AI agents can perform complex functions such as sending emails, checking the weather, interacting with databases, and more.

How to Create Your Own Customer Service Agent

Let's see how you can create a virtual customer service agent using tools NoCode, such as n8n for automations and integrations, and Dify for creating AI agents. With these tools, you can train your agent with personalized data and integrate it with WhatsApp to serve your customers efficiently.

1. Project planning

Before you start building your agent, it’s important to plan your workflow. To do this, you’ll need:

User interface (frontend): For this application, WhatsApp will be the main interface. This makes the process easier, as it avoids the need to develop an application or website from scratch.
Backend and database: Here you can use Supabase, a highly efficient NoCode database to store data about agent interactions with users.
Integration Tool: n8n is a great choice to connect your agent to WhatsApp and manage automations.
Agent creation tool: Dify lets you create powerful, custom AI agents without the need for coding.

2. Creating the AI agent in Dify

Dify is a NoCode tool that lets you create AI agents trained with custom data. You can train your agent with:

PDFs: Instructions, FAQs, and product information can be uploaded in PDF format.
Spreadsheets: Structured data in spreadsheets can be used to answer specific questions, such as courses offered, duration, and prices.
Websites: information available on the company's website can also be used to enrich the agent's knowledge.

So, in Dify, you can create workflows and set up a base prompt with clear instructions on how the agent should behave in customer service, who they represent, and what their duties are. In addition, you can define “knowledge blocks” that organize the training information efficiently.

3. Automation and integration with n8n

n8n is an automation tool that allows you to connect different services, such as WhatsApp and Supabase, to your AI agent. With it, you can create flows that:

They receive messages from WhatsApp and forward them to the AI agent.
Store user interactions in the database.
They respond to the customer with personalized and contextual information.

For example, when receiving a message from a customer asking about a specific course, n8n forwards that message to the AI agent in Dify, which queries the database and responds with the course details to customer support.

4. Integration with WhatsApp

To connect your agent to WhatsApp, you’ll need a WhatsApp API, such as Zapi, that allows communication between your app and the AI agent. The setup is simple:

The API webhook receives the message from the user.
The message is forwarded to n8n, who forwards it to the agent at Dify.
The agent queries its trained data and responds to the user.

This integration allows your AI agent to chat directly with customers via WhatsApp, providing instant and personalized service.

5. Practical examples

Let’s imagine a practical scenario. You have a company that offers online courses, and you want your AI agent to help students find information about courses and enroll. So, your agent could be trained with:

Course spreadsheet: including information such as name, duration, price and registration link.
FAQ PDF: answers to common questions, such as payment methods, access to content, etc.
Company website: for additional information about the company and its offerings.

This way, your agent can answer questions like:

“What courses are available on application development?”
“How do I enroll in the FlutterFlow course?”
“What is the price of the NoCode Startup course?”

So, in all these interactions, the agent consults the data provided and responds accurately, offering a superior customer service experience.

Conclusion: Best Customer Service with NoCode

With the evolution of NoCode technologies, creating highly efficient customer service agents has never been more accessible. Tools like Dify and n8n allow you to build powerful agents.

By adopting these technologies, you not only improve the customer experience, but also optimize your company’s internal processes. So why not take advantage of this revolution in customer service?

So, if you want to learn more about how to create custom customer service agents and integrate them with WhatsApp, check out the NoCodeIA training.

September 24, 2024
5 min

Neto Camarano

Supabase Backend – Everything you need to know

Estimated reading time: 9 minutes

Building scalable and secure applications today requires a robust backend infrastructure. However, for many developers, managing the complexities of servers, authentication systems, and storage can be a daunting task. This is where Supabase comes into play.

Supabase is an open-source Backend as a Service (BaaS) platform that aims to simplify backend development. Providing a PostgreSQL database, real-time data synchronization, user authentication, and file storage through a simple and intuitive API.

In this guide, we'll explore everything you need to know about Supabase, its key features, and how it can help developers streamline their workflow.

What is Supabase?

Supabase is a powerful, open-source alternative to Firebase, offering a database PostgreSQL managed. In order to combine real-time features, user authentication, file storage and even serverless functions.

The platform is designed with the goal of simplifying backend development while providing scalability and security. Unlike traditional backends, Supabase handles much of the heavy lifting, offering a managed backend with minimal configuration.

Developers can focus on building their frontend applications while leveraging Supabase's backend capabilities through an API.

Why choose Supabase?

Supabase’s main appeal lies in its ability to provide developers with a robust backend that doesn’t require complex configuration or management. Traditionally, developers would have to deal with multiple configurations separately.

Supabase brings all of these features together into one cohesive package, reducing the need to juggle multiple services and tools. Another significant benefit is its open source nature.

While Firebase is a closed ecosystem with proprietary technology, Supabase is built with the best tools open source, primarily PostgreSQL. This means you get the flexibility and transparency of open source software without sacrificing the benefits of a managed service.

Now, let’s dive deeper into the key features of Supabase.

1. Leveraging the PostgreSQL database

At the heart of Supabase is PostgreSQL, one of the world's most popular relational database systems. PostgreSQL is a database that offers flexibility, scalability, and performance.

Supabase's decision to use PostgreSQL as its backend database allows developers to leverage the full power of SQL while benefiting from a stable and scalable infrastructure.

Some of the key features of Supabase's managed PostgreSQL database include:

Advanced queries: Supabase supports complex SQL queries including joins, aggregations, and even full-text search.
JSON support: PostgreSQL supports JSON data types, allowing you to work with both structured and unstructured data in the same database.
Row-level security: With Supabase, you can implement fine-grained row-level access control, ensuring that users can only access the data they are authorized to see.

Additionally, the PostgreSQL database is fully integrated with the Supabase API, which means that every table you create in your database automatically generates RESTful endpoints to perform CRUD operations.

Therefore, this Instant API feature is a huge time saver for developers who would otherwise need to manually create endpoints to interact with their data.

2. Seamless API Integration with Supabase

One of the main strengths of this tool is its API-first approach. Developers can interact with the backend exclusively through a RESTful API. This makes it easy to integrate Supabase with different frontend technologies like React, Vue, Angular, and Next.js.

This decoupling of the frontend from the backend allows for greater flexibility, making it easier to swap or update frameworks without having to overhaul the entire backend. This way, with the Supabase API, you can perform CRUD operations, manage user authentication, and handle file uploads and downloads. All without having to write complex server-side code.

The API is also well documented, making it easy for developers to get started. So whether you’re an experienced developer or just starting out, the straightforward documentation and SDKs make Supabase accessible and quick to implement.

3. User authentication made easy

Implementing user authentication is one of the most time-consuming and error-prone aspects of backend development. Supabase simplifies this process by providing a comprehensive, integrated authentication system.

This way, developers can implement secure user registration, login, and session management with just a few lines of code. Supabase supports multiple authentication methods, including:

Email/Password: Traditional login with email and password.
Social Logins: Authentication through popular services like Google, GitHub, and others.
Magic Links: Password-free login via email.

In addition to handling login and registration, Supabase's authentication system also supports password resets, email verification, and JWT-based session management.

4. Real-time data synchronization

Real-time functionality is becoming increasingly important in modern applications. Supabase excels in this area by providing real-time data synchronization through PostgreSQL replication.

So with Supabase, you can easily build applications that react to database changes in real time. For example, if you're building a collaborative text editor, you can use the WebSocket-based event system to ensure that all users see the updates.

This feature is possible without any complex setup or configuration. So once your database is connected to Supabase, you can start listening for changes in real time using the client libraries.

5. Hassle-free file storage

In addition to handling database operations, Supabase also offers file storage. This feature is especially useful for applications that need to store and manage user-generated content.

Supabase’s storage system is secure and comes with built-in access control policies. You can create public and private buckets to manage your files. Additionally, Supabase’s API allows you to easily upload, download, and delete files programmatically.

6. Supabase Edge Functions for Serverless Execution

The tool also supports Edge Functions, which are lightweight serverless functions that run at the edge, close to your users. These functions offer low-latency execution, making them ideal for tasks that need to be executed quickly and efficiently.

So with Supabase edge functions, you can write custom server-side logic in JavaScript or TypeScript and deploy it directly to Supabase. This feature is especially useful for tasks like payment processing, sending transactional emails, or data transformations.

7. Scalability and security

As your application grows, Supabase scales with you. Because Supabase is built on PostgreSQL, it is designed to handle large volumes of data and high levels of concurrent traffic.

Security is another important consideration for any backend, and this tool implements best practices for data encryption, authentication, and access control. So, whether you’re managing sensitive user data or protecting file uploads, Supabase ensures your application remains safe and secure.

Conclusion

Supabase is an incredibly powerful tool for developers who want to simplify backend development while still having access to a scalable, secure, and feature-rich infrastructure. Offering a managed PostgreSQL database, user authentication, real-time sync, file storage, and cutting-edge features, this tool provides a comprehensive solution for building modern applications.

So, if you’re looking for an open-source alternative or simply want to simplify your backend development, Supabase is definitely worth exploring.

Finally, if you want to know how to get the most out of this tool, you need to be part of the NoCodeIA Training! Come and discover everything about the world NoCode with us!

September 19, 2024
6 min

Matheus Castelo

OpenAI o1: the model that revolutionizes no-code platforms

Estimated reading time: 7 minutes

In recent months, OpenAI has been surprising with a series of impressive launches. After the launch of GPT for All, which brought a powerful multimodal model, the company didn't stop there. Recently, they announced a new model, OpenAI o1, which promises to be even more efficient and revolutionary.

If you thought GPT for All was already impressive, get ready to meet the new One Preview. In this article, we’ll explore why this model is gaining acclaim and how it differs from its predecessors.

What is OpenAI o1?

One Preview, or o1, is a beta version of a highly advanced model that is already being tested by select users. Its main differentiator is that it is designed to “think” about responses before providing them.

This means that instead of giving quick, automatic answers like most previous models, One Preview analyzes the question more deeply, taking more time to consider the best solution, especially for complex problems in areas like science, coding, and math.

This slower, more careful approach has a clear purpose: to provide more accurate and detailed answers, something that can be crucial in cases where the quality of the answer is more important than speed.

Why is this model so powerful?

One of the highlights of One Preview is its logical reasoning capabilities. Unlike other models, it uses more time and resources to evaluate the answer before providing it, mimicking the behavior of a human expert.

In tests, One Preview outperformed in areas like science and coding, where complex problems are the norm. In an international math competition, for example, while GPT for All correctly solved 13% of the problems, One Preview achieved an impressive 83% correct answers.

This leap in performance is a clear indication that we are facing a much more robust model, especially for tasks that require advanced reasoning.

Enhanced security with o1

Another important aspect of this new model is safety. Previous AI models often failed to narrow down responses to potentially dangerous questions, such as the creation of weapons or harmful substances.

One Preview brings much tighter security. It not only thinks through the responses, but also evaluates whether the content is safe and ethical before providing it. This means the model avoids providing dangerous information, such as recipes for creating illegal substances or explosives.

This is vital in today’s world, where the ethical use of AI is at the center of global discussions. By improving security, OpenAI demonstrates its commitment to delivering technologies that are not only powerful but also safe for large-scale use.

Who can benefit from One Preview?

If you work on complex science, math, or coding problems, One Preview might be the perfect template for you. It’s designed to handle precisely these types of problems.

Often other models, such as GPT-4 and other solutions from OpenAI and competitors such as AWS and Google Cloud, cannot perform well on more intricate issues. However, with One Preview, which has a more analytical approach, the results are significantly better.

Performance comparisons

In tests, One Preview outperformed GPT for All in several areas. In coding competitions, for example, GPT for All achieved just 11% correct answers, while One Preview achieved 62%.

Impacts for NoCode developers

If you work with NoCode platforms such as Bubble or Flutterflow, get ready for a revolution. The ability to integrate AI with NoCode tools was already impressive, but with One Preview, the possibilities expand even further.

Imagine building an application that not only automates processes but also solves complex problems autonomously using advanced AI. With One Preview, this becomes a reality.

For example, if you’re building a NoCode system to process scientific data or solve equations, One Preview can provide detailed and accurate answers. Plus, its ability to drill down into questions allows it to solve problems in ways that other models simply can’t.

Practical examples of using o1

To better understand the potential of this model, we ran some simple tests on OpenAI’s own website. First, we asked the model to explain Einstein’s Law of Relativity.

One Preview took about five seconds to think, but delivered an extremely detailed and accurate response, something that impresses even those who are already used to interacting with AI models.

We then asked the model to generate Python code to implement this law in a project. Again, it took a few seconds to process the request, but it generated functional and well-structured code.

This type of use is extremely relevant for NoCode developers, who can take advantage of the model's ability to create complex applications without needing to understand all the details of coding.

Price and accessibility

Of course, all this power comes at a cost. OpenAI o1’s full model costs about $15 per million tokens, which is roughly three times more expensive than GPT for All.

However, this price difference is justified by the model's ability to solve complex problems with greater precision. For those looking for more affordable solutions, there is the One Mini, a cheaper version that also offers good results.

It is worth remembering that, for now, o1 is only available to some users, as it is still in beta. However, it should soon be released to a wider audience, and API integrations will allow developers NoCode take advantage of the full power of this tool.

OpenAI o1: The Future of AI Automation

With each new release, OpenAI reinforces its position as a leader in the artificial intelligence market. OpenAI o1 is yet another testament to the company’s commitment to bringing innovations that not only make developers’ lives easier, but also make projects that were previously unimaginable possible.

If you work with NoCode platforms or are interested in intelligent automation, now is the time to start exploring the possibilities that One Preview offers.

In the coming years, we’ll likely see even more impressive advances. In the meantime, let’s take advantage of the innovations that are already available and start planning how we can use them in our projects. If you enjoyed this content and want to learn more about the future of AI, be sure to check out our NoCodeAI Training!

September 17, 2024
5 min

Where there is change there are opportunities

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