One of the most common questions people ask when they want to work without knowing how to code is how to learn NoCode quickly. While NoCode is easier than traditional programming, it still requires time and dedication.
After all, we’re building software, and that involves understanding the basics like project planning, data modeling, design, and more. Here are the steps to learn NoCode quickly so you can start building amazing apps and even make money from them.
Understanding the journey of learning NoCode
Before diving into the step-by-step process of how to learn NoCode, it’s important to understand how the learning journey works. Many people think that learning a new skill is a linear process — take classes, practice, and gradually improve.
However, the reality is that the learning journey often has its ups and downs. When you first start learning something new, you may feel excited and confident at first. But as you delve deeper, you may encounter challenges that make you doubt your progress.
It’s important to realize that feeling confused or frustrated is a normal part of the learning process. This is usually when you’re really starting to understand the skill. So if you feel overwhelmed, remember that it’s a sign that you’re on the right track.
Step 1: Familiarize yourself with NoCode tools
How to Learn NoCode Fast | 6 Essential Steps 4
The first step in learning NoCode quickly is to familiarize yourself with the various no-code tools available. The no-code ecosystem is vast, with hundreds of tools to choose from.
To focus your learning, you need to understand the nature of your project. Are you building a mobile app, a web app, or just a simple website? Once you know what you want to build, research the best no-code tools for that type of project.
At No-Code Startup, we’ve spent months testing and comparing different no-code tools so you don’t have to. We’ve identified the best tools for building robust apps, such as Bubble and FlutterFlow.
A common mistake people make is jumping straight into building their app without understanding the fundamentals of no-code development and software. It is crucial to know how to learn basic NoCode and follow a methodology when creating your application.
Skipping this step can lead to costly mistakes down the road, as you may need to go back and redo parts of your project. At No-Code Startup, we emphasize the importance of understanding the problem you’re solving before diving into the solution.
Start by conceptualizing your system — what types of users will you have, what screens will you need, and what functionality is required? After that, move on to designing your screens, creating a low-fidelity wireframe and eventually a more detailed design. Then, focus on data modeling and finally the actual development of your application.
Step 3: Learn how to learn NoCode through hands-on projects
Theory is important, but the best way to learn NoCode is through hands-on projects. Studies show that active learning, such as discussing, producing, and teaching, helps us retain knowledge much better than passive learning, such as reading or listening.
That’s why at No-Code Startup, we encourage our students to learn by doing. Start with simple projects to understand the basic concepts.
As you gain confidence, gradually take on more complex projects. This step-by-step approach will help you develop your skills and prepare for real-world challenges.
Step 4: Work on real-world projects
How to Learn NoCode Fast | 6 Essential Steps 5
Once you’ve completed a few hands-on projects, it’s time to tackle a real-world problem. Whether it’s a work-related task or a personal project you’ve always wanted to bring to life, applying your skills to a real-world scenario will deepen your understanding and give you the confidence to build more complex applications.
At this stage, you’ll find that the methodology and fundamentals you’ve learned will make the process much smoother. You’ll also be better equipped to troubleshoot and resolve any issues that arise, knowing how to find the answers you need.
Step 5: Surround yourself with like-minded people
Learning is easier when you are surrounded by people with similar goals. Join NoCode developer communities like Bubble Community, where you can ask questions, share experiences, and learn NoCode from others.
At No-Code Startup, we have a community where students can interact, ask technical questions, and support each other. Being part of such a community can provide valuable insights, networking opportunities, and even potential partnerships.
Step 6: Teach Others How to Learn NoCode
The final step to mastering NoCode (or any skill) is to teach it to others. Teaching forces you to organize your thoughts and explain concepts clearly, which reinforces your own understanding. It’s also a way to give back to the community and help others on their learning journey.
As you become more experienced, you can even offer consulting or mentoring services, turning your expertise into a source of income. Teaching not only helps you learn better, but it also establishes you as a go-to expert in the no-code field.
How to learn NoCode in the best way
How to Learn NoCode Fast | 6 Essential Steps 6
Learning how to learn NoCode is a journey full of ups and downs, but by following these steps, you can accelerate your progress and start building amazing apps in no time.
Remember, the key is to focus on the fundamentals, learn through hands-on experience, and stay connected with others in the no-code community.
And as you progress, don’t forget to share your knowledge — teaching is one of the most powerful ways to solidify your skills and grow as a developer. Click and find out more!
Watch our Free MasterClass
Learn how to make money in the AI and NoCode market, creating AI Agents, AI Software and Applications, and AI Automations.
Known as “Castelo”, he discovered the power of No-Code when he created his first startup entirely without programming – and that changed everything. Inspired by this experience, he combined his passion for teaching with the No-Code universe, helping thousands of people create their own technologies. Recognized for his engaging teaching style, he was awarded Educator of the Year by the FlutterFlow tool and became an official Ambassador for the platform. Today, his focus is on creating applications, SaaS and AI agents using the best No-Code tools, empowering people to innovate without technical barriers.
Artificial intelligence has advanced rapidly and AI agents are at the heart of this transformation. Unlike simple algorithms or traditional chatbots, intelligent agents are able to perceive the environment, process information based on defined objectives and act autonomously, connecting data, logic and action.
This advancement has driven profound changes in the way we interact with digital systems and carry out everyday tasks.
From automating routine processes to supporting strategic decisions, AI agents have been playing fundamental roles in the digital transformation of companies, careers and digital products.
What is an AI agent?
For an even more practical introduction, check out the AI Agent and Automation Manager Training from NoCode StartUp, which teaches step by step how to structure, deploy and optimize autonomous agents connected with tools such as N8N, Make and GPT.
One AI agent is a software system that receives data from the environment, interprets this information according to previously defined objectives and executes actions autonomously to achieve these objectives.
It is designed to act intelligently, adapting to context, learning from past interactions, and connecting to different tools and platforms to perform different tasks.
How Generative AI Agents Work
How Generative AI Agents Work
According to IBM, generative AI-based agents use advanced machine learning algorithms to generate contextualized responses and decisions — this makes them extremely efficient in personalized and dynamic flows.
Generative AI agents use large-scale language models (LLMs), such as those from OpenAI, to interpret natural language, maintain context between interactions, and produce complex, personalized responses.
This type of agent goes beyond simple reactive response, as it integrates historical data, decision rules and access to external APIs to perform tasks autonomously.
They operate on an architecture that combines natural language processing, contextual memory and logical reasoning engines.
This allows the agent to understand user intent, learn from previous feedback, and optimize its actions based on defined goals.
Therefore, they are ideal for applications that require deeper conversations, continuous personalization and autonomy for practical decisions.
Watch the free video from NoCode StartUp and understand from scratch how a conversational and automated AI agent works in practice:
Difference between chatbot with and without AI agent technology
While the terms “chatbot” and “AI agent” are often used interchangeably, there is a clear distinction between the two. The main difference lies in autonomy, decision-making capabilities, and integration with external data and systems.
While traditional chatbots follow fixed scripts and predefined responses, AI agents apply contextual intelligence, memory, and automated flows to perform real actions beyond conversation.
Traditional chatbot
A conventional chatbot operates on specific triggers, keywords, or simple question-and-answer flows. It usually relies on a static knowledge base and lacks the ability to adapt or customize continuously.
Its usefulness is limited to conducting basic dialogues, such as answering frequently asked questions or forwarding requests to human support.
Conversational AI Agent
An AI agent is built on a foundation of artificial intelligence capable of understanding the context of the conversation, retrieving previous memories, connecting to external APIs, and even making decisions based on conditional logic.
In addition to chatting, it can perform practical tasks — such as searching for information in documents, generating reports or triggering flows in platforms such as Slack, Make, N8N or CRMs.
This makes it ideal for enterprise applications, custom services, and scalable automations.
Comparison: AI agent, chatbot and traditional automation
What are AI Agents? Everything You Need to Know 12
To delve deeper into the theory behind these agents, concepts such as “rational agent” and “partially observable environments” are addressed in classic AI works, such as the book Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig.
Types of AI Agents
AI agents can be classified based on their complexity, degree of autonomy, and adaptability. Knowing these types is essential to choosing the best approach for each application and to implementing more efficient and context-appropriate solutions.
Simple reflex agents
These agents are the most basic, reacting to immediate stimuli from the environment based on predefined rules. They have no memory and do not evaluate the history of the interaction, which makes them useful only in situations with completely predictable conditions.
Example: a home automation system that turns on the light when it detects movement in the room, regardless of time or user preferences.
Model-based agents
Unlike simple reflex agents, these maintain an internal model of the environment and use short-term memory. This allows for more informed decisions, even when the scenario is not fully observable, as they consider the current state and recent history to act.
Example: a robot vacuum cleaner that recognizes obstacles, remembers areas already cleaned and adjusts its route to avoid repeating unnecessary tasks.
Goal-based agents
These agents work with clear goals and structure their actions to achieve these objectives. They evaluate different possibilities and plan the necessary steps based on desired results, which makes them ideal for more complex tasks.
Example: a logistics system that organizes deliveries based on the lowest cost, time and most efficient route, adapting to external changes, such as traffic or emergencies.
Utility-based agents
This type of agent goes beyond objectives: it evaluates which action will generate the greatest value or utility among several options. It is indicated when there are multiple possible paths and the ideal is the one that generates the greatest benefit considering different criteria.
Example: a content recommendation platform that evaluates user preferences, schedule, available time and context to recommend the most relevant content.
Learning agents
They are the most advanced and have the ability to learn from past experiences through machine learning algorithms. These agents adjust their logic based on previous interactions, becoming progressively more effective over time.
Example: a virtual customer service agent who, throughout conversations, improves their responses, adapts the tone and anticipates doubts based on the most frequently asked questions.
To understand how the use of AI is becoming a key factor in global digital transformation, McKinsey & Company published a detailed analysis on trends, use cases and economic impact of AI in business.
What are AI Agents? Everything You Need to Know 13
AI Agent Use Cases
Companies like OpenAI have been demonstrating in practice how agents based on LLMs are capable of executing complete workflows autonomously, especially when integrated with platforms such as Zapier, Slack or Google Workspace.
The application of artificial intelligence agents is rapidly expanding across various sectors and market niches.
With the evolution of no-code tools and platforms such as N8N, make up, Dify and Bubble, the creation of autonomous agents is no longer restricted to advanced developers and has become part of the reality of professionals, companies and creators of digital solutions.
These agents are especially effective when combined with automation tools, enabling complex workflows without the need for code. Below, we explore how different industries are already benefiting from these intelligent solutions.
Marketing and Sales
In the commercial sector, AI agents can automate everything from the first contact with leads to the generation of personalized proposals.
Through platforms like N8N, it is possible to create flows that collect data from forms, feed CRMs, send personalized emails and track the customer journey.
Additionally, these agents can analyze user behavior and adapt nurturing approaches based on previous interactions.
Service and Support
Companies that handle high volumes of interactions benefit from AI agents trained based on internal documents, FAQs, or databases.
With Dify and Make, for example, you can build assistants that answer questions in real time, automatically open tickets, and notify teams via Slack, email, or other integrations.
Education and Training
In the educational field, agents can be used to guide students, suggest content based on individual progress and even correct tasks in an automated way.
This automation illustrated below shows how AI agents can be practically implemented using N8N. In the flow, we have a financial agent personalized that converses with the user, accesses a Google Sheets spreadsheet to view or record expenses, and responds based on defined logic, allowed categories, and contextual validations.
The agent receives commands like “Show me my expenses for the week” or “Record an expense of R$120 on studies called 'Excel Course'”, and performs all actions automatically, without human intervention.
Automation flow with AI agent in N8N for expense control via Google Sheets
AI Agent FAQs
What can I automate with an AI agent?
AI agents are extremely versatile and can be used to automate everything from simple tasks — such as responding to emails and organizing information — to more complex processes such as reporting, customer service, lead qualification, and integration between different tools.
It all depends on how it is configured and what tools it accesses.
What is the difference between an AI agent and a customer service bot?
While a traditional bot answers questions based on keywords and fixed flows, an AI agent is trained to understand context, maintain memory, and make autonomous decisions based on logic and data. This allows it to take practical actions and go beyond conversation.
Do I need to know how to program to create an AI agent?
No. With no-code tools like N8N, Make, and Dify, you can create sophisticated agents using visual flows. These platforms allow you to connect APIs, build conditional logic, and integrate AI without having to write a line of code.
Is it possible to use AI agents with WhatsApp?
Yes. With platforms like Make or N8N, you can integrate AI agents into WhatsApp using third-party services like Twilio or Z-API. This way, the agent can interact with users, answer questions, send notifications, or capture data directly from the messaging app.
Why Learn to Build AI Agents Now
AI Agent Manager Training
Mastering the creation of AI agents represents a competitive advantage for any professional who wants to stand out in the current market and prepare for the future of work.
By combining no-code tools with the power of artificial intelligence, it becomes possible to develop intelligent solutions that transform operational routines into automated and strategic flows.
These agents are applicable in different contexts, from simple tasks such as organizing emails, to more advanced processes such as generating reports, analyzing data or providing automated service with natural language.
And the best part: all of this can be done without relying on programmers, using accessible and flexible platforms.
Get started today with AI Agent Manager Training, or deepen your automation expertise with the N8N Course to create agents with greater integration and data structure and take the first step towards building more autonomous, productive and intelligent solutions for your routine or business.
Large Language Models (LLMs) have become one of the most talked-about technologies in recent years. Since the meteoric rise of ChatGPT, generative AI-based tools are being explored by entrepreneurs, freelancers, CLT professionals, and tech-curious individuals.
But why is understanding how LLMs work so important in 2025? Even if you don't know how to program, mastering this type of technology can open doors to automation, the creation of digital products, and innovative solutions in various areas.
In this article, we will explain in an accessible way the concept, operation and real applications of LLMs, focusing on those who want to use AI to generate value without relying on code.
What is an LLM?
LLM stands for Large Language Model. It is a type of artificial intelligence model trained on huge volumes of textual data, capable of understanding, generating and interacting with human language in a natural way. Famous examples include:
GPT-4 (OpenAI)
Claude (Anthropic)
Gemini (Google)
Mistral
Perplexity IA
These models function as “artificial brains” capable of performing tasks such as:
Text generation
Automatic translation
Sentiment classification
Automatic summaries
Image generation
Automated service
How do LLMs work?
In simple terms, LLMs are built on Transformer neural networks. They are trained to predict the next word in a sentence, based on large contexts.
The more data and parameters (millions or billions), the more powerful and versatile the model becomes.
Building your own LLM requires robust infrastructure, such as vector storage, high-performance GPUs, and data engineering. That's why most professionals opt for use ready-made LLMs via APIs, like those of OpenAI, Anthropic (Claude), Cohere or Google Gemini.
For those who don't program, tools like Make, Bubble, N8N and LangChain allow you to connect these models to workflows, databases, and visual interfaces, all without writing a line of code.
Additionally, technologies such as Weaviate and Pinecone help organize data into vector bases that improve LLM responses in projects that require memory or customization.
The secret is to combine the capabilities of LLMs with good practices in prompt design, automation and orchestration tools — something you learn step by step in AI Agent Manager Training.
Difference between LLM and Generative AI
Although they are related, not all generative AI is an LLM. Generative AI encompasses many different types of models, such as those that create images (e.g. DALL·E), sounds (e.g. OpenAI Jukebox), or code (e.g. GitHub Copilot).
LLMs are specialized in understanding and generating natural language.
For example, while DALL·E can create an image from a text command, such as “a cat surfing on Mars,” ChatGPT, an LLM — can write a story about that same scenario with coherence and creativity.
Examples of practical applications with NoCode
The real revolution in LLMs is the possibility of using them with visual tools, without the need for programming. Here are some examples:
Create a chatbot with Dify
As Dify Course, it is possible to set up an intelligent chatbot connected to an LLM for customer service or user onboarding.
Automate tasks with Make + OpenAI
Node Makeup Course You learn how to connect services like spreadsheets, email, and CRMs to an LLM, automating responses, data entry, and classifications.
Building AI Agents with N8N and OpenAI
O Agents with OpenAI Course teaches how to structure agents that make decisions based on prompts and context, without coding.
Advantages of LLMs for non-technical people
Access cutting-edge AI without having to code
Rapid testing of product ideas (MVPs)
Personalization of services with high perception of value
Optimization of internal processes with automations
LLMs and AI Agents: The Future of Interaction
The next evolutionary step is the combination of LLMs and AI agents. Agents are like “digital employees” that interpret contexts, talk to APIs and make decisions autonomously. If you want to learn how to build your agents with generative AI, the ideal path is AI Agent Manager Training.
We are living in an era where texts, images and videos can now be created by artificial intelligence. But there is one element that is gaining strength as a competitive advantage: the voice.
Whether in podcasts, institutional videos, tutorials or even automated service, the ability to create realistic artificial voice is changing how companies and creators communicate. And in this scenario, the ElevenLabs AI emerges as one of the global protagonists.
What is ElevenLabs AI? The AI-Powered Voice Revolution 17
What is ElevenLabs?
O ElevenLabs is one of the neural speech synthesizers most advanced on the market. With its technology AI voice cloning and AI-powered text to speech, allows you to create realistic voices in multiple languages, with natural intonation, dynamic pauses and surprising emotional nuances.
Key Features:
Human-quality Text to Speech
Conversational AI with support for interactive agents
AI-generated voices can be used commercially, as long as you respect the platform's Terms of Use and do not violate third-party rights by cloning real voices without authorization.
Is it possible to use ElevenLabs for free?
Yes. ElevenLabs offers a free plan with 10,000 credits per month, which can be used to generate up to 10 minutes of premium quality audio or 15 minutes of conversation
This plan includes access to features like Text to Speech, Speech to Text, Studio, Automated Dubbing, API, and even Conversational AI with interactive agents.
Ideal for those who want to test the platform before investing in paid plans.
What is the best alternative to ElevenLabs?
Other options include Descript, Murf.ai and Play.ht. However, ElevenLabs has stood out for its natural voice, advanced audio editing features with AI, API integration and support for multiple languages.
Their paid plans start from US$ 5/month (Starter) with 30 thousand monthly credits, and go up to scalable corporate versions with multiple users and millions of credits.
You submit a text, choose a voice (or clone one), and AI converts that text into realistic audio in seconds. It can be used via the web dashboard or via API for automated workflows.
Examples of using ElevenLabs AI in practice
1. Video and podcast narration
Ideal for creators who want to save time or avoid the costs of professional voiceovers.
2. Automated service with human voice
Turn cold bots into realistic, empathetic voice assistants.
3. Generating tutorials and training with audio
Companies and CLT professionals can create more engaging internal materials.
4. Applications that “talk” to the user
With tools like Bubble, FlutterFlow or WebWeb, it is possible to integrate AI voice into apps.
How to integrate ElevenLabs with NoCode tools
What is ElevenLabs AI? The AI-Powered Voice Revolution 19
N8N + ElevenLabs API
Allows you to automate voice generation based on dynamic data using visual workflows in N8N. It is ideal for creating processes such as audio customer service responses, automated voice updates, and more.
With the use of AI agents, it is possible to create voice-responsive systems, such as a virtual attendant that speaks to the customer based on a dynamic prompt.
Use the API to insert audio into your apps with interaction triggers or dynamic events.
ElevenLabs and NoCode: Open the door to creating experiences with voice AI
AI-generated voice is already a powerful, accessible and potential-rich reality. ElevenLabs is not just a tool, but an engine for creating immersive, automated and more human experiences.
If you want to learn how to integrate these possibilities with NoCode and AI tools, NoCode Start Up has the ideal paths:
EN 
PT 
ES 