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IT job market: how have no-code platforms transformed jobs?

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Estimated reading time: 10 minutes

In the last ten years, society has faced a major revolution marked by digitalization, connectivity, internet of things and even artificial intelligence. The truth is that today there are technologies so advanced that no sci-fi film from the 1980s was able to predict. 

With such digital transformation, the job market found itself obliged to follow these trends and we currently have a technological reality that is widespread in the corporate world. 

Virtual clocking in and collaborative cloud spaces are just a few examples of the tools used in home office companies in different niches. It is no longer necessary for an organization to be focused on technology sectors to be technological. 

To some extent, all companies use digital solutions, whether to organize its structures or offer services. 

Within this context, O job market in Information Technology (IT) becomes increasingly inflated and more professionals are required. After all, have you ever wondered how these companies manage to develop so many technological solutions? 

However, there are not many qualified professionals to meet this demand. For this reason, the low-code and no-code platforms emerge to solve this problem once and for all. 

These solutions offer affordable and simple programs that allow you to create applications, automate processes, provide reports and dashboards In real time without the need for in-depth knowledge of programming languages.

Are you interested in the subject and want to know how no-code platforms Have they transformed IT jobs? Stay with us in this article, as we will discuss this broad job market and show you what you need to be successful as a no-code programmer.

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IT Job Market: How Have no-code Platforms Transformed Jobs? 3

How is the IT job market?

IT is the acronym for Information Technology, an area that involves a series of activities related to technology, such as databases, hardware, softwares and networks (home or business), used to handle information. In general, the profession is responsible for helping companies work with their data and optimize their processes. 

Some of the main functions of a professional in this area include:

  • Technical support;
  • Schedule;
  • E-commerce development;
  • Database administration;
  • Security.

The IT sector is present in most companies and is essential for maintaining their operational processes. It is an area in constant growth, even with global crises in the job market.

There are several job possibilities for IT professionals, they can work in:

  • Financial sectors;
  • Companies specializing in technology (such as start-ups);
  • Public and private organizations;
  • Banks and insurance companies;
  • Telephone operators;
  • Industries.
  • Hospitals and clinics.

There is a great demand for qualified professionals in different sectors, however There are not enough people to fill these positions. According to a survey carried out by BrazilLAB and Fundação Brava, in partnership with the Center for Public Impact (CPI), the deficit of professionals in the area is expected to continue growing and could reach the number of more than 300 thousand people by the year 2024 . 

It is no surprise that the IT profession was one of the highest paid in the second quarter of 2023, according to the Brazilian Institute of Economics of Fundação Getúlio Vargas.

What is the future of the IT job market?

We've already talked about what the IT job market is like today, but what are the main trends for the future of programming? Keep reading to find out! 

In the business context, the revolution in new technologies has created a new type of consumer: more demanding. Therefore, a culture of speed began that accompanies this increase in demand and the need for more agile and efficient processes

Information is the most precious data in this new form of society, it is almost infinite and an efficient organization in this case is essential. Regarding data processing, global consultancy Gartner identified several rising technological trends, see some of them: 

  • Data mesh;
  • Cyber security mesh;
  • Cloud-native platforms.

Although this is almost common sense in the IT area, few professionals have enough experience to implement these tools. This is when no-code technology stands out, as it allows people without technical programming knowledge to build and implement new solutions effectively. 

Benefits of using no-code for the IT job market

no-code technology is based on a basic premise: ensure the production of technologies in an accessible and simple way. In it, softwares are created through an interface with models that bring together several actions. 

These platforms are widely used by micro and small entrepreneurs, but are becoming increasingly widespread among large companies such as: 

  • Spotify;
  • Amazon;
  • Google;
  • Goal.

Do you want to know the main benefits of no-code and why these tech giants are implementing it? Continue reading. 

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IT Job Market: How Have no-code Platforms Transformed Jobs? 4

Reduction of steps and time worked

With the no-code, systems and tasks can be automated by applications created from templates ready. This way, professionals in the IT sector are free to focus on activities that require their specific skills. This adds great autonomy to the company's teams and helps the overall efficiency of the business.

Best value for money

Companies can save a lot when they don't have to hire highly specialized developers or purchase third-party applications. The no-code development allows internal teams create and update technological solutions quickly and efficiently.

Furthermore, systems simplification and data integration facilitate the management and ongoing maintenance of applications, which reduces the time and resources needed to ensure operations.

High efficiency

Another benefit is the increase in internal productivity, since excessive dependence on the IT department is reduced, as we mentioned previously. This way, employees in each department can meet their own technological needs.

This autonomy eliminates bottlenecks that often arise when IT requests need to be approved and fulfilled. With the ability to develop their own solutions or adjust existing systems, employees can act more independently and direct the progress of their tasks.

What are the skills of the no-code developer in the IT job market?

Now that you understand how no-code can revolutionize the IT market, you may be wondering what it takes to enter this field. We have separated some essential skills for developing no-code in the job market, see: 

Recognize needs

What are the user's true needs and how to solve them through no-code programming? A good codeless developer is able to observe a demand – in society, in the company or in the IT market – and create, from a few commands, something that can revolutionize the entire area of technology. It is this professional who comes out ahead.

Enjoy studying

To become a successful no-code developer, you need to enjoy learning. Despite being easier than traditional programming, no-code requires knowledge of new platforms, coding styles, and more. Being self-taught can bring you many competitive advantages, use the internet to your advantage and invest in no-code courses accessible. 

Work with little management

Developers work with little supervision, so you need to be very organized and become your own boss. This means that you need to be your own motivator and invest in effective planning of your projects. 

It is necessary to know how to work independently and build creative solutions for the business routine before ideas come from leadership.

Knowing how to receive criticism

Receiving constructive criticism is part of every worker's routine and for no-code developers this is no different. In fact, your client or supervisor may have criticisms that lead to building better softwares. 

Don't dismiss opinions and learn to listen and interpret the needs of others in order to implement their requests efficiently on apps and websites.

“Sell the fish”

Last but not least, learn how to sell your idea. Not everyone will understand technical terms or the need for a developer on the team. It's your job explain the benefits of your work, what you can do for the company or client.

How to work with no-code in the IT department?

To implement no-code in the IT department, you first need to follow some tips:

Value automated processes

The first step to implementing a no-code developer in the IT department is understand and show the benefits of codeless programming. You can start by showing interest and talking about your ideas with the person responsible for the sector.

Get to know the no-code platform

Discover no-code platforms of different types, see which big techs use and start there. Seek knowledge in free training or courses that fit your budget. One FlutterFlow course could be a good choice.

Keep an eye out as many software vendors offer specific training programs for no-code developers. There are also free videos and tutorials that can help you at the beginning of your journey.

Present ideas no-code

The next step is to share your ideas with colleagues and superiors within the organization. Be sure to show in a practical way how no-code can be applied to improve efficiency, optimize processes and generate value for the company.

What is the salary of no-code developer? 

According to the Code Source 2023 survey, the average salary of a low-code developer, which includes no-code, in Brazil is R$ 8,256.66.

In addition to formal remuneration, no-code developers can venture out as freelancers or individual micro-entrepreneurs (MEI). Many move towards the mobile environment and discover how to make money creating apps.

So, do you think it’s worth starting to invest in the area? 

No-Code Start-Up helps you on this journey

Now that you know how hot the IT job market is for no-code programmers, how about investing in this sector?

At No-Code Start-Up you will find courses to create incredible applications, startups and e-commerces without using a line of code.

Discover our Bubble training and see how easy it is to be a no-code developer.

Don't miss the opportunity to learn from No-Code Start-Up! 

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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

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.

For an in-depth analysis of the concepts that differentiate rule-based automations and intelligent agents, it is also worth checking out the official MIT documentation on intelligent agents.

Comparison: AI agent, chatbot and traditional automation

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.

AI Agent Use Cases
What are AI Agents? Everything You Need to Know 11

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.

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
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.

Further reading

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

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.

Read more: Transformers Explained – Hugging Face

Own LLMs vs. API usage: what do you really need?

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

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
What is ElevenLabs AI? The AI-Powered Voice Revolution 15

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
  • Studio for longform audio editing
  • Speech to Text with high accuracy
  • Voice Cloning (Instant or Professional)
  • Sound Effects Generation (Text to Sound Effects)
  • Voice Design and Noise Isolation
  • Voice Library
  • Automatic dubbing in 29 languages
  • Robust API for automations with tools like N8N, Make, Zapier and custom integrations
ElevenLabs FAQ
What is ElevenLabs AI? The AI-Powered Voice Revolution 16

ElevenLabs FAQ

Find out more about the company and news from ElevenLabs directly at official website of ElevenLabs and see the API documentation.

Does ElevenLabs have an API?

Yes, ElevenLabs has a complete API that allows you to integrate speech generation with automated workflows.

With this, it is possible to create applications, service bots, or content tools with automated audio.

Discover the Make Course from NoCode Start Up to learn how to connect the ElevenLabs API with other tools.

Are ElevenLabs voices copyright free?

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.

See all available plans on the ElevenLabs website. However, ElevenLabs has stood out for the naturalness of its voice and the quality of its API.

How does ElevenLabs work?

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

NoCode tools
What is ElevenLabs AI? The AI-Powered Voice Revolution 17

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.

Discover the N8N Course from NoCode Start Up

OpenAI Agents + ElevenLabs

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.

See the Agents with OpenAI Course

Bubble/FlutterFlow + ElevenLabs

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:

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