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OpenAI o1: the model that revolutionizes no-code platforms

openai o1 01

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!

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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.

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The market is changing – fast. Artificial intelligence is no longer a trend, it has become a necessity. Companies are cutting costs, optimizing operations and looking for specialists to implement AI in their daily lives. And this is exactly where the AI profession comes in. AI Manager Course.

NoCode AI Manager Course: What it is, Who it is for and What its Objectives are

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Artificial intelligence (AI) is reshaping the way the financial sector operates, from risk analysis to the automation of complex processes. More than a trend, AI has become a strategic tool for financial institutions that want to increase their efficiency, reduce costs and offer personalized experiences. Within this scenario, the use of AI agents for finance has been gaining ground as a practical and accessible application for companies of all sizes.

Financial dashboard with automated charts and visuals representing artificial intelligence

AI Software Development in the Financial Sector

Creating AI-based solutions in the financial context requires robustness, security, and adaptability. Developing this type of software requires an architecture that is prepared to handle large volumes of data, continuous learning, and the ability to provide accurate insights.

In addition, systems need to be able to handle sensitive data, integrate with multiple sources (such as banks, brokerages, and ERPs), and adapt quickly to regulatory changes in the industry. Flexibility and modularity are core elements of any AI architecture for finance.

Integration with Existing Infrastructures

Much of AI’s success in the financial sector depends on its integration with legacy systems. This includes internet banking platforms, CRMs, payment gateways, and compliance tools. Using NoCode platforms such as make up or N8N allows you to create effective connections without the complexity of traditional development.

By the way, if you want to experience in practice how to integrate financial flows with AI, No-Code Start-Up provides a free N8N course with full video on YouTube. It's a great opportunity to explore real automations and understand how to structure secure and intelligent integrations in an accessible way.

With this approach, banks and fintechs can activate intelligent flows based on real data, such as automatic sending of alerts, personalized segmentations and recommendations based on consumer behavior.

Challenges in AI Development for the Financial Sector

Despite the enormous potential, there are challenges that need to be considered. Among the most relevant are:

  • Data quality: models are only effective if fed by clean and organized data.
  • Explainability: It is essential to understand how the AI arrived at a particular recommendation.
  • Cultural resistance: Traditional teams may resist adopting automation and algorithm-based decisions.

As highlighted by Deloitte, the combination of data governance, team training and ethical monitoring of AI is essential to mitigate risks and generate consistent results.

Security and Regulatory Compliance

The financial sector is one of the most regulated in the world. Therefore, all AI applications must comply with standards such as LGPD, GDPR and Central Bank regulations.

The adoption of good practices Data Privacy by Design, end-to-end encryption and role-based access control are just some of the basic requirements. Platforms such as Xano offer robust infrastructure with a focus on security for those who want to develop financial backends with AI.

Digital security illustration with padlock and financial data, symbolizing protection and compliance in AI application

Software Scalability and Resilience

As AI becomes a critical part of operations, it is necessary to ensure that systems are scalable and resilient. This means being able to grow as demand dictates, without compromising performance or security. Cloud computing and the adoption of microservices are essential strategies in this journey.

Companies like Goldman Sachs and Bank of Brazil have already demonstrated, in different contexts, how AI models can be deployed gradually, safely testing hypotheses before scaling to the entire operation.

AI Agents for Finance: Use Cases and Applications in the Financial Sector

1. Automated credit analysis

Companies like Credits use AI to evaluate hundreds of variables — including banking history, spending habits, and public data — to offer personalized credit. This reduces default rates and expands access to credit in a fairer way. According to McKinsey, automation can reduce analysis time by up to 70%.

2. Fraud prevention

O Bradesco and other institutions have implemented machine learning models that detect fraud based on behavioral patterns. When a transaction deviates from the pattern, the system triggers an automatic block or sends an additional verification to the user. According to Visa, the use of artificial intelligence helps prevent fraud totaling approximately US$14T25 billion.

3. Automated investment management

Robo-advisors like the ones from XP Investments use algorithms that analyze investor profiles, financial goals and market conditions to assemble and rebalance portfolios autonomously. CB Insights highlights that these systems are democratizing access to quality financial services, previously restricted to large investors.

4. AI-powered customer service

O Itau has incorporated AI into its digital channels, allowing customers to renegotiate debts, request second copies of bills or consult invoices using natural language. This reduces response time, improves customer experience and frees up human teams for more complex cases. According to Accenture, up to 80% of first-level banking interactions can now be automated using artificial intelligence.

5. Cash flow forecast

Financial management startups use AI agents for finance that integrate data on accounts payable and receivable, seasonality and market trends to predict cash flow for the coming months with high accuracy. Based on this information, more assertive decisions can be made. Harvard Business Review reinforces that this approach reduces the margin of error in financial projections and improves strategic planning.

The Role of AI Agents for Finance

Among all the applications, the AI agents for finance stand out for their versatility and accessibility. They function as intelligent “copilots”, performing tasks such as:

  • Automatic generation of financial reports
  • Sending alerts about targets or deviations
  • Predictive profitability analysis

Using platforms such as Dify and OpenAI, it is possible to configure these agents with natural language, making them easier to use even for those without technical training. This expands access to data intelligence in the financial sector.

The Future of AI in the Financial Sector

Artificial intelligence in the financial sector is no longer a distant promise — it is present in strategic decisions, customer service, and risk management. The adoption of technologies like AI agents for finance represents a leap forward in digital maturity. As technical challenges are overcome and platforms become more accessible, companies of all sizes will be able to use AI not only to automate, but to evolve.

Organizations that master the use of AI ethically, safely, and strategically will be ahead in delivering value and conquering the market. The future of finance is predictive, integrated, and data-driven — and it starts now. Want to learn how to build your own AI-powered financial agents without coding? Access the AI Agent Manager Training and discover the most practical way to apply all this in your context.

How AI is changing the market can be observed in practically all sectors of the economy, and this change is intensifying every day. Artificial intelligence (AI) is being recognized as a disruptive force that is profoundly reshaping the global market. From simple tasks to complex decisions, it has been integrated into processes in various sectors, transforming the way people work, consume and manage businesses.

Furthermore, when observing the effects of this transformation, it becomes clear how much the job market is being reconfigured: new opportunities arise, some professions lose ground and others adapt or are reborn with the support of technology, which demonstrates how AI is changing the market in a broad and profound way.

How AI is changing the job market

AI is accelerating the automation of repetitive and operational tasks. AI systems are already being used to efficiently perform:

  • Customer service with chatbots.
  • Predictive data analysis for sales and marketing.
  • Automated financial and audit processes.
  • Inventory control and logistics.

These changes not only reduce operational costs, they also require the workforce to be retrained for new roles, which reinforces how AI is changing the job market with great intensity.

Representation of people and artificial intelligence collaborating in different professions

Professions affected by artificial intelligence

According to PwC's report on the future of work (source), it is estimated that up to 30% of human tasks could be automated by the mid-2030s. This data shows, in practice, how AI is changing the job market and skills requirements.

Some of the roles most impacted by AI include:

  • Telemarketing operators
  • Administrative assistants
  • Data Analysts (some tasks being replaced by generative AI)

On the other hand, new functions emerge, such as:

  • Prompt Engineers
  • Automation experts with NoCode
  • Conversational Experience Designers
  • Intelligent Agent Managers

Those AI agents, for example, have been increasingly used in companies seeking to automate decisions and perform tasks with minimal human intervention. According to an analysis of the The Verge, large companies such as OpenAI, Google and DeepMind are investing heavily in the development of these systems, which can already act independently in complex corporate processes. They are designed to operate autonomously, learn continuously and integrate with other technologies — which makes them key players in the ongoing digital transformation.

What's Happening Now: How AI is Changing the Marketplace in Numbers

The AI market is experiencing exponential growth. The sector is estimated to surpass US$ 500 billion in value by 2027. There is a global race for innovation, with startups, large companies and governments investing heavily in:

  • Generative models (like ChatGPT)
  • Robotic Process Automation (RPA)
  • Artificial intelligence applied to health, education, law and agribusiness

This movement demonstrates how AI is being positioned as a strategic asset for growth and competitiveness.

Suggested reading:

AI Agent and Automation Manager Training

What are the negative aspects of AI in the job market?

Despite promising advances, important challenges also arise:

  • Structural unemployment: functions terminated without sufficient time for requalification
  • Digital inequality: not everyone has access to technological education
  • Technological dependence: companies become hostages of platforms and algorithms
  • Ethical and privacy issues: inappropriate use of data and biased automated decisions

These factors require public policies, business leaders and civil society to debate limits, transparency and responsibilities in the use of technology.

People using AI tools in a modern workplace

Opportunities and the future of work with AI

The key is in the conscious adaptation. The future of work will be driven by:

  • Continuous learning and professional requalification
  • Mastery of AI tools and NoCode platforms
  • Creating new business models based on data and automation
  • Development and management of autonomous AI agents

Increasingly, professionals and companies will need to adopt a stance proactive and experimental, turning AI into an ally.

Recommended courses:

AI and entrepreneurship: new market frontiers

Artificial intelligence is not only transforming the traditional job market, it is also paving the way for new business models. Digital entrepreneurs are using AI to create scalable products such as intelligent assistants, recommendation systems, and data-driven SaaS platforms. No-code tools combined with AI agents are enabling the emergence of lean, highly automated, and highly personalized startups.

A great example is the AI-based micro-SaaS, which solve very specific problems and can be created by a single person. Platforms like Bubble, FlutterFlow and Make, integrated with OpenAI models, make this scenario not only possible, but accessible.

For those who wish to explore this new territory, we recommend SaaS IA NoCode Training, designed to transform ideas into digital products using the power of artificial intelligence.

How AI is changing the market and shaping the future

Artificial intelligence is changing the market in an irreversible way. It is not only a technological revolution, but also a social, professional and economic transformation. The question is no longer “if” AI will impact your work, but “how will you position yourself in this new era”.

The good news is that there have never been so many accessible tools for those who want to learn AI in practice.

See all No Code Start Up training and courses

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