Mythbusting: Is learning new frameworks always beneficial for the development team?

Mythbusting: Is learning new frameworks always beneficial for the development team?

Mythbusting, Technologies

Curated by: Shaggy

Half of the positive outcomes in software development come from choosing the right approach to it. Keeping your processes updated is critical to ensure that a project goes smoothly, as software development is a complex process that requires careful planning and execution. To that end, there are a variety of different approaches, each with its advantages and disadvantages, that are ultimately chosen by the specific needs and goals of the project. So, with that in mind, let’s talk about frameworks.

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In software development, a framework is a set of tools and libraries providing a common structure for building applications. A web application framework, for example, may include libraries for handling requests and responses, session management, and template rendering, as well as functionalities for routing, authentication, and other common tasks. By providing a structure, frameworks can make development easier by reducing the amount of boilerplate code needed, in addition to providing a consistent approach to solving common problems.

That’s why software developers and project managers are always on the lookout for new tools and frameworks that can make things more efficient, ensuring they remain updated and knowledgeable in the latest trends. However, there is often a trade-off between using the latest and greatest technology and having to learn how to use it effectively; anything new added to an established workflow will include a learning curve, and in some cases, the latest technology can slow down a team rather than help them achieve an outcome more efficiently. 

“Developers may need to spend time learning the new tool properly before they can start using it effectively, especially if the new tool is different enough from what the team is used to, causing more problems than it solves”, says Adolfo Cruz, Partner and PMO at Scio. “Ultimately, whether or not developers benefit from using the latest frameworks in software development depends on the particular case. It’s important to weigh the pros and cons of each new tool before making a decision.”

Is it a good idea to constantly adopt new frameworks?

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There’s no one-size-fits-all answer to this question, but we can see on paper why this might make sense; by using the latest frameworks, a team can take advantage of the most up-to-date features and capabilities, and they are generally more efficient than older ones, which can save your team time and resources in the long run. Moreover, choosing a new framework shows that your team is committed to keeping up with the latest trends and technologies.

“In my opinion, [frequent change of frameworks] can be a negative thing, because sometimes the latest version still has some kinks to work out”, says Carlos Estrada, Lead Application Developer at Scio. “Using a technology that has already been tested by the community or by your team can save you a lot of bugs and headaches. Is not wrong to try the latest framework at every opportunity if you are part of a start-up that’s barely getting off the ground, but for a more established company with clients and expectations, I wouldn’t recommend it.”

With that in mind, adopting a new framework is not something to be taken lightly, and the best timing for this will vary depending on the specific project and the team involved, as well as the resources you can commit to it. To that end, there are a few general factors to keep in mind when deciding whether or not to implement a new framework into your development cycle: 

  • First, consider whether the new framework offers significant advantages over the current one. If it’s simply a personal preference, it may not be worth the time and effort required to switch frameworks. However, if the new framework offers significant improvements in terms of performance or efficiency, it may be worth considering. 
  • Then think about whether the team is ready and willing to learn a new framework. If team members are resistant to change, it may not be worth force-feeding them a new framework, lest it critically disrupts the development of a product. However, if they’re open to learning something new, adopting a new framework can be an excellent way to keep them engaged and excited about the project. 

So logically, there are downsides to this approach if an organization is constantly selecting new frameworks, negating any advantages that the framework might offer in the long run, especially in a field like software development where innovation and disruption are always moving forward.

“Many developers spend lots of time constantly learning the next new framework. There are many existing frameworks, and they move in and out of vogue rapidly. As mobility matures, developers will benefit more from consistent approaches to mobile development as they move across SDKs and frameworks. A consistent approach to security, integration, development, and management enables quality and speed”, are the words of this article on some common myths about software development; although it’s focused on mobile application design, it’s also a bit of good advice for any kind of software work.

So, while it may be tempting to keep trying frameworks to entice new projects, there are some definite advantages to sticking to one specific framework. For starters, using the same framework will help to streamline the development process, since you and your team will already be familiar with the tools and syntax, as well as making it easier to share code between projects, which can be a huge time-saver. And at the very end, using the same framework across multiple projects will give you a better understanding of its strengths and weaknesses, which can help you to develop more efficient and effective code.

But how do you  choose the “best” one?

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Ultimately, there are several compelling reasons to be consistent with your frameworks during every project, and by doing so, you can enjoy a smoother development process and better code quality. However, different projects and challenges might need different approaches, so selecting a framework that makes sense for your organization requires consideration and care. As starting points, you might want to consider the…

  1. Support: Most frameworks are open-source and community-driven. One with a big pool of developers and engineers contributing to it and a direct line of communication in case of any issues will always be preferable. After all, a framework is as good as the people surrounding it, so if their last update was in 2018, no matter how good a framework might be, sooner or later it can leave you behind the curve.
  2. Security: The more security functions you can add through a software framework, the better, so choosing one that allows you this flexibility already makes it hard to top.
  3. Sustainability: The chosen framework keeps up with the Software Development Lifecycle? If not, then you are not working with a tool with a sustainable future, so selecting something scalable and with enough flexibility might be the best course of action.
  4. Documentation: Linked to the ‘Support’ point above, thorough and well-written documentation of the framework is invaluable to learn it quickly, a critical requirement if you are looking for a new framework that makes upgrades easy to implement.
  5. Outcomes: What does it offer to a client and a final user? Does it allow making progress on a project faster (for a client) while making it easy for feedback to be implemented satisfactorily (for an end user)? How a framework works beyond the development cycle is always an important consideration to make.

Ultimately, however, there’s no perfect answer to this question, and it will vary depending on the specific circumstances of each development cycle. And while there are benefits to using different frameworks for different projects, there is also value in being consistent with one particular framework, like reducing training costs and onboarding for new developers, making it easier to share code between different applications. Most importantly, it can promote greater consistency in the quality of the final products, so if you keep these general considerations in mind, you should be able to decide what’s best for your project and team at every turn.

The Key Takeaways

  • Selecting the correct approach to development can make the difference between a good outcome and a bad one.
  • Frameworks are a great example of this: selecting the correct one for a project can make things easier for everyone involved in development.
  • New frameworks are coming up all the time, so weighting their advantages and disadvantages is critical for any business looking to adopt them.
  • There are lots of reasons why having a consistent set of frameworks might work better in the long run than using whatever new one comes up, in terms of time, investment and money.

Scio is an established Nearshore software development company based in Mexico that specializes in providing high-quality, cost-effective technologies for pioneering tech companies. We have been building and mentoring teams of engineers since 2003 and our experience gives us access not only to the knowledge but also the expertise needed when tackling any project. Get started today by contacting us about your project needs – We have teams available to help you achieve your business goals. Get in contact today!

The boom of ClimateTech: Attracting talent to solve the challenges of the future

The boom of ClimateTech: Attracting talent to solve the challenges of the future

Technologies

Curated by: Sergio A. Martínez

ClimateTech’s Momentum and Why Engineering Talent Is Paying Attention

Technology work has always attracted people who enjoy solving real problems and building practical solutions. For decades, the most ambitious engineers gravitated toward industries promising scale, speed, and a sense of building what comes next. But today, the defining challenge of our era is climate change, and its impact is no longer theoretical. Global temperatures keep breaking records, extreme weather disrupts basic infrastructure, and entire industries are under pressure to rethink how they operate.
Engineering leaders across the United States now face a new reality: the next generation of meaningful innovation will come from companies tackling climate problems head-on. That shift has produced a surge of interest in ClimateTech — a category that includes everything from carbon capture software, energy-efficiency platforms, and predictive climate modeling to electric mobility, grid optimization, and agricultural sustainability tools.
For developers, ClimateTech represents a rare combination of complexity, urgency, and purpose. It’s an industry where the problem space is wide open, the stakes are high, and the opportunity to build long-lasting systems is clear. The result is a noticeable migration of engineering talent toward companies with a climate-driven mission, even when that means walking away from established roles in Big Tech.
Part of the appeal is the scale of the potential impact. Engineers who move into ClimateTech frequently mention the desire to build products that reduce emissions, extend renewable-energy availability, or help entire regions adapt to climate volatility. The work offers immediate relevance and long-term value, creating a strong sense of professional purpose that traditional tech roles sometimes fail to match.
ClimateTech’s growth trajectory also signals a long runway for innovation. It is still early enough that developers can influence foundational architectures, yet mature enough that real funding, customers, and technological breakthroughs are already in motion. Especially for engineers with a strong systems mindset — people who want real-world constraints, complex data, and high-impact outcomes — the space feels like a natural progression.
Interest in ClimateTech began accelerating further as industry leaders publicly pivoted their careers toward climate work. As noted in “What Is ClimateTech?,” high-profile figures like Chris Sacca and Bill Gates have launched climate-focused investment funds, while Mike Schroepfer stepped down from his CTO role at Meta to dedicate his time to climate initiatives. Their moves sent a strong signal: this is no longer a fringe sector. It’s one of the most energized areas in technology today.

Why Engineers Are Leaving Big Tech for ClimateTech

The shift toward ClimateTech would not be as notable if it weren’t drawing talent from companies once considered the pinnacle of engineering careers. But over the past few years, more developers have been walking away from stable, well-paying roles at major tech firms to join smaller climate-focused organizations.
One illustrative example comes from Protocol, which reported on the case of Cassandra Xia, a Google engineer who left the company to pursue climate solutions. When she informed Google of her decision, she was encouraged to stay and join an internal climate initiative. But she did not believe those projects would ever reach meaningful scale because they were not aligned with Google’s core business model. Her reasoning reflects a growing sentiment among engineers: internal sustainability projects in large corporations often function more as employee-retention programs than transformative efforts.
This sentiment reveals a broader pattern. For decades, Big Tech companies offered unparalleled growth, learning, and prestige. But as they matured, their priorities shifted. Innovation became incremental, risks were minimized, and internal projects struggled to escape the gravitational pull of business models optimized for advertising, e-commerce, or infrastructure services. Meanwhile, ClimateTech startups emerged with clearer missions, faster decision cycles, and founders driven by purpose as much as profit.
Several characteristics make ClimateTech startups uniquely compelling to engineering talent:
Smaller organizations move faster.
Climate problems require rapid experimentation, iteration, and adaptation. Startups, unencumbered by legacy processes, can ship faster and pivot without bureaucratic drag.
Risk tolerance is built in.
Developing new energy systems, carbon-capture processes, or climate-prediction models requires a level of innovation that inherently carries risk. Startups are designed to tolerate — and even embrace — that uncertainty.
Mission alignment is stronger.
Founders in this space are often personally committed to climate action. That sense of purpose drives culture, hiring, and product direction in ways that resonate deeply with engineers seeking impactful work.
Innovation is the default, not the exception.
Because climate challenges are complex, engineering teams regularly work on systems that blend hardware, software, AI models, and scientific research. This creates intellectually rich environments difficult to replicate inside established corporations.
This does not mean Big Tech lacks opportunities to contribute. But for many engineers, the path to high-impact climate work remains clearer — and more personally rewarding — within the ClimateTech ecosystem.

The Work Ahead: Why ClimateTech Gives Engineers a Clear Sense of Purpose

As the operational and financial consequences of climate change accelerate, organizations across every sector need better tools to plan, adapt, and mitigate risk. This creates an expanding landscape for software, data infrastructure, and predictive technologies. Engineering talent plays a crucial role here.
Modern ClimateTech solutions rely heavily on sophisticated technologies that demand strong engineering fundamentals:
Satellite imagery and geospatial analytics for tracking environmental shifts

AI-driven forecasting models for extreme weather events

Sensor networks for energy grids, industrial plants, or agricultural terrain

Simulation engines modeling air quality, water flow, or material efficiency

Platforms that aggregate and validate emissions data for regulatory compliance

For developers, this work combines traditional software development with real-world systems thinking. It requires high-performing backend architectures, resilient pipelines, human-centered design, and deep collaboration across disciplines. It is not just about writing code; it is about building software that interacts with the physical world in meaningful ways.
This intersection of digital and physical domains provides an unusually strong sense of purpose. Engineers working in ClimateTech consistently report that they feel more connected to the outcomes of their work. Their systems influence how cities prepare for heat waves, how farms optimize water use, or how companies reduce emissions across supply chains.
That sense of contribution is increasingly important to today’s developers, especially those who feel burned out by products designed to maximize engagement metrics or advertising revenue. ClimateTech offers clear, high-stakes problems that require creativity, technical depth, and practical engineering discipline.
Luis Aburto, CEO and Co-Founder of Scio, captures this sentiment clearly: “Companies that take meaningful steps toward climate initiatives will be better positioned to attract software developers looking to use their talent in the best way possible.” His point underscores a broader truth — ClimateTech is not just a new industry. It is becoming one of the most effective magnets for engineering talent.

What Engineering Leaders Should Take Away

ClimateTech’s rise is reshaping the competitive landscape for engineering talent. For CTOs and VPs of Engineering, the implications are direct: your company is no longer competing only with traditional tech firms for top developers. You are also competing with mission-driven organizations promising impact at a global scale.
The demand for engineers capable of working in data-intensive, distributed, and scientifically complex environments is growing quickly. ClimateTech companies offer these engineers meaningful ownership over hard problems. They provide clear missions backed by measurable outcomes. They communicate purpose with authenticity rather than as a marketing exercise.
The key question for most organizations is simple: What can you offer that rivals the sense of purpose engineers find in ClimateTech?
If your company wants to attract and retain top-tier developers, you need to show that the work has long-term relevance. That does not mean rebranding yourself as a climate company. It means identifying engineering initiatives in your roadmap that contribute to sustainability, resilience, or long-term societal value — and resourcing them with real investment rather than symbolic gestures.
Talent sees through one-off announcements or short-term programs. Engineers want commitment, clarity, and an environment where their work connects to outcomes beyond quarterly revenue. ClimateTech companies offer this inherently, which is why they have become powerful competitors for talent.
As more organizations embrace sustainability efforts, those that take meaningful early steps will position themselves ahead of the curve. Developers who care deeply about the future — and represent the most driven segment of the talent pool — are watching. Companies that act decisively now will be the ones that attract them.

FAQ

ClimateTech & Engineering Talent – FAQs

How ClimateTech is reshaping engineering priorities, talent decisions, and long-term impact.

ClimateTech encompasses software and hardware solutions designed to reduce emissions, improve energy efficiency, strengthen climate resilience, or enable industries to operate in more sustainable and measurable ways.

Many developers are seeking purpose, technical challenge, and visible impact. ClimateTech companies often offer all three, along with faster decision-making and fewer corporate constraints than traditional Big Tech environments.

No. ClimateTech spans agriculture, manufacturing, logistics, construction, transportation, insurance, and financial services — any industry adapting operations to climate risk, regulation, or sustainability goals.

By committing to meaningful sustainability initiatives, investing in long-term impactful engineering projects, and giving developers real ownership over work that connects to outcomes beyond short-term metrics.

Is the future of FinTech in the hands of Artificial Intelligence?

Is the future of FinTech in the hands of Artificial Intelligence?

AI, Technologies

Curated by: Sergio A. Martínez

When most people think of Artificial Intelligence, they probably conjure the Hollywood depictions of evil robots that become sentient or self-aware, and then go out of control. However, even if this is the general pop culture stereotype of AI, the real-life technology is very different, already getting implemented in several key industries to streamline processes and improve efficiency. The Financial Technology sector is no different; AI is starting to have a major impact, from chatbots and digital assistants to complex algorithmic trading, reshaping the way we conceive and manage our finances.

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And it’s not just big banks and financial institutions that are benefiting from the power of AI — FinTech startups use it to create innovative products and services that change the way we bank, invest and save, bringing more personalized experiences to customers by analyzing their spending patterns and providing tailored recommendations. 

So, while AI continues to evolve, its increasingly significant footprint on the FinTech industry is making financial services more efficient and accessible, playing a crucial role by helping to automate various tasks, from customer service to fraud detection. For example, AI-powered chatbots can provide personalized financial advice, and Machine Learning is being used to develop predictive analytics tools that can help identify investment opportunities, or red flags to watch out for.

As a result, AI will only become more embedded in FinTech, transforming the financial sector by automating processes and providing real-time insights, and helping cut costs. Perhaps most importantly, AI will give organizations the ability to make sense of the vast troves of data they collect daily, using Machine Learning algorithms to identify patterns and trends that would be impossible for humans to spot. As a result, this technology is poised to profoundly impact the financial sector in the years to come.

However, the popularity of AI solutions in FinTech has also raised questions within the industry; after all, with machines driving more financial systems, is their traditional risk management ready to keep pace with an increasingly automatized approach that brings innovations and disruption almost daily? For any business, it’s important to stay ahead of the curve and make sure you are using AI to benefit your customers and your bottom line, and risk assessment needs to be considered to succeed at it. But what will it look like?

Risk assessment in FinTech: A job for robots?

As FinTech companies become increasingly reliant on AI, the need for robust risk management processes is more important than ever. By its very nature, FinTech is a highly innovative sector, which means new risks are constantly emerging. And while AI and Machine Learning can help to identify these risks promptly, as well as monitor any exposure to them by automating data analysis and providing accurate results, gaps still exist.

“One of the main concerns is that AI systems can be biased against certain groups of people, due to the data that they are trained on. If an AI system is only exposed to data skewed in favor of one group of people, it will produce biased results”, says Rod Aburto, Service Delivery Manager and Co-Founder of Scio. “And beyond that, AI systems can be hacked or tampered with, which could lead to disastrous consequences. These tools are still far from perfect, and there is always the possibility that they’ll make mistakes with serious implications, but despite these potential risks, AI remains promising.”

Combining Machine Learning and AI can help financial institutions to make more informed decisions, like assessing the impact of new regulations on a business model or identifying risks associated with their products and services, but the availability of data is an important matter: the final assessment is only as good as the information entered into the system. This means FinTech companies need to be especially vigilant in their risk assessments due to the rapidly changing nature of the information. For example, if a training dataset is predominantly male, a Machine Learning algorithm may learn to associate certain traits with being male, leading to biased results when applied to a broader population. 

In the specific case of FinTech, a way that bias can be introduced is through algorithms that have been designed to achieve a particular outcome, rather than being impartial. An algorithm designed to identify creditworthy individuals is likely to be biased against low-income applicants, leaving people in need of these kinds of services out. 

Another challenge for FinTech companies reliant on AI to provide their services is that they are vulnerable to attacks by hackers who could exploit weaknesses in these systems. Hackers could gain access to sensitive customer data or even manipulate the algorithms used by FinTech companies, leading to disastrous outcomes. So when it comes to FinTech, security should be a top concern, and data breaches and security vulnerabilities can have a devastating impact on both consumers and businesses when machines cannot make critical decisions on their own. That’s why it’s so important for fintech firms to have robust security measures and know the risk involved in pure automatization.

A balanced future in FinTech

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“FinTech is an industry constantly evolving, and there are many good practices around risk assessment, but at its core, risk assessment in FinTech should focus on three key areas: regulatory compliance, customer impact, and business resilience”, continues Rod Aburto about the challenges in pure automatization. “And when it comes to implementing AI tech in this area, responsibility, due diligence, and human intervention are key to ensure they work as intended. These machines are more than capable of digesting data and coming up with insights than almost any person but are not infallible. Without human expertise guiding and implementing actual useful results, it’s easy to render any outcome from AI less than ideal.”

AI is revolutionizing the fintech industry at every level, from software development to customer service. According to the International Data Corporation (IDC), “the worldwide market for AI software, hardware, and services is expected to surpass $500 billion by 2024”. As more FinTech companies are using AI to create new and innovative products and services that make financial services more efficient and accessible, it’s important to keep in mind that an ethical, responsible, and effective implementation of AI is one where humans are kept in the loop and can be made accountable for any mistake. AI will only become more embedded in FinTech as the industry evolves, and for businesses, it’s important to stay ahead of the curve and make sure you are using AI to benefit your customers and your bottom line.

So, by conducting responsible and human-curated risk assessments, FinTech companies can stay ahead of the curve and make sure that their products and services are as safe and secure as possible.

The Key Takeaways

  • The FinTech sector is one of the most innovative, and technologies like AI and Machine Learning are finding implementations in almost every area.
  • AI brings a speed of automation of processes, insights, and results without precedent, and while exciting, challenges arise alongside these tools.
  • Traditional risk assessment needs to keep pace with the technological revolution and has to keep a watch out for weaknesses and biases in these systems.
  • Human intervention and interaction will still be necessary for the foreseeable future, guiding these systems and achieving their best outcomes.

Scio is an established Nearshore software development company based in Mexico that specializes in providing high-quality, cost-effective technologies for pioneering tech companies. We have been building and mentoring teams of engineers since 2003 and our experience gives us access not only to the knowledge but also the expertise needed when tackling any project. Get started today by contacting us about your project needs – We have teams available to help you achieve your business goals. Get in contact today!

Good Test Case design in QA: Quality at every step of the process

Good Test Case design in QA: Quality at every step of the process

Outsourced Engineering Team, Product Development, Scio, Software Development, Technologies

Curated by: Sergio A. Martínez

Creating software can be compared to solving a big, complex puzzle. A developer needs to take a bunch of pieces (code, algorithms, requirements, deadlines, etc.) and put them together in the right way to create a functioning product that satisfies everyone involved, from clients to final users. And just like with a puzzle, there is no single «right» way to develop software; it depends on the individual developer’s preferences and style, where some may start by laying out all of the pieces and looking for patterns, while others may start assembling pieces and then adjust as they go along. 

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And the biggest challenge is that if even one piece is out of place, it can throw the entire system off balance. This is why, besides having a good team of developers able to see the big picture and break it down into manageable tasks, a good QA Tester is so critical to obtaining the best possible outcome during development. Only then can you hope to create a successful piece of programming.

That’s why having a good approach to QA is so important; having experienced testers whose toolset matches the requirements of the product, capable of coming up with a plan for how they will test the code as they write it, as well as having a deep understanding of what “quality” means for the project, is a must in any team. 

So, in that sense, we want to take a look into one of the most important processes of QA: test cases. Because beyond running automated tests and manual testing, QA involves a systematic approach where developers can avoid costly mistakes and create products that meet customer expectations. And in practice, how can you design the perfect test case? What considerations should you have, and what’s the best approach to document and keep track of the sometimes messy process of QA?

Test cases are simple: Just think of everything

When it comes to software development, well-designed test cases are essential. By carefully planning out each test case, developers can ensure that their code will be thoroughly tested for errors, and taking the time to design comprehensive test cases can save a lot of time and effort in the long run. But how should you approach this task in practice? Is there a trick to designing a good Test Case?

“It depends on the project”, says Angie Lobato, a Quality Assurance Analyst at Scio with a wide range of expertise in everything QA. “The ISTQB already mentions that 100% thorough testing is not something that is possible, so it comes down to the priorities of the team, the requirements, the severity of the bugs, and the timelines set to deliver the product, as well as how much time the person in charge of QA has.”

This is why knowing how to design a test case is so important; considering all the challenges that software development already faces, being able to write an efficient, timely, and thorough test case is a valuable skill, keeping in mind things like… 

  • Thinking about the expected behavior of the system under test. What should it do in various scenarios?
  • Choosing input values that will exercise all relevant parts of the system.
  • Designing tests that will detect errors, but also verify that the system behaves as expected.
  • Keeping track of all tests performed, including pass/fail status and any observations made.

However, saying this is easier said than done; it can be difficult to create comprehensive test cases that cover all possible scenarios, and as software becomes more complex, replicating customer environments to test for all potential issues requires some intuition and minute attention to detail. That’s why the design of your test cases has to start with a script as the basis of the test, documented and shared to see exactly what you are trying to accomplish. For this process, Angie tells us that…

“I first need to validate that the Test Case (TC) related to the specific item I’m checking doesn’t exist yet, and do whatever is necessary, like adding, taking out or updating steps to not end up with a suite of repeated test cases”, she explains. “To design the script, it’s always good to create them in their respective suite, with a link to the requirement so everybody in the team can easily find them (I’ve personally used TFS, Azure DevOps, and Jira) depending on the tools utilized during the project. For the script itself, I define the objective of the Test Case, as well as the preconditions and postconditions it needs. Once that has been taken care of, I start to retrace the steps necessary to reach the item I need to test. I add each needed step to achieve the objectives of the test case with their expected result, and finally, I validate the final results where the change needed to be reflected.”

As you can see, there’s a lot of documentation involved in designing a test case, and having the proper formats to keep everything in order (like this one) helps to make sure that each test is accomplishing what it needs to. And according to Angie, a good test case needs a couple of characteristics to make it good:

  • A good test case has a clear objective stated and is updated to the latest version of the project. 
  • Has all the necessary testing data to execute it without creating repeated information. 
  • Has defined all the preconditions and postconditions of the product. 
  • And most importantly, don’t try to test more than one thing in a single case.
  • However, if you need to, changing the parameters of the test is necessary to make that clear. 
  • An ideal test case shouldn’t have more than 10 steps in total.

Ensuring quality at a distance

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As anyone who has ever been involved in software development knows, QA is a critical part of the process, and a good test case can help to ensure that the final product meets the requirements of the customer and is free of issues, especially in the current development landscape where remote collaboration is becoming a given. 

For a Nearshore development team like the ones at Scio, a well-crafted, carefully designed test case is invaluable, helping to ensure that the team and the client is on the same page concerning the expected results of the testing process, and providing a clear and concise way to communicate those expectations to everyone involved. 

In other words, a good test case can help to streamline the testing process and make it more efficient, so taking the time to create a good test case is well worth the effort for any remote software development team. 

“Any company that outsources software development knows that collaboration is key to success. A good QA team is essential to ensuring that the final product meets the standards”, says Adolfo Cruz, PMO Director, and Partner at Scio. “In a Nearshore setting, they are especially beneficial because they ensure that any problems are found and fixed quickly before they have a chance to cause major problems. As a result, well-designed test cases play a vital role in ensuring the success of a remote relationship.”

The Key Takeaways

  • Quality is necessary at every step of the process of developing software, not only a concern in the final product.
  • A good example is test cases, how important they are to the process of QA, and what good practices get involved in designing one.
  • A well-designed test case is straight to the point, meticulous, and tries to think of all the context around the product in order to ensure the best quality possible.
  • Also, the process of designing a good test case is doubly important when working on a project remotely, helping keep everyone on the same page and track all the changes and corrections necessary to bring the best possible outcome. 

Scio is a Nearshore software development company based in Mexico where we believe that everyone deserves everyone should have the opportunity to work in an environment where they feel like a part of something. A place to excel and unlock their full potential which is the best approach to creating a better world. We have been collaborating with US-based clients since 2003, solving challenging programming puzzles, and in the process showcasing the skills of Latin American Engineers. Want to be part of Scio? Get in contact today!

Is the FinTech sector responsible for the financial education of its users?

Is the FinTech sector responsible for the financial education of its users?

Entrepreneurship, FinTech, Technologies

Curated by: Sergio A. Martínez

A Changing Financial Landscape

The last decade reshaped personal finance. Digital payments, mobile banking, alternative lending platforms, and investment apps have moved financial decision-making from branch offices to smartphones. For millions of users, FinTech is now the first point of contact with financial products. As a result, these platforms influence not only how people transact but also how they learn, compare, and interpret financial decisions.
This shift raises a critical question for engineering leaders in FinTech organizations: Where is the line between providing a product and shaping financial behavior? The answer is increasingly relevant as regulators, customers, and investors look for signs of accountability in how financial technologies guide users.
Rod Aburto, Co-Founder and Service Delivery Manager at Scio, frames it clearly: “More people rely on FinTech solutions to make financial decisions. Budgeting apps, P2P lending, micro-investment tools—these platforms promise convenience, but they also shape financial behavior. With that influence comes a question of responsibility.”
The debate centers on whether FinTech providers should go beyond usability, compliance, and feature design to actively promote financial literacy. Some argue that users alone must understand the tools they adopt. Others believe FinTech providers must offer transparency, education, and context to prevent misinformed decisions that can generate real financial harm.
FinTech has become a powerful enabler, but the industry’s responsibility in user education is more complex than a simple yes or no. It touches ethics, product strategy, customer trust, and the long-term sustainability of the sector itself.

Section 1: The Expanding Role of FinTech in User Decision-Making

FinTech platforms began as alternatives to slow, traditional financial institutions. They offered faster onboarding, simplified interfaces, and frictionless engagement. Over time, however, their role expanded significantly. Today, FinTech tools not only process transactions but also shape how people perceive risk, spending, saving, investing, and creditworthiness.
Consumers now expect digital platforms to act as guides as much as they act as tools. The interface of a budgeting app interprets financial categories on the user’s behalf. Micro-investment tools frame portfolio decisions with nudges, projections, and risk settings. Debt-management apps offer automation that can either empower or mislead users depending on how transparent the mechanism is.
This creates a grey area: When does a FinTech product move from service delivery to behavioral influence?
Many users—especially first-time borrowers, young professionals, small business owners, and gig workers—adopt FinTech tools precisely because they lack traditional financial knowledge. Without clear guardrails or context, they may misunderstand the implications of interest rates, repayment schedules, balance automation, or micro-investing risk.
For engineering and product teams, this context matters. Inadequate disclosure or confusing workflows may increase conversion in the short term but damage trust, retention, and regulatory standing in the long term. FinTech success depends heavily on reducing friction, yet frictionless onboarding without transparency can backfire.
This is why several industry analysts argue that FinTech providers have at least a partial obligation to guide decision-making responsibly. Not to act as advisors, but to design flows that:
explain how a product works,

communicate risk in plain language,

avoid hidden decision-paths,

and provide contextual guidance where complexity exists.

The question is not whether FinTech should replace professional advisors. It cannot and should not. Instead, the challenge is building products that allow users to make informed decisions without needing advanced financial training.
As Simon Pearson from HedgeThink notes, “Financial education has become a long-term policy priority. As technology shapes financial behaviors, education must follow technology, not lag behind it.”
FinTech providers now stand at the intersection of usability and responsibility. The path they choose will define how the public perceives digital finance over the next decade.

Section 2: Where FinTech Education Matters Most—Marketing, Security, and Communication

If user education is becoming part of the FinTech mandate, where should it exist? The most practical areas—those with the greatest influence—are marketing transparency, security expectations, and direct communication. These elements shape how users interpret a product long before they make their first transaction.
Marketing Transparency
Marketing is the first place where expectations can be misaligned. Clear, honest messaging helps users understand what the product does, what it doesn’t, and what assumptions they will have to carry. Many FinTech marketing campaigns still rely on excitement—“fast approval,” “instant payouts,” “no hassle”—while burying critical conditions in footnotes or unclear screens.
Responsible marketing avoids ambiguity by:
describing product capabilities plainly,

clarifying any limitations,

avoiding exaggerated claims,

and emphasizing long-term outcomes instead of short-term gains.

Users should know what they are signing up for before providing personal data, linking accounts, or accepting terms. The line between persuasion and clarity becomes a strategic decision for engineering leaders, who must ensure teams understand how marketing promises align with real product functionality.
Security and Data Transparency
Security is another area where education has real impact. Users often underestimate how their data is collected, used, stored, or shared. FinTech companies handle extremely sensitive data, and while robust internal security systems are essential, they must also translate into clear communication with customers.
Rod Aburto emphasizes this point: “FinTech customers and platforms are frequent targets of digital attacks and fraud. Transparency about risk and security measures is as important as the technology itself.”
Security education includes:
explaining what data is collected and why,

outlining user responsibilities (password management, device trust, MFA),

ensuring customers recognize phishing or fraud scenarios,

providing visible pathways for reporting suspicious activity.

A secure system builds trust, but a secure system explained well builds loyalty.
Ongoing Communication and Customer Context
FinTech companies must keep users informed as features evolve, terms shift, or regulatory updates require changes. Communication is not a one-time onboarding event. It is a relationship.
Proactive communication should:
notify users about meaningful product changes,

share relevant updates that impact account behavior,

offer accessible support channels,

and foster a rhythm of transparency rather than reaction.

Clear communication is an educational tool in itself. It moves the product from a transactional service to a reliable financial partner—one that respects the user’s ability to make informed decisions when guided with clarity.

Comparative Module: Where User Education Impacts FinTech

Area
Why It Matters
What Users Need
Marketing Shapes first impressions and expectations Clear value, limitations, and risks
Security Protects user trust and reduces fraud Data transparency and practical guidance
Communication Maintains alignment and reduces confusion Timely updates and accessible support

Section 3: The Real Limits of FinTech Education

Although FinTech platforms influence financial behavior, there are limits to how much they can—-and should—educate users. Financial literacy involves a deep understanding of economic principles, risk assessment, long-term planning, and scenario analysis. These skills cannot be fully transferred through onboarding modules or app tooltips.
Three core boundaries define what FinTech can realistically provide:
1. FinTech Cannot Replace Professional Advice
Even the most intuitive apps cannot replicate the nuance of professional financial planning. Advisors evaluate long-term goals, income stability, tax impact, market cycles, and behavioral tendencies. Context is key, and automated systems cannot fully account for individual complexity.
FinTech excels at tactical decisions—budgeting, categorization, approximations, simulations—but strategic financial guidance remains beyond its scope. Users still carry the responsibility of seeking expert counsel for major decisions.
2. Simplicity Often Hides Complexity
FinTech wins by minimizing friction. Yet simplifying complex financial mechanisms can create false confidence. Users may assume that if a tool is easy to use, it must also be safe or low-risk. In reality, interfaces often compress layers of complexity:
dynamic interest rates,

compounding risk,

tax implications,

third-party data handling,

algorithmic decision-making.

This does not mean FinTech should become more difficult to use. Instead, the challenge is transparency without overwhelming the user. Clear context allows users to understand the mechanism without needing a finance degree.
3. User Behavior Still Drives Outcomes
Financial literacy depends on habit formation, emotional control, and long-term discipline. No app can prevent impulsive spending, speculative investing, or ignoring payment reminders. Technology enables choices, but behavior determines results.
Despite these limits, FinTech still plays a valuable educational role. It serves as a gateway, offering access, visibility, and structure for people who may never have engaged in personal finance before. The responsibility of the industry lies in designing tools that respect users’ decision-making capabilities and clarify risk without inducing fear or confusion.
FinTech cannot solve financial literacy alone, but it can meaningfully raise the baseline.

Section 4: Designing FinTech with Ethical Responsibility

As FinTech matures, engineering leaders are reevaluating product ethics. The industry is shifting from rapid growth to long-term sustainability. Trust, clarity, and responsible design are becoming key differentiators, especially as regulators intensify their focus on digital finance.
Responsible FinTech design starts with an ethical framework that guides product decisions. This includes:
Setting Clear Expectations
Users should understand:
what the product is designed to do,

what it cannot do,

what the user is responsible for,

and what risks accompany its use.

Proactive clarity prevents misuse more effectively than disclaimers hidden in dense terms.
Balancing Simplicity with Honesty
Engineering teams often streamline interfaces to reduce friction. But when simplification removes important context, users may underestimate financial consequences.
Responsible simplification means:
preserving clarity around cost, risk, and outcomes,

providing additional detail where needed,

offering optional deeper explanations,

and using design patterns that avoid misleading defaults.

Designing for Trust
Trust is the backbone of any financial service. FinTech teams can strengthen trust with:
transparent workflows,

consistent interface patterns,

understandable data practices,

and predictable user experiences.

This is particularly relevant in cross-border or emerging markets, where user expectations vary widely. Internal teams must design systems assuming a broad audience with diverse levels of financial knowledge.
Building for Long-Term Relationships
The most successful FinTech platforms today differentiate through reliability and customer alignment, not just UI design. In this sense, FinTech organizations can learn from strong service-delivery models such as nearshore engineering partnerships, where transparency and communication define the relationship. (Internal link placement suggestion: link to a Scio article on aligned nearshore engineering practices.)
FinTech products built with ethical clarity reduce user confusion, improve retention, and strengthen long-term adoption. As competition increases, ethical design becomes a strategic advantage rather than a compliance requirement.

Conclusion: Shared Responsibility in a Digital Financial World

FinTech platforms have become essential tools for modern financial life. Their influence is growing, and with that influence comes a measure of responsibility. While FinTech companies should not replace advisors or assume full responsibility for user literacy, they do play a role in ensuring transparency, context, and trust.
The user still carries responsibility for understanding financial decisions. Yet FinTech providers must design systems that respect the user, communicate clearly, and avoid obscuring complexity in ways that could distort decision-making.
This shared responsibility model—technology enabling access, companies ensuring clarity, and users seeking knowledge—reflects a healthy financial ecosystem.

FAQ

Transparency & Financial Education in FinTech – FAQs

How clear communication and education shape trust, adoption, and long-term outcomes in FinTech products.

They should not replace professional advisors, but they do have a responsibility to provide clear explanations, transparent terms, and practical context around risk so users can make informed decisions.

Plain-language descriptions of how products work, what security measures are in place, and which user responsibilities directly affect financial outcomes.

They can raise the baseline by simplifying concepts and increasing access, but full financial literacy still requires deeper knowledge, experience, and personal discipline beyond any single platform.

Because users rely entirely on digital interfaces to understand complex financial mechanisms. Clear communication builds trust, reduces misuse, and supports long-term adoption.

The Rubber Duck Method: What is the explanation behind this debugging approach?

The Rubber Duck Method: What is the explanation behind this debugging approach?

Project Management, Technologies

Curated by: Sergio A. Martínez

Debugging software is an important, if often tedious, the task for any programmer. Finding and removing errors generating crashes, freezes, or incorrect results is critical to ensuring the quality of a piece of software, and while some bugs can be fixed with a few simple tests, more difficult ones require special approaches and techniques. And thankfully, there are many resources available to help programmers debug their software; after all, with patience and perseverance, even the most difficult bugs can be squashed.

The Rubber Duck Method: What is the explanation behind this debugging approach?

One such technique is the popular Rubber Duck method, which may already be familiar to a seasoned developer. In short, the Rubber Duck method is a debugging approach in which developers explain their code line by line to an inanimate object, such as a rubber duck. This may sound silly, but it’s an incredibly effective way to find and fix mistakes. 

“Computers process information differently than humans do. Anyone who’s first learning to program understands this well. What’s hard about programming for a beginner isn’t really big hard esoteric concepts, but that you’ve got to be so painfully exacting in how you describe everything to a (dumb) computer. That’s why we do rubber duck debugging.”

However, have you ever been curious about why this approach works? What exactly happens in our brains when we verbalize a problem to someone else (even if that someone just happens to be a bath toy), that could lead to a solution that was obvious all along? And what is the best way to implement this method to finally find and solve that bug that has been bothering you all week?

The challenge of language

Computers are dumb. And we don’t mean that in a Luddite, anti-tech sort of way, we mean it in the original definition of “dumb”: incapable of human speech. And speech here is more than just talking; speech includes context, mood, choice of words, familiarity, and an infinity of other variables that a computer can’t understand (yet).

Of course, this doesn’t mean that we cannot communicate with computers, it just means that we use specialized languages to do so, and every single one of them works with the principle that computers are dumb: unless you tell a machine exactly what they need it to do, or how to react when something happens, they will not produce a desirable outcome. Thoughtful Code put it best:

“‘Is it cold outside?’ is a question that most humans, having some idea of the weather, will answer pretty easily. They’ll say something like, “No, it’s pretty nice.” Asked that question, a computer — or a really finicky and hyper-rational person — will need you to define each of those words.”  

A computer understands the most literal and absolute terms and learning to manipulate those terms is the basic principle of programming. This also means that computers don’t make mistakes, people do. So, if something within the instructions given to the machine doesn’t add up, then the program will not work as intended, and finding the exact place where the communication between a person and a computer got out of alignment can be a challenge. Here’s where the rubber duck comes in handy, thanks to the way we process language.

Here’s a fun fact: did you know that reading, writing, and speaking are located in completely different parts of our brain? Our understanding of the way we use and apply language is always evolving, but it is understood that we use different functions depending on the type of language we employ, which is why it’s so useful to verbalize a problem to find a solution: you involve a completely different part of your mind to help.

Of course, the Rubber Duck method is not useful only in software development, but since computers are very linguistically complex tools (being probably the only ones we need to “speak to” to use), verbalization is useful here, forcing developers to slow down and think about the minute details of their code, which can help to spot mistakes that they would otherwise overlook. As the blog “The Psychology Behind Rubber Duck Debugging” puts it:

“A lot of times, I’ve experienced some programmers that will ask my help about a specific bug they are fixing. I will then ask them how their application and their code works. I literally have no idea how to fix a program that is not mine and have no idea about the flow. However, I let them explain the flow of the process and the connection between functions and files. Oftentimes, they think of a solution before I even understand what is happening. Many people have been so thankful for me — for doing literally, nothing.”

Programmers understanding themselves

The Rubber Duck Method: What is the explanation behind this debugging approach?

You can see the same principle at work in the classroom. Teachers probing students with questions are intended to make sure a lesson has been learned, forcing the students to consider and explain it by themselves. The only difference is that a programmer using the Rubber Duck method is taking both roles (teacher and student) at once. 

In other words, this method allows developers to share their thoughts with a neutral party, questioning and probing themselves regarding their code, which can help identify areas of confusion or misunderstanding. And most importantly, it encourages developers to develop a clear and concise explanation of their code, which can be useful for future reference. 

“The real magic doesn’t happen on the rubber duck itself (sorry, Duck Norris). However, it happens in our minds. It uses the same psychological principle wherein we are encouraged to explain to ourselves why we did such actions and have a self-realization about what we’ve done. It is usually used by most psychologists to fully understand a person and, at the same time, for the person to understand himself/herself fully.”

And understanding yourself is fundamental to being a good programmer. Just like writing any other thing (a novel, or a sheet of music), everyone has their own style, approach, and technique when coding an application, which makes the ability to explain what you wrote so important; if you aren’t able to understand your process inside and out, then debugging will always be a challenge, especially when working as part of a team, where the code must always be in sync. In fact, the Rubber Duck method can be used as a form of collaboration, as another programmer can serve as your rubber duck and offer feedback or suggestions while you go through your code trying to find an answer.

“When working on a software development project, it’s important to have a good collaboration method in place, and the rubber duck method is one way to ensure that everyone on the team is on the same page”, says Jesús Magaña, Senior Project Manager at Scio. “It can help a developer to articulate his or her thought process, and as a result, team members can quickly identify any gaps in understanding and address them before they cause problems. Additionally, the rubber duck method can help to uncover errors in logic or coding syntax, and overall is an effective way to ensure that everyone on the team can contribute.”

In a Nearshore development environment, where collaboration has come a long way in recent years, the Rubber Duck method can also be useful to bring keep everyone on the same page by improving communication, helping maintain contributions clear, and easing the challenge of solving a tough bug even in remote settings (where a developer may not have anyone to immediately bounce ideas or solutions during debugging), which can help projects to come together more easily. After all, Nearshore software development has its challenges, but by using the proper approach (or bath toy), teams can overcome obstacles and build better software together.

The Key Takeaways

  • A bug in the code is basically a mistake in communication between a developer and a computer.
  • Following this, it’s no wonder that approaches to problem-solving like the Rubber Duck method can help to find the precise place where a code is not working.
  • Although you only need something to talk to (like a rubber duck), this process can involve many people in a team, offering advice and feedback.
  • However, in remote setups (like with a Nearshore development partner), having a way to find and fix bugs without the insight of anyone else can be a valuable resource.

Scio is a Nearshore software development company based in Mexico where we believe that everyone deserves everyone should have the opportunity to work in an environment where they feel like a part of something. A place to excel and unlock their full potential which is the best approach to create a better world. We have been collaborating with US-based clients since 2003, solving challenging programming puzzles, and in the process showcasing the skills of Latin American Engineers. Want to be part of Scio? Get in contact today!