.svg)
Are you still measuring 2026 talent with old-fashion tools?
A couple of years ago we received a curious call from a tech recruitment agency. They had a serious problem with one of their clients. The last two developers they had hired seemed to fit perfectly on paper and had passed the interviews without raising any suspicions. However, when they started working on the project, the reality was very different: obvious technical difficulties, low resolution capacity, and performance well below expectations. Both ended up being "let go." As a consequence, the client lost trust and began to look at every new candidate the agency presented with huge suspicion.
The solution they adopted was simple but unusual at the time: stop relying solely on CVs and interviews and validate real capabilities through technical tests adapted to the context of the project. We received this request, and we took care of designing specific challenges for the client's exact tech stack, architecture, and concrete needs. The result confirmed something we see constantly: knowing a technology and proving that you know how to work with it are two very different things. Software development cannot be proven solely on paper. It needs practical evidence.
The most interesting part was that not only did the client regain trust in the selection process. The candidates themselves highly valued the detailed technical feedback they received (from our evaluators). Today, the client continues to work with that agency, and the agency continues to turn to us to validate profiles before presenting them.
This happens because the mass evaluation tools on the market stay on the surface. To understand why our approach works where traditional platforms fail, here is the real difference:
The market standard vs. Squad Challenge
How does Squad Challenge evaluate the use of AI?
1. We Don’t Evaluate Whether You Use AI. We Evaluate Whether You Know How to Work With It.
Banning ChatGPT, Claude, or Copilot in a technical assessment today is like banning Stack Overflow ten years ago. Nobody works that way anymore. According to GitHub, more than 90% of developers already use AI tools at some stage of their workflow. The question is no longer whether they use them. The question is whether they master them or simply depend on them.
At Squad Challenge, we start from a simple premise: AI is already part of the workplace. Just as we don’t judge candidates for using an IDE or a framework, we don’t penalize the use of AI. What we measure is something far more valuable to employers: the ability to deliver high-quality results when everyone has access to the same tools.
2. Hallucinations Are Real. And Surprisingly Expensive.
Consulting firms have been warning about this for months. McKinsey highlights AI’s ability to significantly boost software development productivity, while Gartner warns that those gains can quickly disappear if quality isn’t kept under human supervision. Translated into CTO language: generating code is easy. Maintaining it is where the fun begins.
That’s why we introduce scenarios where blindly following AI recommendations tends to end badly. Outdated dependencies. Half-baked design patterns. Elegant-looking decisions that quietly introduce scalability or maintainability issues. We’re not trying to catch anyone out. We’re trying to see whether the candidate can recognize when the machine is making things up. Because today’s models write code remarkably well. They also invent solutions with an absolutely impressive level of confidence.
3. The Outcome Matters. The Reasoning Matters More.
One of the most consistent findings across Deloitte and Accenture studies on AI-assisted teams is that performance differences don’t come from the tool itself. They come from the people using it. Two developers can receive exactly the same AI-generated code snippet. One builds a solid product. The other creates six months of technical debt.
That’s why our evaluators look at the entire process. Not just what gets delivered, but how the candidate got there. We review decisions, iterations, pivots, and technical reasoning. If someone truly understands a solution, they can explain why they chose one approach and rejected another. If they simply accepted the first AI-generated answer because it sounded convincing, that usually becomes obvious within a few minutes.
4. Architecture Is Still a Human Sport.
Today’s LLMs are incredibly good at solving specific tasks. They refactor functions, generate tests, write documentation, and produce entire components in seconds. But when a problem requires business context, long-term thinking, or balancing technical trade-offs, things change quickly. Very quickly.
That’s why we evaluate the areas that organizations still consider critical, according to Gartner, Thoughtworks, and McKinsey: simplification skills, technology selection, separation of concerns, dependency management, and architectural judgment. Because companies don’t hire developers to write a function. They hire people to make decisions that will still affect the product three years from now, long after everyone has forgotten the original prompt.
5. Speed Is No Longer the Differentiator. Judgment Is.
A few years ago, it was impressive to see someone produce thousands of lines of code in a matter of days. Today, any AI model can do that in minutes. The scarcity is no longer in generation. It’s in validation. In fact, several recent studies show that bottlenecks are shifting away from development and toward code review, architecture, quality assurance, and compliance.
That’s why Squad Challenge doesn’t try to measure who can code the fastest. We try to measure who makes the best decisions. Who knows when to accept an AI suggestion. When to modify it. And, most importantly, when to ignore it completely. Because in the age of artificial intelligence, value no longer comes from writing more code than everyone else. It comes from knowing which code is actually worth writing.
Excellent code + Technical criteria: The true balance
We’re looking for engineers who truly understand their craft.
We evaluate two things above all else. First, the ability to build high-quality solutions that are clean, well-structured, and easy to maintain over time. Second, the judgment to leverage existing tools instead of reinventing the wheel, choosing the right frameworks, libraries, and technologies for the problem at hand and delivering value to the business as efficiently as possible.
The real difference lies in who does the evaluation.
Every Squad Challenge is reviewed by independent experts from our community of more than 25,000 developers, including over 8,000 certified professionals. This ensures objective, consistent assessments aligned with modern engineering best practices, while reducing the bias and noise that often plague traditional hiring processes.
And because these reviews are performed by people who build software for a living, every candidate receives personalized feedback from experienced professionals. The result is a much stronger signal of real-world engineering ability than any automated test score or traditional interview process can provide.
This approach allows us to identify technical skills and long-term potential with far greater accuracy. But it also creates a better experience for candidates, regardless of the final hiring decision. Instead of being judged by an impersonal algorithm or a generic assessment, developers receive expert feedback that helps them understand their strengths, identify areas for improvement, and continue growing in their careers.
The outcome is a premium hiring experience that builds trust, strengthens employer brand, and turns technical assessments into something candidates actually find valuable.
Stop investing in automated filters that barely measure what really matters.
In a world where AI can help candidates breeze through superficial coding tests in a matter of minutes, the competitive advantage no longer comes from evaluating theoretical knowledge or memorized answers. It comes from understanding how people think, make decisions, and solve real problems.
If you want to build high-performing engineering teams, you need to evaluate both the quality of the code and the judgment behind it. Practical challenges combined with expert human review make it possible to identify the professionals who can build maintainable software, tackle complex problems, and create value from day one.
Want to identify your best engineers before you hire them?
Let’s talk.
The outcome is a premium hiring experience that builds trust, strengthens employer brand, and turns technical assessments into something candidates actually find valuable.
