We at HackerEarth love pair programming. Before you call out for being biased though, hear us out. Over the years we have spent perfecting our interview platform FaceCode, we have heard from many hiring managers that using a pair programming interview tool is one of the best ways to assess a candidate’s coding abilities in real time.

Let’s look at what these managers have told us:

With modern pair programming interview tools, employers must be well-informed about the coders’ unique skills set, ability to collaborate, solve problems, and strong analytical thinking
Interviewers must be able to deduce the coders’ agility in coding, the complexity of the code used, proficiency in using features such as CodeEditor, auto-suggest, and much more
A modern interview approach must evaluate how well coders handle ambiguity. It must highlight their attitude toward the challenge and aptitude for learning
The interviewer learns about the interviewee’s skills and personality, while the interviewee learns about whom they will be working with and what a typical workday looks like

What is pair programming?

Pair programming is a collaborative coding technique where two programmers work together at one workstation. One, the “driver,” writes code while the other, the “navigator,” reviews each line of code as it is typed in. The roles switch frequently to keep both partners engaged. This approach not only improves code quality by facilitating immediate feedback and error correction but also enhances learning and knowledge sharing between the pair. It’s particularly effective in tackling complex problems and learning new technologies. Companies often use pair programming to foster a collaborative environment and develop a more cohesive team dynamic, ultimately leading to more robust and error-free software.

What is a pair programming interview?

A pair programming interview is a style of interviewing candidates where the interviewer and candidate share a coding platform to solve a programming problem together. With pair programming, you can test 3 skills in developers: problem-solving, teamwork, and communication skills.

It can be a great way to identify talented developers. That’s not to say pair programming interviews (a.k.a pair coding interviews) can not go wrong.

Pair Programming_ Tips — Source: ASCIIville

DISCLAIMER: No known coders have been harmed during pair programming interviews. Also, breath mints are just good to carry along for any scenario. Just saying.

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Laughs aside, the main reason why pair programming interviews go south is that the rules of engagement are not specified; or followed. As a wise man said, a football game without rules is just a brawl. So, let’s list down some of the oft-repeated mistakes in a pair coding interview and how one can avoid them.

Pair Programming Interview Mistakes

Mistake 1: Not agreeing on rules beforehand

Pair programming has a simple structure. There’s a DRIVER and a NAVIGATOR. Simply put, the driver writes code while the other, the observer or navigator, reviews each line of code as it is typed in.

There are many ways this driver-navigator relationship can work:

Ping-Pong pairing: In this Developer A starts the process by writing a failing test or the ‘PING’. Developer B then writes the implementation to make it pass i.e. the ‘PONG’. Each set is then followed by refactoring the code together.

Strong-style pairing: In this, the navigator is usually the person who has more experience with the setup or task at hand, while the driver has lesser experience (with the language, the tool, the codebase, or because they are fresh out of college). The experienced person usually ends up being in the navigator role and guides the driver.

Pair development: Pair development is not a ‘style’ of pair programming per se. It’s more of a methodology. While the above two styles can be used for developing code in real time, pair development can be used to create a user story or feature. This goes beyond just coding and allows the pair to handle many different tasks as a team.

So, before you invite a candidate over for a code pair interview, ensure you know which style you are going to use and lay down the rules clearly. If you are switching roles between driver and navigator, make sure that the rules of discussion and expectations are clear from the get-go.

Mistake 2: Lack of proper conflict resolution mechanisms

It is important to settle conflicts well as a pair, and one way of doing it is to agree at the outset on which role has the final say. Between the driver and the navigator, one role needs to have the ‘casting vote’.

That said, this mechanism should not deter either of the pair from asking questions, or raising red flags. The goal of the pair programming role is to provide the candidate with something close to a ‘real-world experience’, i.e. they work on actual problems that your team solves in their workday. At the same time, the interviewer gets a first-hand glimpse at the candidate’s problem solving skills, and ability to collaborate.

Don’t forget this in an attempt to be ‘right’ during your pair programming routine. Agreeing to a mutually suitable arrangement at the outset aligns expectations and provides a fairly straightforward method of conflict resolution.

Mistake 3: Thinking there is just one ‘right’ answer

There are 11287398173 ways to write FizzBuzz. Remember this when you are in the middle of your next pair programming interview.

As interviewers, a very easy mistake to make is to believe that there is just one right way to approach a problem. Experienced hiring managers know that while it is perfectly alright to usually have an answer in mind to a given question, it is also important to listen and see what the interviewee’s answer is.

Most of the time, you’ll find that the candidate’s approach is different from yours. If you keep an open mind, you might even be surprised by their creativity! Rigidity in thought is a no-no for any interviewer; this typically demonstrates that they are not open to new ideas and only serves to alienate candidates.

This is also important for interviewees. Many times, candidates get trapped in the rabbit hole of ‘pleasing’ the interviewer. They look for solutions that they think will appease the interviewer. It is important to be aware of this behavior. Use the opportunity to showcase your skill-set, instead of behaving like a mind reader and trying to say and do things that will impress the manager. Ask clarifying questions, understand the boundary conditions or the corner cases, and then do your own thing!

Mistake 4: Not communicating enough

Okay, we get it. Not everyone likes chatter when they are coding. Some coders like music, others like radio silence.

The whole purpose of a pair-programming interview is to communicate. Let’s rephrase that a bit. The sole purpose of a pair-programming interview is to communicate effectively with your partner and build something collaboratively.

Interviewers need to set the tone here. Please tell your candidates clearly what kind of communication you expect from them. Do you want them to finish their coding and then walk you through their code, or do you want a play-by-play commentary? While doing so, please be cognizant of the fact that you do not come across as intimidating, and allow the candidate the flexibility to understand and solve the problem in their own time and space.

Interviewees would do better to ditch the YOLO approach on this one and use the session to show their planning and communication skills.

Benefits of pair programming interviews

Pair programming interviews offer a number of benefits to both employers and candidates.

Benefits for employers:

Assess real-time problem-solving skills: Pair programming interviews allow employers to see how candidates approach and solve problems in a real-time setting. This is much more informative than traditional whiteboard interviews, which can be more artificial and less indicative of a candidate’s actual coding skills.

Evaluate communication and teamwork skills: Pair programming interviews also allow employers to evaluate candidates’ communication and teamwork skills. This is important because tech workers often need to be able to work effectively with others on complex projects.

Identify potential culture fits: Pair programming interviews can also help employers to identify potential culture fits. By observing how candidates interact with each other and with the interviewer, employers can get a better sense of whether candidates would be a good fit for the company culture.

Benefits for candidates:

More skill-oriented process: Pair programming interviews give candidates a more realistic opportunity to demonstrate their skills. Candidates are able to work with the interviewer to solve a problem, and they are able to ask questions and get feedback as they go. This can help candidates to perform better than they might in a traditional whiteboard interview.

Better understanding of the company culture: Pair programming interviews also give candidates a better understanding of the company culture. By interacting with the interviewer and seeing how the interviewer works, candidates can get a sense of how the company values collaboration and teamwork.

Opportunity to network with potential colleagues: Pair programming interviews can also be an opportunity for candidates to network with potential colleagues. By working with the interviewer, candidates can learn more about the company’s projects and technologies. Candidates can also make a good impression on the interviewer and other potential colleagues.

Tips to conduct a pair programming interview

Ensuring your pair programming interviews are effective requires a balanced approach:

Set clear expectations: Before the session, clearly communicate the objectives, tools to be used, and the problem’s scope.

Use real-world scenarios: Instead of abstract problems, use challenges that reflect real tasks your team faces. This provides valuable insights into the candidate’s practical skills.

Ensure role clarity: Specify who is the “driver” (the one writing the code) and the “observer” (the one reviewing and suggesting) and switch roles midway to ensure a balanced assessment.

Prepare a list of pair programming interview questions: Create a list of pair programming interview questions to check the candidate’s ability to design code

Maintain respectful communication: Encourage open dialogue. The candidate should feel comfortable asking questions, suggesting alternatives, or admitting when they don’t know something.

Embrace silence: Allow the candidate to think. Not every moment needs to be filled with talk.

Provide tools and documentation: Ensure the candidate has access to necessary tools and can refer to documentation if needed. This mirrors real-world conditions.

Focus on the Journey, NOT just the Solution: Remember, the goal is to understand how the candidate thinks and collaborates. A perfect solution isn’t the only indicator of a good fit.

Conclude with feedback: Dedicate the last few minutes to provide feedback. Highlight what went well and areas of improvement. This can be incredibly valuable for both the candidate and your company’s reputation.

When done right, pair programming can yield awesome results!

These are just some of the things we have learned from our discussions with hiring managers and candidates. We hope that they help you in your next interview. Another important aspect of a good pair programming interview is using the right tool, and HackerEarth’s FaceCode can help you with that. The key to having a good technical code pair interview is creating a familiar environment for the candidates, so they can relax and focus on the task at hand. FaceCode, with its built-in code editor and easy-to-access question library, allows you to do that easily.

We hope you ace your next pair programming interview – whether you are an interviewer or a candidate. Good luck!

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

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My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

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Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

Clean, semantic HTML
Smooth CSS transitions
Keyboard navigation support
ARIA labels for accessibility
Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

No DNS-prefetch hints for external image domains
Missing width/height attributes causing layout shift
No preload directives for critical resources
Missing CSS containment properties
No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

No 'use strict' directive in JavaScript
Missing package.json for dependency management
No test files
Missing README documentation
No .gitignore or version control setup
Could use functional array methods for cleaner code
Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

Security vulnerabilities
Performance/Efficiency issues
Performance optimization opportunities
Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

What actually improved (and by how much)
What new issues I might have introduced
Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

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What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

How to implement Content Security Policy correctly
Why DOM depth matters for rendering performance
What CSS containment does and when to use it
How to structure code for better maintainability
Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

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Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin — This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way — Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

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Accurately assess real-world development skills.
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No need to switch between different tools or manage complex setups.
Focus purely on showcasing skills, not environment configurations.
Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

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A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable or Hostinger Horizons enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

4 Essential Mistakes To Avoid During Pair Programming Interviews

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