David Heinemeier, the creator of Ruby on Rails tweeted:

Several organizations still use whiteboard interviews as a standard process to hire developers.

In a whiteboard interview, developers are given a problem statement for which they have to provide the solution on a whiteboard.

The most common tasks include recalling algorithms and writing them bug-free on the whiteboard.

The important thing to consider is that a whiteboard is not a code editor. Developers can’t actually run the code to see if it works, let alone benchmark it.

Hence, many developers dislike whiteboard-based interview questions. It’s easy to find someone or the other venting about it on various social media platforms.

The problem is not just limited to whiteboard interview processes. Developers around the world face a lot of challenges during interviews pertaining to lengthy recruitment processes, being ghosted by recruiters, coding in an uncomfortable environment, being asked irrelevant questions, etc. The phrase, “the recruitment process is broken,” is used so commonly by developers that it has become a cliché.

Unfortunately, most of these issues are falling on deaf ears. This ultimately gives rise to negative candidate experience. Negative candidate experience can cost companies more than just losing out on good candidates. It can even result in a significant monetary loss. The most famous example is that of Virgin Media where a bad candidate experience cost the company 5.4 million USD per annum.

This is where developer assessments come into play. When developers apply for a job, major organizations consider technical assessments as an integral part of the interview process. Here are a few points on how developer assessments can improve candidate experience:

With developer assessment tools, candidates can code from anywhere in an environment of their choice. They do not need to travel long distances to give interviews, code on whiteboards, or get rejected based on a phone conversation during the screening process.
Developer assessment tools ensure that interviews are structured. This means that all the candidates are asked the same set of questions and interviewers do not know the specifics of each candidate such as gender, age, ethnicity, etc. This assures the candidate that the hiring decision will be unbiased and they will be benchmarked the right way.
Irrespective of what the hiring decision is, candidates, feel that they have had a fair shot at showcasing their skills through an engaging process of developer assessments without any human bias.

So, how can you ensure a seamless candidate experience using developer assessments?

We, at HackerEarth, are aware that enabling a good candidate experience is extremely important. When it comes to technical hiring, HackerEarth’s Assessment software optimizes candidate experience to help you stand apart from your competitors.

Here are 5 ways how HackerEarth Assessment ensures a better candidate experience:

1. Let candidates use the assessment platform in the language of their choice

We understand that developers live in every corner of the world.

Hence, HackerEarth’s Assessment software supports various spoken languages so that developers can use the platform easily.

The languages that are supported include:

English
Japanese
Chinese
French
Portuguese
Russian

This instills a sense of belonging among candidates and they are bound to be happy.

2. Know the value of a candidate’s time

“You know why everyone loves a vacation? Because it’s the only time it’s okay to waste time.”

If you’re on the hunt for a new candidate to fill a job position, do whatever you can to save their time.

Time is a great equalizer, and every minute that a candidate uses for one task can be used for another, especially during interviews.

HackerEarth has a user-friendly coding environment in which candidates can write code in any language.

When they compile their code, they are shown errors in real time and this helps them review their code and make it better. They can also run their code against custom input and output.

One of the features that HackerEarth’s coding environment has is code stubs. Code stubs are boilerplate code that is required whenever a candidate writes code.

For example, the following C++ code is a code stub. This will be available to candidates in the code editor when they select C++ as the programming language:

#include <iostream>

#include <string>

using namespace std;

int main()

{

}

In this example, the candidate can focus on writing the code that will help in solving the problem statement. This saves the candidates time allowing them to focus more on the approach that they want to follow.

Another feature in HackerEarth’s Assessment software that saves a candidate’s time is the Autocomplete feature.

This feature in which the code editor predicts and displays the name of the related functions, methods, standard classes and objects, operators that you are typing.

For example, when a candidate types java.util, they see suggestions of various functions that can then be imported into their code by pressing Ctrl and the space bar.

improve candidate experience using developer assessments

Also, you can check whether a code submitted by the candidates is written efficiently or not. We use an open-source platform, SonarQube, to inspect code quality. It performs automatic reviews of code to detect bugs, vulnerabilities, etc.

The code quality score is determined by calculating the average of four key metrics: maintainability, reliability, security, and cyclomatic complexity. In other words, the code-quality score is an average value of key metrics that represent the best practice to write code.

3. Let candidates know if something is wrong

With HackerEarth’s Assessment software, candidates get proactive alerts in their test environment if there’s any error pertaining to network failure, server error, errors in loading JavaScript files, etc.

This eliminates confusion, making it easier for them to fix their code before they submit it.

4. Conduct online video interviews

Online video interviews are great and serve as a valuable tool for providing a seamless candidate experience. HackerEarth’s live interview platform lets candidates take an interview from the comfort of their home or a location of their choice.

All they need is a working webcam and a computer with a working Internet connection.

HackerEarth’s Assessment software integrates interviews with a candidate’s Google calendar. Relevant emails are automatically sent to candidates when interviews are scheduled, rescheduled, or canceled.

It also has a default system check where a candidate’s system is automatically checked for the following:

Versions of the operating system and browser
Whether the JavaScript language is enabled
Dimensions of the screen size that is being used
Whether cookies are enabled
Whether the candidate’s webcam and microphone/speaker are working

In addition to writing code in real-time, candidates can explain technical concepts via high-quality video calls. Using the multi-room text chat in video interviews, candidates can easily communicate with their recruiters.

Online interviews can connect the best candidates with the best companies out there. However, it is important for both candidates and recruiters to be aware of things that they need to do to ensure that the interview is hassle-free.

5. Light side vs. dark side

You must have heard some coders tossing phrases such as “I am much better at reading dark text on a white background” or “The dark background minimizes distraction. It lets you focus on the only light source, which is your desktop/laptop.”

So, what do we choose? The light theme or the dark theme?

We understand that different developers have different perceptions about coding and themes are a personal preference. Hence, HackerEarth’s Assessment software lets developers code in a theme of their choice—light or dark—whichever they are comfortable with.

Other best practices

So far, we have spoken about providing a seamless candidate experience using developer assessments. Here are other small tips to keep in mind to ensure that you attract the right talent, make their experience worthwhile, and retain them.

Write accurate job descriptions

Job descriptions allow you to make informed hiring decisions. Most importantly, before a candidate actually applies for a job, a clear job description is what motivates them to do so.

Let’s take a look at a few examples of good and bad jobs posts.

Bad job post

Source: Upwork Global Inc.

Good job post

Source: HackerEarth

A good job description uses a clear job title, speaks directly to candidates, describes tasks, and most importantly, sells your job.

They provide the required information to candidates to help them assess if they are suitable for the position.

Remember that the candidate is also evaluating your organization and you based on such small but important details.

Address the company culture with enthusiasm

Company culture is what makes the company; it is the inherent personality of an organization.

Also, it is the top concern for millennials in particular. Hence, it is not enough to simply tell candidates that your organization offers a great company culture.

You have to give the candidate an accurate view of what it’s actually like to work for your organization. Start by citing examples of employees who have been in the organization for a long time and what culture means to them, define your core values, etc.

Make faster hiring decisions

Faster hiring decisions do not mean you make a rush hire. It means that you value the candidate’s time and want to make the interview process as seamless as possible.

For faster hiring, organizations can:

Schedule interviews shortly after receiving the application
Ask for work samples ahead of time
Make the candidate meet multiple parties in one day

Keep candidates in the loop

Candidates may get frustrated if they send in applications for a job role and never hear from the company or fill an online job application and get an email saying their profile will be reviewed.

No one ever says by whom and by when. Also, after they appear for an interview and if they are not selected, they often hear recruiters say, “We shall get back to you.”

Be modest. Let candidates know whether they have made the cut or not. If they have not been selected, send them encouraging emails listing their areas of improvement, which can help them in their next job application.

This opens up a door of positivism and respect in the candidate’s mind for your organization.

Do not let them wonder where they stand. It is always a wise thing to keep them informed, no matter what the hiring decision is.

Here’s an example of a good rejection email.

Source: Beamery

Do your homework

Research what qualifies as a competitive salary for the open position. It is important that candidates with the desired skill sets, who strive to do their best, and who can perform exceptionally well, feel sufficiently compensated for their worth.

Final thoughts

To sum up, high-quality talent expects a high-quality candidate experience. Starting from the initial recruiting process—sending emails or conducting phone calls—to rolling out a job offer, candidates these days expect the best out of an interview process.

We hope this article will help you provide a seamless candidate experience during your next tech assessment.

Feel free to get in touch by writing to me at ashmita@hackerearth.com

Happy hiring!

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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:

For Recruiters & Hiring Managers:

Accurately assess real-world development skills.
Gain deeper insights into candidate proficiency.
Hire with greater confidence and speed.
Reduce candidate drop-off from technical friction.

For Candidates:

Enjoy a seamless, efficient assessment experience.
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

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

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.

How to improve candidate experience using developer assessments

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